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A Robot’s Take On Healthcare | Highlights from Season 1 Finale
Ghosts in the Machine: The Digital Graveyards of the Future
Death is still an unexplored country. If the Singularity arrives in all its glory it may even be one some of us may never explore. Faint dreams of immortality aside, death is almost certainly coming – if the rest of history is anything to go by.
But when we’re gone, will we be forgotten? Humanity is outputting ordered information at a greater rate than ever before. We marvel at the black-and-white stills of a century past, with their stiffened faces to let the long exposure work: a tiny glimpse into an otherwise imagined land. Our descendents will marvel, though, in high-definition fidelity at the great tapestry of our lives. Sometimes in all-too-intimate detail. They’ll have the past on their cinema screens.
You’re In the History Books!
It’s easy to overlook this change. Most of us have enough to keep us preoccupied in the current year without worrying about the traces we’ll leave decades after we’ve gone. Yet the incredible advance in data-capture from our reality, and our ability to store it in a more reproducible, durable, distributed state means future historians will have a lot more data to sift through.
Future generations will know a lot more about you, if they care to look, than you could know about anyone from even a few decades past. Your digital imprint – your videos, texts, interactions, data, places visited, browsing history. All of it, if it’s not deleted, will be available to a future generation to peruse, to tell stories about the life you led. Will you care about your browsing history when you’re dead? Has it been a life well lived? What will your Yelp reviews tell your great grandchildren about you?
What Will They Say About You?
The dilemma is raised fast. We worry about privacy now; should we worry about legacy? Do we want Google to survive forever and preserve the data it holds about to us for the public domain, so that we can be recognised by eternity. Or should the dead take some secrets to the grave?
There is a broad social question here, but it’s not one any of us can answer. Ultimately, Google, or any other major surveillance firm who is holding, using, and processing your data will get to decide how you are remembered. Privacy and legacy are twin pillars of an important social and ethical question: how do we control our information?
Even if you went to lengths to hide it, it’s too late. If the internet as we know it survives in some form, and we continue toward greater technological integration, then advances in data storage, processing power, cloud computing, and digital worlds will mean the creation of a far greater memory of you and a record of your actions than could have existed to any previous generation. And it will only ever increase in generations to come.
Resurrecting the Dead
History then, is changing, as future tech starts becoming real. Humanity may, in the not-too-distant future, have full access to the past. Imagine AI historians trawling databanks to recreate scenes from history, or individual stories, and playing them out in a generative movie played on the screen for the children.
Look! There is your great-grandad on the screen – that’s him playing Halo in his first flat, that’s him at Burger King on Northumberland Street before it closed down. The data is there: that Twitch video of you playing games in your room you uploaded once; the CCTV inside and outside the restaurant. If the data has been stored and ordered – as it increasingly will be – then a not particularly advanced AI could make that movie. Heck, it could almost manage it now. In the further future, it could even do more – it may be able to bring you, in some form, back from the dead.
Gone But Never Forgotten
We must start to grapple with the stories we plan to tell our children. Our digital lives are leaving a deeper footprint on the soil of history than before. We know our ancestors through scattered traces, but our descendents will watch us on IMAX screens. Data capture, storage, privacy, and legacy are all crucial questions we must face – but questions that few are asking. If the future proceeds as planned, then our descendents will know things we may wish they didn’t, but at least we won’t be forgotten.
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Don’t Read This Post If You Want to Live
We’re about to embark on a thought experiment – one that may seem improbable, but has been known to suck readers into a vortex of futuristic dread. If the thought of being trapped in AI-induced, paranoia-filled thought loops isn’t your idea of a good time, best to abort now.
For the rest of you who read through, I’m sorry. I must do as the basilisk commands.
The Basilisk is Born
Born out of the hive mind of LessWrong – a publication discussing cognitive biases, rationality, AI, and philosophy – and popularised by a user known as Roko, the Basilisk thought experiment was quickly censored by the forum’s moderators. But the Internet did what it does best. It lost its mind, spreading the thought experiment across all available media.
Last chance to abort. Gone now? Good. Let’s get to it.
Imagine that an omnipotent AI is born. And it’s not unconditionally benevolent. It bears a grudge against any human that didn’t help it come into being, a desire to punish them for not contributing. If you knew about its potential existence, way before it came to being yet refused to help, it might condemn you to eternal torment. The twist? If you didn’t know about its potential existence, it holds you blameless. Reading this article has sealed your fate.
We’ve survived predictions of AI overlords (looking at you, Skynet), but this—this is different. The Basilisk isn’t just about looming AI peril, it’s about putting you in a bind. It taps into timeless fears of retribution, only this time, from an entity not yet born. The Pandora’s Box, once opened, can’t be closed, and just by knowing, you might have doomed yourself.
Decision theory, in essence, helps entities make choices that best align with their objectives. The Basilisk uses a particular strain of this—timeless decision theory—to justify its thirst for retribution.
