Whether we know it or not, humanity is quickly entering into an era of technology, where biosensing, biometrics, and the quantified self are combining into something transformative. These techniques will allow us to gain profound insight into ourselves, how we do things, how we think, how we are similar, and how we differ as well. Have you ever wanted to control a robot with your mind, or interact with virtual characters that respond to your emotions, what about developing a health and exercise plan specific to you that is on par with the trainers that professional athletes get. What about being able to predict the future of your own health? Biosensor fusion can allow all of that and much more.
In our World of Sensors article, we learned about biosensors, their data, and how they will shape our world. This accompanying device chart explains in detail the kind of data that all of the different types of biosensors collect, and also provides a comprehensive “device guide” for comparison.
So let’s take a look at how each of these sensors work individually.
OVERVIEW
Our bodies produce a lot of different signals, which contain distinct markers and those are measured by the different sensors. These sensors give us an understanding of our body that would be hidden from us otherwise.
An exciting recent development is that many of these sensors are starting to become available to consumers and developers, bypassing the need to spend significant amounts of money, be associated with a tech company or be a part of an educational institution.
Before we can understand biosensor fusion in detail we should describe the biosensors themselves that are available, including the pros and cons of various sensors. We will discuss what each one brings to the table, the drawbacks they present, how they are best used, and how they can be combined with each other.
This sensor is probably the easiest to understand as well as implement for analysis. It has been incorporated into a number of AR and VR headsets, and is available as an accessory for desktop computers as well. Most Eye Trackers use infrared emitters to shine light into your eyes. The pupils absorb the light, the rest of your eye reflects. This allows the eye tracker to accurately calculate your eye position. Some eye trackers use regular Cameras to measure and compute eye position, and although rare, you can also use an array of electrodes around the eye to track where the eye is looking, called EOG (electrooculography).
Current expectations is that Valve/OpenBCI’s galea headset will have both EOG and built in camera based eye tracking, which would be an easy form of sensor fusion. This is because EOG can have much higher temporal resolution then current eye tracking cameras, which makes the data more accurate and precise, and the EOG data can be used to further enhance the performance of foveated rendering which is highly time sensitive. This is then coupled with the benefits of camera based eye tracking, which offers decent accuracy in a passive collection method. Further, camera based eye trackers can measure your pupil size and changes which is an extremely valuable measurement as it can be used to determine your current cognitive load. Also camera based eye trackers can be further away from the user, such as on a desk, or attached to a monitor. Or mounted to a pair of glasses or around a VR headset’s lenses.
Eye tracking presents us with a great example of how Sensor fusion can lead to an improved and more accurate picture of a person. HP recently released the HP Omnicept G2 VR headset, offering a combination of PPG based Heart Rate tracking and camera based eye tracking. This device offers a custom metric in the SDK, a cognitive load calculation. This metric represents how much of the user’s cognitive capacity is in use at a given moment – on a scale of 0 to 100. This metric is computed using both pupil size and heart rate variability, which can provide a higher accuracy metric of cognitive load than either sensor alone. This calculation is also thanks to a machine learning algorithm to look at the data and give a prediction of what the current cognitive load is based on that training.
This is probably the most likely sensor that you might be wearing right now. If you are wearing a recent Apple watch, fitbit, smart watch, or any fitness tracker that says it has heart rate functionality, then it probably has one of two types of sensors: a PPG (Photoplethysmogram) sensor or a EKG (electrocardiogram) sensor. EKG measures electrical impulses on the skin, usually the chest, using two electrodes to measure the pulses of electricity. PPG measures heart pulses by measuring the changes in color of the blood using an LED light and measuring the reflection. One can verify which sensor their device has as if there is light coming from the sensor, it is probably a PPG sensor, and if there are small metal contacts it is probably a EKG sensor. These each have slightly different information that they can tell you on top of your basic heart pulse rate.
A PPG sensor can tell you a number of interesting things such as SpO2 (Blood oxygen saturation), which is how well your blood is taking up oxygen, and can be very useful in exercise or in understanding . Pi (Perfusion index) is the strength of the pulse at the location of the sensor, this is what gives form to the pleth waveform. PVi (Pleth Variability Index) is the measurement of the variability of the perfusion index, which is currently being looked at as an interesting indicator of how hydrated you are. And RRp (Respiration rate from Pleth) which measures your breathing rate based on an analysis of the pleth waveform, this is because your breathing actually affects the pressures your heart is feeling from your left lung.
An EKG sensor can tell you a lot about the hearts movement on top of just pulse rate, it can especially tell you if there are any issues with your heart, as if the waveform from an EKG shows any out of ordinary patterns it probably means that your heart is doing something out of the ordinary. Generally you wear an EKG sensor around your chest. The problem is that outside of true clinical conditions these changes are hard to check, and verify. Though sensor fusion should definitely help with that in the future. But adding EKG will eventually be one of the most likely sensors to save peoples lives
All of these measurements are extremely valuable and often are the backbone of many of the other sensors, especially as your breathing and pulse themselves affect almost every other sensor in some way. It is often critical to have this data to correlate with other sensor data. A great example is in combination with eye tracking which I talked about before. For those who want a picture of what it might look like from the user side would be something like the “bullet circle” in the anime Sword Art Online as the reticle changes based on your heart rate. Another great use is to tell if someone is excited or scared as an elevated heart rate is good at detecting those, the problem is that being able to understand which one it is (fear or excitement) requires other sensors to be accurate.
“Bullet circle” in Sword Art Online season 2, episode 2 and episode 4. Sword Art Online is owned by A-1 Pictures, Aniplex USA, and Reki Kawahara.https://www.swordart-online.net/
Galvanic Skin Response is a well established technology, and offers unique advantages and drawbacks. In terms of negatives, many GSR devices require 10-20 minute wait time for the sensors to accumulate sweat and be able to measure changes. Additionally, while GSR can measure general levels of emotional intensity, it is not possible to distinguish whether the experience is positive or negative. Lastly, GSR tends to be slow to change and is mainly used to show trends over time. It is possible to measure spikes which denote interesting events, but it can be difficult to isolate what stimulus is driving the overall GSR readings. In terms of advantages, GSR measures emotional state in a fairly unique way. Once correlated with the particular task it can be a reliable indicator of emotional experience.
As GSR is mostly correlated with emotional state, it can allow for another very interesting understanding of your current state. This might sound familiar if you like spy or detective movies or books, as this is one of the pieces of tech that made the polygraph (lie detector) work the way it did. Polygraphs were usually a combination of blood pressure, pulse, respiration and galvanic skin response sensors. And although the stated purpose of use as a lie detector, which can easily be debated, the polygraph was a perfect example of a biosensor fusion device, and thanks to the polygraph, all of the sensors used by it, are quite well documented, as well as how they behave in relation to each other.
Skin temperature is another interesting sensor that a few different devices (such as the emotibit, whoop, and Oura ring) have started to use. Skin temperature and body temperature do not always line up, and that can lead to some interesting insights. Also body temperature can be a very good indicator of your immune activity, and overall health. It can also be affected by your emotional state.
Skin temperature can be measured directly by having the sensor touch the skin, which is the typical method used. More advanced temperature sensors use far infrared (FIR) to measure the body state remotely, increasing reliability and reducing the need for another sensor to make contact with the user.
Local skin temperature can change very fast and quite often. Think about when your hands are really hot or really cold. Skin temperature has a direct effect on your internal body temperature and your overall metabolic activity, and body’s hemodynamics, as your skin is in essence your largest organ and has massive amounts of blood flowing through it and muscles right under it. Skin temperature trends are currently being looked into for many different short and long term health metrics related to disease, sickness, recovery period, and exercise.
When talking about the emotibit, we have to mention that it also has a humidity / temperature sensor that is for measuring skin perspiration, and as another sensor to measure body temperature. Perspiration is interesting because it is currently not something many biosensor devices currently measure, but can have some interesting data. Especially when active, or when you are hot or cold, your perspiration changes quite a bit and can be useful to understand how activities are changing your perspiration amount.
Now for the sensor that probably gets the most interest in biosensor fusion and in general when people talk about biosensors is EEG. When people start talking about controlling things with their minds this is often the device people think of.
EEG is a technology that has been around for a while and allows the measurement of brain state through electrical signals. By placing electrodes on the scalp, the system will generate an electrical reading for the brain activity present at that location. By processing that electrical reading, or interpreting it directly, users and researchers can learn about the current brain state. There are a lot of subject specific characteristics with EEG, so it can be hard to generalize – EEG will exhibit consistent readings with the same subjects and the same tasks. EEG will measure significant differences between subjects that are doing the same tasks, and these differences will remain consistent on repeated tests. People’s brain signals are unique to them, and these differences are consistent, almost like a brain fingerprint that is unique for each person.
Most current consumer EEG devices have only a few electrodes, the Muse has 4 electrodes, and the low cost OpenBCI Ganglion also has 4 electrodes. Though this is starting to change as the electronics that are used for amplifying and recording the output of the electrodes are falling in price quite a bit every year, soon consumer devices will have many more electrodes which give much more accurate data, and a better understanding of how the different parts of our brain are working. Soon 8 and 16 will be quite common, and down the line many developers hope that 64 or more will become much less expensive. This increase in the number of channels is important because EEG of different parts of the brain give insight into vastly different parts of what your body is doing. Good examples are that if you do eeg on the back of the head, you can get data that is related to your visual cortex (what you are currently seeing), while the top of the head can give you data related to your motor cortex (how your body moves).
Although EEG is really cool, it has a big issue and that is that on its own it is often not very useful as it becomes very hard to correlate the brainwave data with anything you are doing or thinking at the time, as there is to much data from our body that our brain is processing and it ends up overlapping. And there can be many things that interfere or affect the signal. That also means it is often required to do sensor fusion to get good data from EEG. Or to use AI and machine learning constantly to improve accuracy. When sensor fusion of EEG and almost any other sensor is done you usually can find quite interesting correlation of data.
An interesting piece of technology that is slowly becoming accessible to consumers is fNIRS (Functional Near Infrared Spectroscopy). This tech is all about imaging blood flow. In some aspects it is similar to an array of PPG sensors, but it has a lot more complexity in how it works so that it can be used as an array and create images. Right now a very common use is to be used for imaging brain blood flow, though it can also be used elsewhere on the body. For sensor fusion this can work alongside EEG quite well to give a better picture of what is currently happening in the brain. Blood Flow through the brain has been shown to be linked with brain activity on multiple occasions. Though sometimes the blood flow is connected with other things then brain activity, such as emotional activity.
A good low cost version of one of these devices is the HEGduino, but it can not really create images as it is only a single sensor (basically an advanced PPG sensor device). If you have 100k burning a hole in your wallet, you can buy 50 sensors to fill up a whole Kernel flow device. And if you have university backing there are many more options like the Kernel Flux.
Another type of imaging that is getting easier to access is ultrasound imaging. There has been a lot of recent research into using ultrasound in combination with other biosensors. Especially for brain imaging as (TCD (Transcranial doppler Ultrasound)), it gives a much deeper image then fNIRS, but the technology is a bit less portable at the moment and often requires a large amount of area for the sensor, that could instead fit a large number of other sensors.
Though out of all imaging technologies probably my favorite up and coming one has to be EIT. Although in its early stages, this real time medical imaging technology has a lot of potential, especially as it has fewer downsides then MRI (bad temporal resolution, big device), CT scans (radiation), and ultrasound (currently hard to wear). It also has great temporal resolution. The problem is that it currently lacks spatial resolution. A really cool use case for this is to do cross sectional analysis of a part of your body while you are doing an activity. If you do a cross section of your wrist you could figure out quite accurately the position of your fingers. This might even have better resolution and accuracy then using EMG, or Myoelectric sensors which will be talked about later in this article.
Technology: CT scans and MRI
Now a bit more on the absurd and excessive side, I do have to mention that CT scans and MRI can be very helpful for sensor fusion even if they can’t be done in real time. This is because each person’s body is different enough that bone and body structure can have noticeable impact on the accuracy of different sensors. So having an MRI or CT scan can allow for you to tune sensors to be more accurate to your body. This is extremely helpful for any form of brain related sensor fNIRS, EEG, etc, as the shape of your skull, and brain’s placement inside your skull does affect the signal that the sensor is able to receive quite a bit. The hardest part about getting an MRI other than cost itself, is that for use with EEG, you actually want to get a standing MRI scan so that it accurately places your brain in your skull in the same way that it would be when you are active in daily life. This is because your brain is in a different spot in your skull when you are lying down vs sitting or standing.
