Bitcoin’s By Month: Is Redtember and Uptober For Real?

Posted on by Werner

Bitcoin’s price movements have always been a subject of intense tea-leaf reading and speculation, and over the years, various trading adages have emerged, attempting to capture patterns in Bitcoin’s monthly performance. From wisdoms like “Sell in May and Go Away”, “Wake Me Up When September Ends”, to Redtember and Uptober, crypto investors are skeptical about certain months. Is this pure superstition?  

Let’s dig into historical data to examine the validity of these popular sayings and provide insights into Bitcoin’s monthly price trends. Remember that historical performance is not equal to future performance.

Examining Bitcoin’s Monthly Returns

Let’s analyze the comprehensive table of Bitcoin’s monthly returns from 2013 to 2024. 

Credit: Coinglass

Month-by-Month Breakdown

  1. January: New Year Here and Chinese New Year incoming

With an average return of 3.35%, January usually shows moderate growth. Notable years include 2013 with a 44% gain and 2015 with a -33.05% loss. Since 2020 we’ve seen more green, as institutions enter with quarterly budgets, looking to start allocating earlier on in the year. 

  1. February: Usually quiet, but with outliers

Historically strong, February boasts the second-highest average return of 15.66%. The standout year was 2013, with an impressive 61.77% increase.

  1. March: Upwards

Another solid performer, March had an average 13.42% return from 2013-24. March of 2013 was exceptional, with a staggering 172.76% gain.

  1. April: Fools’ Gold? 

Consistent growth was seen in April, averaging 12.98%. Both 2013 and 2019 saw gains exceeding 50%. Not so fast though: it dropped in April of this year. 

  1. May: Go away!

While it has a positive mean return of 7.94%, May showed significant fluctuations over the years 2013 to 2024. Many crypto investors believe it’s the best time to temporarily pack your toys away and wait till Q4 before allocating again, hence the saying “Sell in May and Go Away”. In 2024, at least, they were right. 

  1. June: The summer doldrums start

The first month with a negative mean return, although a small one of -0.35%.

  1. July: Fireworks

This one’s a surprise. As the summer holidays peak, there’s a surprising return to positive territory with a 7.56% average, showing steady but unspectacular performance. 

  1. August: No-man’s land

Barely positive at a 1.75% mean return, August has historically been unremarkable.

  1. September: Redtember? 

Notorious for negative performance, with a mean return of -4.92%. This month has earned the moniker “Redtember” in crypto circles. It’s usually a slow month, as the summer holidays hangover means everyone is slow to get to serious business in financial circles. 

  1. October: Uptober

Aha, Uptober! The start of Q4 and the end of the year is coming into sight. Budget remainders can be allocated with greater freedom, and all systems are firing. October was historically surprisingly strong, with an average return of 22.90%, challenging the negative sentiment often associated with autumn months.

  1. November: The best month on average

The best-performing month on average, with an impressive 46.81% return. 2013 saw an extraordinary 449.35% gain.

  1. December: Holiday season bring mixed gifts

Closes the year with a respectable 5.45% average gain.

Credit: Tesfu Assefa

Evaluating Popular Trading Adages

Now, let’s examine some well-known trading sayings in the context of Bitcoin’s historical performance.

“Sell in May and Go Away”

This adage, borrowed from traditional stock markets, suggests divesting in May and reinvesting in November. For Bitcoin:

  • May itself averages a 7.94% gain, contradicting the “sell” advice.
  • The subsequent months show mixed results:
    • June: Slightly negative (-0.35%)
    • July: Positive (+7.56%)
    • August: Marginally positive (+1.75%)
    • September: Negative (-4.92%)
    • October: Strongly positive (+22.90%)

The data indicates that strictly adhering to this adage for Bitcoin would have resulted in missed opportunities, particularly in July and October. However, it would also have avoided September’s typical downturn.

“Redtember”

September’s reputation for poor performance is well-supported by the data:

  • Average return: -4.92%
  • Median return: -6.04%
  • Negative years: 9 out of 12
  • Worst performances: 2014 (-19.01%), 2019 (-13.38%), 2020 (-7.51%)

While there have been exceptions (2015: +2.35%, 2016: +6.04%, 2023: +3.91%), the trend clearly leans negative, lending credibility to the “Redtember” moniker.

“Uptober”

October’s strong performance supports the “Uptober” nickname:

  • Average return: 22.90%
  • Median return: 27.70%
  • Positive years: 8 out of 12
  • Standout years: 2013 (+60.79%), 2017 (+47.81%), 2021 (+39.93%)

Despite some negative years (2014: -12.95%, 2018: -3.83%), October generally shows strong positive performance, validating the “Uptober” concept.

Broader Trends: Quarterly Analysis

Expanding our view to quarterly performance reveals interesting patterns:

  • Q1 (Jan-Mar): Strongest quarter, averaging 56.47% returns.
  • Q2 (Apr-Jun): Positive but cooler, with 26.89% average returns.
  • Q3 (Jul-Sep): The weakest quarter, averaging just 4.95%.
  • Q4 (Oct-Dec): Very strong, with an 88.84% average return.

These quarterly trends suggest a general pattern of strength in the latter part of the year and early months, with a lull during the summer. This lull in summer may be the meaning of ‘sell in May and go away’.

Bitcoin and Ethereum by Quarter

Credit: Coinglass

What is maybe a bit more clear in this historical data, is that Q2 and Q4 were opportune times for Bitcoin’s price to move, and Q3 was a time to HODL after the Q2 growth. Every year is different, and news like a recession, a Bitcoin spot ETF approval, or a halving event can have a dramatic effect on the price of all crypto assets. 

Ethereum, on the other hand, has so far been a coin for all seasons. ETH’s Q2 performance has been incredible, with only two red blips in 2022 and 2024. This can be attributed to technological events like the Merge and specific upgrades like EIP-1559 and proto-danksharding. 

Implications for Investors

While these patterns are intriguing, it’s crucial to approach them with caution:

  1. Historical Trends vs. Future Performance: Past patterns don’t guarantee future results. Two things known for their unpredictability are the cryptocurrency market and the future.
  2. Market Evolution: As the crypto market matures, it may behave differently to an earlier version of itself, and historical patterns may become irrelevant.
  3. External Factors: Global economic conditions, regulatory changes, and technological developments can override historical patterns.
  4. Volatility Considerations: Bitcoin’s high volatility means significant deviations from averages are common.
  5. Long-Term Perspective: Short-term trading based solely on monthly patterns can be risky. A long-term investment strategy might be more suitable for many investors.

Conclusion

The analysis of Bitcoin’s monthly performance reveals some consistent patterns, particularly the challenges of September and the strengths of October and November. However, it’s important to view these trends as part of a larger picture rather than definitive trading signals.

Things in Cryptoland are always green or red but rarely black and white. There’s a lot of nuance and macro-economic factors at play – or is it lunar horoscope influence?

Investors should consider multiple factors when making decisions:

  • Conduct thorough research on market fundamentals.
  • Stay informed about regulatory developments and technological advancements.
  • Consider the broader economic context.
  • Diversify investments to manage risk.
  • Align trading or investing strategies with personal financial goals and risk tolerance.

While historical data are interesting, the cryptocurrency market’s dynamic nature means that adaptability and comprehensive analysis remain key to navigating this exciting but volatile space.

Therefore, you have two options: zoom out completely to an annual view, or take it day by day. 

Oh, and of course invest in projects that you’ve properly researched and believe in!

Let us know your thoughts! Sign up for a Mindplex account now, join our Telegram, or follow us on Twitter. 

Psychedelics and the Coming Singularity: A Conversation with David Jay Brown about his book

Posted on by RU Sirius

It used to be a bit of a secret in tech circles. But given the recent rise in the social and even political acceptance of psychedelic drug and plant use in the USA and elsewhere, now it can be told. Psychedelics and extreme technological change go together like peanut butter and jelly.

This new book of interviews by David Jay Brown casts a wide net. It chases after what the various interview subjects think about the Singularity, but beyond that, as Brown says in my conversation with him, he asks them about, “Simulation Theory… DMT entities… psychedelics and ecological awareness, God, and death.”

A diverse cast of characters are on the receiving end of Brown’s inquiries, and almost every one of them remarks that Brown really asks the big questions. My personal favorite interviewees include the great trippy graphic novelist/comic book writer and bon vivant Grant Morrison, the writer Erik Davis whose recent book High Weirdness looks at far out psychedelic, sci-fi, techno-visions of the 1970s through the lens of the works of Philip K. Dick, Robert Anton Wilson and Terence McKenna, and the almost indescribable Bruce Damer who seems to be engaged in projects related to almost everything –including space science, virtual reality and the origin of life, just to name a few. Damer also curates the DigiBarn in Santa Cruz, which holds libraries and archives of Timothy Leary and Terence McKenna. These are among the many wildly brilliant interview subjects.