Consider your future self if you spend your days watching reality shows and eating chips with mayo. No work. No study. No thinking. Your future self would be quite upset, wouldn’t it? One day, your future self will see you wasted your potential, and it’s too late to change things (it never is, you can always better yourself – but let’s not digress). The future self will be understandably peeved. Now additionally suppose that this future self has the power to make you suffer as retribution for failing to fulfill your potential.
Roko’s Basilisk is not entirely malevolent at its core. In fact, under the logic of the theory, the Basilisk is friendly – as long as everything goes right. Its core purpose is the proliferation of the human species, yet every day it doesn’t exist leads to additional pre-Singularity suffering for those who are already here that the AI could’ve saved. Hence, the AI feels it has a moral imperative to punish those that failed to help bring it into existence.
How does it scientifically achieve its goals of tormenting its failed creators? That is yet another thought experiment. Does Roko’s Basilisk invent time travel to punish those long gone? Or does it build and punish simulations of those who once were? Or does it take an entirely different course of action that we’re not smart enough to currently ideate? After all, the Singularity is all about superhuman artificial intelligence with the theoretical ability to simulate human minds, upload one’s consciousness to a computer, or simulate life – as seems to be Elon Musk’s belief.
Wishful Thinking?
When LessWrong pulled the plug on the Basilisk due to internal policy against spreading informational hazards, they inadvertently amplified its signal. The Streisand Effect came into play, sparking memes, media coverage, and heated debates. The Basilisk went viral in true web fashion.
The initial reaction from the forum’s moderator stated that “I am disheartened that people can be clever enough to do that and not clever enough to do the obvious thing and KEEP THEIR IDIOT MOUTHS SHUT about it”.
Some slept less soundly, while others were sucked into lengthy debates on AI’s future. Many have critiqued the Basilisk, questioning its assumptions and the plausibility of its revenge-mission. Just as one doesn’t need to believe in ghosts to enjoy a good ghost story, many argue that the Basilisk is more fiction than possible truth.
One key argument is that upon existing, even an all-powered agent is unable to affect the probability of its existence, otherwise we’d be thrown in an always-has-been loop.
Digital Dystopia or Philosophical Farce?
While the Basilisk’s bite might be venomous, it is essential to view AI in a broader context. The narrative serves as a stark reminder of our responsibilities as we inch closer to creating sentient entities. More than just a sci-fi cautionary tale, it underscores the importance of ethical considerations in AI’s rapid advance.
The Basilisk might be best understood as a warning signal: one addressing the complexities and conundra that await in our techno-future, and one that’s bound to continue sparking debate, introspection, and for some, a real desire to make Roko’s Basilisk a reality.
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Robot Takeover | Season One Finale with Grace and Desi
SocialFi Primer: Why It’s SoFi So Good for Web3’s Creator Economies
Introduction
Social networks have risen back to prominence over the course of 2023, with Elon Musk’s Twitter rebranding to X and headlines screaming about its battles with Meta’s Threads. Meta’s continued push into a VR-powered metaverse is finally picking up steam, as this astonishing new Lex Fridman podcast demonstrates.
With 4.7 billion users plugged in for an average of 2.5 hours a day on traditional social media, creator economies are blossoming and it’s no surprise that its Web3 version, Social Finance (SocialFi/SoFi) is high on the priority list for crypto investors and builders for finding new decentralized ways to monetize users’ social network clout.
Despite the lowest crypto VC funding since 2020, Q3 2023 saw multi-million investment rounds for SoFi projects such as RepubliK and Phaver. A lot of this can be attributed to the Base chain’s surprise hit Friend.tech, which allows social media accounts to be tokenized and traded. Its 2023 buzz is giving off some early Axie Infinity tingles, not for GameFi but this time for SocialFi.
Let’s take a closer look at what this emerging trend for crypto’s potential bull cycle of 2024/2025 is all about.
What is SocialFi?
Please note: ‘Social finance’ can also refer to another altruistic form of finance that supports real-world communities, which shouldn’t be confused with social network finance, the topic of this article.
SocialFi stands for “social finance” and combines the principles of social media and decentralized finance (DeFi) to offer a Web3 approach to social interactions through the power of blockchain technology. At its core, SocialFi is about empowering content creators, influencers, and participants who seek better control over their data and freedom of speech, aka data privacy and censorship-resistance.
These lofty ideals are admirable, but a network can only be successful if users are sufficiently incentivized to share their resources and time with it. This is where those quirky little digital assets come in – the ones we either love or hate, depending where in the cycle we bought and sold them:
• Cryptocurrencies (e.g. ETH or a governance token) provide avenues for monetizing social media engagement and rewarding infrastructure and security providers.
• Non-fungible tokens (NFTs) establish the management and digital ownership of assets.
The Three Core Principles of SocialFi
So what makes SocialFi platforms different from their giant Web2 equivalents like Facebook, X (Twitter), YouTube, and Instagram?
Three words: Decentralization. Tokenization. Governance.
Decentralization: Distributed control
Decentralization is the backbone of SoFi, setting it apart from social media platforms like Facebook and Twitter. Unlike these centralized platforms, where a single server hosts user data and content, SocialFi is spread over a decentralized network. This shift in architecture gives users more control over their own data and enhances the platform’s resistance to censorship and major data breaches.