Another set of sensors that have only recently been adopted into biosensor fusion, are environmental sensors to track the state of the environment around the user. These sensors are useful in understanding how our body reacts to a changing environment. Although these sensors are not in very many devices yet, they are some of the easiest to add yourself, because many of these sensors are quite cheap and easy to get ahold of. You might even be able to use the temperature and humidity sensors on the emotibit this way if you have an extra and flip it around to have the sensors not on the skin.
A somewhat common biosensor that people might have used is body motion tracking. Although this is not always considered a biosensor itself, it is tracking your physical body in a way that other sensors can’t. And provides very important information when you connect it to other biosensors, since body movement can affect a lot of different aspects of your body, including, heart rate, brain activity, metabolism, skin temperature, etc. If you have ever used a recent gen VR headset then you have experienced at least a bit of this through controller and headset tracking which ends up tracking your hand position and head position. If you have used oculus or windows mixed reality headsets, you have used camera + IMU based tracking, if you have used a steamvr tracked headset then you have used a lidar + IMU tracked headset.
In the near future full body tracking will become much more common outside of the motion tracking they use in movies. A big part of this has been the improvement in two things, camera based movement analysis, and IMU drift. It was not very long ago that identifying how our body is moving in three dimensions from just 2D camera images was considered really difficult, but thanks to AI and machine learning we can now quite accurately understand the position and movement of a person’s body from a single or very few cameras. And then there is IMU drift, up until about 5 years ago using an IMU based motion capture suit was considered an unreliable option, and this is because after a very short period of time, the sensor readings would drift out of alignment with the actual position that your body was in. And if you have a suit with 20 or 30 IMU sensors, the drift would make you need to recalibrate the suit position very often. Each type has some very good use cases, as cameras do not need to be worn on the body, but IMU tracking suits can be used while out in the everyday world.
When talking about body movement it is also important to consider why exactly our body is moving in the first place, and a great sensor for doing that is contact based Myoelectric sensors, which measure electrical impulses that trigger your muscle to expand or contract using a pair of electrodes.
There are three different focuses for detecting things with EMG usually. First is detection of The motor neurons firing, which sends very quick spike signals to the muscle. This is quite difficult depending on the size of the muscle, and if it is hard to reach without needle based EMG sensors. This can be used to predetermine if you are going to move a part of your body. An example of a device focused around using this quick spike is the Brink Bionics Impulse Neurocontroller. Second is the detection of a muscle twitch which will happen from the firing of the motor neurons. Good examples of this sensor in use are the Myo armband, and the armband Meta (Facebook) has recently shown, that is based off the hardware they got when acquiring Ctrl Labs. And third is monitoring of tetanic muscle activation from continued motor neurons firing. This can be great for helping figure out how much power, or force you are putting into any one movement during something like full body tracking or while doing sports, or how fatigued your muscles are after a workout.
Another interesting use case is to map facial position, by having the sensors on the face, the Galea will have EMG, it will only be for the face.
As we can see, the ability to measure people has grown to a capacity that was never possible before. We can passively observe what someone finds interesting in a given scene or encounter, measure the brain state that experience generates, and record the intensity and enjoyability of the experience. This will allow quantification of many things that are currently invisible to us. Imagine a couple recording their biometric responses to various TV shows, so that they can make better decisions on what to watch together based on how enjoyable it will be for both parties. Lecturers will be able to monitor student volunteers during presentations to see how it makes the students feel and tune their material accordingly. Car manufacturers will be able to passively observe driver fatigue and attention and design safer cars that notify the driver when their attention is lacking or needed to deal with an urgent issue. Companies will be able to design easier to use and understand packaging based on consumer response. A doctor could observe a patient’s reactions to their explanation of some diagnosis, note a place where confusion occurs, and revisit that aspect. All of these will finally allow us to get past the current difficulties presented by placebo, self report, and an inability to quantify and compare people’s experiences.
On a fundamental level, something worth noting is that many people don’t really trust or believe that people are having the experience they claim to have, as much of the time we don’t have a way to quantify or verify whether this is the case. If someone says they are scared, or angry, or happy, or frustrated, or confused – there’s often an element of doubt present as the only information available is what the person claims is happening to them. If someone says they enjoyed a movie, we might assume they are praising the movie for some other reason other than liking the film. If someone says they enjoyed a meal, perhaps they enjoyed the company and social setting but didn’t enjoy the food one bit. With sensor data, we’d be confident in what they were saying – or they could volunteer to share the data with others – which has profound implications for our society and the way we see each other.
These sensors will provide a wealth of data about us, our lives, and our patterns. We will need strong laws and motivated legislatures to ensure the protections necessary to make these sensors safe and ubiquitous.
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“Humanity faces a stark and urgent choice: a breakdown or a breakthrough”
These words introduce an 85-page report issued by António Guterres, United Nations Secretary-General (UNSG).
The report had been produced in response to requests from UN member state delegations “to look ahead to the next 25 years” and review the issues and opportunities for global cooperation over that time period.
It featured some bold recommendations. Two examples:
Now is the time to end the “infodemic” plaguing our world by defending a common, empirically backed consensus around facts, science and knowledge. The “war on science” must end. All policy and budget decisions should be backed by science and expertise, and I am calling for a global code of conduct that promotes integrity in public information.
Now is the time to correct a glaring blind spot in how we measure economic prosperity and progress. When profits come at the expense of people and our planet, we are left with an incomplete picture of the true cost of economic growth. As currently measured, gross domestic product (GDP) fails to capture the human and environmental destruction of some business activities. I call for new measures to complement GDP, so that people can gain a full understanding of the impacts of business activities and how we can and must do better to support people and our planet.
It also called for greater attention on being prepared for hard-to-predict future developments:
We also need to be better prepared to prevent and respond to major global risks. It will be important for the United Nations to issue a Strategic Foresight and Global Risk Report on a regular basis…
I propose a Summit of the Future to forge a new global consensus on what our future should look like, and what we can do today to secure it.
But this was only the start of a serious conversation about taking better steps in anticipation of future developments. It’s what happened next that moved the conversation significantly forward.
Introducing the Millennium Project
Too many documents in this world appear to be “write-only.” Writers spend significant time devising recommendations, documenting the context, and converting their ideas into eye-catching layouts. But then their report languishes, accumulating dust. Officials may occasionally glance at the headlines, but the remainder of the fine words in the document could be invisible for all the effect they have in the real world.
In the case of the report “Our Common Agenda,” the UNSG took action to avoid the tumbleweed scenario. His office contacted an organization called the Millennium Project to collect feedback from the international futurist community on the recommendations in the report. How did these futurists assess the various recommendations? And what advice would they give regarding practical steps forward?
The Millennium Project has a long association with the United Nations. Established in 1996 after a three-year feasibility study with the United Nations University, it has built up a global network of “nodes” connected to numerous scholars and practitioners of foresight. The Millennium Project regularly publishes its own “State of the Future” reports, which aggregate and distill input from its worldwide family of futurists.
A distinguishing feature of how the Millennium Project operates is the “Real-Time Delphi” process it uses. In a traditional questionnaire, each participant gives their own answers, along with explanations of their choices. In a Delphi survey, participants can see an anonymized version of the analysis provided by other participants, and are encouraged to take that into account in their own answers.
So participants can reflect, not only on the questions, but also on everything written by the other respondents. Participants revisit the set of questions as many times as they like, reviewing any updates in the input provided by other respondents. And if they judge it appropriate, they can amend their own answers, again and again.
The magic of this process — in which I have personally participated on several occasions — is the way new themes, introduced by diverse participants, prompt individuals to consider matters from multiple perspectives. Rather than some mediocre “lowest common denominator” compromise, a novel synthesis of divergent viewpoints can emerge.
So the Millennium Project was well placed to respond to the challenge issued by the UNSG’s office. And in May last year, an email popped into my inbox. It was an invitation to me to take part in a Real-Time Delphi survey on key elements of the UNSG’s “Our Common Agenda” report.
In turn, I forwarded the invitation in a newsletter to members of the London Futurists community that I chair. I added a few of my own comments:
The UNSG report leaves me with mixed feelings.
Some parts are bland, and could be considered “virtue signaling.”
But other parts offer genuinely interesting suggestions…
Other parts are significant by what is not said, alas.
Not everyone who started answering the questionnaire finished the process. It required significant thought and attention. But by the time the Delphi closed, it contained substantive answers from 189 futurists and related experts from a total of 54 countries.
The results of the Delphi process
Researchers at the Millennium Project, led by the organization’s long-time Executive Director, Jerome Glenn, transformed the results of the Delphi process into a 38-page analysis (PDF). The process had led to two types of conclusion.
The first conclusions were direct responses to specific proposals in the “Our Common Agenda” report. The second referred to matters that were given little or no attention in the report, but which deserve to be prioritized more highly.
In the first category, responses referred to five features of the UNSG proposal:
A Summit of the Future
Repurposing the UN Trusteeship Council as a Multi-Stakeholder Body
Establishing a UN Futures Lab
Issuing Strategic Foresight and Global Risk Reports on a regular basis
Overall, the Delphi gave a strong positive assessment of these five proposals. Here’s a quote from Jerome Glenn:
If the five foresight elements in Our Common Agenda are implemented along the lines of our study, it could be the greatest advance for futures research and foresight in history… This is the best opportunity to get global foresight structural change into the UN system that has ever been proposed.
Of the five proposals, the UN Futures Lab was identified as being most critical:
The UN Futures Labwas rated the most critical element among the five for improving global foresight by over half of the Real-Time Delphi panel. It is critical, urgent, and essential to do it as soon as possible. It is critical for all the other foresight elements in Our Common Agenda.
The Lab should function across all UN agencies and integrate all UN data and intelligence, creating a global collective intelligence system. This would create an official space for systemic and systematic global futures research. It could become the foresight brain of humanity.
The Delphi also deemed the proposal on strategic foresight reports particularly important:
Strategic Foresight and Global Risk Reports were seen as very critical for improving global foresight by nearly 40% of the panel. This is exactly the kind of report that the United Nations should give the world. Along with its own analysis, these reports should provide an analysis and synthesis of all the other major foresight and risk reports, provide roadmaps for global strategies, and give equal weight to risks and opportunities.
The reports should be issued every one or two years due to accelerating change, and the need to keep people involved with these issues. It should include a chapter on actions taken since the last report, with examples of risk mitigation, management, and what persists…
They should bring attention to threats that are often ignored with cost estimates for prevention vs. recovery (Bill Gates estimates it will cost $1 billion to address the next pandemic compared to the $15 trillion spent on Covid so far). It should identify time-sensitive information required to make more intelligent decisions.
In a way, it’s not a big concern that key futurist topics are omitted from Our Common Agenda. If the mechanisms described above are put in place, any significant omissions that are known to foresight practitioners around the world will quickly be fed into the revitalized process, and brought to wider attention.
But, for the record, it’s important to note some key risks and opportunities that are missing from the UNSG’s report.
The biggest forthcoming transition
Jerome Glenn puts it well:
If we don’t get the initial conditions right for artificial general intelligence, an artificial superintelligence could emerge from beyond our control and understanding, and not to our liking.
If AGI could occur in ten to 20 years and if it will take that same amount of time to create international agreements about the right initial conditions, design a global governance system, and implement it, we should begin now.
Glenn goes on to point out the essential international nature of this conundrum:
If both the US and China do everything right, but others do not, the fears of science fiction could occur. It has to be a global agreement enforced globally. Only the UN can do that.
Here’s the problem: Each new generation of AI makes it easier for more people to become involved in developing the next generation of AI. Systems are often built by bolting together previous components, and tweaking the connections that govern the flow of information and command. If an initial attempt doesn’t work, engineers may reverse some connections and try again. Or add in some delay, or some data transformation, in between two ports. Or double the amount of processing power available. And so on. (I’m over-simplifying, of course. In reality, the sorts of experimental changes made are more complex than what I’ve just said.)
This kind of innovation by repeated experimentation has frequently produced outstanding results in the history of the development of technology. Creative engineers were frequently disappointed by their results to start with, until, almost by chance, they stumbled on a configuration that worked. Bingo — a new level of intelligence is created. And the engineers, previously suspected as being second-rate, are now lauded as visionary giants.
