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Living in 1999 felt like standing on the edge of an event horizon. Our growing obsession with technology was spilling into an outpouring of hope, fear, angst and even apocalyptic distress in some quarters. The dot-com bubble was swelling as the World Wide Web began spreading like a Californian wildfire. The first cell phones had been making the world feel much more connected. Let's not forget the anxieties over Y2K that were escalating into panic as we approached the bookend of the century.

But as this progress was catching the imagination of so many, artificial intelligence (AI) was in a sorry state only beginning to emerge from a debilitating second 'AI winter' which spanned between 1987 and 1993.

Some argue this thawing process lasted as long as the mid-2000s. It was, indeed, a bleak period for AI research; it was a field that "for decades has overpromised and underdelivered", according to a report in the New York Times (NYT) from 2005.

Funding and interest was scarce, especially compared to its peak in the 1980s, with previously thriving conferences whittled down to pockets of diehards. In cinema, however, stories about AI were flourishing with the likes of Terminator 2: Judgement Day (1991) and Ghost in the Shell (1995) building on decades of compelling feature films like Blade Runner (1982).

It was during this time that the Wachowskis penned the script for The Matrix a groundbreaking tour de force that threw up a mirror to humanity's increasing reliance on machines and challenged our understanding of reality.

It's a timeless classic, and its impact since its March 31 1999 release has been sprawling. But the chilling plot at its heart namely the rise of an artificial general intelligence (AGI) network that enslaves humanity has remained consigned to fiction more so than it's ever been considered a serious scientific possibility. With the heat of the spotlight now on AI, however, ideas like the Wachowskis' are beginning to feel closer to home than we had anticipated.

AI has become not just the scientific, but the cultural zeitgeist with large language models (LLMs) and the neural nets that power them cannonballing into the public arena. That dry well of research funding is now overflowing, and corporations see massive commercial appeal in AI. There's a growing chorus of voices, too, that feel an AGI agent is on the horizon.

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People like the veteran computer scientist Ray Kurzweil had anticipated that humanity would reach the technological singularity (where an AI agent is just as smart as a human) for yonks, outlining his thesis in 'The Singularity is Near' (2005) with a projection for 2029.

Disciples like Ben Goertzel have claimed it can come as soon as 2027. Nvidia's CEO Jensen Huang says it's "five years away", joining the likes of OpenAI CEO Sam Altman and others in predicting an aggressive and exponential escalation. Should these predictions be true, they will also introduce a whole cluster bomb of ethical, moral, and existential anxieties that we will have to confront. So as The Matrix turns 25, maybe it wasn't so far-fetched after all?

Sitting on tattered armchairs in front of an old boxy television in the heart of a wasteland, Morpheus shows Neo the "real world" for the first time. Here, he fills us in on how this dystopian vision of the future came to be. We're at the summit of a lengthy yet compelling monologue that began many scenes earlier with questions Morpheus poses to Neo, and therefore us, progressing to the choice Neo must make and crescendoing into the full tale of humanity's downfall and the rise of the machines.

Much like we're now congratulating ourselves for birthing advanced AI systems that are more sophisticated than anything we have ever seen, humanity in The Matrix was united in its hubris as it gave birth to AI. Giving machines that spark of life the ability to think and act with agency backfired. And after a series of political and social shifts, the machines retreated to Mesopotamia, known as the cradle of human civilization, and built the first machine city, called 01.

Here, they replicated and evolved developing smarter and better AI systems. When humanity's economies began to fall, they struck the machine civilization with nuclear weapons to regain control. Because the machines were not as vulnerable to heat and radiation, the strike failed and instead represented the first stone thrown in the 'Machine War'.

Unlike in our world, the machines in The Matrix were solar-powered and harvested their energy from the sun. So humans decide to darken namely enslaving humans and draining their innate energy. They continued to fight until human civilization was enslaved, with the survivors placed into pods and connected to the Matrix an advanced virtual reality (VR) simulation intended as an instrument for control while their thermal, bio-electric, and kinetic energy was harvested to sustain the machines.

"This can't be real," Neo tells Morpheus. It's a reaction we would all expect to have when confronted with such an outlandish truth. But, as Morpheus retorts: "What is real?' Using AI as a springboard, the film delves into several mind-bending areas including the nature of our reality and the power of machines to influence and control how we perceive the environment around us. If you can touch, smell, or taste something, then why would it not be real?