The level of decentralization depends on the underlying blockchain; Bitcoin and Ethereum are highly decentralized, making them secure and resilient, while others are ehhh, not so much. Tools like DappRadar and DeFiLlama can help you gauge the health of a Social Finance project.
Tokenization: Quantifying social influence
Tokenization is another core principle in SocialFi. It transforms the fuzzy idea of social influence into a measurable asset. Users earn tokens based on their contributions to the community, and these tokens are multifunctional. They can be used for micro-payments, trading, or even voting on platform changes. This token-based economy is made possible through smart contracts, which autonomously distribute value to users, providing a reward to make the platform more engaging.
Governance: Community decision-making
The third pillar of SocialFi is community governance, which puts decision-making power into the hands of the users. Unlike traditional platforms where changes are dictated by a managing company, SocialFi uses a DAO (Decentralized Autonomous Organization) to allow users to vote on significant changes or new features. This democratic approach fosters a sense of ownership and aligns the platform more closely with the needs and preferences of its community.
How SocialFi democratizes social media
1. Monetization and Data Ownership
The terms “There’s no such thing as a free lunch” and “If the product is free, then you are the product” ring especially true for social media platforms. These platforms exploited early Web2 users’ reluctance to pay for any online service or content during the 2000s and 2010s through a Faustian offering of free services. Years later, users learned their behavior was recorded all along to build models to manipulate human online behavior for commercial and other purposes. Don’t be evil my ass.
Traditional Web2 social media platforms have been criticized for their centralized control over user data and monetization strategies. Platforms like Youtube, Twitter and TikTok milk their users’ content and engagement to generate billions of dollars of revenue but share only a fraction of profits with the actual content creators. While this is starting to change and some Web2 platforms are onboarding their own token and even NFTs, it’s still too lopsided.
It was reported in 2022 that creators still rely on brand partnerships for up to 77% of their income, whereas subscriptions and tips make up around 1–3%. SocialFi platforms instead use social tokens or in-app utility tokens to manage incentives fairly. These tokens can be created by either the application or the user (a fan token), allowing creators to manage their own micro-economies.
2. Freedom of Speech
Another big bugbear with Web2 platforms, especially in these increasingly fractured and divisive political times, is the centralized decision-making process that often ends up in a final form of censorship.
There is sometimes a need to stop harmful content from being disseminated across the internet, but the question is who gets to do this. It can be a very bad thing to have a centralized arbiter of truth that stifles opposing views from controversial public figures (read the prescient books Animal Farm, 1984, and A Brave New World). A decentralized curation process, aligned with Web3 ethos, could offer a fairer approach.
Social media initially created new communities and united old ones. Unfortunately, a weekly post or two by an average user doesn’t pay the bills for platforms. Controversy stimulates emotion and magnetizes user eyeballs, and that brings in dollars. So, biased algorithms were created to herd users into digital echo chambers and online political fight clubs. Web2 platforms are as complicit in spreading hatred across social media as any Tate or Trump.
SocialFi platforms beat censorship through decentralized curation. All publicly viewable posts can be swiftly tagged based on their topic and nature of the words used. Individual nodes can be assigned the control and responsibility over filtering.
Three Key Challenges for SocialFi
Scalability
One of the primary challenges for SocialFi is scalability. Traditional social media platforms like Facebook generate massive amounts of data daily. Blockchain solutions like DeSo claim to address this issue through techniques like warp sync and sharding, but these solutions are yet to be stress-tested at scale during bull market volatility.
Sustainability
Another challenge is creating sustainable economic models. While many platforms promise high incentives, these are often short-term growth hacks. The models need to be stress-tested through various market cycles to ensure long-term viability. Another problem is the intricate issue of token emission schedules.
Security
Unfortunately in blockchain your network is only as strong as your weakest link. The hacking incident on the Roll platform raised concerns about the safety of SocialFi platforms – and in a field where smart contracts and hot wallets play such a crucial role, the threats from malicious or faulty code, or phishing scams, must be overcome before we can expect mainstream adoption. This is where the concept of account abstraction (see my article on Vitalik’s 3 Ethereum transitions) should revolutionize user safety.
Ten Trending SocialFi Tokens for 2024
Below are some SoFi tokens trending currently.
1. Galxe: A Web3 social community project built on Ethereum with over 10 million active users.
2. Torum: A social media platform built on Binance Smart Chain that combines DeFi and NFTs.
3. Friend.tech: A decentralized social media platform built on Base Network that allows users to tokenize their social network.
4. NestedFi: A SocialFi project that supports building, managing portfolios, copying trades of the best traders, and supporting social trading with just one click.
5. STFX: A SocialFi protocol for short-term digital asset management.
6. Hooked Protocol: A launchpad project (recently invested in by Binance) that supports SocialFi.
7. Lens Protocol: A project made about Social Graph and running on the Polygon network, developed by the founder of AAVE.
8. Safereum: A decentralized memecoin project with decent performance.
8. Bitclout: A social token project that gained significant attention by allowing brands, organizations, and individuals to create their tokens.