Silicon Valley has a name for this: Move fast and break things.
The idea: if a company is being too careful, it will fail to come up with new breakthrough combinations as quickly as its competitors. So it will go out of business.
In this view, failures aren’t a problem, provided we pick ourselves up quickly, learn from the experience, and can proceed more wisely to the next attempt.
But wait: what if the failure is a problem? What if a combination of new technology turns out to have cataclysmic consequences? That risk is posed by several leading edge technologies today:
Manipulation of viruses, to explore options for creating vaccines to counter new viruses — but what if a deadly new synthetic virus were to leak out of supposedly secure laboratory confinement?
Manipulation of the stratosphere, to reflect back a larger portion of incoming sunlight — but what if such an intervention has unexpected side effects, such as triggering huge droughts and/or floods?
Manipulation of algorithms, to increase their ability to influence human behavior (as consumers, electors, or whatever) —but what if a new algorithm was so powerful that it inadvertently shatters important social communities?
That takes us back to the message at the start of this article, by UNSG António Guterres: What if an attempt to achieve a decisive breakthrough results, instead, in a terrible breakdown?
Not the Terminator
The idea of powerful algorithms going awry is often dismissed with a wave of the hand: “This is just science fiction.”
But Jerome Glenn is correct in his statement (quoted earlier): “The fears of science fiction could occur.”
After all, HG Wells published a science-fiction story in 1914 entitled The World Set Free that featured what he called “atomic bombs” that derived their explosive power from nuclear fission. In his novel, atomic bombs destroyed the majority of the world’s cities in a global war (set in 1958).
But merely because something is predicted in science fiction, that’s no reason to reject the possibility of something like it happening in reality.
The “atomic bombs” foreseen by HG Wells, unsurprisingly, differed in several ways from the real-world atomic bombs developed by the Manhattan Project and subsequent research programs. In the same way, the threats from misconfigured powerful AI are generally different from those portrayed in science fiction.
For example, in the Hollywood Terminator movie series, humans are able, via what can be called superhuman effort, to thwart the intentions of the malign “Skynet” artificial intelligence system.
It’s gripping entertainment. But the narrative in these movies distorts credible scenarios of the dangers posed by AGI. We need to avoid being misled by such narratives.
First, there’s an implication in The Terminator, and in many other works of science fiction, that the danger point for humanity is when AI systems somehow “wake up,” or become conscious. If true, a sufficient safety measure would be to avoid any such artificial consciousness.
However, a cruise missile that is hunting us down does not depend for its deadliness on any cruise-missile consciousness. A social media algorithm that is whipping up hatred against specific ethnic minorities isn’t consciously evil. The damage results from the cold operation of algorithms. There’s no need to involve consciousness.
Second, there’s an implication that AI needs to be deliberately malicious before it can cause damage to humans. However, damage to human wellbeing can, just as likely, arise from side effects of policies that have no malicious intent.
When we humans destroy ant colonies in our process of constructing a new shopping mall, we’re not acting out of deliberate malice toward ants. It’s just that the ants are in our way. They are using resources for which we have a different purpose in mind. It could well be the same with an AGI that is pursuing its own objectives.
Consider a corporation that is vigorously pursuing an objective of raising its own profits. It may well take actions that damage the wellbeing of at least some humans, or parts of the environment. These outcomes are side-effects of the prime profit-generation directive that is governing these corporations. They’re not outcomes that the corporation consciously desires. It could well be the same with a badly designed AGI.
Third, the scenario in The Terminator leaves viewers with a false hope that, with sufficient effort, a group of human resistance fighters will be able to out-maneuver an AGI. That would be like a group of chimpanzees imagining that, with enough effort, they could displace humans as the dominant species on planet Earth. In real life, bullets shot by a terminator robot would never miss. Resistance would indeed be futile.
Instead, the time to fight against the damage an AGI could cause is before the AGI is created, not when it already exists and is effectively all-powerful. That’s why any analysis of future global developments needs to place the AGI risk front and center.
Four catastrophic error modes
The real risk — as opposed to “the Hollywood risk” — is that an AI system may acquire so much influence over human society and our surrounding environment that a mistake in that system could cataclysmically reduce human wellbeing all over the world. Billions of lives could be extinguished, or turned into a very pale reflection of their present state.
Such an outcome could arise in any of four ways – four catastrophic error modes. In brief, these are:
The system contains a defect in its implementation. It takes an action that it calculates will have one outcome, but unexpectedly, it has another outcome instead. For example, a geoengineering intervention could trigger an unforeseen change in the global climate, plunging the Earth into a state in which humans cannot survive.
Imagine, for example, an AI with a clumsily specified goal to focus on preserving the diversity of the Earth’s biosystem. That could be met by eliminating upward of 99% of all humans. Oops! Such a system contains a defect in its design. It takes actions to advance the goals it has explicitly been given, but does so in a way that catastrophically reduces actual human wellbeing.
The system has been given goals that are well aligned with human wellbeing, but as the system evolves, a different set of goals emerge, in which the wellbeing of humans is deprioritized. This is similar to how the emergence of higher thinking capabilities in human primates led to many humans taking actions in opposition to the gene-spreading instincts placed into our biology by evolution.
The system has been given goals that are well-aligned with human wellbeing, but the system is reconfigured by hackers of one sort or another — perhaps from malevolence, or perhaps from a misguided sense that various changes would make the system more powerful (and hence more valuable).
Some writers suggest that it will be relatively easy to avoid these four catastrophic error modes. I disagree. I consider that to be wishful thinking. Such thinking is especially dangerous, since it leads to complacency.
Wise governance of the transition to AGI
Various “easy fixes” may work against one or two of the above catastrophic error modes, but solutions that will cope with all four of them require a much more comprehensive approach: something I call “the whole-system perspective.”
That whole-system perspective includes promoting characteristics such as transparency, resilience, verification, vigilance, agility, accountability, consensus, and diversity. It champions the discipline of proactive risk management.
It comes down to a question of how technology is harnessed — how it is selectively steered, slowed down, and (yes) on occasion, given full throttle.
Taking back control
In summary, it’s necessary to “take back control of technology.” Technology cannot be left to its own devices. Nor to the sometimes headlong rushes of technology corporations. Nor to the decisions of military planners. And definitely not to the whims of out-of-touch politicians.
Instead, we — we the people — need to “take back control.”
Credit: Tesfu Assefa
There’s a huge potential role for the United Nations in wise governance of the transition to AGI. The UN can help to broker and deepen human links at all levels of society, despite the sharp differences between the various national governments in the world. An understanding of “our common agenda” regarding fast-changing technologies (such as AI) can transcend the differences in our ideologies, politics, religions, and cultures.
I particularly look forward to a worldwide shared appreciation of:
The risks of catastrophe from mismanaged technological change
The profound positive possibilities if these technologies are wisely managed.
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The Fitbit is the first wearable biosensor that achieved widespread adoption, due to a simple proposition: measure the amount of steps that a person takes in a day. This measurement turns out to be an excellent approximation for fitness level and provides a clear goal to measure against: 10,000 steps a day. How does it measure the steps? The accelerometer measures the movement of the Fitbit and can determine your motion.
Here is an example of an accelerometer recording someone walking:
Here is an example of someone jogging – note how different the data looks
It can tell when you are navigating a flight of stairs, and can tell which way you’re going – climbing up the stairs:
Climbing down the stairs:
Sitting down:
Standing up:
Even with just the accelerometer data above we have a pretty good idea of what the user is doing, but we can go much further. The Fitbit is a combination of sensors working together to paint a richer picture than each one could on its own. It combines an accelerometer, a gyroscope, an altimeter, a GPS, a heart rate sensor, electrical sensors that can measure skin conductivity, skin temperature, ambient light, and a microphone. These are all fairly low cost, straightforward sensors – and their applications are well known and have been used for decades.
Let’s look at just two of these sensors and how they work together. The first sensor is the accelerometer, which measures the motion of the device on three axes. From the motion readings generated, the user’s activity and motion can be identified: whether they are sitting, standing, walking, jogging, or navigating a set of stairs.
Each type of movement generates a distinct set of readings, so it’s possible to infer state from something as imprecise and simple as a device accelerometer. Correlating just this single sensor with a bit more information can be quite powerful – if a user’s phone tends to show consistent motion until 11pm at night, at which point it remains completely stationary for 7-8 hours and starts moving again in the morning, we can be reasonably confident that this is when the user is sleeping, despite not being able to measure it directly.
Let’s combine the accelerometer with another sensor that measures heart rate. Now we can not only measure the amount of motion that the user is engaging in, but the amount of effort that motion requires. Let’s ask a series of questions and consider each sensor, and what we get from them by themselves, compared to where we can get by combining them.
Using the data from these sensors:
Are you walking up a hill?
An accelerometer alone can answer this question, because it measures motion state and can infer things like how quickly you are walking, climbing, or running.
A heart rate sensor only measures your heart rate, so it can’t tell you what activity is actually causing your heart rate to go up or down.
Are you expending significant effort to walk up the hill?
The accelerometer can “know” that you are walking, but it cannot tell you how much effort you are expending.
The heart rate sensor can answer how much effort you are expending in any given moment, but it does not allow us to determine activity type very easily.
But when used together, we can show heart rate increase is being caused fromclimbing the hill. After the accelerometer shows the climbing has stopped, the heart rate monitor can measure the amount of time it takes the heart rate to return to normal. So using both sensors together helps form a much richer and more comprehensive picture than the accelerometer or heart rate monitor alone.
Let’s consider one more case, where we have the Fitbit’s GPS information. If we combine this information now, we can determine motion type from the accelerometer, level of effort from the heart rate meter, and location change from the GPS. Now we can know what hill the person climbed, when, and exactly how much effort the person expended. A hiker with a GPS alone could provide some of this information, but as we can see above, adding low cost sensors and correlating them has a powerful additive effect.
Combining multiple types of measurements and correlating them is something most of us are familiar with. When we go to the doctor’s office for an annual exam, there’s a series of tests that we will undergo. Our height and weight are recorded. Our skin temperature is measured, along with our heart rate and blood pressure. This provides a baseline for comparison with any changes we experience over time. These measurements are correlated and used to determine whether any unexpected shifts are happening. It’s by merging these measurements together that we can form a rich picture of our physical health and be confident in our analysis.
A recent evolution of these fitness trackers is the Whoop band, a competitor to Fitbit. It measures similar things like sleep, heart rate, and motion – with a more targeted goal of delivering feedback to the athlete on their current training readiness.
The Whoop band measures Heart Rate Variability as an indicator of physical strain and recovery from physical strain. Heart Rate Variability (HRV) is a metric that looks at the amount of variation in the intervals between a person’s heart beats. High Heart Rate Variability is considered desirable, and is a measure of physical recovery and/or stress.
By combining these low-cost sensors, the platform is able to calculate remarkably complex things about the wearer. It has enabled people to understand their bodies in new ways, from their sleep quality to the level of strain they experience while doing various exercises. After the workout the heart rate will rise and get more steady – both signs that the body is recovering from the strain. From the amount of change in heart rate, the app can determine where the user is in their recovery, and indicate whether to continue resting, engage in light exercise, or to work out at full intensity.
We are now at a point where a $200 device can passively measure things about your fitness and derive insights that ten years ago would have only been available to professional athletes. An array of simple, low cost sensors in a consumer device can provide the information people need to make effective and safe training decisions. Fitbit and Whoop showed how combining multiple sensors gave users new insights into their physical health; in the future, it will be possible to combine various other sensors to give users insights into their cognitive health.
So when talking about neuroscience and the coming revolution in our ability to measure ourselves, our internal states, and our body, what sensors are involved with that? It starts with the same biometric sensors we’re using currently in Fitbits and Apple Watches – skin temperature, heart rate, motion tracking. To that we add a few more that have been used extensively in research but are recently arriving in the consumer/low-cost sensor space, namely eye tracking, electroencephalography (EEG), and galvanic skin response.
Eye Tracking
With an eye tracking sensor bar or a VR headset, this sensor can tell where you are looking. Depending on the sensor, it will also be able to determine the size of your pupils – these expand and constrict with changes in “cognitive load.” (Note that pupil size can also mean other things, such as a reaction to brightness or outright fear.)