I met David Jay Brown in the mid-1980s, back when I was publishing High Frontiers, a predecessor to Mondo 2000 (as Mindplex is its descendent). Since then I’ve been consistently impressed by his output as a writer and interviewer. He has written for Scientific American, Wired and other periodicals. Books include ‘Mavericks of the Mind’, ‘Conversations on the Edge of the Apocalypse’, ‘The New Science of Psychedelics: At the Nexus of Culture, Consciousness, and Spirituality’ and ‘Women of Visionary Art’.

RU Sirius: What made you decide to combine the themes of psychedelics and the Singularity, and how did you choose the people you would interview?

David Jay Brown: I think the rise in Artificial Intelligence and the psychedelic renaissance are two of the most important forces driving the future evolution of consciousness right now, and that they dovetail in a way that mutually compliments one another. Psychedelics have inspired the development of computer technology and software development from the beginning, and the ecological awareness, personal boundary dissolution, creativity, and spiritual elevation that psychedelics often bring provide an essential balance to the foundational development of AI, its integration into the world, and may assist in reducing the possible dangers that it could bring. 

As with all my interview collections, the people that I chose to interview are simply the people whose work are inspiring me, whose books I’ve been reading, and I let it organically grow as I proceed with the project. 

Another major theme in the book is about exploring the extended state DMT research and the possibility of non-human entity contact, which I think will also be major factor influencing the future evolution of consciousness, and a number of the people in the collection – such as Andrew Gallimore, David Luke, and Carl Hayden Smith – were chosen for the role that they’re playing in this research and exploration. In addition to contemplating the symbiotic relationship between AI and psychedelics, I see both as catalysts for expanding human potential and pushing the boundaries of what it means to be conscious in the 21st century. AI has the potential to radically transform our cognitive processes, but it needs the human element – the kind of insight that psychedelics offer – to help guide its ethical development and avoid purely mechanistic outcomes. By engaging with these two powerful forces, we’re not only enhancing our technological abilities but also deepening our relationship with the natural world, the cosmos, and the unknown realms of consciousness. 

The interviewees were chosen not just for their expertise, but also because they represent a wide spectrum of perspectives – scientific, philosophical, spiritual – on these issues. They are the thought leaders pushing the envelope in their respective fields, and I wanted their diverse voices to reflect the complexity of the Singularity and the transformative role that psychedelics may play in shaping it.

RU: There’s not a lot of Kurzweilian hard-science singularitarianism in the book. How would you respond to that? It’s a very expansive view of the theme (which I like). What versions of singularities do you find most compelling?

DJB: I was corresponding with Ben Goertzel, and he was supposed to be in the collection, but he got too busy as my deadline was approaching and so, unfortunately, he didn’t make it into this book. I interviewed Ray Kurzweil for two of my previous collections, and corresponded with him as I was doing this book as well, but exploring the notion of the Singularity was really only one of the themes in the book, and the title was chosen by my publisher after the book was completed. The notion of the technological Singularity, that Kurzweil and others have popularized, as a reference to the future time when digital electronic minds become more intelligent than all human minds combined – which is a poetic application of the term borrowed from astrophysics, describing the point where the known laws of physics break down, like the center of a Black Hole or whatever state of the universe existed before the Big Bang, and future predictions can’t be made – was a good question to stimulate the imagination of my interviewees.

Other questions that were explored for the same reason had to do with Simulation Theory, the possible reality of the DMT entities, the relationship between psychedelics and ecological awareness, thoughts on the concept of God, and what happens to consciousness after death.

The notion of the Singularity is similar to Terence McKenna’s idea of an attractor or transcendental object at the end of time where novelty becomes infinite, and Teilhard de Chardin’s notion of the Omega Point, the final evolutionary point of unification between God and humanity. It’s a great concept to stimulate our thinking about what could be, during a future time where reality blurs with the imagination. The Singularity, as Kurzweil and others have framed it, often focuses on technological advancements leading to a superintelligence that reshapes our reality, but my approach intentionally opens the door to more speculative, expansive interpretations of this transformative moment. I find versions of the Singularity compelling when they incorporate a sense of mystery, where not just technology but consciousness itself becomes the focal point of evolution. This includes models like McKenna’s timewave theory, where consciousness reaches a crescendo, or de Chardin’s Omega Point, suggesting an inevitable spiritual awakening or divine integration. In these frameworks, the Singularity isn’t just a technological event – it’s a spiritual and philosophical one, where the boundaries between human, machine, and the universe dissolve. The potential for contact with non-human intelligences, whether through DMT or other means, might also play a role in how we conceptualize future consciousness expansion. For me, the most compelling version of the Singularity is one that embraces not only the scientific possibilities but also the profound unknowns of existence.

RU: You wrote “the kind of insight that psychedelics offer – to help guide its ethical development and avoid purely mechanistic outcomes.” I myself have a line in my memoir that psychedelics may be necessary to lubricate an otherwise brittle and mechanistic tech future. But a lot of the high end techies that are into psychedelics like Musk or Thiel seem to develop superman or supremacist ideas and attitudes. When I interviewed Martin Lee about ‘Acid Dreams’ for High Frontiers back in 1987 he said psychedelics can be “exaggerants” that bring out whatever potential is inside a person in an outsized fashion. What do you think about the lessons learned about the unreliability of psychedelic use in turning out enlightened or compassionate humans?

DJB: Yes, “exaggerants” is a good term, and I understand your concern. As Stan Grof points out, psychedelics are non-specific brain amplifiers, and they don’t inherently bring out the positive attributes of people. They amplify what’s already there. I recall hearing stories about Pentagon officials doing acid and dreaming up new mass-killing technologies, and we all know what happened with Charles Manson and his followers. There are many people with sociopathic personalities in positions of great power, and according to Tim Ferriss all of the billionaires that he knows, without exception, are using psychedelics. 

But sociopaths only make up 1 to 4 percent of the population. More than 96 percent of human beings, I think, have good intentions, and this gives me great hope as psychedelics awaken the masses. I think that as critical thresholds of positive psychedelic awareness are reached, this will drive compassionate action, ecological awareness, and enlightened conscious evolution. Also, people are complex, and often it’s not black and white as to what’s dark and what’s light, especially as consequences unfold in often unexpected and surprising ways, and I do trust that a higher intelligence inherent in the natural world is helping to guide us in ways that may not be obvious to our rational minds. While psychedelics can certainly amplify darker tendencies in some individuals, particularly those in positions of power or with pre-existing sociopathic traits, I believe the broader cultural awakening that psychedelics foster will lean toward a more compassionate and interconnected world. The key lies in set and setting, as well as in education and integration practices. With responsible use and proper guidance, psychedelics have the potential to help individuals confront their shadows, fostering deep healing and growth. But you’re right – it’s unreliable to assume that psychedelics alone will result in enlightened or compassionate humans.

Transformation requires intention, community support, and ethical frameworks to truly guide individuals toward positive change. In this way, psychedelics are tools, not cures – they provide the potential for insight, but the outcome depends on how that insight is applied.

RU: I loved McKenna and his vision. Back in the High Frontiers days in the mid-1980s, he told us that the interior of the human was going to be externalized, and what we imagine will simply come to be and this was years before everyone was talking about VR. Still, Timewave Zero predicted a massive transformation culminating in December of 2012. Believers will say that the transformation happened under the surface and it’s just not visible, which is what all religionists and cultists say when a prophecy fails. But the Black Mirror reality of today is not something Terence would like. (Terence himself didn’t take the predictions as seriously as others who adapted it) 

So the question here is whether you think faith can be excessive and – just as the hyper-rationalists could use some influence from the visionary or spiritual – a lot of psychedelicists could use a dose of rationality.

DJB: I certainly agree that balancing faith and rationality is a good idea, and that too much of either can be excessive. I always viewed Terence as more of a storyteller and a poet than a scientist, and as much as I think many of insights are quite compelling, I take much of what he said with a grain of salt, and you’re right, I don’t think that Terence even took his own predictions that seriously. He actually changed the date for the end of his novelty-accelerating Timewave model a number of times, and December 21st of 2012 was chosen to align with the end of the Mayan calendar, so I see the endpoint that he predicted as more of poetic interpretation of where human evolution is headed – that seems to resonate with what Kurzweil describes as the Singularity, what de Chardin means by the Omega Point, and what Timothy Leary and Robert Anton Wilson mean by the actualization of the 8th brain circuit, which also seem more poetic than scientific. 

