Category Archives: Artificial General Intelligence

Artificial intelligence aficionados are betting that the technology will help solve humanity's biggest problems, from wars to global warming, but in practice, these may be unrealistic ambitions for now.

"It's not about asking AI 'Hey, this is a sticky problem. What would you do?' and AI is like, 'well, you need to completely restructure this part of the economy,'" said Michael Littman, a Brown University professor of computer science.

Littman was at the South By Southwest (or SXSW) arts and technology festival in Austin, Texas, where he had just spoken on one of the many panels on the potential benefits of AI.

"It's a pipe dream. It's a little bit science fiction. Mostly what people are doing is they're trying to bring AI to bear on specific problems that they're already solving, but just want to be more efficient.

"It's not just a matter of pushing this button and everything's fixed," he said.

With their promising titles ("How to Make AGI Beneficial and Avoid a Robot Apocalypse"), and the ever presence of tech giants, the panels attract big crowds, but they often hold more pragmatic objectives, like promoting a product.

At one meeting called "Inside the AI Revolution: How AI is Empowering the World to Achieve More," Simi Olabisi, a Microsoft executive, praised the tech's benefits on Azure, the company's cloud service.

When using Azure's AI language feature in call centers, "maybe when a customer called in, they were angry, and when they ended the call, they were really appreciative. Azure AI Language can really capture that sentiment, and tell a business how their customers are feeling," she explained.

The notion of artificial intelligence, with its algorithms capable of automating tasks and analyzing mountains of data, has been around for decades.

But it took on a whole new dimension last year with the success of ChatGPT, the generative AI interface launched by OpenAI, the now iconic AI start-up mainly funded by Microsoft.

OpenAI claims to want to build artificial "general" intelligence or AGI, which will be "smarter than humans in general" and will "elevate humanity," according to CEO Sam Altman.

That ethos was very present at SXSW, with talk about "when" AGI will become a reality, rather than "if."

Ben Goertzel, a scientist who heads the SingularityNET Foundation and the AGI Society, predicted the advent of general AI by 2029.

"Once you have a machine that can think as well as a smart human, you're at most a few years from a machine that can think a thousand or a million times better than a smart human, because this AI can modify its own source code," said Goertzel.

Wearing a leopard-print faux-fur cowboy hat, he advocated the development of AGI endowed with "compassion and empathy," and integrated into robots "that look like us," to ensure that these "super AIs" get on well with humanity.

David Hanson - founder of Hanson Robotics and who designed Desdemona, a humanoid robot that functions with generative AI - brainstromed about the plus and minuses of AI with superpowers.

AI's "positive disruptions...can help to solve global sustainability issues, although people are probably going to be just creating financial trading algorithms that are absolutely effective," he said.

Hanson fears the turbulence from AI, but pointed out that humans are doing a "fine job" already of playing "existential roulette" with nuclear weapons and by causing "the fastest mass extinction event in human history."

But "it may be that the AI could have seeds of wisdom that blossom and grow into new forms of wisdom that can help us be better," he said.

Initially, AI should accelerate the design of new, more sustainable drugs or materials, said believers in AI.

Even if "we're not there yet... in a dream world, AI could handle the complexity and the randomness of the real world, and... discover completely new materials that would enable us to do things that we never even thought were possible," said Roxanne Tully, an investor at Piva Capital.

Today, AI is already proving its worth in warning systems for tornadoes and forest fires, for example.

But we still need to evacuate populations, or get people to agree to vaccinate themselves in the event of a pandemic, stressed Rayid Ghani of Carnegie Mellon University during a panel titled "Can AI Solve the Extreme Weather Pandemic?"

"We created this problem. Inequities weren't caused by AI, they're caused by humans and I think AI can help a little bit. But only if humans decide they want to use it to deal with" the issue, Ghani said.

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Will AI save humanity? U.S. tech fest offers reality check - Japan Today

It is becoming increasingly clear that the advent of artificial general intelligence (AGI) is upon us. OpenAI includes in its mission that it aims to maximize the positive impact of AGI while minimizing harm. The research organization recognizes that AGI wont create a utopia, but they strive to ensure that its benefits are widespread and that it doesnt exacerbate existing inequalities.

Some say that elements of AGI will be seen in GPT-5 that OpenAI says is currently in prerelease testing. GPT-5 is anticipated to be available by the end of this year or in 2025.

Others suggest that Magic AI, the expanding artificial intelligence (AI) developer and coding assistant, may have already developed a version of AGI. With a staggering ability to process 3.5million words, Aman Anand writes in Medium, It is important to remember that Magics model is still under development, and its true capabilities and limitations remain to be seen. While the potential for AGI is undeniable, it is crucial to approach this future with caution and a focus on responsible development.

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Meanwhile Google Gemini 1.5 Pro version is leaping ahead of OpenAI models with a massive context capability:

This means 1.5 Pro can process vast amounts of information in one goincluding 1 hour of video, 11 hours of audio, codebases with over 30,000 lines of code or over 700,000 words. In our research, weve also successfully tested up to 10million tokens.

Accelerated by the intense competition to be the first to achieve AGI, it is not unreasonable to expect that at least certain of the parameters commonly describing AGI will conceivably be achieved by the end of this year, or almost certainly by 2026. AI researchers anticipate that

an AGI system should have the following abilities and understanding:

AI researchers also anticipate that AGI systems will possess higher-level capabilities, such as being able to do the following:

Given those characteristics, lets imagine a time, perhaps in four or five years, in which AGI has been achieved and has been rolled out across society. In that circumstance, it would seem that many of the jobs now performed by individuals could be more efficiently and less expensively completed by agents of AGI. Perhaps half or more of all jobs worldwide might be better done by AGI agents. At less cost, more reliability and instant, automatic updating, these virtual employees would be a bargain. Coupled with sophisticated robotics, some of which we are seeing rolled out today, even many hands-on skilled jobs will be efficiently and effectively done by computer. All will be immediately and constantly updated with the very latest discoveries, techniques and contextual approaches.