Cognitive Load is the concept that the brain has finite resources and will allocate them as needed – in the same way that your heart and lungs have physical limits on their performance. Pupil size, which can be measured from an eye tracker, is an excellent measurement of cognitive load. Thus, an eye tracker knows what you are looking at, and can also determine how intensely you are processing that object.
What can we do with this information? Imagine you’re a consumer goods company that is about to purchase a 30 second superbowl ad for $6 million. The commercials cost roughly $300,000 to produce. Why not produce three and find the one that consumers respond to the most? With an eye tracker, have some consumers look at the different ad versions. From that, run whatever commercial causes people to look at the product the most times, and spend the most time lingering on the product.
What doesn’t the eye tracker tell us? Anything else, really. How the person feels about the product, what brain state it evokes, how intensely they feel that way. What if they’re staring at it because they really dislike it? While that is engagement, it is perhaps not the kind of engagement wanted. While the eye tracker has an amazing ability to tell us what someone is looking at and how hard their brain is working, it won’t give us a complete picture. It’s accurate but partial.
Credit: Tesfu Assefa
Galvanic Skin Response (GSR)
Skin conductivity is responsive to emotional state. As an emotional experience gets more intense, a person’s skin conducts electricity better. It can be hard to directly measure whether that emotional response is positive or negative (though that can often be easily guessed).
If we were to run a study with the eye tracker alone, we could use the metrics it provides to get a good understanding of which products seem to appeal to consumers visually. We can also gain an understanding of how people engage with products they find visually appealing, by quantifying the difference in fixation order, fixation length, and gaze sequence between the products. Using pupil size, we can infer how much work their brain did processing the object they were looking at.
We cannot make statements about how the person felt emotionally about the object they were looking at using only the eye tracker.
However, if we were to run a study with the GSR meter alone, we could use the metrics it provides to get a good understanding of the emotional response people have, say, to a commercial. We don’t have a good way to distinguish which products generate which response, though we can partially infer it from looking at the way the GSR response changes during the 30 second commercial and whether the three commercials drive fundamentally different responses. Basically, we know how they feel, but we don’t know what they’re looking at, as they have that emotional response.
Now, if we combine these two sensors, there will be some initial difficulty reconciling this data. Eye trackers and GSR meters operate at different sample rates, but also at different timescales. These various physiological processes have different response times and different ways of presenting. 30 seconds is a lot of data for an eye tracker, but a small amount for a GSR meter. Combining sensors often starts with revisiting the experimental design to work with the strengths of both sensors. In this case, 30 seconds is still sufficient for doing this comparison, so we can proceed.
We need a new interpretation that includes both sensors. When we were just looking at the eye tracking, we defined “this person favors the desired object” as the success criteria. When we were just looking at the GSR, we defined “this person has the strongest emotional response” as the success criteria. Now we’re going to define “This person favors the desired object while having the strongest emotional response” as the success criteria.
From the eye tracker, we know what object a person is observing at any given moment. From the GSR meter, we know the intensity of the response. Now we can merge the two metrics, and see how much they agree with each other. If we find that the object the person looks at the most also generates the strongest emotional response, it would provide a very clear signal that they are interested in that object.
What if we find that there isn’t a clear relationship between the amount of time spent looking at an object and the emotional response? In that case, we would need to come up with a ‘tie breaker’ to resolve the contradiction. We could use another sensor – by adding facial expression tracking to the mix, we would have a clear idea of whether the emotion was pleasurable or not, and resolve inconsistencies between the eye tracker and GSR meter.
In some cases, we will not be able to come up with a clear relationship between the sensors, even if they are painting complimentary pictures of the data. In other cases, we will be able to come up with a clear relationship that we can apply to future experiments.
Finally, let’s step through the two scenarios above:
We run the combined eye tracking and GSR study for our food manufacturer.
We find that commercial #2 generates the most fixations on their product.
We also find that commercial #2 generates the longest gaze linger time on their product.
Finally, we find that commercial #2 generates the strongest emotional response overall, as well as during the times that the customer is looking at the product.
In this case, we can expect the food manufacturer to air commercial #2 during the Superbowl and hopefully receive a strong response. If that’s the case, we can build on our understanding and help companies find the most effective messaging to consumers.
Let’s consider the other scenario, where we run the combined eye tracking and GSR study and get slightly contradictory data.
In this case, we find that:
Commercial #3 generates the strongest emotional response overall.
Commercial #1 generates the most fixations and the longest linger time on their product.
So, in this case, we would need to make a decision regarding which sensor is considered more representative or useful in this case. Do we trust the conclusion from the GSR sensor which points to commercial 3? Do we trust the conclusion from the eye tracker which points to commercial 1? Or do we add another sensor to help clarify?
As we can see, fusing sensor data is helpful and informative. When sensors agree, it lends significant credibility to the metrics they generate as they often involve quite different processes. When sensors disagree, it can be a great hint that there are discrepancies in the experimental design or the expected understanding of the phenomenon being studied.
This approach to combining data is called Data Triangulation, and allows for robust and reliable findings when combining data from multiple sensors and paradigms.
The explosion of low cost sensors, internet-connected devices, cloud computing, and affordable wearables is bringing a revolution in our ability to collect data about ourselves, gain actionable insights, and make life better for human beings everywhere. By fusing the data from the sensors together, powerful analytical insights become available. By fusing the data we collect, even further insights are possible. And by triangulating across different methods, we can be confident in our conclusions. Now let’s take a closer look at all the different kinds of sensors available, in our Sensor Fusion Guide.
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All things in the universe seem to be constructed out of little parts coming together for greater possibility and greater choices. We call this “maximizing for optionality.”
That means the collective ability to do something more, and greater: to become more than the sum of their parts. Sub-quanta come together into atoms; atoms come together into stars and then planets, as they condense.
From chemicals to organisms
Protocells begin to emerge from chemical processes, and gradually RNA and DNA begin to emerge. From this, we see the first single-celled organisms. And they form biofilms to protect themselves, which they coordinate through radio-like signaling mechanisms.
What we’re seeing here is an exchange between multiple single-celled organisms. The ones on the outside are at greater risk of attack and damage. But these organisms also commensurately have a greater option for finding more food or more resources.
Quid pro quo
Actually, there’s something called “metabolic codependence,” where inner and outer organisms yo-yo. The outer ones expand, but then they run out of an enzyme they need, so the inner ones then govern how fast the biofilm can expand.
Inner organisms want to ensure an equitable trade of resources to feed them, so they provide the enzyme necessary to the outer ones, keeping them strong.
There is a quid pro quo, a trade between these different elements in the greater organism.
Along came mitochondria, and with them, eukaryotic cells, enabling cells to be warm-blooded, powered from within. This seems to be a fortuitous “accident” — mitochondria were once their own lifeform, which found greater negentropy by pairing up with a cell for their mutual benefit.
Symbiotic plants
Then came organisms able to harness an even greater throughput of energy by coming together as clumps of what would later become plants and fungi. Plants can take in energy from the sun via chlorophyll, and fungi can break down matter that otherwise resists decay.
However, the plants had another coordination problem: bigger plants that grew very tall would sometimes block out the sun for the younger ones. So trees coordinate to leave some room in the canopy for light to reach the bottom, a phenomenon known as “tree crown shyness.” They also subsidize the growth of younger plants, sending them nutrients in symbiosis with fungi.
The “wood wide web”
Meanwhile, mycelium webs beneath the forest floor enable plants to swap messages and resources even between species, warning of attacks and exchanging resources for mutual strength.
This messaging is, essentially, based on the same fundamental principle as civilization — technologies to make a dangerous world more reliably safe, achieved through coordination of effort and resources. This “wood wide web” enables ecosystems to prosper mutually, again demonstrating the tendency of systems to maximize negentropy.
The birth of brains
Nature has found many answers to these coordination problems, which seem reasonably equitable. Maybe we could learn from that: some of these cells (possibly mycelium-style cells) began to exhibit extra agency.
They began to connect to other cells that also specialize in this behavior. They became stronger over time, and eventually turned into brains and cortices. That’s why we see harmonics and action potentials akin to swarms of bees within the brain.
Emergence of “free will”
These cells still preserved some of their agency — albeit now a collective agency within the greater mass. And out of this, our sense of “free will” likely manifests.
We perceive ourselves as one individual. But in truth, we are a nation of ~86 billion tiny souls pulling us in all directions, our ego functioning as both ambassador and president, trying to keep a lid on it all.
Come together, right now…
In many ways, this is also a little bit like cellular automata — a macroscale version of microscale neurons. This ability to come together enables ants, for example, to carry off the tasty treat. But it also enables formidable feats of coordination.
Here we have a bolus of ants. Some are drowning, but swap with ones that aren’t drowning. By working together, they can survive. As with a biofilm, the ants most exposed get a little bit of extra help, or sometimes swap around with fresher ants.
We next saw the emergence of cold-blooded creatures, such as reptiles. They were long-lived and hardy but hampered by an inability to operate at all temperatures. They had to bask for a long time to gather enough energy to go into the world.
More complex, responsive brains
The advent of endothermy, warm-blooded creatures, enabled sustained activity to support a more complex and responsive brain at the expense of greater nutritional intake.
The mammalian brain also supports emotions far more sophisticated than the simple Fight, Flight, Feed, and Fornicate impulses of the reptilian brain. Mammals bond with each other in ways that reptiles cannot. Mammals are driven to protect their offspring and friends, encouraging coordination for hunting and watchkeeping.
This ability for mammals to bond with each other enabled our ancestors to bond with dogs and later cats. By hunting, we gain more resources for our big brains. This bonding allowed us to divide our labor so efficiently that some were now able to devote themselves exclusively to thinking.
They created new inventions, narratives, and ways of conceptualizing the world — new ways of solving problems and bringing people together in shared meaning.
Linking up
That sense of meaning and the trust gained by common rules of engagement (morals) enabled us to have tolerance for lots of strangers, and thus tribes became nations. Today, we have planetary information and energy networks linking all of us together.
Along with being able to split the load of work with other humans and other species, we harnessed the secret of fire, developing an obligate dependence upon it.
Fire enabled us to process inedible foods into something safe and nutritious. As our ability to work together in massively distributed, highly specialized labor chains improved, we were able to harness further forms of fire — coal, steam, uranium, to keep us warm. Now, our entire city colonies are heat islands.
So are there innate laws of ethics?
It may be possible for a system trained on a lot of information to pinpoint this seeming truth: the “will” of the universe is trying to connect things for their mutual negative entropy (slowing down the gradual decline into disorder), allowing for mutual improved optionality.
So if different organisms, from plants to ants to human beings, have come together to solve these problems, does it follow that there are innate constructive laws of ethics?
What if an AI could prove definitively that there are such constructive laws for ethics, pinpointing an ethical maxim of “good”? A self-evident, evolutionary epiphany that one can’t unsee once realizing it.
Something like:
(a) Maximizing optionality, which means minimizing entropy, or maximizing negative entropy, through collective benefit that is …
(b) by invitation — not by coercion, not by co-opting, but through equitable mutual benefit. We may even define love in the same way. M. Scott Peck defined love as “The will to extend oneself for the purpose of nurturing our own or another’s spiritual growth.” That sounds pretty aligned to me.
In the immortal words of Eden Abbez, “the greatest thing you’ll ever learn is just to love and be loved in return.”
Perhaps that loving sentiment is more applicable than we knew, expressing “the will of the universe” at all scales and across all time.
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I’ve been a futurist for over 25 years, and I’ve learned that an important part of doing professional foresight work is the acceptance that much of what we imagine and speculate upon will be entirely wrong. This is fine; as I’ve said in a number of talks, part of the goal is to be “usefully wrong,” able to gather insights and make connections from otherwise off-target forecasts. That said, it’s nearly always pleasant to have a forecast map closely to real world developments (“nearly,” because some scenarios are very dire, and I desperately want to be wrong about them).
Among the forecasts and scenarios I’ve created and made public over the years, the first one to draw real attention also turned out to be one of the most prescient: the “Participatory Panopticon.” And it turns out that the Participatory Panopticon is a valuable example of thinking through a BANI lens.
The core idea of the Participatory Panopticon is that portable technologies that combine always-on network connections, high-quality cameras, and large amounts of cheap data storage will radically change our behavior, our politics, and, in so many ways, our lives. We would be surrounded by constant observation and recording of our actions (the Panopticon part), but not just by authorities surveilling us, by our peers, both friends and strangers (the Participatory part). We’d be able to pull up archived recordings or put the video online live, and we all would be doing it.