As for the idea that we are living in ‘Black Mirror’ reality today, I think that this perspective is open to interpretation. Most certainly the dark aspects of the world have been amplified. Those in power have sought greater and greater methods of controlling the human herd, and they now have access to technologies that can seemingly enslave much of humanity. But this tension between enslavement and liberation isn’t really new, it’s just been greatly amplified. We live in a dualistic universe, and I think that there will always be a Yin-Yang balance of dark and light forces. It seems that our species is wobbling on the edge of either planetary suicide or divinity status, and this growing tension has continued to escalate and intensify. But I think it has always seemed this way. In Tim Rayborn’s book ‘A History of the End of the World’, he makes it clear that this inclination toward apocalyptic thinking has been present throughout human history, as has the notion that humanity is on the verge of a Golden Age of Enlightenment. Are we headed toward an environmental apocalypse and global mass extinction, or will our wayward species overcome the immense challenges that currently face us, and become all-powerful and immortal superhuman masters of space and time? These teetering polarized possibilities have now become so extreme that it seems like our species is facing an evolve-or-die intelligence test, but I suspect that it has always seemed this way and will always seem this way in our dualistic universe. I know that many people think that the world has gotten too dark to be optimistic, however, I suggest that we consider that two things may have influenced this perspective regarding optimism and excitement about future possibilities: the acceleration and intensification of both positive and negative forces, as I’ve described, as well as the aging process. Young people seem more optimistic than the older generations. I don’t think the world has just gotten worse; I think it’s gotten both better and worse at the same time, and the aging process tends to decrease neophilia and increase neophobia. This happens in all animal species and humans are no exception. I see all the dystopian possibilities that everyone else my age sees – the division in our country, the climate crisis, the rise of racism, the threat of nuclear war, etc. – but I also see enormous positive potential as well – the psychedelic revolution raising ecological awareness, AI evolution promising advances in medicine and virtually every field of human endeavor, incredible scientific advances, astonishing new technologies, and young people seeing through the corruption in our two-party political system. I suspect that this Yin-Yang nature of world will continue to accelerate and intensify, but that utopia or dystopia will never fully arrive. It will always be some weird mix of the two, with infinite evidence to suggest that either light or dark perspectives will prevail. 

I also think that part of the appeal of Terence’s ideas, and the broader psychedelic movement, is the invitation to engage with uncertainty and paradox. The poetic, visionary nature of his predictions encouraged people to look beyond rigid frameworks of understanding, allowing them to imagine and co-create potential futures. But as with all visionary insights, it’s essential to temper them with discernment. Faith can become excessive when it blinds us to reality, just as rationality can become constricting when it limits our capacity for awe and wonder. Psychedelicists, like everyone else, benefit from balancing intuition with logic, especially as the stakes grow higher. In today’s world, where technologies like AI and VR blur the line between imagination and reality, it’s more important than ever to remain grounded while exploring new possibilities. As the collective tension increases, so too does the responsibility on each of us to use our tools – whether technological or visionary – wisely. 

In a sense, our challenge is to prevent the ‘Black Mirror’ reality from consuming us, while still allowing space for the unprecedented opportunities of this evolutionary moment. After all, if we can harmonize the rational and the visionary, perhaps we can transcend the dualistic cycles of history and consciously shape a more balanced, compassionate future.

Credit: Tesfu Assefa

RU: Why DMT? In other words, of all the psychedelics, and there are many of these chemical and plant wonders, what is it about DMT that fascinates us in this accelerating time? What are your thoughts?

DJB: There are several reasons why DMT is special and unique in the world of psychedelics, as well as profoundly relevant to our accelerating time of information overload. Let me first provide a little background on DMT. DMT is endogenous to the human body, it’s found in all mammalian species, and throughout the plant world. It’s ubiquitous throughout nature, and no biochemist can tell you what function it serves in any of these places. It seems to be both a neurotransmitter and a hormone in the human body, but no one really has a clue as to why it’s there. It’s a very simple molecule, derived from the essential amino acid tryptophan, and its place in the natural world is a profound mystery. 

In studies with rodents, we know that DMT levels escalate in their brains after cardiac arrest, which lends credence to the popular idea that it mediates the near-death experience in human beings. When ingested in sufficient quantities, DMT experiences generally become an order of magnitude more intense than that of any of the other psychedelics, and it quite literally transports one to another world, which is commonly referred to as “hyperspace” by the psychonauts who have traveled there. One becomes completely immersed in a multi-dimensional reality that completely replaces our three-dimensional world, and it is often reported as seeming “more real than real”. It doesn’t appear to be a hallucination; it seems completely real and one remains fully lucid during the experience. And most profoundly, this new world appears to be populated by hyper-intelligent, non-human entities that seem to take great interest in communicating with us in various ways.

Surprisingly, there is an incredible amount of similarity among the reports from psychonauts as to what these beings look like and how they act towards us. This phenomenon provides the basis for my latest book, ‘The Illustrated Field Guide to DMT Entities’, which is an attempt to create a taxonomy of these beings, and was just published by Inner Traditions. 

In the book I describe 25 of the commonly encountered DMT entities – such as gray aliens, jesters, reptilians, octopoid beings, and “the self-transforming machine elves,” that McKenna famously refers to – and Sara Phinn Huntley, Alex Grey, Luke Brown, Harry Pack, and other brilliant artists illustrate them. 

This phenomenon is being taken seriously by a number of prestigious scientists – such as Rick Strassman, Andrew Gallimore, and David Luke – who have organized studies at Imperial College in London and elsewhere to put people into extended-state DMT sessions. This is being done with the intention of sending DMT voyagers into longer periods where they can access hyperspace and communicate with these strange entities, to see if there truly is an objective reality to them, and what can be learned from them. In my book ‘Psychedelics and the Coming Singularity’, I interview Gallimore and Luke at length about this phenomenon, as well as Carl Hayden Smith, who is one of the brave subjects in the Imperial College extended-state DMT studies that are currently underway. I think this is some of the most exciting scientific research in human history, as we appear to be building technologies that allow us to consistently and reliably communicate with more advanced species – like the contact with alien beings that has been long-anticipated in our science fiction stories – and this is eerily resonant with the recently leaked Pentagon footage of UAPs, and the reports of secret government programs that some military insiders claim exist with the goal of reverse-engineering the technology of crashed alien vehicles.

But what I’m describing is largely from a Western perspective, as many indigenous peoples in the Amazon basin, who have been using a DMT-based jungle brew known as ayahuasca since prehistory, have long reported contact with powerful spirits and advanced beings during their shamanic journeys. To them, this isn’t news at all, but we’re discovering this at a new technological level, which may provide a more sophisticated medium for communication with these advanced beings. Remember, it was McKenna who first began popularizing DMT back in the 1980s and 1990s, and it was basically an obscure drug at the time. We were just talking about how McKenna predicted that novelty would accelerate with his Timewave model as we evolved – well, he accurately predicted the explosion of interest in DMT that resonates with the explosion of information and novelty that we are presently experiencing. 

Another aspect that makes DMT so compelling is its potential to challenge our understanding of reality itself. The fact that so many people report similar experiences, with recurring motifs and entities, raises the question of whether DMT provides access to a shared, objective dimension that exists independently of our usual perception. This idea blurs the line between the subjective and the objective, and it forces us to reconsider the nature of consciousness. Is DMT merely unlocking hidden aspects of our minds, or is it truly opening a doorway to other worlds? These are questions that challenge the core of our scientific and philosophical paradigms. Additionally, in the context of our rapidly accelerating technological world, where we are exploring artificial intelligence and virtual realities, the DMT experience stands out as a reminder that there may be even more advanced, mysterious layers to existence than we can currently fathom. It’s as if DMT serves as a bridge between the ancient wisdom of indigenous practices and the cutting-edge frontiers of modern science.

RU: Is there anything you would like to tell the Mindplex readers about what they might learn or gain in perspective from reading this book?

DJB: We’re currently facing a unique juncture in our evolutionary journey, where the human adventure can branch into a multitude of new directions. It’s an extraordinary time that’s filled with great peril and much promise. Never before in human history have so many human minds been interconnected at the speed of light, thanks to the Internet and other evolving electronic communication technologies. All of human knowledge is now available to everyone nearly instantly, and our collective intelligence has been elevated substantially – yet our collective stupidity paradoxically threatens our very existence. I suspect that this ability and interconnection is greatly amplifying human potential and this gives me enormous hope, but there is also much at stake and we could be on the verge of our own extinction. 

In my book I bring together the perspectives and ideas from some of the most brilliant minds and far-sighted visionaries on the planet to help us navigate through these difficult times and offer out-of-the-box solutions to some of our most pressing problems. These genius luminaries not only offer hope for our wayward world, but also an excitement about the future that rekindles the optimistic enthusiasm that many of us had back in the 1990s, when ‘Mondo 2000’ magazine saw its heyday, and the future seemed bright and filled with great promise – when the internet, transhumanism, virtual reality, brain technologies, and psychedelics were viewed as fantastic agents of liberation, mind expansion, and evolutionary catalysts, instead of as dark tools of oppression, consumerism, slavery, and control. 

By reading ‘Psychedelics and the Coming Singularity’, I hope that people will gain not only a deeper understanding of these transformative forces but also a renewed sense of agency and responsibility. The world may feel chaotic and uncertain, but within this turbulence lies the potential for radical positive change. Through the wisdom and insights of the great thinkers featured in this book, I believe readers will come to see that we are not passive participants in this unfolding story. Rather, we have the power to consciously shape the future and co-create a reality that aligns with our highest aspirations.