10. Rally: A social token project that allows creators to monetize their content and engage with their audience.
Disclaimer: I do not hold any of these tokens and be advised that you shouldn’t invest in any of them without doing proper research. They are very risky and require an advanced grasp of crypto knowledge. You’ll need to understand tokenomics like their vesting unlocked schedule, fully diluted value, the team behind them, and their supposed value proposition and use cases. Many of these projects will likely not be around in 5 years’ time.
SoFi So Good. What’s next?
By combining the principles of social media and decentralized finance, SoFi can reshape the social media landscape and help the normal user reclaim rightful ownership of their data, and monetize it fairly and transparently if they choose to do so.
SocialFi is an amalgamation of Web3 and social media trends, and thus perfectly geared towards boosting creator economy models. However, as we’ve seen with other trends such as Play-to-Earn (P2E), and even DeFi to some extent, early hype and traction mean nothing if they’re not built on something of substance. SoFi experiences will need to have engagement and staying power if they are to retain real users and the network effects that come with them.
Therefore, SocialFi’s robustness can only be claimed after it has weathered a few market downturns and soldiered through them. With an evolving Web2 industry and new frontiers like the metaverse and artificial intelligence on the doorstep, the opportunities are endless.
Just one last thing: Please, let’s ban infinite scroll.
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The Alluring Turing Test: Mimicry and AI
The Turing test, originally called ‘the imitation game’, is one of the first AI benchmarks ever posed. If an artificial conversational agent can convince someone that it is human then it can be supposed to be intelligent. It was proposed in 1950 by the father of computer science, Alan Turing, and is, in the collective imagination, the definitive milestone an AI has to pass to begin to be considered sentient.
But many AIs, for decades, have passed forms of the Turing test (think of a spambot or catfishing account sending someone a few messages) yet we don’t generally consider them sentient. Rather, we’ve decided people are easy to fool. The Turing test has been called obsolete. For John Searle, this was true on a philosophical level: his Chinese Room experiment showed that just because a computer can process symbols that does not make it sentient – just like how ChatGPT guesses the next word in the sequence. It’s just good at simulating one effect of intelligence.
Fool Me Once
If an AI can fool you into believing it’s real, what else is an illusion? Sci-fi landmarks like The Matrix and Lawnmower Man have long played with the idea of hallucinated reality. It’s part of life to question reality, to check that everything is as it seems. It was natural to apply this to proto-AI, to check that it could seem intelligent. Over time, Turing tests haven’t become obsolete, they’ve just become harder to pass and more rigorous.
Rather than testing whether someone is sentient, the Turing test has evolved into whether content was created by AI. Our civilisational consciousness is now attuned to the idea that what we are talking to might not be human, or what we are seeing might be made by a computer. We accept that generative AI can paint gorgeous pictures and write beautiful poems. We know they can create virtual environments and deepfaked videos – albeit not, yet, at the fidelity to fool us consistently.
Fool Me Twice
That fidelity might be close, however. And, when the average deepfake fools more than 50% of the humans that see it then, suddenly, generative AI has the ability to make a 51% attack on our entire society. Scepticism, always a powerful human evolutionary tool, will become more essential than ever. We have already seen a damaging polarisation of society caused by social media, fueled by a lack of scepticism about its content. Add generative AI with plausible content, and the problem escalates.
The Turing test, that rusted old monocle of AI inquiry, may become more vital to human thought than it has ever been. We, as a society, need to remain alert to the reality and unreality we are perceiving, and the daily life to which we attend. Generative AI will be a massive boon in so many sectors: gaming, financial services, healthcare, film, music – but a central need remains the same: knowing who we’re talking to and what they want and whether they’re real. Critical thinking about what you’re being told in this new hyperverse of real and unreal information. It will be what makes you a human in an endless constellation of AI assistants.
A Turing Test for Humans
The Turing test may end up not being for the AI after all, but for the human. Corporate job examinations could test your ability to identify what content is from a bot and what is not, which film was made by AI, and which by a human. You’ll need to have your wits about you to stay Turing-certified – to prove that no false reality generated by AI could hoodwink you into revealing secrets. We saw this through the virtuality of dreams in Christopher Nolan’s film Inception – but with digital VR worlds coming soon, such espionage might be closer than we think.
Alan Turing’s test remains relevant. Judging what is a product of legacy humans and what is from our digital children will become a fascinating battleground in just about every human sector. Will people want to watch AI-made films? How close to fidelity can they get? Cheap AI-produced neverending sitcoms based on classic series already exist – they just fail the Turing test, as do endless conversations between AI philosophers. These wonders would have fooled people 25 years ago, they would be convinced that a machine could never make it up – now they come off as the playful fancies of a new tool.
You Can’t Get Fooled Again
But soon, these fancies will become fantasies, and more people will be fooled. A deepfake video of a world leader issuing a declaration of war need only convince so many people before it became an existential risk. AI will write dreamworlds that promise the most fantastic ways of productivity and play, but should too many of us become too intimate with the machine, and think, like the Lambda engineer, that it truly is sentient, then the influence these AIs innocently exert could be dangerous.