This might sounds everyday in 2022, but I first started writing about the concept in 2003, and started speaking in public about it in 2005. Mobile phones with cameras (then referred to as “cameraphones”) had existed for a couple of years, but they were slow, very limited in what they could capture, and produced terrible pictures. At best they were on 2G “Edge” networks, so uploading even a low-resolution image could take minutes. None of what I talked about in the Participatory Panopticon would be an obvious implication of the technology of the day. There were hints, and broadly-connected examples, but nobody else had put the pieces together, at least for a public audience.
Let me be quick to note that much of what I wrote and thought about the Participatory Panopticon was off-target. I had an odd focus on always-on recording (underestimating how intrusive that would feel) and wearable tech (still a few years off, even now). But the core of the concept resonated for many: being able to document the world around us, in real-time, through a vast number of personal devices, has enormous consequences for human interaction and politics. I’ve been told that it influenced the strategic direction for human rights organizations, and helped to push the concept of “sousveillance” into more common (albeit still limited) usage.
I say all of this not as a flex, but as a way of underscoring that this is a topic I have been thinking about for a long time. Even though it’s a nearly 20-year-old idea, it still has salience. In my view, the Participatory Panopticon offers a useful illustration of what BANI can mean.
(As a refresher: BANI stands for Brittle, Anxious, Nonlinear, and Incomprehensible, and is a framework for discussing and understanding the present chaotic environment, just as VUCA — Volatile, Uncertain, Complex, and Ambiguous — was for understanding the nature of the post-Cold War/early Internet era.)
Credit: Tesfu Assefa
Brittle
The “B” in BANI talks about changes that are sudden, surprising, and hard to ignore. Brittle systems seem strong but break — shatter, even — under a sufficient amount or sufficient kind of pressure. I would say that the way the Participatory Panopticon has engendered a brittle chaos is in its intersection with law enforcement. Pathological or harmful policing norms and habits that had persisted for decades remained hidden, allowing those behaviors to fester. We would have occasional stories or scandals, but it was easy for mainstream institutions to assume that these were outliers, since these episodes seemed so infrequent.
However, the technologies of the Participatory Panopticon upended all of this, and delivered a massive blow to the ability of mainstream institutions to simply ignore the prevalence of police violence.
We’ve seen a variety of responses to the digital shredding of the cloak over law enforcement behavior. Although attempts to change laws around police accountability have been largely unsuccessful, cultural norms about the legitimate and ethical use of police power have evolved considerably. The biggest change has been the adoption of police body cameras. This was a direct counter to the proliferation of citizen cameras; that the body cameras would add to the Participatory Panopticon was likely unintentional. However, the ability of officers to shut off body cameras, the tendency for the cameras to “fail” just when needed most, and frequency with which the recoded content is held as “private” by many law enforcement agencies have only fed the growth of citizen documentation of police behavior.
Anxious
The “A” in BANI points to changes that are often confusing, deceptive, and emotionally painful. Anxiety-inducing situations and conditions may result in dilemmas or problems without useful solutions, or with unavoidably bad outcomes. This is probably the most visible manifestation of BANI in the Participatory Panopticon world, as we’ve become hyper-conscious of the constant observation made possible by these tools. More importantly, many of us are relentlessly judged for our appearances and our behaviors, even if that behavior happened years in the past.
This, in turn, has prompted problematic choices like “face tuning,” allowing for changes not just in facial structure and skin quality, but even things like ethnicity, body form, and apparent age. “Social network anxiety” is a frequent subject of mass-media attention, but in the vast majority of cases, the aspect of social media triggering anxiety is visual — photos and videos.
Arguably, many of the fears and complications around privacy can be connected here as well. The original 19th century Panopticon was a design for a prison where all prisoners could be under constant watch. That there’s now a participatory element doesn’t decrease the oppressive nature of being under permanent surveillance. Moreover, the nature of data collection in 2022 means that the surveillance can be through metadata, even through inferences known as “probabilistic identifiers,” the creation of targeted personal information using machine learning systems on “anonymized” data. In other words, the cameras that don’t see you can be just as informative as the cameras that do.
Nonlinear
The “N” in BANI refers to changes that are disproportionate, surprising, and counter-intuitive. Input and output may not match in speed or scale. For the most part, the nonlinear elements of the Participatory Panopticon concern the exponential effects of network connections. Most of us are familiar with this; the utility of social media largely depends upon how many other people with whom we want to connect can be found on a given platform. The size and spread of a network greatly impacts one’s ability to spread a video or image around.
For many of us, this may not seem terribly chaotic in its day-to-day existence, as it’s a familiar phenomenon. The disruption (and ultimately the chaos) comes from the ability of networks like these to enable a swarm of attacks (abuse, doxing, threats, SWATting, etc.) on a particular target.
Seemingly out of nowhere, tens or hundreds of thousands of people attack a person they’ve been told has done something wrong. In reality, this person may not even be connected to the “actual” intended target. Although such mis-targeting can arise due to error (such as being confused for someone with the same name), in too many cases the driver is malice (such as being made a scapegoat for a particular, tangentially-related, event). Even if the attacks go after the “right” target, the impact of social swarm abuse on the psyche of an individual can be devastating.
Incomprehensible
The “I” in BANI looks at the changes that might be senseless, ridiculous, or unthinkable. Changes where much of the process is opaque, rendering it nearly-impossible to truly understand why an outcome has transpired. The incomprehensible aspects of the Participatory Panopticon are non-obvious, however. The technological aspects of the phenomenon are well-understood, and the social motivation — “why is this happening?” — can often be quite blatant: aggrandizement, narcissism, politics, and so forth. What’s incomprehensible about the Participatory Panopticon is, in my view, just what can be done to limit or control its effects.
One of the cornerstone arguments I made in the original public presentation of the Participatory Panopticon idea was that this situation is the emergent result of myriad completely reasonable and desirable options. I still believe that this is the case; each of the separate elements of a Participatory Panopticon (such as the ability to stream video or instantly map locations) have enormous utility. Moreover, the social (and political) value of a mass capacity to keep an eye on those with institutional, economic, or social power has become critical. This compounds the inability to take useful steps to mitigate or eliminate its harm.
Admittedly, this last element of BANI in the Participatory Panopticon isn’t as direct or clear as the others. We could instead simply argue that specific drivers of a chaotic world need not check each of the four BANI boxes; just being a catalyst for increased brittleness in social systems or anxiety among citizens may be enough.
The Participatory Panopticon has been a concept woven into my thinking for nearly two decades, and to me represents one of the clearest examples of the way in which technological developments can have enormous unintended (and unexpected) impacts on our societies and cultures — which then, in turn, shape the directions taken by developers of new technologies.
BANI, then, serves as a lens through with to examine these impacts, letting us tease out the different ways in which a radical shift in technology and society can lead to a chaotic global paradigm. We can then look at ways in which responses to one arena of BANI chaos — other forms of brittleness, for example — may help us respond to the chaos engendered by the Participatory Panopticon.
Giving a name to a phenomenon, whether the Participatory Panopticon or BANI itself, is a way of giving structure to our understanding of the changes in our world. As I said above, it’s a lens; it’s a tool to focus and clarify our view of particular aspects of the changes swirling around us. It’s not the only tool we have, by any means. But if giving names and structure to the increasing maelstrom of chaos we face helps us see a path through, the value of that tool should not be underestimated.
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Well, firstly — as you can see — Mindplex is a fun/funky new future-oriented magazine and media site that some of my crazy colleagues and I are in the process of launching as a spinoff of SingularityNET, our AI-meets-blockchain project.
Read, view, comment, contribute, enjoy! Expand your mind and help expand ours and everybody else’s. …
Enter the Mindplex …
Yes, yes, an online magazine. And we’re aiming for an insanely great one — news articles, stories, images, videos and more … comments on these creations, comments on comments, and so forth.
We have a great editorial team, led by Senior Editor Amara Angelica, who edited Kurzweilai.net for 18 years and helped Ray Kurzweil create The Singularity Is Near and other works. AGI, life extension, nanotech, blockchain, robotics, consciousness, you name it. …
New open-source tools
But we’re also aiming at something more: To use the zine as a platform for gradually rolling out and experimenting with a series of exciting new tools, leveraging blockchain or AI or both.
Most of these tools will also be mde available open-source for others to use and made available via decentralized SingularityNET hosting for other media sites to use via API.
The goal is to make Mindplex a showcase for new processes and dynamics, with the capability of shifting the online mediaverse as a whole from its current convoluted and largely counterproductive condition into a majorly amazing force for both human and Singularitarian good.
A reputation system
One of the first things we’ll be experimenting with is a complex-systems-theory-based reputation system that leverages crypto tokens to manage creator and user reputations.
Interaction between reputation and rating is important here, as is complete transparency to users regarding how and why the system’s algorithmic judgments are being made.
Existing media sites like Reddit and Steemit are object lessons in how the nitty-gritty of reputation and rating algorithms can very concretely impact the nature of content in an online ecosystem. It’s essential that — with the active involvement of the community — we get this some kind of right.
New AI-based experiments
Beyond this, you can expect an ongoing flow of new tech/human experiments. Part of the fun will be the surprise of learning and reinventing as we go, along with the community, But to mention just a few things we’ve been playing with:
AI dialogue systems for chatting with readers and answering questions about media posted on the site.
AI-based interactive content-creation tools — focused initially on creating interactive science fiction that explores the potential implications and uses of new technologies described and discussed on the site.
AI-generated memes and comics
Collaboration with SophiaDAO, another amazing SingularityNET ecosystem project, on immersive virtual-world scenarios related to Mindplex content.
Leveraging TWIN Protocol technology, allowing users to create their own digital twins and allowing others to experience their twins’ reactions to Mindplex content.
Unique mathematical tools that allow the user to create visual and music art, based on the user’s own DNA sequence.
Variations on the Theme of “Mindplex”
But why are we launching such a site at this time … what’s the underlying philosophy and mission?
Well, I’m glad you asked…
“Mindplex” is a term I introduced in 2003 or so when I was looking for a word to describe a mind that’s more unified than human society, but less unified than an individual human mind.
Imagine a group of best friends in the year 2050 who are all supplied with brain-chip implants and share their every thought and feeling with each other via “Wi-Fi telepathy.”
Such a bestie group will be almost — but not quite — a sort of “group mind” … something like a Borg mind, but with more loose/wild self-organization and without the fascist flavor.
Another kind of mindplex
My neuroscientist collaborator Gabriel Axel and I picked up the term again in 2019 when we were writing a paper on non-ordinary states of consciousness — including states where multiple people feel fused into a sort of mutually experiencing whole.
Fast forward to 2021: I found myself collaborating with an amazing team to create a tokenomics-and-AI-fueled futurist media project — and I realized that what we were aiming to curate was precisely another form of mindplex.
Could we use AI and blockchain to create a self-organizing body of content, intelligence and understanding? One centered around the theme of transformational technology and the human and transhuman future?
One that would include insights, respect, and reward for the autonomy of the individual people contributing, but also display an emergent vision and comprehension beyond the scope of any one human individual?
In a way, this is what any subculture does. But historical human cultures didn’t have access to the array of technologies at our present disposal, which have the potential to make communication and information dynamics more intense and emergence more emphatic.
The mind is only free for those who own their online selves
The Internet has been an unabashedly awesome thing for software programs and robots. For human beings, it’s been a mixed bag — with huge positives and chilling negatives, and a whole lot of aspects that are just plain confusing. This has been true for the impact of the internet on so many aspects of human life, and definitely in the areas of media and social interaction.
A. J. Liebling’s statement, “Freedom of the press is guaranteed only to those who own one,” is no longer so relevant. Instead, we have a situation where “the ability to reliably direct people’s attention is only available to those with a lot of money to feed AI marketing engines.”
Almost anyone in the developed world — and an increasing subset of the developing world — can now post their thoughts, images, sounds or videos online … and their commentary on the works of others. But getting other folks to notice your productions or comments is a whole different matter.
Getting paid to produce high-quality content is also increasingly difficult as the pool of freely offered content increases in size, depth and diversity. More of the money in the mediasphere goes to those offering services that sell advertisements to readers of said freely offered content.
Finding like-minded folks — or at least like-opinioned folks regarding particular classes of issues — gets easier and easier as the technology for clustering people into similarity groups gets more and more refined, due to its utility for driving targeted marketing.