Ultimately, this book invites readers to embrace both the mysteries and challenges of this pivotal moment, and to step boldly into the unknown with curiosity, courage, and a sense of hope for what’s to come.

Let us know your thoughts! Sign up for a Mindplex account now, join our Telegram, or follow us on Twitter. 

Ten Explosive Web3 Trends For 2025

Posted on by Werner

As we hurtle towards the final quarter of 2024, Web3 continues to reshape industries and redefine our digital interactions, while attention remains on the yo-yo prices of cryptocurrencies. Look closer and there’s a lot happening.

From the rise of AI-powered decentralized applications to the tokenization of real-world assets (RWA), the trends emerging in the Web3 space are not just technological advancements, but paradigm shifts that promise to revolutionize the way we interact with the digital world. Let’s delve into the top ten Web3 trends that are making waves in 2024 and are set to shape the landscape in 2025.

1. AI-Web3 Symbiosis: Intelligent Decentralized Systems Are Coming

The integration of Artificial Intelligence (AI) with Web3 technologies in 2024 is pushing the boundaries of what’s possible in decentralized applications (dApps) and distributed ledger technologies.

Oracle networks 

One notable AI-powered smart contract platform is the oracle network Chainlink, which has seen a 300% increase in adoption rates since the beginning of 2024. By leveraging machine learning algorithms, Chainlink has dramatically improved the efficiency and accuracy of oracle networks, reducing transaction times by up to 75% and enhancing the reliability of data feeds.

Decentralized Healthcare Records

The collaboration between AI and Web3 is also revolutionizing data management and content verification. 

In the healthcare sector, encumbered by convoluted data practices that inhibit the sharing of vital medical information across different parties, companies like MedicalChain are using AI algorithms within Web3 frameworks to enable secure, decentralized management of medical records. Techniques such as secure multi-party computation, homomorphic encryption and federated learning allow AI models to analyze sensitive data ‘in a black box’, preserving individual privacy. Medical information remains encrypted and inaccessible to unauthorized parties, yet the AI analysis can still glean insights from it.

2. The RWA Tokenization Revolution

The tokenization of real-world assets (RWA) has gained significant traction in 2024, spearheaded by traditional finance giants like BlackRock. The total market cap of tokenized real-world assets hit $5 trillion in the middle of the year. This trend is making traditionally illiquid assets more accessible and tradable.

Real estate protocols have tokenized over $1 billion worth of properties by Q2 2024. This has allowed fractional ownership of prime real estate, easing access to a market that was previously out of reach for many investors.

In the art world, platforms like Maecenas have tokenized masterpieces like Picassos worth over $500 million, allowing art enthusiasts to own fractions of world-renowned artworks. This trend is changing investment paradigms and reshaping how we perceive ownership in the digital age.

3. Sustainable Blockchain: Crypto Goes Green

Environmental concerns have been a significant hurdle for blockchain adoption, but 2024 has seen a decisive shift from the very energy-hungry Proof-of-Work to Proof-of-Stake. The Ethereum network’s transition to Proof-of-Stake (PoS) in late 2022 blazed a trail, and now we’re seeing the fruits of this green revolution.

Cardano, another proof-of-stake blockchain, reported a 99.9% reduction in energy consumption compared to traditional Proof-of-Work systems. This approach has attracted environmentally-conscious investors and developers, with Cardano’s DeFi ecosystem growing 200% in the first half of 2024.

Carbon-neutral blockchains are becoming the norm rather than the exception. Algorand, which achieved carbon negativity in 2021, has offset over 10 million tons of carbon emissions through its sustainability program by mid-2024.

4. DeFi 2.0: The Next Generation of Decentralized Finance

Decentralized Finance (DeFi) is a new creature in 2024, finally addressing many of the challenges that plagued its early incarnations. DeFi 2.0 platforms are focusing on improved security, scalability, and user experience.

Aave, a leading DeFi protocol, has introduced AI-powered risk assessment tools, reducing the instances of bad debt by 80% compared to the previous year. This has instilled greater confidence in the DeFi ecosystem, attracting institutional investors who were previously wary of the risks associated with decentralized lending and borrowing.

Another milestone in the DeFi space is the rise of cross-chain interoperability. Polkadot’s parachains have facilitated seamless asset transfers across different blockchain networks, with over $50 billion in cross-chain transactions recorded in the first quarter of 2024.

5. The Metaverse Economy: Virtual Worlds, Real Value

The metaverse has gained traction in 2024, with major tech companies and startups alike building immersive digital worlds. These virtual realms are not just for gaming and socializing; they’re becoming hubs of economic activity.

Decentraland, a leading metaverse platform, has seen its virtual real estate market cap surpass $2 billion in 2024. Major brands like Nike and Gucci have established virtual stores in Decentraland, with Nike reporting that 15% of its digital sales now come from its metaverse presence.

The rise of the metaverse has also fueled the growth of virtual economies. Play-to-earn games like Axie Infinity have created new income streams for players, particularly in developing countries. In the Philippines, over 100,000 people now earn a living wage purely from play-to-earn games, marking a significant milestone in the gaming industry.

6. Decentralized Social Media: Taking Back Control

2024 has seen a surge in decentralized social media platforms, as users seek alternatives to centralized platforms plagued by data privacy concerns and algorithmic manipulation.

Mastodon, a decentralized social network, has grown its user base to over 50 million by mid-2024, a tenfold increase from the previous year. This growth has been fueled by its commitment to user privacy and its resistance to censorship, contrasted with growing discontent with centralized platforms.

Another notable player in this space is Mirror, a decentralized publishing platform that allows writers to tokenize their content. By the end of 2024, Mirror had facilitated over $100 million in direct reader-to-writer payments, revolutionizing the economics of online content creation.

Credit: Tesfu Assefa

7. NFTs Beyond Art: Utility Tokens in the Real World

The NFT art market has cooled since its 2021 peak, but 2024 has seen a resurgence of NFTs in practical applications. Utility NFTs, which provide real-world benefits to holders, have gained significant traction.

Ticketing giant LiveNation has partnered with blockchain platform Flow to issue NFT tickets for concerts and events. These NFT tickets prevent fraud and allow artists to engage with fans long after the event, creating new revenue streams through digital memorabilia and exclusive content.

In education, blockchain-based platforms like Learning Economy Foundation have issued over 1 million verifiable credential NFTs as of mid-2024. These NFTs serve as tamper-proof records of academic achievements and professional certifications.

8. DAOs: The Future of Organizational Governance

Decentralized Autonomous Organizations (DAOs) have come of age in 2024, moving beyond crypto-native applications to disrupt traditional organizational structures.

MakerDAO, one of the oldest and largest DAOs, has expanded its reach beyond the crypto world. In a groundbreaking move, it acquired a chartered bank in the USA, bridging the gap between DeFi and traditional finance. This milestone marks the first time a DAO has owned a regulated financial institution.

The DAO model has also gained traction in the nonprofit sector. The Ocean Cleanup DAO, launched in early 2024, has raised over $100 million for ocean conservation efforts, demonstrating the power of decentralized governance in addressing global challenges.

9. Zero-Knowledge Proofs: Privacy in a Transparent World

As concerns about data privacy continue to grow, zero-knowledge proofs (ZKPs) have emerged as a critical technology in the Web3 ecosystem. ZKPs can verify information without revealing the information itself, striking a balance between transparency and privacy.

Zcash, a privacy-focused cryptocurrency that utilizes ZKPs, has seen its adoption rate increase by 500% in 2024. Major financial institutions, including JPMorgan Chase, have begun integrating Zcash’s technology into their blockchain solutions, signaling a shift towards privacy-preserving finance.

In the field of identity verification, Microsoft’s ION project, which uses ZKPs for decentralized identifiers, has over 100 million users by the end of 2024, marking a significant step towards self-sovereign identity.

10. Prediction Markets: Data-Driven Forecasting

Crypto-based prediction markets are all the rage now, particularly in the context of political events like the 2024 U.S. election. Platforms such as Polymarket process hundreds of millions of dollars weekly, offering real-time insights into public sentiment on various outcomes, such as the presidential election in the USA, which recently became their first market with $1 billion in betting volume.

Prediction markets use blockchain technology in the backend to ensure transparency and efficiency, attracting both casual users and respected forecasters. Prediction markets are not limited to political outcomes. They’re increasingly used in fields such as:

  • Economic forecasting
  • Sports betting
  • Entertainment industry predictions
  • Scientific research outcomes

By providing financial incentives for accurate predictions, these markets aggregate knowledge from sources around the world and from every sector of society, outperforming traditional polling and forecasting methods.

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Cooperative Learning: How Videos and Text Are Helping AI Understand the World

Posted on by Nathan

The field of artificial intelligence has made remarkable strides in recent years, but one persistent challenge remains: teaching machines to understand complex information from multiple sources. Researchers from Sakana AI recently explored this issue in their paper, “Cooperative Learning of Disentangled Representations from Video and Text.” They introduce a new approach that enables AI systems to learn by combining visual and textual data, offering new potential for improving how machines comprehend and process the world around them.