Strip away the barren dystopia, the self-aware AI, and strange pods that atrophied humans occupy like embryos in a womb, and you can see parallels between the computer program and the world around us today.

When the film was released, our reliance on machines was growing but not final. Much of our understanding of the world today, however, is filtered through the prism of digital platforms infused with AI systems like machine learning. What we know, what we watch, what we learn, how we live, how we socialize online all of these modern human experiences are influenced in some way by algorithms that direct us in subtle but meaningful ways. Our energy isn't harvested, but our data is, and we continue to feed the machine with every tap and click.

Intriguingly, as Agent Smith tells Morpheus in the iconic interrogation scene a revelatory moment in which the computer program betrays its emotions the first version of the Matrix was not a world that closely resembled society as we knew it in 1999. Instead, it was a paradise in which humans were happy and free of suffering.

The trouble, however, is that this version of the Matrix didn't stick, and people saw through the ruse rendering it redundant. That's when the machine race developed version 2.0. It seemed, as Smith lamented, that humans speak in the language of suffering and misery and without these qualities, the human condition is unrecognizable.

By every metric, AI is experiencing a monumental boom when you look at where the field once was. Startup funding surged by more than ten-fold between 2011 and 2021, surging from 670 million to $72 billion a decade later, according to Statista. The biggest jump came during the COVID-19 pandemic, with funding rising from $35 billion the previous year. This has since tapered off falling to $40 billion in 2023 but the money that's pouring into research and development (R&D) is surging.

But things weren't always so rosy. In fact, in the early 1990s during the second AI winter the term "artificial intelligence" was almost taboo, according to Klondike, and was replaced with other terms such as "advanced computing" instead. This is simply one turbulent period in a long near-75 year history of the field, starting with Alan Turing in 1950 when he pondered whether a machine could imitate human intelligence in his paper 'Computing Machinery and Intelligence'.

In the years that followed, a lot of pioneering research was conducted but this early momentum fell by the wayside during the first AI winter between 1974 and 1980 where issues including limited computing power prevented the field from advancing, and organizations like DARPA and national governments pulled funding from research projects.

Another boom in the 1980s, fuelled by the revival of neural networks, then collapsed once more into a bust with the second winter spanning six years up to 1993 and thawing well into the 21st century. Then, in the years that followed, scientists around the world were slowly making progress once more as funding restarted and AI caught people's imagination once again. But the research field itself was siloed, fragmented and disconnected, according to Pamela McCorduck writing in 'Machines Who Think' (2004). Computer scientists were focusing on competing areas to solve niche problems and specific approaches.

As Klondike highlights, they also used terms such as "advanced computing" to label their work where we may now refer to the tools and systems they built as early precursors to the AI systems we use today.

It wasn't until 1995 four years before The Matrix hit theaters that the needle in AI research really moved in a significant way. But you could already see signs the winter was thawing, especially with the creation of the Loebner Prize an annual competition created by Hugh Loebner in 1990.

Loebner was "an American millionaire who had given a lot of money" and "who became interested in the Turing test," according to the recipient of the prize in 1997, the late British computer scientist Yorick Wilks, speaking in an interview in 2019. Although the prize wasn't particularly large $2,000 initially it showed that interest in building AI agents was expanding, and that it was being taken seriously.

The first major development of the decade came when computer scientist Richard Wallace developed the chatbot ALICE which stood for artificial linguistic internet computer entity. Inspired by the famous ELIZA chatbot of the 1960s which was the world's first major chatbot ALICE, also known as Alicebot, was a natural language processing system that applied heuristic pattern matching to conversations with a human in order to provide responses. Wallace went on to win the Loebner Prize in 2000, 2001 and 2004 for creating and advancing this system, and a few years ago the New Yorker reported ALICE was even the inspiration for the critically acclaimed 2013 sci-fi hit Her, according to director Spike Jonze.

Then, in 1997, AI hit a series of major milestones, starting with a showdown starring the reigning world chess champion and grandmaster Gary Kasparov, who in May that year went head to head in New York with the challenger of his life: a computing agent called 'Deep Blue' created by IBM. This was actually the second time Kasparov faced Deep Blue after beating the first version of the system in Philadelphia the year before, but Deep Blue narrowly won the rematch by 3.5 to 2.5.