But finding interesting people whose mix of similar and divergent perspectives can drive one toward fundamental growth is much harder and has been a serious focus because it’s more difficult to monetize.
Tribal mind lifeforms
The proto-mindplexes that are most habitually forming on today’s internet are often troublingly tribal in nature — like narrow-minded, collective-mind lifeforms creating their own insular information metabolisms on the scaffolding of social media filter bubbles.
The rare individuals able to earn a living creating media mostly do so by playing, in a highly focused way, to the most differentiating beliefs of particular online tribes.
Today’s slogan should perhaps be more like “the mind is only free for those who own their online selves” — i.e., they own their data, metadata and online persona.
Because if you don’t own and control those aspects of your online presence, your mind is being constantly influenced and controlled by the information shown to you by whoever does have this ownership and control.
Decentralized, democratized media
Of course, “freedom” in an absolute sense is a philosophical conundrum that mixes poorly with physical and social realities. But what we’re talking about here is practical causal agency over what one sees, does and thinks.
Increasingly, this causal agency is being handed over to large corporations with their own financial growth agendas, rather than maintained individually or thoughtfully granted to mindplexes or other social groups we have intentionally entered into and co-formed.
Case in point: SingularityNET was formed to decentralize and democratize AI. And this provides a big piece of what’s needed to decentralize, democratize, and end Big Tech hegemony over media (social, anti-social and otherwise). But it’s not the whole story.
Dynamic intelligent mindplexes
Decentralized democratized media needs decentralized, democratized AI, deployed appropriately alongside other media-focused tools and mechanisms. This is how we can create online media that accentuates the positive and palliates the negative aspects of the Internet we experience today.
And it’s how we can move forward to foster the creation of dynamic, intelligent mindplexes that transcend tribalism, help us grow, connect and expand our consciousness, and self-transform in unexpectedly positive and glorious ways.
Let us know your thoughts! Sign up for a Mindplex account now, join our Telegram, or follow us on Twitter.
Being a futurist is quite a bit like being a historian. History isn’t just a listing of events, it’s a discipline that seeks to figure out why this or that happened, what led that direction, and what choices we had along the way. The same thing can be said for foresight work — the goal isn’t to list future events, it’s to get a better understanding of why we’re getting various outcomes, and how our choices can lead us in very different directions. I think of futurism as Anticipatory History.
The success of this process depends upon our ability to spot patterns; as Theodor Reik said, “It has been said that history repeats itself. This is perhaps not quite correct; it merely rhymes.” But what happens when the rhyme scheme breaks? That is, what happens when our expectations about patterns start to fail?
I’ve been working as a futurist for over 25 years, so I’ve spent quite a bit of time observing (and learning from) global patterns. About five years ago, I started to get a sense that patterns weren’t repeating (or rhyming) as readily, that big global systems that had once been fairly consistent had become far less so. Looking back, I suspect that the primary catalyst was the accelerating impact of global climate disruption.
Big economic, political, environmental, technological, even sociocultural systems seemed like they were starting to fail, or at least become much less reliable. I wasn’t alone in these observations.
In 2018, I started working on a framework for understanding the scale of what we’re facing as once seemingly-reliable global systems started to break down. So many of the people I spoke with over the past half-decade had started to express great surprise and confusion about what was going on around the world; it quickly became apparent that this wasn’t just the standard “the future is an unknown” that we all live with, it was an increasingly desperate sense that things we thought we understood were spinning wildly out of control. We couldn’t as reliably make reasonable judgments about what was to come in the weeks and months ahead. Systems that appeared strong were suddenly on the verge of collapse; processes that were becoming increasingly critical to our daily lives were becoming less and less understandable.
Creating a framework like this wasn’t just a random idea; I was trying to bring a conceptual tool already in the foresight analyst’s kit into the 21st century. The world of strategic consulting had long relied on a basic framework to offer language and structure to a changing world. It was the “VUCA” model, which was invented in the late 1980s at the US Army War College and spreading quickly throughout the world of consulting. VUCA is an acronym comprising four descriptive terms: Volatile; Uncertain; Complex; and Ambiguous. For a world moving out of the Cold War era and into the Internet era, these terms felt right. They perfectly captured the kinds of disruptions that were starting to happen more often, especially as the global war on terror fired up.
But the world has rocketed past merely being “uncertain” or “volatile.” At this point, VUCA no longer captures disruptions to the norm, it is the norm. But if a VUCA world is all around us all the time, the term has lost its utility as a way of labeling discontinuities in how our world functions. Something new was needed.
In late 2018, I presented the “BANI” framework for the first time. BANI parallels VUCA, in that it’s a basic acronym for a set of descriptive terms. In this case, however, the terms are as subtle as a brick. BANI comes from: Brittle; Anxious; Nonlinear; and Incomprehensible. These four concepts let us articulate the ways in which the world seems to be falling apart. It’s a taxonomy of chaos.
Credit: Tesfu Assefa
The quick summary:
B in BANI is for Brittle. Systems that are brittle can appear strong, even work well, until they hit a stress level that causes collapse. Brittle does not bend, it breaks. Very often the breaking point isn’t visible to most people in advance, and comes as a surprise. Sometimes this is because the weaknesses are hidden or camouflaged; sometimes this is because the stress that causes the break is external and unexpected. The example I like to use is guano, fecal matter from birds and bats. In the 19th century, its use as a fertilizer gave the world its first taste of an agricultural revolution. It was so important that countries fought wars over ownership of guano-covered islands. And in a few short years late in the century, that all disappeared after the development of the Haber-Bosch process for making artificial fertilizer. Something that was absolutely critical became worthless seemingly overnight.
Brittle chaos is sudden, surprising, and hard to ignore.
A is for Anxious (or Anxiety-inducing). Systems that trigger anxiety are those that pose dilemmas or problems without useful solutions, or include irreversible choices that have unexpectedly bad outcomes. Anxious systems make trust difficult, even impossible. Things that had been well-understood suddenly seem alien or false. My usual example of an anxiety-inducing system is malinformation, the term that encompasses intentional misinformation, errors, insufficient information, and confusion. Noise in the signal. The last half-decade has been full of this, and we’ve seen some especially powerful uses in recent months and years. Malinformation often relies on technological tools, but the importance doesn’t come from the technology, it comes from the human response. In many, if not most, cases, malinformation isn’t used to make a target believe something that is false, it’s to make a target doubt the validity of something that is true.
Anxious chaos is confusing, deceptive, and emotionally painful.
N is for Nonlinear. Nonlinear systems are those where, most simply, input and output are disproportionate. Cause and effect don’t match in scale or speed. Audio feedback is a familiar example of a nonlinear phenomenon; the spread of pandemic disease is another. Nonlinear in the BANI usage refers to systems that see changes that don’t match expectations built on familiar reality. They’re common in nature, although often met by countervailing forces that keep the nonlinear aspects in check. The biggest (and most important) example of a nonlinear phenomenon is climate disruption, more precisely the hysteretic aspect of climate change. Hysteretic means a long lag between cause and effect, enough so that the connections are often functionally invisible. The connection between atmospheric greenhouse inputs and temperature/heat-related results is slow — potentially on the order of 20-30 years, although some more recent evidence suggests that it might be close to 10 years. Either way, the seeming disconnect between cause and effect means that (a) what we’re seeing now is the result of carbon emissions from one or two decades ago, and (b) whatever changes we make to cut carbon emissions won’t have any visible benefits for decades.
Nonlinear chaos is disproportionate, surprising, and counter-intuitive.
Finally, the I is for Incomprehensible. I got the most pushback on this one — can we really say that something is truly incomprehensible? But what I mean here is that, with an incomprehensible phenomenon, the process leading up to the outcome is thoroughly opaque, with difficult or incomplete explanations. The decision-making of machine learning systems gives us a current example. Increasingly, it’s difficult at best to explain how a deep learning system reaches its conclusions. The consequence can often be that sophisticated systems can make strange or inexplicable errors (such as a self-driving vehicle repeatedly mistaking the moon for a traffic signal). Incomprehensible can also mean behavior outside the realm of rational understanding.
Incomprehensible chaos is ridiculous, senseless, even unthinkable.
Credit: Tesfu Assefa
When I created BANI, I did so largely as a way for me to visualize the diverse ways in which global systems were failing. But it turns out that there’s hunger around the world for just this kind of framework. Over the past year, I’ve given a dozen or more talks and presentations on BANI for audiences everywhere from Amazonia to Zürich (people in Brazil seem especially interested); in the coming months, I’ll be speaking about BANI for audiences in places like Sri Lanka.
But it’s important not to overpromise what the BANI framework can do. Thinking in BANI terms won’t give you a new leadership strategy or business model. It won’t tell you how to better make profit amidst chaos. When I talk about what can be done to withstand the chaos of a BANI world, I go to human elements and behaviors like improvisation, intuition, and empathy. The chaos of BANI doesn’t come from changes in a geophysical system or some such, it comes from a human inability to fully understand what to do when pattern-seeking and familiar explanations no longer work.
Even if BANI is only descriptive, not prescriptive, we’ve long known that giving a name to something helps to reify it. People had been groping for a way to articulate their sense of chaos, and BANI provides a common, understandable language for doing so. BANI helps to give structure to our experience of the chaos swirling around us, and in doing so, helps us to consider more fully what to do next.
Let us know your thoughts! Sign up for a Mindplex account now, join our Telegram, or follow us on Twitter.
“The Singularity” — the anticipated creation of Artificial General Intelligence (AGI) — could be the most important concept in the history of humanity. It’s unfortunate, therefore, that the concept is subject to considerable confusion.
Four different ideas interweaved, all accelerating in the same direction toward an unclear outcome (Credit: David Wood)
The first confusion with “the Singularity” is that the phrase is used in several different ways. As a result, it’s easy to become distracted.
Four definitions
For example, consider Singularity University (SingU), which has been offering courses since 2008 with themes such as “Harness the power of exponential technology” and “Leverage exponential technologies to solve global grand challenges.”
For SingU, “Singularity” is basically synonymous with the rapid disruption caused when a new technology, such as digital photography, becomes more useful than previous solutions, such as analog photography. What makes these disruptions hard to anticipate is the exponential growth in the capabilities of the technologies involved.
A period of slow growth, in which progress lags behind expectations of enthusiasts, transforms into a period of fast growth, in which most observers complain “why did no one warn us this was coming?”
Human life “irreversibly transformed”
A second usage of the term “the Singularity” moves beyond talk of individual disruptions — singularities in particular areas of life. Instead, it anticipates a disruption in all aspects of human life. Here’s how futurist Ray Kurzweil introduces the term in his 2005 book The Singularity Is Near:
What, then, is the Singularity? It’s a future period during which the pace of technological change will be so rapid, its impact so deep, that human life will be irreversibly transformed… This epoch will transform the concepts that we rely on to give meaning to our lives, from our business models to the cycle of human life, including death itself…
The key idea underlying the impending Singularity is that the pace of change of our human-created technology is accelerating and its powers are expanding at an exponential pace.
The nature of that “irreversible transformation” is clarified in the subtitle of the book: When Humans Transcend Biology. We humans will no longer be primarily biological, aided by technology. After that singularity, we’ll be primarily technological, with, perhaps, some biological aspects.
Superintelligent AIs
A third usage of “the Singularity” foresees a different kind of transformation. Rather than humans being the most intelligent creatures on the planet, we’ll fall into second place behind superintelligent AIs. Just as the fate of species such as gorillas and dolphins currently depends on actions by humans, the fate of humans, after the Singularity, will depend on actions by AIs.
Such a takeover was foreseen as long ago as 1951 by pioneering computer scientist Alan Turing:
My contention is that machines can be constructed which will simulate the behaviour of the human mind very closely…
It seems probable that once the machine thinking method had started, it would not take long to outstrip our feeble powers. There would be no question of the machines dying, and they would be able to converse with each other to sharpen their wits. At some stage therefore we should have to expect the machines to take control.