The Problem with Single-Source Learning

In most machine learning models today, AI systems are trained to recognize patterns using either video data or text data—but rarely both at the same time. While this method has led to great advances in image recognition and natural language processing, it has its limitations. When AI only learns from one source, it lacks the rich context that human perception naturally incorporates. For example, a machine might recognize a scene in a video, but it might not fully grasp the meaning without understanding the accompanying text or spoken language.

Disentangled Representations: A New Approach

Merging Models in the Data Flow Space (Layers) (Credit: Sakana.ai)

To overcome these limitations, the researchers propose a method called disentangled representation learning, where the AI system separates important factors from both videos and text. These factors might include objects in a scene, actions being performed, or the relationship between words and visuals. By disentangling these elements, the model can learn more effectively from both sources, capturing a more complete understanding of the world.

Specifically, disentangled representation learning helps in several ways:

  1. Separation of Key Factors: By isolating different elements such as objects in a scene, actions being performed, and the relationships between words and visuals, the AI can more clearly distinguish and analyze each component. This separation allows the model to focus on specific aspects of the data, leading to a more comprehensive understanding of each source.
  2. Enhanced Contextual Understanding: The method combines the visual and textual data in a way that integrates context. For example, understanding a video of a cooking process becomes more accurate when the AI also processes the recipe text, linking the ingredients and steps with the visual cues. This results in a richer and more nuanced representation of the information.
  3. Improved Learning Efficiency: By disentangling these elements, the AI can learn more efficiently from both sources. It avoids the confusion that may arise from treating the data as a monolithic whole, allowing for better alignment and interpretation of visual and textual information.
  4. Real-World Applicability: This approach enables the AI to better handle real-world scenarios where data is inherently multimodal. For instance, in autonomous driving, disentangled learning helps in correlating visual inputs (like road signs) with textual instructions (like speed limits), thus improving decision-making.

The novelty of this approach lies in how the system learns cooperatively. Rather than treating video and text as independent sources of information, the model uses both in tandem, allowing the text to provide context for the visuals and vice versa. This cooperative learning leads to richer representations, where the AI understands more than just the surface-level features of the video or the literal meaning of the text.

Training AI to Learn Like Humans

This cooperative learning approach mirrors the way humans process information. When we watch a video, we don’t just see the images on the screen—we also use language to explain what’s happening, drawing connections between our senses. For instance, in a documentary, we understand the visuals of animals in their habitat through the narrator’s explanation, which adds layers of meaning to what we see.

Examples of an answer by EvoVLM-JP (Credit: Sakana.ai)

In the same way, this method allows AI to combine video and textual data, learning richer, disentangled representations of the real world. The model is trained to align video clips with textual descriptions, helping it to better understand how specific scenes in a video correspond to the descriptions in text. This multimodal learning opens up new possibilities for AI systems to handle tasks that require deep understanding across different types of data.

Potential Applications of Cooperative Learning

The implications of this research are vast. One potential application is in autonomous systems, such as self-driving cars, which must constantly analyse visual and verbal information to make decisions. By disentangling the visual and textual components, an AI-powered car could better understand road signs, traffic signals, or verbal instructions from passengers.

Another area where this could have a significant impact is content recommendation systems. With a deeper understanding of both videos and textual content, systems like YouTube or Netflix could offer more personalised recommendations, matching videos to users based on a nuanced understanding of both the video content and the textual descriptions or subtitles.

Challenges and Future Directions

While this cooperative learning model shows great promise, it also comes with challenges. For one, aligning text with videos in a meaningful way requires high-quality data and well-labelled examples. Moreover, disentangling representations in a way that consistently improves performance remains a difficult task, especially in diverse real-world scenarios.

The researchers also acknowledge that more work is needed to explore how this model performs across different types of videos and texts, as well as how it might be extended to other modalities, like audio or sensor data.

Credit: Tesfu Assefa

Conclusion

The paper “Cooperative Learning of Disentangled Representations from Video and Text” offers a new perspective on how artificial intelligence can learn more effectively from multiple data sources. By allowing AI to learn cooperatively from both video and text, the researchers are helping push the boundaries of machine perception. This approach holds the potential to revolutionize fields from autonomous systems to content recommendation, paving the way for AI that can understand the world with a level of depth and context that’s more human than ever before.

Reference

Sakana.AI. “Evolving New Foundation Models: Unleashing the Power of Automating Model Development,” March 21, 2024. https://sakana.ai/evolutionary-model-merge/.

Wang, Qiang, Yanhao Zhang, Yun Zheng, Pan Pan, and Xian-Sheng Hua. “Disentangled Representation Learning for Text-Video Retrieval.” arXiv.org, March 14, 2022. https://arxiv.org/abs/2203.07111.

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Rethinking Machine Learning: Stephen Wolfram’s Case for Simplicity

Posted on by Nathan

This article reviews Stephen Wolfram’s latest work on simple machine learning models, published on August 24. Wolfram, a British-American computer scientist and physicist, is widely recognized for his pioneering advancements in computer algebra and his foundational role in theoretical physics. Over the last three decades, he has developed the Wolfram Language, which powers tools like Mathematica and Wolfram|Alpha. Known for shaping modern science and education, Wolfram’s contributions, including his influential 2002 book A New Kind of Science, continue to impact cutting-edge fields like machine learning.

Researchers and engineers have spent years trying to understand the intricate workings of machine learning (ML). But Stephen Wolfram suggests we might be missing a crucial point: Could there be a simpler, more fundamental explanation behind ML’s success? In his recent exploration, Wolfram delves into the possibility that minimal models might help explain the underlying structure of ML systems, offering a fresh take on this complex field.

Machine Learning: Not Just Layers of Neurons

At the heart of ML, we often picture layers of neurons, processing data through complex algorithms. The more layers, the more power—right? Wolfram questions this assumption. Rather than seeing machine learning models as just “black boxes” stacked with neurons, he proposes a new way of thinking: rule-based systems. These systems might help us see how machine learning really works without needing to overcomplicate things.

 A random collection of weights that are successively tweaked with biases to “train” the neural net to reproduce a function. The spikes near the end come from “neutral changes” that don’t affect the overall behavior) (Credit: Wolfram, “What’s Really Going on in Machine Learning? Some Minimal Models.”)

The Emergence of Simple Rules

One of the key insights Wolfram brings forward is that simple rules could give rise to the same kind of patterns we see in ML models. These simple rules, when applied over time, generate incredibly complex behaviors, much like we observe in natural systems. Wolfram argues that even though ML models seem complex, they might be governed by simple underlying principles—ones that are easy to overlook because of the complicated structures we build on top of them.

A pattern generated by a 3-color cellular automaton that through “progressive adaptation”. The rule applied here is that the pattern it generates (from a single-cell initial condition) survives for exactly 40 steps, and then dies out (i.e. every cell becomes white). (Credit: Wolfram, “What’s Really Going on in Machine Learning? Some Minimal Models.”)

Could Simple Models Replace Deep Learning?

Wolfram suggests that if we embrace minimal models, we might be able to make machine learning more understandable. For instance, we can take cellular automata—simple systems where each “cell” follows a set of local rules which can generate behaviors just as intricate as the multi-layered systems we see in ML today. In essence, we don’t always need deep learning to replicate complex behaviors; simple models can often get us the same results.

How Minimal Models Explain ML’s Success

So, why does this matter? Wolfram’s argument gives a new perspective on the success of ML models. He believes that much of what makes machine learning effective might not be the depth or complexity of the model, but the fact that these models can tap into a universal rule-based approach. Even the simplest rules, given enough time, can build up to create the complicated behaviours we see in modern AI systems.

Another pattern that survives the 50 steps using the “rule array”. At first it might not be obvious to find such a rule array, however the simple adaptive procedure easily manages to do this. (Credit: Wolfram, “What’s Really Going on in Machine Learning? Some Minimal Models.”)

The Future of Understanding Machine Learning

Wolfram’s work invites researchers to think beyond the technicalities of neurons and layers. He challenges the ML community to explore simpler frameworks to explain machine learning’s achievements. Could this lead to more efficient models? Or perhaps unlock new ways to innovate in AI? As more researchers investigate the concept of minimal models, we may find that these simple principles have been there all along, guiding the complex systems we’ve created.

Key Take-Aways

While machine learning has always been regarded as a highly complex field, Wolfram’s insights into minimal models provide a refreshing, almost philosophical take. As the field progresses, we may see a shift toward exploring more fundamental, rule-based systems that simplify our understanding of artificial intelligence. And in this simplicity, we might uncover the true power behind machine learning’s continued evolution.

Credit: Tesfu Assefa

Validating Wolfram’s Minimal Models in Practice

While Wolfram’s idea of using simple rules to explain machine learning (ML) is interesting, it’s important to consider a different perspective. Right now, ML systems, especially deep learning models, work really well because of their complex structures and the huge amounts of data and computing power they use.