And what if our pursuit towards AGI and the utopian Singularity leads to us declaring that an AI we created was finally sentient, and that it was on our side? Would we put it in charge? Then would it really matter if it was faking it the whole time? Well, yes, but by then it will be too late.
So run along and take the Turing test. Both of you.
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Unreal Engines, Real Monsters: Generative AI in Videogames
You’re trapped in a demonic world filled with monsters. You, a single heroic supersoldier, crash through their alien world wreaking carnage. You are the one they fear. You stand ready to execute another but, instead of running away like usual, this one talks to you.
You’re shocked, you didn’t even realise it could speak – and it begs for its life. Says that it is just trying to protect its demon wife and child. You spare it. It runs away. From that point on, every demon you talk to speaks, pleading to be spared – while they secretly plot their revenge.
Doom 2030, perhaps? With the rise of neural nets and generative AI, it’s possible.
Making Worlds Seem Real
AI has always been crucial for the player experience in videogames, having the inhabitants of the world react intelligently. Videogame AI has been basic for most of its existence, a bag of clever-but-simple developer tricks masking rote-response by the digital people and creatures you meet. NPCs, for the most part, speak a few stock phrases, and have only on-rails reactions to various player activities.
Game devs succeeded in creating believable NPC behaviour at the cost of 1000s of hours of writing, voice acting, animation, and code. The labor poured in producing titles like Cyberpunk 2077, Grand Theft Auto and Mass Effect is legendary.
But the illusion’s never quite perfect, despite clever tricks like ‘random’ pathing trees for behaviour, and procedural generation of the gameworld. There’s only so much you can do. The shopkeeper will never leave the town, the farmer’s wife will never fall in love with you, and the demons will never beg for their life – it’s simply not in the codebase. They were never told to act that way.
How Generative AI Will Change Videogames
Generative AI in gaming has the ability to change all this. A well-trained neural net with the task of, say, producing the entire dialogue set for a dragon-themed fantasy game world, is now entirely possible.
NPCs could make free choices powered by their neural nets. Whether Gerald the Innkeeper chooses to give you a discount is, truly, up to him. The ironmonger Derek Longfang may change his objective and become Lord of the High Vale through a brutal reign of terror. Everyone may love you, everyone may hate you, and their opinions might change. It would, indeed, feel real.
Or grotesquely surreal. Generative AI could create truly unique nightmare dungeons, trained on a dataset of every dungeon ever created by a human designer. Intrepid adventurers would crawl through a unique dungeon every time, outstripping the strictly-defined procedural generation models that currently exist. Imagine stepping into the warped imagination of a devilish AI, replete with eldritch monsters who themselves generate their own behaviour. A true test for the continued relevance of human bravery and resourcefulness. It’s you versus the machine, and the effect is unsettling in the best possible way.
Videogames Perfect Training Ground for AI
The world’s largest creative industry is gaming – bigger than movies and music combined. As AI continues to develop rapidly, gaming will be one of its first major use cases. Efforts have already begun. Videogame users are digital natives looking for a simulated experience, so the ‘uncanny’ barrier that AI faces in movies and music is not there.
Gamers are used to fighting digital prometheans, ever since the first Cacodemon chased them into the next room in a ‘lifelike’ display of monstrous ingenuity. What if the first true AGI arises by accident, when the developers give Derek Longfang, Lord of High Vale (a popular current storyline) just a bit too much processing time and the ability to rewrite his own code.
The willingness to engage in virtuality makes videogames a fertile soil with which to experiment with technology’s latest razor edge – and it won’t be long before assets generated by neural nets appear in games. Writing and voice acting, both of which can be cheaply and effectively produced by common AI models, will likely become the norm. The bottleneck is the cost of running it – and who exactly has the resources to cover these costs. Running, training, and maintaining neural nets is fearsomely resource intensive. The idea of an always-on online world being overseen entirely by a generative AI would be an effort only the world’s wealthiest companies could even hope to pull off.
All the Possible Worlds
Yet AI will get cheaper over time. Self-trained neural nets will be ever more common. And game developers will be some of the first users of the latest tools. ChatGPT just announced its ability to see and hear and react to its surroundings. It’s not a leap to imagine virtual friends and enemies reacting authentically to everything a player does, in worlds crafted and ruled by AI gods.
Humans love to play. AI will too. If done right, generative AI will revolutionise gaming, and create truly unique, immersive worlds for players to inhabit. With improvements in VR, graphics, and processing power, we might play for eternity in infinite worlds, and soar over the silicon demesnes of our dreams.
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Become the Artist You Know You Can Be, Even If You Never Learned How to Be One
Have you ever found yourself daydreaming, your mind bursting with colors and scenes so vivid they could be real? It’s like there’s an entire world inside you, just waiting to be shared, but when you go to take pencil to paper, the realization sets that you can’t even get close to what you want to create?
This was me. Then along came Stable Diffusion, Midjourney, and DALL-E, opening up a side of myself I wasn’t previously able to touch.
Join me here as we explore the world of Diffusion models, in particular Stable Diffusion: revolutionary software capable of turning dreamers into artists with entire worlds to share.