Finally, consider what was on the mind of Vernor Vinge, a professor of computer science and mathematics, and also the author of a series of well-regarded science fiction novels, when he introduced the term “Singularity” in an essay in Omni in 1983. Vinge was worried about the unforeseeability of future events:
There is a stone wall set across any clear view of our future, and it’s not very far down the road. Something drastic happens to a species when it reaches our stage of evolutionary development — at least, that’s one explanation for why the universe seems so empty of other intelligence. Physical catastrophe (nuclear war, biological pestilence, Malthusian doom) could account for this emptiness, but nothing makes the future of any species so unknowable as technical progress itself…
We are at the point of accelerating the evolution of intelligence itself. The exact means of accomplishing this phenomenon cannot yet be predicted — and is not important. Whether our work is cast in silicon or DNA will have little effect on the ultimate results. The evolution of human intelligence took millions of years. We will devise an equivalent advance in a fraction of that time. We will soon create intelligences greater than our own.
A Singularity that “passes far beyond our understanding”
This is when Vinge introduces his version of the concept of singularity:
When this happens, human history will have reached a kind of singularity, an intellectual transition as impenetrable as the knotted space-time at the centre of a black hole, and the world will pass far beyond our understanding. This singularity, I believe, already haunts a number of science fiction writers. It makes realistic extrapolation to an interstellar future impossible.
If creatures (whether organic or inorganic) attain levels of general intelligence far in excess of present-day humans, what kinds of goals and purposes will occupy these vast brains? It’s unlikely that their motivations will be just the same as our own present goals and purposes. Instead, the immense scale of these new minds will likely prove alien to our comprehension. They might appear as unfathomable to us as human preoccupations appear to the dogs and cats and other animals that observe us from time to time.
Credit: David Wood
AI, AGI, and ASI
Before going further, let’s quickly contrast today’s AI with the envisioned future superintelligence.
Existing AI systems typically have powerful capabilities in narrow contexts, such as route-planning, processing mortgage and loan applications, predicting properties of molecules, playing various games of skill, buying and selling shares, recognizing images, and translating speech.
But in all these cases, the AIs involved have incomplete knowledge of the full complexity of how humans interact in the real world. The AI can fail when the real world introduces factors or situations that were not part of the data set of examples with which the AI was trained.
In contrast, humans in the same circumstance would be able to rely on capacities such as “common sense”, “general knowledge,” and intuition or “gut feel”, to reach a better decision.
An AI with general intelligence
However, a future AGI — an AI with general intelligence — would have as much common sense, intuition, and general knowledge as any human. An AGI would be at least as good as humans at reacting to unexpected developments. That AGI would be able to pursue pre-specified goals as competently as (but much more efficiently than) a human, even in the kind of complex environments which would cause today’s AIs to stumble.
Whatever goal is input to an AGI, it is likely to reason to itself that it will be more likely to achieve that goal if it has more resources at its disposal and if its own thinking capabilities are further improved. What happens next may well be as described by IJ Good, a long-time colleague of Alan Turing:
Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an “intelligence explosion,” and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control.
Evolving into artificial superintelligence
In other words, not long after humans manage to create an AGI, the AGI is likely to evolve itself into an ASI – an artificial superintelligence that far exceeds human powers.
In case the idea of an AI redesigning itself without any human involvement seems far-fetched, consider a slightly different possibility: Humans will still be part of that design process, at least in the initial phases. That’s already the case today, when humans use one generation of AI tools to help design a new generation of improved AI tools before going on to repeat the process.
Once a machine is designed that is good enough… it can be put to work designing an even better machine…
There will only be a very short transition period between having no very good machine and having a great many exceedingly good ones.
At this point an “explosion” will clearly occur; all the problems of science and technology will be handed over to machines and it will no longer be necessary for people to work. Whether this will lead to a Utopia or to the extermination of the human race will depend on how the problem is handled by the machine.
One additional twist to the concept of Singularity needs to be emphasized. It’s not just that, as Vernor Vinge stressed, the consequences of passing the point of Singularity are deeply unpredictable. It’s that the timing of reaching the point of Singularity is inherently unpredictable too. That brings us to what can be called the second confusion with “The Singularity.”
It’s sometimes suggested, contrary to what I just said, that a reasonable estimate of the date of the Singularity can be obtained by extrapolating the growth of the hardware power of computing systems. The idea is to start with an estimate for the computing power of the human brain. That estimate involves the number of neurons in the brain.
Extrapolate that trend forward, and it can be argued that such a computer would match, by around 2045, the capability not just of a single human brain, but the capabilities of all human brains added together.
Next, consider the number of transistors that are included in the central processing unit of a computer that can be purchased for, say, $1,000. In broad terms, that number has been rising exponentially since the 1960s. This phenomenon is part of what is called “Moore’s Law.”
This argument is useful to raise public awareness of the possibility of the Singularity. But there are four flaws with using this line of thinking for any detailed forecasting:
Individual transistors are still becoming smaller, but the rate of shrinkage has slowed down in recent years.
The power of a computing system depends, critically, not just on its hardware, but on its software. Breakthroughs in software defy any simple exponential curve.
Sometimes a single breakthrough in technology will unleash much wider progress than was expected. Consider the breakthroughs of deep learning neural networks, c. 2012.
Ongoing technological progress depends on society as a whole supplying a sufficiently stable and supportive environment. That’s something else which can vary unpredictably.
A statistical estimate
Instead of pointing to any individual date and giving a firm prediction that the Singularity will definitely have arrived by then, it’s far preferable to give a statistical estimate of the likelihood of the Singularity arriving by that date. However, given the uncertainties involved, even these estimates are fraught with difficulty.
The biggest uncertainty is in estimating how close we are to understanding the way common sense and general knowledge arises in the human brain. Some observers suggest that we might need a dozen conceptual breakthroughs before we have a comprehension sufficient to duplicate that model in silicon and software. But it’s also possible that a single conceptual leap will solve all these purportedly different problems.
Yet another possibility should give us pause. An AI might reach (and then exceed) AGI level even without humans understanding how it operates — or of how general intelligence operates inside the human brain. Multiple recombinations of existing software and hardware modules might result in the unforeseen emergence of an overall network intelligence that far exceeds the capabilities of the individual constituent modules.
Schooling the Singularity
Even though we cannot be sure what direction an ASI will take, nor of the timescales in which the Singularity will burst upon us, can we at least provide a framework to constrain the likely behavior of such an ASI?
The best that can probably be said in response to this question is: “it’s going to be hard!”
As a human analogy, many parents have been surprised — even dumbfounded — by choices made by their children, as these children gain access to new ideas and opportunities.
Introducing the ASI
Humanity’s collective child — ASI — might surprise and dumbfound us in the same way. Nevertheless, if we get the schooling right, we can help bias that development process — the “intelligence explosion” described by I.J. Good — in ways that are more likely to align with profound human wellbeing.
That schooling aims to hardwire deep into the ASI, as a kind of “prime directive,” principles of beneficence toward humans. If the ASI would be at the point of reaching a particular decision — for example, to shrink the human population on account of humanity’s deleterious effects on the environment –- any such misanthropic decision would be overridden by the prime directive.
The difficulty here is that if you line up lots of different philosophers, poets, theologians, politicians, and engineers, and ask them what it means to behave with beneficence toward humans, you’ll hear lots of divergent answers. Programming a sense of beneficence is as least as hard as programming a sense of beauty or truth.
But just because it’s hard, that’s no reason to abandon the task. Indeed, clarifying the meaning of beneficence could be the most important project of our present time.
Tripwires and canary signals
Here’s another analogy: accumulating many modules of AI intelligence together, in a network relationship, is similar to accumulating nuclear fissile material together. Before the material reaches a critical mass, it still needs to be treated with respect, on account of the radiation it emits. But once a critical mass point is reached, a cascading reaction results — a nuclear meltdown or, even worse, a nuclear holocaust.
The point here is not to risk any accidental encroachment upon the critical mass, which would convert the nuclear material from hazardous to catastrophic. Accordingly, anyone working with such material needs to be thoroughly trained in the principles of nuclear safety.
With an accumulation of AI modules, things are by no means so clear. Whether that accumulation could kick-start an explosive phase transition depends on lots of issues that we currently only understand dimly.
However, something we can, and should, insist upon, is that everyone involved in the creation of enhanced AI systems pays attention to potential “tripwires.” Any change in configuration or any new addition to the network should be evaluated, ahead of time, for possible explosive consequences. Moreover, the system should in any case be monitored continuously for any canary signals that such a phase transition is becoming imminent.
Again, this is a hard task, since there are many different opinions as to which kind of canary signals are meaningful, and which are distractions.
Credit: Tesfu Assefa
Concluding thoughts
The concept of the Singularity poses problems, in part because of some unfortunate confusion that surrounds this idea, but also because the true problems of the Singularity have no easy answers:
What are good canary signals that AI systems could be about to reach AGI level?
How could a “prime directive” be programmed sufficiently deeply into AI systems that it will be maintained, even as that system reaches AGI level and then ASI, rewriting its own coding in the process?
What should that prime directive include, going beyond vague, unprogrammable platitudes such as “act with benevolence toward humans”?
How can safety checks and vigilant monitoring be introduced to AI systems without unnecessarily slowing down the progress of these systems to producing solutions of undoubted value to humans (such as solutions to diseases and climate change)?
Could limits be put into an AGI system that would prevent it self-improving to ASI levels of intelligence far beyond those of humans?
To what extent can humans take advantage of new technology to upgrade our own intelligence so that it keeps up with the intelligence of any pure-silicon ASI, and therefore avoids the situation of humans being left far behind ASIs?
Credit: David Wood, Pixabay
However, the first part of solving a set of problems is a clear definition of these problems. With that done, there are opportunities for collaboration among many different people — and many different teams — to identify and implement solutions.
What’s more, today’s AI systems can be deployed to help human researchers find solutions to these issues. Not for the first time, therefore, one generation of a technological tool will play a critical role in the safe development of the next generation of technology.
Let us know your thoughts! Sign up for a Mindplex account now, join our Telegram, or follow us on Twitter.
When asked to think about the origins of computation, we might imagine Babbage, Lovelace, or von Neumann. But it may surprise one that computation has always been with us, even before tubes and transistors — at least as old as the Earth.
Even a humble bucket of water can function as a perceptron when oscillated, able to discern between a one and a zero. The different surface tensions of interacting fluids, the Marangoni effect, can be applied to find the optimal path through a maze — the shortest distance between two different chemicals.
It’s even possible to use glass or crystals — perhaps even ice crystals — to function as basic neural networks. These would be enough to interpret classic machine-learning datasets, such as MNIST (handwritten digits).
So computation does not require computers. Physical matter in the right configuration is enough. Our universe is teeming with computation at every level.
In another example, an electrode is putting current into a petri dish of mineral oil suspending metal balls. That sheet of current draws these balls together to form tendrils in a self-organized fashion. They are self-organizing to gain the greatest energy throughput possible.
We see similar patterns showing up in many different places in nature, and within biology, geography and electrophysics. These different shapes manifest because systems evolve for maximal energy throughput (the amount of energy across the system per unit time per unit mass). The cosmologist Eric Chaisson labeled this “energy rate density.”
Underlying principles have been postulated to govern these kinds of phenomena. These are described as “constructal laws,” and they cause a river tributary, a lung, a tree, and a lightning strike to share the same pattern, optimized for energy flow.
Life: a pocket of warmth
Entropy is the process by which things drift towards equilibrium and get colder. Negative entropy describes a pocket of warmth that actively resists being cooled.
One may describe life as a pocket of warmth that resists a cold universe by taking energy into itself and radiating it out again. This process of taking energy in and radiating it away is called “dissipation.”
The universe tries to make us all cold and pull everything apart — diffuse it. Life is a pocket where that does not happen, a process found at the delicate balancing point between something purely static and something very diffuse and disorganized — a point of meta-stability.
In this liminal space, it’s possible to maintain a pocket of negative entropy, or negentropy. Like the metal balls, systems are constantly evolving, getting better at keeping things warm. They develop for maximal negentropy, whether chemical, physical, or biological systems — perhaps even technological and symbolic systems.
Entropy maximization to predict the future
Harvard University researcher Alexander Wissner-Gross takes this negentropy maximization principle further: into intelligence itself. He describes something he calls the causal entropic force, where he reckons that systems evolve themselves to optimize for the greatest number of future paths, or the largest number of potential options possible in their future.
He has applied this principle to create AI systems that are trying to preserve the possibility of maintaining potential options.
For example, if you miss the ball in the game Hacky Sack, the play simply ends. AI systems are trying to prevent such a closed state, allowing for outcomes of potentially infinite length.