Here are some key points to think about:

  1. Can Simple Models Replace Complex Ones?: Building and training minimal, rule-based models to perform the same tasks as current deep learning systems might be much harder. We need to see if these simpler models can actually do what deep learning models do, especially when it comes to handling big tasks with the resources we have.
  2. Evaluate Performance: We should create and test practical versions of these simple models on real-world problems. Compare how well they perform against today’s deep learning models.
  3. Check Scalability and Resources: Look at how these minimal models scale up and how much data, computing power, and energy they need. Compare these needs with the requirements of current deep learning systems.
  4. Practical Testing: To really understand if Wolfram’s approach works, we should test these minimal models in practice and see if they can achieve similar results with less complexity.

By exploring these aspects, we can better understand whether simple models could be a practical alternative to the complex systems we use today or if the success of current ML models depends on their complexity and extensive resource use.

Reference

Wolfram, Stephen. “What’s Really Going on in Machine Learning? Some Minimal Models.” Stephen Wolfram Writings, August 22, 2024. Accessed September 1, 2024. https://writings.stephenwolfram.com/2024/08/whats-really-going-on-in-machine-learning-some-minimal-models/.

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Beyond the Hype: The Urgent Need for Education and Oversight in the Age of Super AI

Posted on by Hruy Tsegaye

Human history becomes more and more a race between education and catastrophe.

H. G. Wells 

The quest to create Artificial Superintelligence (ASI) is more than just a technological ambition: it is a profound philosophical endeavor that poses existential questions about humanity’s future. As we stand on the precipice of this transformation, we need a comprehensive system of checks and balances around the development of Super AI. Without it, we risk consequences that could reshape—or potentially endanger—the world as we know it. Exploring the current agents, their motivations, and their influences in detail is a task we can no longer ignore; however, examining these elements in a balanced way requires true courage. In this article, we will highlight the diverse motivations driving the creation of Super AI, the inherent dangers associated with these motivations, and the critical need for a regulated approach that balances innovation with ethical oversight.

The Drive to Build for the Sake of Innovation

Among the groups pursuing the development of Super AI are those driven by the sheer allure of innovation. These are the scientists, technologists, and enthusiasts who view the creation of Super AI as the ultimate achievement in human ingenuity—a testament to our capacity to push the boundaries of what is possible. Their motivations are not rooted in power or profit but in the intellectual satisfaction of creating something never seen before. This drive for innovation is beautiful and admirable in its way, and it can lead to groundbreaking discoveries, yet it also harbors significant risks.

The primary danger lies in the lack of foresight and responsibility. If you think innovation is inherently good, you miss any bad ethical and societal implications of the technology. These groups may be so busy asking “can we build it?” that they neglect to ask “should we build it?”. This tunnel vision can lead to the release of super intelligent and conscious AI systems that are poorly understood, insufficiently tested, and potentially harmful. The pursuit of scientific glory without safeguards could result in the development of Super AI that acts unpredictably, beyond human control, and eventually annihilates the world as we know it, or disrupts societal norms and values leading to dystopia.

The Hunger for Power: Economic and Military Motivations

Another major force propelling the advancement of Super AI is the pursuit of power—both economic and military. Governments and big corporations are heavily invested in AI research, driven by the promise of gaining a strategic edge over their rivals. Economically, Super AI offers the potential to revolutionize industries, automate complex processes, and create new markets. Militarily, the development of AI-enhanced weaponry and intelligence systems could redefine global power dynamics, making nations that possess advanced AI capabilities the dominant forces on the world stage.

However, the race for AI supremacy is fraught with peril. The pursuit of economic and military dominance through Super AI can lead to a dangerous arms race, where competition drives speed at all costs, overshadowing safety and ethics. In this scenario, the focus isn’t creating AI that is beneficial for humanity – it’s creating AI that helps a select few win power and dominance. The risks include the autonomous weapons, surveillance systems that infringe on human rights, and economic models that exacerbate inequality. Super AI power in the hands of a few entities—be they nations or corporations—raises the specter of a world where the majority of humanity is subject to the whims of AI-driven elites.

Some players see the globe as a ruthless competition. They are incapable of thinking of the other side as anything but an adversary. In such a worldview, the adversary poses a perpetual clear and present danger, justifying massive investment, moral flexibility, and risky gamble. 

The world must urgently identify any and all circumstances where universal limits on Super AI can be established. Without such measures, it is a short, quick race towards a third world war.

The Idealists: Saving or Replacing Humanity

In contrast to the power-seekers, there are those who view Super AI as a tool to transcend humanity’s limitations. These idealists envision Super AI as a savior—a means to solve global challenges like clean energy, longevity, pollution and climate change, disease, and poverty. Some even entertain the notion that Super AI could replace humanity, creating a new form of existence that is free from human and biological flaws. While these visions are rooted in a desire to improve the human condition, they too carry profound risks.

The danger with this idealistic approach is the assumption that Super AI will inherently act in humanity’s best interest, or that evolving towards synthetic intelligence is superior to what nature has provided. These perspectives often underestimate the complexity of aligning AI’s goals with human values, especially when those values are diverse, subjective, contradictory, and subject to change. Additionally, the idea of replacing the current form of humanity with some sort of Super AI synthetic lifeform overlooks the ethical questions surrounding the value of preserving the ‘meat-based human’ form and human agency. Similarly, it disregards the scientific aspects of unknown factors, such as whether humans can exist solely as conscious beings without their biological bodies, and for how long. Will living forever lead to stagnation and gradual extinction? Even more complicated practical questions rooted in economic disparity are ignored: can the less developed world afford such Super AIs? How can we mitigate the effect of the current inequality? Which part of humanity is going to be saved and which will be left behind? There are many questions that these groups tend to ignore in their rush to ‘save’ humanity. If left unchecked, such ambitions could result in scenarios where the group makes decisions that disregard individual freedoms, cultural identities, economical handicaps, and the intrinsic worth of human experience.

Credit: Tesfu Assefa

The Doomers vs. the Accelerationists

Two groups amass at opposite poles of Super AI development: the Doomers and the Accelerationists. This division could polarize society into pro-tech and anti-tech factions. This division might escalate into a conflict that extends beyond intellectual debate, potentially leading to societal fragmentation, unrest, and even violence. 

The Doomers oppose the idea of developing Super Intelligence, viewing it as the existential threat that could end the world as we know it. They argue that unleashing a Super AI is akin to opening Pandora’s box. The danger posed by this group lies in their extreme resistance to any AI advancements. Their absolute stance against Super AI can create an environment where dialogue and compromise become impossible, hindering any efforts to establish a balanced approach to Super AI regulation.

On the opposite side are the Accelerationists. They advocate for the rapid and unrestrained development of Super AI. They believe that technological progress should be pursued at any cost, often dismissing the potential risks associated with such advancements. Furthermore, they believe that it’s too late to save humanity and the planet without AI – Super AI is the only way out of our crises. The Accelerationists are dangerous because of their tendency to overlook or downplay the existential threats posed by Super AI, including the possibility of unintended consequences that could be catastrophic for humanity. Their refusal to consider safety measures or listen to the concerns of the opposition can create a reckless rush toward Super AI development, ignoring critical ethical considerations and safety protocols. This stubborn, one-sided view heightens the risk of creating dangerous Super AI systems. It also deepens the divide between those who advocate for caution and those who push for unbridled advancement, making consensus and cooperative regulation increasingly difficult. 

Religious Fundamentalists and Conspiracy Groups

There are more factions in the debate. There are Religious Fundamentalists and Conspiracy Groups, who often view Super Intelligence through a lens of apocalyptic prophecy. Many in these groups see Super AI as a doomsday weapon, either created deliberately to bring about humanity’s downfall or as a harbinger of divine judgment. 

Some are deterministic, believing that the advent of Super AI is an inevitable part of a predestined fate. They adopt a fatalistic attitude, feeling powerless to influence the course of events. Others believe that humanity has the agency to alter this course and should actively resist or sabotage any and all AI development in an effort to avert the perceived doom.

The primary danger posed by these groups is the irrational and often destructive nature of their discourse. Their arguments are typically grounded in subjective interpretations, religious dogma, or conspiracy theories rather than rational, objective, and evidence-based considerations. This approach can lead to extreme measures, such as sabotage, misinformation campaigns, or violence, which not only disrupt the constructive dialogue necessary for responsible Super AI development but also contribute to backlash and social destabilization. The imagery of Super AI as an apocalyptic threat can fuel fear and paranoia, making it even more challenging to engage in meaningful discussions about the potential benefits and risks of Super AI. It’s hard to develop sound policies and regulations in this climate of fear and irrationality, ultimately leading to increased risk of Super AI being built without proper oversight and ethical grounding.

Credit: Tesfu Assefa

The Need for a Check and Balance System

Given these varied and conflicting motivations, a robust check and balance system is essential in developing Super AI. Such a system would provide oversight, ensure ethical considerations are prioritized, and prevent any single entity from monopolizing Super AI’s power. However, creating this system is not without its challenges.