"This highly publicized match was the first time a reigning world chess champion lost to a computer and served as a huge step towards an artificially intelligent decision making program," wrote Rockwell Anyoha in a Harvard blog.

It did something "no machine had ever done before", according to IBM, delivering its victory through "brute force computing power" and for the entire world to see as it was indeed broadcast far and wide. It used 32 processors to evaluate 200 chess positions per second. I have to pay tribute, Kasparov said. The computer is far stronger than anybody expected.

Another major milestone was the creation of NaturallySpeaking by Dragon Software in June 1997. This speech recognition software was the first universally accessible and affordable computer dictation system for PCs if $695 (or $1,350 today) is your idea of affordable, that is. "This is only the first step, we have to do a lot more, but what we're building toward is to humanizing computers, make them very natural to use, so yes, even more people can use them," said CEO Jim Baker in a news report from the time. Dragon licensed the software to big names including Microsoft and IBM, and it was later integrated into the Windows operating system, signaling much wider adoption.

A year later, researchers with MIT released Kismet a "disembodied head with gremlin-like features" that learns about its environment "like a baby" and entirely "upon its benevolent carers to help it find out about the world", according to Duncan Graham-Rowe writing in New Scientist at the time. Spearheaded by Cynthia Greazeal, this creation was one of the projects that fuelled MIT's AI research and secured its future. The machine could interact with humans, and simulated emotions by changing its facial expression, its voice and its movements.

This contemporary resurgence also extended to the language people used too. The taboo around "artificial intelligence" was disintegrating and terms like "intelligent agents" began slipping their way into the lexicon of the time, wrote McCorduck in 'Machines Who Think'. Robotics, intelligent AI agents, machine surpassing the wit of man, and more: it was these ingredients that, in turn, fed into the thinking behind The Matrix and the thesis at its heart.

When The Matrix hit theaters, there was a real dichotomy between movie-goers and critics. It's fair to say that audiences loved the spectacle, to say the least with the film taking $150 million at the US box office while a string of publications stood in line to lambast the script and the ideas in the movie. "It's Special Effects 10, Screenplay 0," wrote Todd McCarthy in his review in Variety. The Miami Herald rated it two-and-a-half stars out of five.

Chronicle senior writer Bob Graham praised Joe Pantoliano (who plays Cypher) in his SFGate review, "but even he is eventually swamped by the hopeless muddle that "The Matrix" becomes." Critics wondered why people were so desperate to see a movie that had been so widely slated and the Guardian pondered whether it was sci-fi fans "driven to a state of near-unbearable anticipation by endless hyping of The Phantom Menace, ready to gorge themselves on pretty much any computer graphics feast that came along?"

Veteran film director Quentin Tarantino, however, related more with the average audience member in his experiences, which he shared in an interview with Amy Nicholson. "I remember the place was jam-packed and there was a real electricity in the air it was really exciting," he said, speaking of his outing to watch the movie on the Friday night after it was released.

"Then this thought hit me, that was really kind of profound, and that was: it's easy to talk about 'The Matrix' now because we know the secret of 'The Matrix', but they didn't tell you any of that in any of the promotions in any of the big movie trailer or any of the TV spots. So we were really excited about this movie, but we really didn't know what we were going to see. We didn't really know what to expect; we did not know the mythology at all I mean, at all. We had to discover that."

The AI boom of today is largely centered around an old technology known as neural networks. Despite incredible advancements in generative AI tools, namely large language models (LLMs) that have captured the imagination of businesses and people alike.

One of the most interesting developments is the number of people who are becoming increasingly convinced that these AI agents are conscious, or have agency, and can think or even feel for themselves. One startling example is a former Google engineer who claimed a chatbot the company was working on was sentient. Although this is widely understood not to be the case, it's a sign of the direction in which we're heading.

Elsewhere, despite impressive systems that can generate images, and now video thanks to OpenAI's SORA these technologies still all rely on the principles of neural networks that many in the field don't believe will lead to the sort of human-level AGI, let alone a super intelligence that can modify itself and build even more intelligence agents autonomously. The answer, according to Databricks CTO Matei Zaharia, is a compound AI system that uses LLMs as one component. It's an approach backed by Goertzel, the veteran computer scientist who is working on his own version of this compound system with the aim of creating a distributed open source AGI agent within the next few years. He suggests that humanity could build an AGI agent as soon as 2027.