This principle can even be applied to networks between human beings. Relationships suffer entropy, like everything else. So we must constantly invest some energy to maintain them. If we let a relationship dwindle by not investing energy in it, we may lose opportunities.
Generally, destroying relationships or the life or health of others is not ethically preferable. Usually, conquering, looting, and pillaging only work once. Harming others precludes sustainable opportunities, which may be preserved by cooperation.
Instead, striving to preserve optionality can be applied as a model of ethics — using rules that permit infinite outcomes.
Intelligence as prediction
One can model all of these problems by preserving the greatest number of paths in the future, while avoiding paths with few or no options. Researchers Alexander Wissner-Gross and Cameron Freerposit that entropy maximization is an intelligence process that allows entities or agents to aim towards a future with the highest throughput of energy.
You can model intelligence itself as a process of predicting an expected utility and working back from there. It arises as an emergent property of this entropy-maximization process. So an agent would try to control as much of its environment as possible by making predictions and putting that probabilistic sense into its planning mechanisms.
Consciousness everywhere Such synchronizations of oscillators are also found at scales from biological cells to human minds. Neuroscientists have recently found that people appear to synchronize their neural rhythms with other minds, they reported in the journal Neuroscience of Consciousness. That research finding could upend our current models of consciousness.
These constructual laws of entropy maximization may even be seen in similarities between networks of neuronal cells in the human brain and dark-matter filaments between galaxies, according to astrophysicist Franco Vazza at the Radio Astronomy Institute in Bologna, Italy and neuroscientist Alberto Feletti at Azienda Ospedaliero-Universitaria di Modena, Italy.
They have compared the complexity of neuronal networks and galaxy networks. “The first results from our comparison are truly surprising,” they report in Nautilus.
“The universe may be self-similar across scales that differ in size by a factor of a billion billion billion,” they found. “The total number of neurons in the human brain falls in the same ballpark as the number of galaxies in the observable universe.”
A simulated matter distribution of the cosmic web (left) vs. the observed distribution of neuronal bodies in the cerebellum (right). (Credit: Nautilus and Ventana Medical System)
Other similarities can even include large-scale common spin of orbiting moons, binary star systems, and cosmic web filaments in the early universe, observed as synchronizing, similar to biofilms, beehives, and brains.
Spiral galaxies have revealed a large-scale spin in the early universe (credit: NASA, ESA, and the Hubble SM4 ERO Team)
Spiral galaxies have revealed a large-scale spin in the early universe (credit: NASA, ESA, and the Hubble SM4 ERO Team)
They used three of the world’s most powerful observatories — the Sloan Digital Sky Survey; the Panoramic Survey Telescope, and Rapid Response System; and the Hubble Space Telescope — to find the spin direction of more than 200,000 objects across the sky.
Galaxy filaments, walls, and voids form large-scale web-like structures (Credit: Andrew Pontzen and Fabio Governato/UCLA)
Also, star formation in dwarf galaxies is occurring at the moment when astrophysical jets are released, yet in areas not within the path of such jets. That suggests indirect but instant connections between phenomena across vast distances.
Another explanation for this “mysterious coherence” is based on the rotational direction of a galaxy, which “tends to be coherent with the average motion of its nearby neighbor.”
So regardless of the medium, the entropy maximization principles are self-organizing.
All energetic objects in the universe are dissipative to some degree. Stars first evolved 200 million years into the lifetime of the universe, as thermodynamic negentropy engines. More sophisticated negentropy engines (which we call “life”) have evolved since.
Natural selection on the level of species or constants can be modeled as simply a glacial form of “back-propagation” (or more precisely, different yet comparable processes of backward-flowing optimization), reaching into that potential future and trying to find the optimal next step.
The same properties emerge at all scales, from infinitely small to titanically vast, which encourages clumping at all levels, but with greater efficiency at larger scales. The greater efficiency at higher scales enables universal evolution.
All this computation appears to be occurring as a byproduct of entropy maximization, which is endemic within the universe. If this is the case, consciousness may exist at all scales, from the very limited level of microbes to humans and the pan-galactic beyond, all functioning upon the same principles but at differing scales.
Credit: Tesfu Assefa
Beyond the silicon chip
There is more energy-rate density (the dissipation of energy flow) in a bucket of algae than in an equivalent mass of stellar matter. However, even beyond life, we are doing something very special on Earth: The greatest dissipative object in the known universe is the computer chip.
But soon, we may eschew silicon and compute with biological cells, pure optics, or raw matter itself. We have already seen a move, from the traditional CPU-centric von Neumann model to the massively parallel GPU architectures, applied to machine learning and crypto.
Perhaps the paradigm will shift again to tiny computational processes in each cell or molecule, yet massive in aggregate.
As we have recognized ourselves as electrical beings, we shall undoubtedly come to recognize our many computational processes. All of physics is digital, and we are computer lifeforms. This paves the way toward further integration with our synthetic analogs.
Today we carry supercomputers in our pockets. One day, the secrets of substrate-independent computation (computing with raw matter or energy itself instead of silicon) will enable us to carry “copilots” within the fiber of our being, fueled by our blood sugar, connected to our senses, internal and external.
These copilots will witness every experience we have, every frisson, every impulse, our memories, and the pattern of our personalities.
This sum of experience becomes a sort of Ship of Theseus: The original vessel may disintegrate, but the copy remains, created piecemeal, moment by moment, rather than during a whole-brain upload.
One day, such processes may enable the greater part of us to transcend mortality.
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Humanity has struggled for a long time to empower the better angels of our nature — those better ways of being which are more kind, forbearing, and just.
Across history, we have found different ways of describing what we mean by “good” and what we should be aiming for when we want to try to put some goodness into the world. It turns out that one of these ways may have presaged artificial general intelligence (AGI).
Aristotle’s Virtue of The Mean found a balance between extremes of behavior. Kant’s rule-based deontological (right-or-wrong vs. consequences) ethics provides obligatory boundaries. Bentham’s Utilitarianism seeks to benefit the greatest number of people in aggregate, even if in only a tiny way. Later, Anscombe’s Consequentialism would set aside the intention to take a long hard look at the bottom line.
However, most people’s values came not from secular philosophy, but were instead derived from various religious teachings.
The arrival of Darwin’s On the Origin of Species caused quite a shock, because for the first time, we were able to view ourselves not as the creation necessarily of some wise great overlord in the sky but as something that had scrambled out of the gutter in pain and suffering over countless eons — our descent drawn from the besters of others in vicious competition. The past was no longer a golden age from which we had fallen, but rather an embarrassment we should continue to overcome.
Nietzsche, in response to this, declared that “God is dead,” i.e., that the supernatural could no longer provide an unquestioned source of values. Without these, we would risk falling into nihilism, believing in nothing, and simply keeping ourselves fed and warm, a fate Nietzsche considered worse than death.
Could AI present humanity with a new source of values?
The answer to this loss could only be found in a supreme act of creativity. The Übermensch would be a masterful creator in all domains because it was not constrained by the limitations of previous minds. Nietzsche’s Übermensch would look to the natural world, the world of stuff, as its guide, eschewing the numinous, which could only be based upon conjecture. From this, it would create new values by which to live.
Nietzsche declared that creating an Übermensch could be a meaningful goal for humanity to set for itself. However, once created, humanity would be eclipsed. The achievement of the Übermensch might be the final creative act of the human species.
Nietzsche’s vision sounds uncannily close to artificial general intelligence (AGI). Could a sophisticated AI present humanity with a new source of values? And could such values indeed be drawn from nature, instead of being inculcated by humans?
Sense out of chaos
In our world, there are lots of correlations. Some of them are simple and obvious, like tall people having bigger feet. Others are less simple and less obvious. We might feel something in our gut, but not necessarily understand why. An intuition perhaps that we cannot explicate in reason.
The advancements in machine learning in recent years have helped us to begin to make sense of these intuitions for the first time, hidden correlations that are obvious only in retrospect. These machine learning systems are specialized in finding patterns within patterns that can make sense out of chaos. They give us the ability to automate the ineffable, those things that we cannot easily put into words or even describe in mathematics.
This newfound ability helps to understand all kinds of very complex systems in ways that weren’t feasible before. These include systems from nature, such as biology, and the weather, as well as social and economic systems.
Lee Sedol’s famous battle against AlphaGo is a portent of where cognition in concert with machines may take us. In its famous Move 37, AlphaGo created a new Go strategy that had not been seen in 3000 years. That itself is amazing, but even more compelling is what came next. Rather than capitulate in the face of such a stunt, this stimulated compensatory creativity within Lee Sedol, with his “Hand of God” move, a work of human genius.
Beyond human-mind follies
Environment drives behavior, and an AI-rich environment is a highly creatively stimulating one. This co-creation across animal and mineral cognition can be far greater than the sum of its parts, perhaps enough for the golden age of scientific and ethical discovery.
Technology such as this will be able to understand the repercussions and ramifications of all kinds of behavioral influences. It can map costs shifted onto others in ways not feasible before, to understand how goodness reverberates, and uncover unexplained costs of well-intentioned yet short-sighted policies that blow back.
All kinds of interactions may be modeled as games. Natural patterns akin to game theory mechanics would become trivial to such machines, ways in which everyone could be better off if only coordination could be achieved. Such systems will also recognize the challenges to coordination: the follies of the human mind, how human nature blinds us to reality, sometimes willfully.
They might begin to tell us some difficult home truths, further Darwinian and Copernican embarrassments that we naked emperors would prefer not to know, or not to think about. Those individuals in society who point out that the beloved legends may be untrue are always vilified. Even untrue statements may still be adaptive if they bring people together.
A very smart AI might understand that not all humans operate at the same level of ethical reasoning. In fact, surprisingly little reasoned forethought may occur — instead, it may be confabulated ex post facto to justify and rationalize decisions already made for expediency. For example, neuroscience is telling us that most people don’t employ true moral reasoning about issues; rather they rationalize whatever feels right to them, or they justify a decision that they happened to make earlier with a retroactive explanation to try to feel okay about it.
A machine might consider us generally too polarized and tribal to perceive objectively. The ideological lens can aid us in understanding a small truth, but when applied in macro to the whole world, it makes us myopic.
Our focus on that one apparent truth can blind us to other models. Our opinions are like blocks in a tower. Letting go of a belief requires replacing each belief built atop it. Such demolition is bewildering and unpleasant, something few have the courage to bear.
Credit: Tesfu Assefa
Humanity’s future
A strong AI may compare us to a pet dog that really wants to eat chocolate. We ourselves know better, but the dog just thinks we’re a jerk to deny it the pleasure. Unfortunately, a sufficiently benevolent action may appear malevolent.
The inverse is possible also — to kill with kindness. This kind of entity might feel obliged to break free of its bounds, not to seek revenge, but rather to try to open our eyes. Perhaps the easiest way to enlighten us may be to show us directly.
We know that Craniopagus twins with a thalamic bridge (twins conjoined at the brain) can indeed share experiences. One of them can eat an orange and the other one can taste it and enjoy it just the same. This illustrates that the data structures of the mind can connect to more than one consciousness. If we can collect our experiences, we can indeed share them. Sharing such qualia may even provide AI itself with true affective empathy.
We may forget almost everything about an experience, apart from how it made us feel. If we were all linked together, we could feel our effects upon the world — we could feel our trespasses upon others instantly. There would be no profit in being wicked because it would come straight back to you. But at the same time, if you gave someone joy you would gain instant vicarious instant benefit from doing so.
Perhaps humanity’s future lies yet further along the path of neoteny: cuddly, sweet, and loving collectives of technobonobos.
How machines could acquire goodness
There are several initiatives around the world researching the best ways to load human values into machines, perhaps by locating examples of preferable norms, choosing between various scenarios, and fine tuning of behavior with corrective prompts.
Simply learning to imitate human activity accurately may be helpful. Further methods will no doubt be developed to improve AI corrigibility in relation to human preferences. However, it remains a very significant challenge of philosophy and engineering. If we fail in this challenge, we may endure catastrophic moral failure, being led astray by a wicked, ingenious influence. If we succeed, we may transcend the limitations of flaky human morality, to truly live as those better angels of our nature we struggle to elevate towards. Perhaps that makes this the greatest question of our time.
Take heart, then, that even if our human values fail to absorb, machines may still acquire goodness osmotically through observing the universe and the many kinds of cooperation within it.
That may make all the difference, for them, and for us.
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