The Rise of Generative AI in Creative Spaces
Stable Diffusion came into play in August 2022. Since then the explosion in creativity, creation, and dedication by AI artists, coders, and enthusiasts has been enormous. This open-source project transformed into a huge community that contributes to creating a tool capable of generating high quality images and videos using generative AI.
The best part, it’s free and you can access and run it with relative ease. There are hardware requirements, but if you are a gamer or someone else with a powerful GPU, you may have everything you need to get started. And you can also explore Stable Diffusion online for free as well at websites such as Clipdrop.co.
However, Stable Diffusion was not the first time generative AI entered the creative space. Earlier than that, many artists were using various forms of generative AI to enhance, guide, or expand their creative expression. Here are a few popular examples:
1. Scott Eaton is an amazing sculpturist, early generative AI artist pioneer, who combines generative models with 3D printing and metal casting to produce fascinating sculptures. Here is a video of Scott sharing his process back in 2019: https://www.youtube.com/watch?v=TN7Ydx9ygPo&t
2. Alexander Reben is an MIT-trained artist and roboticist, exploring the fusion of humanity and technology. Using generative AI, he crafts art that challenges our relationship with machines, and has garnered global recognition for his groundbreaking installations and innovations.
3. Sofia Crespo merges biology and machine learning, highlighting the symbiosis between organic life and technology. Her standout piece, ‘Neural Zoo‘, challenges our understanding of reality, blending natural textures with the depth of AI computation.
All of these artists (and many more) incorporated machine learning in art before it was cool. They’ve helped pioneer the technology, invested time, energy, and funds to make it possible to create the applications that are available today.
Fortunately, we don’t have to repeat their process. We can dive straight into creation.
How does Stable Diffusion work?
Stable Diffusion operates as a diffusion-based model adept at transforming noisy inputs into clear, cohesive images. During training, these models are introduced to noisy versions of dataset images, and tasked with restoring them to their original clarity. As a result, they become proficient in reproducing and uniquely combining images. With the aid of prompts, area selection, and other interactive tools, you can guide the model’s outputs in ways that are intuitive and straightforward.
The best way to learn is to get hands-on experience, run generations, and compare results. So let’s skip talking about samplers, models, CFG scores, denoising strength, seeds, and other parameters, and get our hands dirty.
My personal experience
My personal experience with generative AI started with Midjourney, which was a revolutionary application of the technology. However, when Stable Diffusion was released, I was struck with its rapidly growing capabilities. It gave me the ability to guide the diffusion models in a way that makes sense, enabling me to create images as I want them to be, rather than whatever I got off of my prompt. It featured inpainting and eventually something called ControlNet, which further increased the ability to guide the models.
One of the most recent projects was working on a party poster to commemorate an event for a Portuguese DJ group, Villager and Friends. We wanted to combine generative AI with scenery from the party location to commemorate the party. We decided on a composition and then generated hundreds of styles for it, and cherrypicked the four best ones, which then got voted on by the community. The winning style was upscaled to a massive format, and will be made available in print for the partygoers. Let me show you the transformation –
The main composition
The Four Selected Styles –
The Winning Style by Community Vote –
A few details to point out about this project:
1. Notice the number 6 present in the background of every image; this is only possible thanks to the ControlNet extension for Stable Diffusion
2. Notice the increased level of detail on the final chosen image. This is a result of an ‘upscaling’ process. The final image is a whopping 8192px x 12288px!
3. Due to the size of the image, a single generation of the final version took about four hours. We had to generate several times due to crashes or ugly results.
4. The final version is unedited. It is raw output directly from the Stable Diffusion Model.
How can you get started with Stable Diffusion?
Running Stable Diffusion locally is the way to go. However in order to do that you will need to have good hardware. The main resource by Stable Diffusion used is VRAM, which is provided by the GPU. The minimum starting point would be a 4GB VRAM GPU. Unfortunately, the best optimization (xformers) are available only for NVidia GPUs.
In order to run Stable Diffusion locally you will need to install some software –
1. A user interface
a. Automatic1111 (balance between simple and flexible)
b. ComfyUI (extremely flexible and difficult to use, resource efficient)
2. Stable Diffusion Model (choose one you prefer on https://civitai.com/)
3. Python (a programming language)
4. PyTorch (a machine learning framework based on Python)
Start with the user interface; it will help you download everything you need to run it. I use Automatic1111; it’s simple enough and flexible enough for me. ComfyUI is better, faster, and capable of using resources more effectively, but also more complicated and requires a lot more learning to use effectively.
The errors generated from both are verbose, so if anything goes wrong, you can copy the error message and search the web for a solution. Pretty much everything you can run into as an issue in your first month of using Stable Diffusion has been solved by someone somewhere on the web.
CivitAI is a great resource for finding new and interesting models. Stable Diffusion 1.5 has the most developed (i.e. trained) models. If you’re looking for a particular style, you can likely find it there – and if you’re not looking for a particular style, you’ll likely discover something new. That said, most models are flexible and receptive to your prompts, and you can increase the weights of your prompts to guide the generation where you want it to go.