There are so many reasons why The Matrix has remained relevant from the fact it was a visual feast to the rich and layered parables one can draw between its world and ours.

Much of the backstory hasn't been a part of that conversation in the 25 years since its cinematic release. But as we look to the future, we can begin to see how a similar world might be unfolding.

We know, for example, the digital realm we occupy largely through social media channels is influencing people in harmful ways. AI has also been a force for tragedy around the world, with Amnesty International claiming Facebook's algorithms played a role in pouring petrol on ethnic violence in Myanmar. Although not generally taken seriously, companies like Meta are attempting to build VR-powered alternate realities known as the metaverse.

With generative AI now a proliferating technology, groundbreaking research found recently that more than half (57.1%) of the internet comprises AI-generated content.

Throw increasingly improving tools like Midjourney and now SORA into the mix and to what extent can we know what is real and what is generated by machines especially if they look so lifelike and indistinguishable from human-generated content? The lack of sentience in the billions of machines around us is an obvious divergence from The Matrix. But that doesn't mean our own version of The Matrix has the potential to be any less manipulative.

Continued here:

As 'The Matrix' turns 25, the chilling artificial intelligence (AI) projection at its core isn't as outlandish as it once seemed - TechRadar

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Some researchers think that AI could eventually achieve general intelligence, matching and even exceeding humans on most tasks.Credit: Charles Taylor/Alamy

Sudden jumps in large language models apparent intelligence dont mean that they will soon match or even exceed humans on most tasks. Signs that had been interpreted as emerging artificial general intelligence disappear when the systems are tested in different ways, reported scientists at the NeurIPS machine-learning conference in December. Scientific study to date strongly suggests most aspects of language models are indeed predictable, says computer scientist and study co-author Sanmi Koyejo.

Nature | 4 min read

Reference: NeurIPS 2023 Conference paper

A robotic chemist might be the ideal laboratory partner: it scours the literature for instructions, designs experiments and then carries them out to make compounds including paracetamol and aspirin. The system, called Coscientist, is powered by several large language models, including GPT-4 and Claude. It can do most of the things that really well-trained chemists can do, says Coscientist co-developer Gabe Gomes. The team hasnt yet made Coscientists full code freely available, because some applications are likely to be dangerous.

Nature | 4 min read

Reference: Nature paper

A large language model can predict peoples health, earnings and likelihood of a premature death. The system was trained on the equivalent of sentences that were generated from the work and health records of around 6 million people in Denmark. For example, write the researchers, a sentence can capture information along the lines of In September 2012, Francisco received twenty thousand Danish kroner as a guard at a castle in Elsinore. When asked to predict whether a person in the database had died by 2020, it was accurate almost 80% of the time, outperforming other state-of-the-art models by a wide margin. Some scientists caution that the model might not work for other populations and that biases in the data could confound predictions.

Science | 4 min read

Reference: Nature Computational Science paper

Research into the boundaries between conscious and unconscious systems is urgently needed, a trio of scientists say. In comments to the United Nations, theoretical computer scientist Lenore Blum, mathematicians Jonathan Mason and Johannes Kleiner all of the Association for Mathematical Consciousness Science call for more funding for the effort. Some researchers predict that AI with human-like intelligence is 520 years away, yet there is no standard method to assess whether machines are conscious and whether they share human values. We should also consider the possible needs of conscious systems, the researchers say.

Nature | 6 min read

(Y. Yamauchi et al./Front. Robot. AI (CC-BY-4.0))

Reference: Frontiers in Robotics and AI paper

Whether machine-learning algorithms run on quantum computers can be faster or better than those run on classical computers remains an unanswered question. Some scientists hope that quantum AI could spot patterns in data that classical varieties miss even if it isnt faster. This could particularly be the case for data that are already quantum, for example those coming from particle colliders or superconductivity experiments. Our world inherently is quantum-mechanical. If you want to have a quantum machine that can learn, it could be much more powerful, says physicist Hsin-Yuan Huang.