A key risk is that the safety system will be monopolized by a special interest, under the guise of regulation. If the power to develop and control AI is concentrated within a select group of regulators, it could be a new form of tyranny—where decisions about AI’s development and deployment are made by a few, without sufficient accountability or representation of broader societal interests. This concentration of control could stifle innovation, suppress dissenting voices, and result in AI technologies that reflect the biases and agendas of the few rather than the needs of the many.

To mitigate this risk, a balanced regulatory approach should involve multiple stakeholders, including governments, international bodies, private sector groups, and civil society. Transparency, accountability, and inclusivity must be the cornerstones of any regulatory framework. The system should be dynamic and adaptable, capable of evolving with the rapid pace of AI development and responsive to new ethical, legal, and societal challenges.

The Current State: Hype, Noise, and the Real Science

The current landscape of Super AI development is thick with hype, misinformation, and sensationalism, muddying the waters for anyone who wants to establish checks and balances. Companies have exaggerated claims about the capabilities and potential of AI to get newspaper inches and investor dollars. This systematic disinformation makes it difficult to discern the true state of AI research and assess the actual risks and benefits.

For example, headlines often proclaim that AI is on the verge of achieving human-like consciousness, or that it will imminently render entire industries obsolete. While such claims generate excitement and investment, they can also lead to unrealistic expectations and misguided policy decisions. We need a reality-grounded, evidence-based approach to regulation that can discern the AI’s actual capabilities rather than its claimed ones. Policymakers and the public must be informed by credible scientific insights rather than sensationalist narratives.

Credit: Tesfu Assefa

Conclusion

The development of Super AI is one of the most consequential endeavors humanity has ever undertaken. It has the potential to revolutionize our world, solve intractable problems, and redefine what it means to be human. However, without a well-structured check and balance system, the pursuit of Super AI could also lead to unintended consequences that threaten our very existence.

A comprehensive approach to regulation – one that respects innovation while safeguarding against misuse – is an absolute necessity. This system must be inclusive, transparent, and adaptable, ensuring that Super AI reflects the diverse interests and values of humanity. As we navigate this uncharted territory, we must remain vigilant, asking not just what Super AI can do, but what it should do, and for whom. The answers to these questions will shape the future of our species, and it is imperative that we approach them with the gravity and foresight they deserve.

There is nothing more unsatisfactory than not reaching a clear conclusion. In this case, the only assured recommendation I can make is the urgent need to integrate the topic of Super AI into the existing education system in a universal and state-of-the-art manner. This must be accomplished very quickly. I began the article with the H.G. Wells’ quote because I believe it perfectly sums up the main problem.

In light of the profound impact that Super AI will have on the future, it is essential that learning about Super AI becomes a mandatory component of education systems worldwide, starting as early as elementary school. Introducing curricula that cover the technical aspects of AI along with its ethical and philosophical implications will equip future generations with the knowledge and the critical thinking skills needed to navigate and shape the AI-driven world they will inherit. An early understanding of Super AI’s potential and pitfalls will empower young minds to approach AI development responsibly and thoughtfully, helping humanity remain in control of this powerful technology. These educational programs should instill a sense of ethical responsibility, emphasizing the importance of aligning Super AI advancements with human values and societal needs. As the future architects of our world, today’s children must be prepared not just to use Super AI but to guide its evolution in a way that benefits all humanity. As H.G. Wells wisely noted, “Civilization is a race between education and catastrophe,” and ignorance is more dangerous than knowledge itself.

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Quantum Random Number Generators: Ushering in a New Era of Mobile Security

Posted on by Hope

The latest innovation in Samsung’s mobile lineup, the Samsung Galaxy Quantum 5, introduces Quantum Random Number Generators (QRNGs) as a key feature for enhanced security. QRNGs leverage the inherent unpredictability of quantum mechanics to generate truly random numbers, which are crucial for encryption, authentication, and secure communications.

But what does this mean for everyday users? How does QRNG technology differ from the methods traditionally used in smartphones, and what role does it play in securing your personal data on the Galaxy Quantum 5? Let’s explore the mechanics of this advancement and compare QRNG with more conventional approaches.

Traditional RNG vs. Quantum RNG: What’s the Difference?

Most digital systems today, including smartphones, rely on Pseudo-Random Number Generators (PRNGs) for generating random numbers. PRNGs use algorithms and an initial seed value to produce sequences that appear random. However, because they are deterministic-algorithm-based, PRNGs are deterministic—meaning that if you know the seed or the algorithm, you can predict the sequence of numbers.  Due to the nature of the algorithm, it’s very hard to actually predict what a deterministic random number is going to do, but in principle it’s possible if you set a large enough computer at the task for a long enough time.

Here’s a basic example of a PRNG in Python:

python

import random

# Seed the PRNG

random.seed(42)

# Generate a pseudo-random number

prng_number = random.randint(0, 100)

print(f”Pseudo-Random Number: {prng_number}”)

In contrast, Quantum Random Number Generators (QRNGs) use quantum phenomena to produce truly random numbers. QRNGs generate numbers based on the random behavior of quantum particles like photons, making them inherently unpredictable and non-reproducible. This provides a higher level of security for cryptographic purposes.

How Does QRNG Work in the Galaxy Quantum 5?

At the heart of Samsung’s Galaxy Quantum 5 is a QRNG chipset developed by ID Quantique. This chipset, measuring just 2.5mm x 2.5mm, is currently the world’s smallest QRNG. It works by detecting random quantum states of photons to generate truly random numbers, which are then used to create encryption keys and protect sensitive data.

Here’s a simple look at how a QRNG works in practice:

python

from qiskit import QuantumCircuit, transpile, Aer, execute

# Create a Quantum Circuit with one qubit

circuit = QuantumCircuit(1, 1)

# Apply a Hadamard gate to the qubit to put it in a superposition

circuit.h(0)

# Measure the qubit’s state

circuit.measure(0, 0)

# Use AerSimulator to execute the circuit

simulator = Aer.get_backend(‘aer_simulator’)

compiled_circuit = transpile(circuit, simulator)

result = execute(compiled_circuit, simulator, shots=1).result()

# Get the random bit (either 0 or 1)

random_bit = result.get_counts()

print(random_bit)

This code uses a quantum circuit to generate a truly random bit. In this example, the Hadamard gate is applied to a qubit to create a superposition—meaning the qubit exists in both the 0 and 1 states at the same time. When measured, the qubit collapses into either 0 or 1, generating a truly unpredictable bit.

In the quantum approach – unless the laws of physics as we currently understand them are wrong in some significant and relevant way – there is in principle no way for anyone to predict what numbers will be randomly generated.   The laws of physics say there is no pattern.

In the Galaxy Quantum 5, this principle is applied on a larger scale, with the QRNG generating random numbers that are used for encryption, authentication, and securing data in applications like mobile banking, social media, and external storage.

Credit: Tesfu Assefa

Samsung Galaxy Quantum Series Comparison

The Samsung Galaxy Quantum series has progressively integrated QRNG technology to enhance mobile security. The Galaxy Quantum 5, the latest in the series, features a smaller, more advanced QRNG chipset compared to its predecessors. While the earlier models like the Galaxy Quantum, Quantum 2, and Quantum 4 introduced QRNG technology in various capacities, the Galaxy Quantum 5 expands its application significantly.

Compared to the previous models, the Galaxy Quantum 5 boasts a QRNG chipset that is not only smaller (2.5mm x 2.5mm) but also includes enhanced encryption capabilities and a Quantum Indicator feature. This indicator notifies users when an application is using quantum-secured services, providing additional transparency. The Galaxy Quantum 5 supports a wider range of QRNG-based apps, including those for games and service applications, and offers improved encryption for external memory.

The earlier models, such as the Galaxy Quantum and Quantum 2, provided basic QRNG-based encryption, with limited app support. The Quantum 4 introduced QRNG-based encryption for external memory, but it was the Galaxy Quantum 5 that expanded the scope of QRNG applications, including enhanced authentication and encryption of information.

Applications in the Samsung Galaxy Quantum 5

The Galaxy Quantum 5 integrates quantum technology deeply into its security features. Partnered with Samsung Knox, the device uses QRNG to enhance protection in various ways:

  • Authentication: Whenever you use biometric data like fingerprints or facial recognition to log in, the encryption keys used to protect this data are generated by QRNG, ensuring they can’t be predicted or replicated.
  • Secure Payments and Banking: QRNG provides extra layers of security for financial apps and mobile banking, protecting sensitive information like payment details and banking credentials.
  • Social Media and Gaming: With QRNG technology, even apps unrelated to finance—like social media and games—can take advantage of quantum-enhanced security, ensuring your data is safe during login and in-app transactions.

Additionally, a “quantum indicator” feature alerts users when QRNG is actively securing an application, adding transparency and peace of mind.