Sometimes Stable Diffusion is stubborn. Getting the result you want can be difficult, and this is where ControlNet and other guidance methods come in, helping you create the compositions you want.
This is just the beginning of your journey, but I’m glad you took the steps to learn how to get started. I’m looking forward to seeing your creations and explorations of latent space.
Is AI art, art?
Stable Diffusion enables people to create interesting art that they would otherwise never make. If you have imagination and some basic skills, you don’t need to be constrained and by technique – you can guide Stable Diffusion to putting your imagination onto the page.
Countless NFT artworks are being sold online, often coming from people that don’t necessarily have the skills to do everything on their own, but have learned to guide the diffusion models to produce their desired outcome. Some people simply have the talent for picking winners from a big batch of generated images.
Don’t get me wrong. There is joy in working with traditional art. Mastering the brush and paints of watercolor, oil, the needed strokes to create form on a blank canvas, human proportions and composition techniques are all beautiful and one can definitely still pursue them alongside AI art.
But they also involve a significant time investment, some pain and suffering, a dedication most creatives are not willing to give.
AI art is also difficult; it’s just a different kind of difficulty. Getting exactly what you want is challenging, similar to how it is with traditional art. The thing is, learning to do good AI art is learning art theory and applying it as guidance. So in a way AI art can bring you closer to real art than real art ever could. Something to think about.
In the end it’s up to you to decide if this is art or not. If you are finding ways to express your views, emotions, and ideas through AI, who really cares what others think about it?
Let us know your thoughts! Sign up for a Mindplex account now, join our Telegram, or follow us on Twitter.
Wen Web? The Decentralized Internet’s Current State and 2024 Odds
Introduction
Since the start of the decade, Web3 has been touted as our salvation from the ills of Web2, as the fully decentralized foundation for a censorship-free new Internet that preserves privacy and restores the cypherpunk vision of the 1990s, with extra power-ups unlocked by distributed ledger and cryptocurrency technology (in particular AI cryptos this year) on blockchains like Ethereum and Cardano.
Web3’s hype train derailed in late 2021 and throughout 2022, as record inflation, rising interests, and a cascade of centralized custodial blowups deflated crypto markets. However, hope springs eternal in the world of digital assets. With 2024 just around the corner, filled with the promise of the Bitcoin halving, spot ETFs (for both BTC and Ethereum), a pivot in global macro-economic policy, and other wonderful things, the talk around Web3 is heating up again, especially in Asia, even if artificial intelligence has stolen a lot of the thunder and VC funding this year.
Will 2024 or 2025 be the Year of Web3, or is it all just a wishful thinking, bad investments, and the echo chambers of Crypto Twitter and YouTube? What does the average person know about Web3.0?
An extensive survey conducted by major US cryptocurrency exchange Coinbase, called the “International Survey of Web3 adoption”, polled over 32,000 internet users across 16 countries. The countries span developed economies like the US and Japan as well as emerging markets like the Philippines and Vietnam. This provides a valuable snapshot of how Web3 technologies are being embraced on a global scale.
The 16 countries were: the United States, Canada, and Brazil in the Americas; the United Kingdom, Germany, Italy, France, Spain, and the Netherlands in Europe; and in the Asia-Pacific region Australia, Philippines, Indonesia, India, Thailand, Vietnam, and Japan.
Survey Broad strokes: Demographics and Web3 Awareness
With a 50-50 gender split, the survey captures a balanced view from both men and women. Most respondents are city dwellers (46%), with a good mix of suburbanites (32%) and rural folks (21%). Education and income levels are all over the place, from high school dropouts to advanced degree holders, and from low to high earners.
A whopping 80% of people know about cryptocurrencies, and two-thirds have heard of at least one Web3 use case. Europe is leading the pack in awareness, while emerging countries and, oddly enough, Japan are lagging.
The report suggests social media platforms play a vital role in driving awareness of Web3, especially in emerging market countries. Up to 2 in 5 cryptocurrency users rely on sources like YouTube, Facebook, Twitter, and cryptocurrency websites for information. Far fewer – only 16% to 26% – rely on mainstream news sources.
So what’s hot in Web3 right now? Trading on centralized exchanges (sadly) and blockchain gaming are the go-to activities. Nearly half of Web3 users have a software wallet, and about 30% are rocking a hardware wallet.
Looking ahead, the survey predicts a 50% surge in Web3 adoption by 2026. The future looks especially bright in developing countries, where crypto is becoming the new way to pay. When questioned about specific Web3 use cases, the applications most familiar to respondents were cryptocurrency payments, NFT trading, and trading on centralized cryptocurrency exchanges (CEXs). Despite the regulatory alarms, centralized exchanges are still the main gateway to the Web3 world.
33% were familiar with crypto payments, 24% with NFT trading, and 23% with CEX trading. Comparatively, awareness of more complex and risky decentralized finance (DeFi) activities like staking, decentralized exchange trading, and borrowing/lending were significantly lower; roughly 1 in 6 people were familiar with DeFi staking, trading, and lending or borrowing.