Nature | 9 min read

This year could see the decline of the term large language model as systems increasingly deal in images, audio, video, molecular structures or mathematics. There might even be entirely new types of AI that go beyond the transformer architecture used by almost all generative models so far. At the same time, proprietary AI models will probably continue to outperform open-source approaches. And generating synthetic content has become so easy that some experts are expecting more misinformation, deepfakes and other malicious material. What I most hope for 2024 though it seems slow in coming is stronger AI regulation, says computer scientist Kentaro Toyama.

Forbes | 25 min read & The Conversation | 7 min read

We've never before built machines where even the creators don't know how they will behave, or why, says Jessica Newman, director of the AI Security Initiative. Thats particularly worrying when AI is involved in high-stakes decisions, such as in healthcare and policing. Researchers and policymakers agree that algorithms need to become more explainable, though its still unclear what this means in practice. For AI to be fair, reliable and safe, we need to go beyond opening the black box, says Newman, to ensure there is accountability for any harm that's caused.

Nature Podcast | 38 min listen

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Psychologist Ada Kaluzna says that using AI in her scientific writing could disrupt her ability to learn and think creatively. (Nature | 5 min read)

Happy new year! Today, Im mesmerized by this short documentary about AI art, made (in large parts) by AI. In truth, there is never going to be a first truly AI-generated documentary because it always will involve labour of some kind, says filmmaker Alan Warburton. Labour is what makes it watchable.

Help this newsletter to have a great start into 2024 by sending your feedback to ai-briefing@nature.com.

Thanks for reading,

Katrina Krmer, associate editor, Nature Briefing

With contributions by Flora Graham

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AI & robotics briefing: Why superintelligent AI won't sneak up on us - Nature.com

In the decades to come, 2023 may be remembered as the year of generative AI hype, where ChatGPT became arguably the fastest-spreading new technology in human history and expectations of AI-powered riches became commonplace. The year 2024 will be the time for recalibrating expectations.

Of course, generative AI is an impressive technology, and it provides tremendous opportunities for improving productivity in a number of tasks. But because the hype has gone so far ahead of reality, the setbacks of the technology in 2024 will be more memorable.

More and more evidence will emerge that generative AI and large language models provide false information and are prone to hallucinationwhere an AI simply makes stuff up, and gets it wrong. Hopes of a quick fix to the hallucination problem via supervised learning, where these models are taught to stay away from questionable sources or statements, will prove optimistic at best. Because the architecture of these models is based on predicting the next word or words in a sequence, it will prove exceedingly difficult to have the predictions be anchored to known truths.

Anticipation that there will be exponential improvements in productivity across the economy, or the much-vaunted first steps towards artificial general intelligence, or AGI, will fare no better. The tune on productivity improvements will shift to blaming failures on faulty implementation of generative AI by businesses. We may start moving towards the (much more meaningful) conclusion that one needs to know which human tasks can be augmented by these models, and what types of additional training workers need to make this a reality.

Some people will start recognizing that it was always a pipe dream to reach anything resembling complex human cognition on the basis of predicting words. Others will say that intelligence is just around the corner. Many more, I fear, will continue to talk of the existential risks of AI, missing what is going wrong, as well as the much more mundane (and consequential) risks that its uncontrolled rollout is posing for jobs, inequality, and democracy.

We will witness these costs more clearly in 2024. Generative AI will have been adopted by many companies, but it will prove to be just so-so automation of the type that displaces workers but fails to deliver huge productivity improvements.

The biggest use of ChatGPT and other large language models will be in social media and online search. Platforms will continue to monetize the information they collect via individualized digital ads, while competition for user attention will intensify. The amount of manipulation and misinformation online will grow. Generative AI will then increase the amount of time people spend using screens (and the inevitable mental health problems associated with it).

There will be more AI startups, and the open source model will gain some traction, but this will not be enough to halt the emergence of a duopoly in the industry, with Google and Microsoft/OpenAI dominating the field with their gargantuan models. Many more companies will be compelled to rely on these foundation models to develop their own apps. And because these models will continue to disappoint due to false information and hallucinations, many of these apps will also disappoint.