What’s Next for Quantum Technology in Smartphones?

The Samsung Galaxy Quantum 5 represents a significant advancement in mobile security through its integration of QRNG technology. As quantum technology continues to evolve, we may see more devices adopting QRNG and other quantum-based solutions, offering stronger protection for personal data.

For now, the Galaxy Quantum 5 is primarily available in South Korea, but it sets a new standard for mobile security. The introduction of QRNG technology could pave the way for broader adoption of quantum-enhanced security in mobile devices worldwide.

Conclusion

The Samsung Galaxy Quantum 5 is a notable step forward in mobile security with its use of Quantum Random Number Generator (QRNG) technology. This device provides an additional layer of protection against potential security threats, especially in high-risk applications like mobile banking and secure communications.

For those interested in exploring the technology further, including a web-based implementation of QRNG, you can find the complete project on my GitHub: https://github.com/Hope-Alemayehu/trulyRandom.

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What is Nosana: Decentralized GPU Computing for AI Inference

Posted on by Werner

Introduction

The marriage between artificial intelligence and blockchain technology is viewed by many now as slightly overhyped. Many projects have failed to live up to expectations, and only a handful like Artificial Superintelligence Alliance (a merger between SingularityNET, Fetch.AI and Ocean Protocol) and Render stand out. 

However, compelling new potential use-cases abound. One is Nosana (NOS), an up-and-coming Solana-based project that aims to revolutionize access to GPU computing power. By creating a decentralized blockchain marketplace for GPU resources, Nosana addresses a critical issue in AI, and also taps into the growing decentralized physical infrastructure (DePIN) sector led by projects like io.Net and Render.

Let’s delve into what makes Nosana a potential game-changer in the world of decentralized computing.

What is Nosana?

Nosana is a decentralized and open-source cloud computing marketplace built on the Solana blockchain. It focuses on AI inference and GPU power distribution, utilizing community-contributed computing resources to run tasks for open-source projects and AI workloads. In short, the project aims to connect AI inference needs and decentralized GPU resources. But what’s AI inference?

Understanding AI Inference

AI inference is the process of applying a trained AI model to new data to get real-time predictions or solutions. It’s the stage where AI models put their learned knowledge to practical use. If you’re an Internet user, you’re probably already using many of these applications on a daily basis without knowing. Some examples include:

  • Real-time object recognition in image, text or video streams
  • On-the-fly language translation
  • Personalized content recommendations on streaming platforms

Nosana specializes in providing computational power for these inference tasks, which are becoming increasingly important as AI applications proliferate across industries.

Key Features of Nosana

FeatureDescription
Decentralized GPU NetworkAllows GPU owners to rent out idle hardware to AI users, creating a peer-to-peer marketplace for computing power.
AI-powered CI/CD AutomationAims to reduce software bugs and enhance user trust by incorporating AI into the software development pipeline.
Nosana ExplorerProvides real-time insights into network performance and statistics, offering transparency to users and stakeholders.
Developer-friendly APIs and Flexible PricingMakes it easier for projects of various sizes to access computing power, potentially lowering the barrier to entry for AI development.
Environmental FriendlinessBy utilizing existing hardware, Nosana potentially reduces the need for additional energy-intensive data centers.

What AI Issues Does Nosana Aim to Solve? 

Nosana tackles three main issues in the AI and computing sectors:

  1. GPU Shortages

The global shortage of GPUs, particularly high-end ones for AI tasks, has been a significant bottleneck in AI development. Nosana provides access to a network of distributed GPU resources, potentially alleviating this shortage.

  1. Idle Compute Utilization

Many GPUs, especially in personal computers, sit idle for long periods. Nosana allows owners of unused GPU power to monetize their resources, improving overall efficiency in the computing ecosystem.

  1. High Public Cloud Pricing

Centralized cloud services charge a lot for AI computing tasks. Nosana offers a more cost-effective alternative, potentially making AI development more accessible to a broader range of organizations and individuals.

Just How Big is Nosana’s AI Potential?

As demand for AI applications increases, so does the need for efficient, cost-effective computing power. Nosana’s decentralized approach could provide several advantages over centralized competitors:

  1. Web3 Scaling

The ability to tap into a global network of GPUs that brings significant scale to AI projects. This could be particularly beneficial for startups and researchers who need to scale their AI operations quickly without massive upfront investments.

  1. Cost Efficiency

AI compute is still very expensive, costing firms like Google and OpenAI billions each year to run. By utilizing idle resources, Nosana may offer more competitive pricing compared to centralized cloud providers, reducing the operational costs of AI projects, making them more viable and sustainable.

  1. Democratizing AI

Democratization of tech is a term bandied around quite a lot in the crypto world, however it could have some substance this time. Lower costs and easier access to computing power could enable more developers and small businesses to work on AI projects. This democratization could lead to more diverse and innovative AI applications across various sectors.

  1. Reducing Latency

Decentralized networks can potentially reduce latency by allowing users to access GPU resources located closer to their geographical position. This could be crucial for real-time AI applications.

  1. Web3 Resilience

A decentralized network is inherently more resilient to outages or attacks compared to centralized cloud services, potentially offering more reliable computing power for critical AI tasks.

How Does Nosana Differ From Competitors? 

Nosana operates in a competitive field alongside projects like Render, Akash, and Golem. However, its specific focus on AI inference, and its integration with the Solana blockchain, set it apart. Here’s how Nosana compares to some of its competitors:

  1. Render: Both focus on decentralized GPU computing. Render has a stronger emphasis on graphics rendering, whereas Nosana specializes in AI inference.
  2. Akash: Akash provides a more general-purpose decentralized cloud computing platform, while Nosana is more focused on GPU resources for AI tasks.
  3. Golem: Golem offers a broader range of computing resources, whereas Nosana concentrates specifically on GPU power for AI.

As of early 2024, Nosana has a relatively small market cap compared to some competitors, potentially indicating room for growth if the project gains traction.

Credit: Tesfu Assefa

Tokenomics and Market Performance

Here’s what you need to know about Nosana’s tokenomics. This information is from CoinMarketCap and the Nosana whitepaper:

  • Total Supply: 100 million NOS tokens
  • Circulating Supply: Approximately 82 million
  • Market Cap: Around $97 million (as of Sept 2024)
  • Token Distribution:
    • 30% private sale
    • 10% public sale
    • 15% team/advisors
    • 20% ecosystem/community
    • 25% foundation reserve

The NOS token has shown strong relative strength in the market, maintaining an upward trend since late 2023 despite overall market fluctuations. 

Staking and Rewards

Nosana offers variable staking options for token holders, with potential annual percentage yields (APY) of up to 40% for long-term stakers. These high yields may not be sustainable in the long run but they currently provide an attractive incentive for token holders to participate in securing the network. Please note that token emissions and unlocks can really destroy a token’s price. 

Partnerships and Ecosystem

Nosana has partnerships with several notable companies in the tech industry, including:

  1. Chaingenius: A blockchain technology company focusing on security and scalability.
  2. HCL Technologies: A global IT services company that uses Nosana to enhance software development processes.
  3. HashiCorp: A software company specializing in multi-cloud infrastructure automation tools.
  4. Akamai: A content delivery network and cloud service provider.

These partnerships could provide Nosana with valuable industry connections and customers.

Risks and Considerations

Nosana does show promise, but potential investors should consider several factors:

  1. Market Competition: The decentralized computing space is crowded and rapidly evolving. Nosana will need to continually innovate to maintain a competitive edge.
  1. Technological Challenges: Ensuring consistent performance across a decentralized network presents non-trivial technical hurdles. Issues like quality of service, data privacy, and network stability will need to be addressed.
  1. Adoption Hurdles: Convincing traditional AI developers to switch to a decentralized solution may require significant effort. Nosana will need to demonstrate clear advantages on cost, performance, and reliability.
  1. Regulatory Uncertainty: The evolving regulatory landscape for cryptocurrencies and decentralized platforms could impact Nosana’s operations. Compliance with emerging regulations will be crucial for long-term success.
  1. Token Volatility: As with many cryptocurrency projects, the NOS token may experience significant price volatility, which could affect its utility within the ecosystem.

Conclusion

Nosana represents an innovative approach to providing GPU computing power for AI applications. Its focus on AI inference, decentralized structure, and its integration with Solana make it a project worth watching in the AI and blockchain space. The potential to democratize access to AI computing resources, and create a more efficient marketplace for GPU power, could be really impactful.

However, as with any early-stage project in a rapidly changing field, potential investors should conduct thorough research and consider the associated risks. The success of Nosana will depend on its ability to deliver on its technological promises, to build a robust ecosystem of users and providers, and navigate the complex landscape of AI and blockchain technologies.

As the AI revolution expands into new corners of both Web2 and Web3, projects like Nosana may play a crucial role in shaping the future of decentralized computing and AI development. Whether Nosana can capitalize on its potential and become a leader in this space remains to be seen, but it certainly presents an intriguing vision for the future of AI DePIN infrastructure.

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