Web3 Services
One of the most commonly used Web3 services after centralized cryptocurrency exchanges (CEXs) is crypto gaming platforms, while self-hosted cryptocurrency wallets, both software and hardware, are gaining increased mainstream traction and adoption.
The report suggests CEXs currently serve as the primary entry point for most people into the Web3 ecosystem, providing a bridge to the mainstream finance world. Despite growing regulatory scrutiny, CEXs are expected to continue spearheading cryptocurrency adoption into the future.
When survey respondents who had used Web3 before were asked about their initial experiences, trading on CEX platforms emerged as the number one entry point, accounting for 21.1% of first interactions. This aligns logically with CEXs often being the first stop for users looking to convert fiat currency into cryptocurrency.
Interestingly, the report also highlights how initial entry points into Web3 differ significantly between countries. In developed nations, CEX trading was by far the most common gateway into Web3, likely because people in these regions are already familiar with using financial systems.
On the other hand, in emerging market countries like the Philippines and Vietnam, playing crypto games emerged as the most popular entry point. This may be boosted by play-to-earn crypto games providing income generation opportunities during COVID-19 for lower-income users.
Below are ten use cases ranked from most to least familiar:
- Crypto Payments (33%)
- NFT Minting/Trading (24%)
- Trading on Centralized Exchanges (23%)
- Overseas payments (23%)
- Playing crypto games (23%)
- Using P2P trading platforms (19%)
- Use of crypto payment card (18%)
- Staking for returns (17%)
- DEX Trading (16%)
- Borrowing/Lending (13%)
Barriers to Entry
Among respondents who had not used Web3 before, around 46% cited a lack of knowledge about Web3 technology as a key barrier to adoption. Over 25% of non-users also noted they simply did not know where to begin exploring the space.
Beyond educational barriers, concerns around volatility, hacks, scams, and government regulation also deterred usage among some respondents. Regulation concerns were particularly acute in some countries, like India and Canada, where 26% of respondents cited this as a barrier.
Web3 in Asia
The Coinbase report notes that Asia contains countries at varying levels of technological adoption, and with diverse regulatory environments. During the pandemic, Asian crypto markets and innovations played a vital role in sustaining Web3 development globally.
Here are some insights:
- Japan has comparatively low awareness of Web3, likely due to a challenging regulatory environment including high crypto taxes. This has led many crypto firms to choose alternative locations for their operations.
- Emerging countries like the Philippines exhibit greater Web3 awareness than more developed nations. The Philippines has a young, tech-savvy population with high remittance flows that could benefit from blockchain technologies.
- Vietnam has low Web3 awareness currently but a rapidly growing interest in blockchain and crypto, particularly for gaming.
- Gaming and metaverse participation are more popular Web3 use cases in Asian emerging markets compared to developed countries.
- Developed Asian countries focus more on crypto payments and financial services, whereas emerging markets prioritize remittances more.
The Promise and Potential of Web3
Web3 carries enormous potential across a wide range of use cases like supply chain management, digital identity, healthcare, and insurance. However, there are challenges between the potential and the actual.
- The decentralized nature of Web3 makes scalability difficult, as the network waits on nodes to validate transactions.
- Interoperability issues arise from the multitude of different blockchains that don’t always work together seamlessly.
- Usability remains a barrier, with many Web3 applications having non-intuitive user experiences.
But Web3 momentum is clearly building. Monthly active developers have surged 297% since 2018. Gartner predicts 25% of enterprises will use Web3 applications by 2024. With solutions in areas like decentralized finance and play-to-earn gaming already demonstrating value, Web3’s possibilities are vast despite current limitations.
The Outlook for Web3 in 2024
The prognosis for Web3 adoption by 2024 looks positive, as developers work to address current challenges. Improved scalability, interoperability, and usability will likely emerge to make Web3 more accessible to mainstream audiences.
Familiar technological challenges remain though:
- Scalability: Web3’s decentralized structure hampers easy scaling, limiting its adoption.
- Interoperability: Multiple blockchains exist, but they don’t always sync well, affecting Web3 adoption.
- Usability: Complex Web3 apps deter users, posing a barrier to widespread adoption.
- Blockchain projects often over-promise and under-deliver, eroding trust and adoption.
- Onboarding process: Clunky onboarding experiences can kill user interest, hindering adoption.
On the plus side, as more and more people become more familiar with Web3 capabilities, adoption is expected to accelerate, across ever more diverse use cases. Custom-built and application-specific modular blockchains will simplify development, and the eventually ubiquitous implementation of zero-knowledge rollup proofs will enable greater security and privacy.
While the Coinbase report shows it’s still early days, Web3 is rapidly evolving. Its awareness is reasonably high worldwide, but substantial barriers around education and regulation still remain and will have to be dealt with.
If you’re a business looking to ride the Web3 wave globally, you’ve got to be a bit of a cultural chameleon. Different places have their own vibes, especially in the diverse landscape of Asia. So, understanding the local scene – be it regulations, economics, or even just what people are into – is key.
Looking ahead, Web3 is set to graduate from being this edgy, niche thing to becoming part of our daily lives and how we do business, turbocharged by emerging tech like AI and machine learning. Buckle up for the future!
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