Calls for antitrust and regulation will intensify. Antitrust action will go nowhere, because neither the courts nor policymakers will have the courage to attempt to break up the largest tech companies. There will be more stirrings in the regulation space. Nevertheless, meaningful regulation will not arrive in 2024, for the simple reason that the US government has fallen so far behind the technology that it needs some time to catch upa shortcoming that will become more apparent in 2024, intensifying discussions around new laws and regulations, and even becoming more bipartisan.

Link:

Get Ready for the Great AI Disappointment - WIRED

As we teeter on the brink of the AGI era, a profound contemplation of a compatible and thriving variant of capitalism becomes indispensable. This requires an exhaustive and intricate exploration into a reimagined economic framework, robust enough to accommodate the profound, multifaceted impacts of AGI.

This is A Blog Series:

AGI, with its unprecedented intellectual capabilities, heralds a tectonic shift not only in technology but also in the foundational pillars of our economic and social structures. Its potential to overhaul industries, inaugurate new markets, and redefine employment necessitates an extensive reevaluation of our current economic paradigms.

Here, I present ten dimensions that are core for the future architecture for rethinking capitalism.

In the threshold of the AGI era, reimagining our economic frameworks is a strategic and ethical imperative. Envisioning the future of capitalism as a flexible, ethical, and inclusive system, capable of leveraging AGIs benefits while mitigating its risks, requires a collective, interdisciplinary collaboration. This vision emphasizes the necessity of constructing an economic model that is resilient and responsive to the rapid technological and societal changes brought about by AGI.

This complex and comprehensive journey calls for visionary thought and a collaborative approach to navigate the intricate realms of the AGI age. Your perspectives are critical in contributing to a deep understanding and strategic direction for global capitalism in this transformative era. Lets engage in this rich and elaborate exploration together.

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Part 3 Capitalism in the Age of Artificial General Intelligence (AGI) - Medium

Artificial General Intelligence could be a risk, an opportunity and a tool that could change everything.

Since OpenAI launched ChatGPT, and since its exponential leap in quality with GPT-4, there is a term that continues to be constantly repeated among experts who are immersed in the development of AI: the arrival of Artificial General Intelligence. This concept can be heard with all kinds of arguments, those who believe that it is going to be a revolution that will change everything forever, and among those who are totally against it because they believe that there are many dangers related to it. Therefore, our goal today is to try to decipher what Artificial General Intelligence is and when it could reach all of us.

What is AGI

Artificial General Intelligence, or AGI (Artificial General Intelligence) is a conceptual idea that describes a computer capable of thinking and acting like a human at the level of reasoning and intelligence. In this framework, supercomputers are already being created that want to imitate human brains, but there is still a long way to go to ensure that they are as intelligent as us or that have that point of creativity that current AI lacks.

Currently AIs, no matter how good they are, cannot solve errors that are not within their learning base. That is, current AIs are normally perfect for fulfilling a very broad series of functions, but they must always be channeled in such a way that these AIs have a basis in the knowledge they have learned. In fact, AI sometimes makes mistakes on really basic issues, such as childrens math problems, or invents data that never existed.

On the other hand, the AGI would not make these errors. It would be a perfect knowledge machine, capable of operating through its own uniqueness and autonomously with great knowledge in all fields and reasoning with its knowledge as we humans would or in an even better way.

Now, this is a hypothetical term, which means that we may never see it completed.

Arrival date of General Artificial Intelligence

It is really difficult to give an exact date when the AGI will arrive. At the moment it is clear that AI is advancing by leaps and bounds and it is believed that thanks to this process of innovation and development it will arrive sooner or later. However, no one within the industry dares to give a specific date on this issue. For the vast majority, in fact, it is not even a necessity, since they first want to focus on those problems that need to be solved at this very moment.

It is almost impossible to know when AGI will come into our lives.

On many occasions, when talking about General Artificial Intelligence, it is done with the idea of being attractive to investors so that they inject the large sums of money that are necessary for the very expensive development of AI. This makes some venture to give dates, like the CEO of SingularityNET, who assured that it would arrive by the year 2031.

Unfortunately, at the moment it is impossible to know when it will arrive, but everything indicates that there is a very long journey to reach it. Whats more, it could be a chimerical event that may never come, since the point could come at which human beings find an insurmountable development ceiling for current technologies.

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Artificial General Intelligence (AGI): what it is and why its discovery can change the world - Medium