AGI is expected to be followed by artificial superintelligence (ASI):

ASI refers to AI technology that will match and then surpass the human mind. To be classed as an ASI, the technology would have to be more capable than a human in every single way possible. Not only could these AI things carry out tasks, but they would even be capable of having emotions and relationships.

What, then, will individual humans need to learn in higher education that cannot be provided instantly and expertly through their own personal ASI lifelong learning assistant?

ASI may easily provide up-to-the-minute responses to our intellectual curiosity and related questions. It will be able to provide personalized learning experiences; sophisticated simulations; personalized counseling and advising; and assess our abilities and skills to validate and credential our learning. ASI could efficiently provide recordkeeping in a massive database. In that way, there would be no confusion of comparative rankings and currency of credentials such as we see today.

In cases where we cannot achieve tasks on our own, ASI will direct virtual agents to carry out tasks for us. However, that may not fully satisfy the human-to-human and emotional interactions that seems basic to our nature. The human engagement, human affirmation and interpersonal connections may not be fulfilled by ASI and nonhuman agents. For example, some tasks are not as much about the outcome as they are the journey, such as music, art and performance. In those cases, it is the process of refining those abilities that are at least equal to the final product.

Is there something in the interpersonal, human-to-human engagement in such endeavors that is worthy of continuing in higher education rather than solely through computer-assisted achievement? If so, does that require a university campus? Certainly, the number of disciplines and therefore the number of faculty and staff members will fall out of popularity due to suppressed job markets in those fields.

If this vision of the next decade is on target, higher education is best advised to begin considering today how it will morph into something that serves society in the fourth industrial revolution. We must begin to:

Have you and your colleagues begun to consider the question of what you provide that could not be more efficiently and less expensively provided by AI? Have you begun to research and formulate plans to compete or add value to services that are likely to be provided by AGI/ASI? One good place to begin such research is by asking a variety of the current generative AI apps to share insights and make recommendations!

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Artificial general intelligence and higher education - Inside Higher Ed

A few weeks ago, I was having a chat with my neighbor Tom, an amateur chemist who conducts experiments in his apartment. I have a longtime fascination with chemistry, and always enjoy talking with him. But this conversation was scary. If his latest experiment was successful, he informed me, it might have some part to play in curing cancer. If it was a failure, however, there was a reasonable chance, according to his calculations, that the experiment would trigger an explosion that levels the entire apartment complex.

Perhaps Tom was lying, or maybe hes delusional. But what if he really was just one test tube clink away from blowing me and dozens of our fellow building residents sky high? What should one do in this situation? After a brief deliberation, I decided to call 911. The police rushed over, searched his apartment and decided after an investigation to confiscate all of his chemistry equipment and bring him in for questioning.

The above scenario is a thought experiment. As far as I know, no one in my apartment complex is an amateur chemist experimenting with highly combustible compounds. Ive spun this fictional tale because its a perfect illustration of the situation that we all of us are in with respect to the AI companies trying to build artificial general intelligence, or AGI. The list of such companies includes DeepMind, OpenAI, Anthropic and xAI, all of which are backed by billions of dollars. Many leading figures at these very companies have claimed, in public, while standing in front of microphones, that one possible outcome of the technology they are explicitly trying to build is that everyone on Earth dies. The only sane response to this is to immediately call 911 and report them to the authorities. They are saying that their own technology might kill you, me, our family members and friends the entire human population. And almost no one is freaking out about this.

Its crucial to note that you dont have to believe that AGI will actually kill everyone on Earth to be alarmed. I myself am skeptical of these claims. Even if one suspects Tom of lying about his chemistry experiments, the fact of his telling me that his actions could kill everyone in our apartment complex is enough to justify dialing 911.

One doesnt need to accept this line of reasoning to be alarmed when the CEO of the most powerful AI company thats trying to build AGI says that superintelligent machines might kill us.

What exactly are AI companies saying about the potential dangers of AGI? During a 2023 talk, OpenAI CEO Sam Altman was asked about whether AGI could destroy humanity, and he responded, the bad case and I think this is important to say is, like, lights out for all of us. In some earlier interviews, he declared that I think AI willmost likely sort of lead to the end of the world, but in the meantime there will be great companies created with serious machine learning, and probably AI will kill us all, but until then were going to turn out a lot of great students. The audience laughed at this. But was he joking? If he was, he was also serious: the OpenAI website itself states in a 2023 article that the risks of AGI may be existential, meaning roughly that they could wipe out the entire human species. Another article on their website affirms that a misaligned superintelligent AGI could cause grievous harm to the world.

In a 2015 post on his personal blog, Altman wrote that development of superhuman machine intelligence is probably the greatest threat to the continued existence of humanity. Whereas AGI refers to any artificial system that is at least as competent as humans in every cognitive domain of importance, such as science, mathematics, social manipulation and creativity, a SMI is a type of AGI that is superhuman in its capabilities. Many researchers in the field of AI safety believe that once we have AGI, we will have superintelligent machines very shortly after. The reason is that designing increasingly capable machines is an intellectual task, so the smarter these systems become, the better able theyll become at designing even smarter systems. Hence, the first AGIs will design the next generation of even smarter AGIs, until those systems reach superhuman levels.

Again, one doesnt need to accept this line of reasoning to be alarmed when the CEO of the most powerful AI company thats trying to build AGI says that superintelligent machines might kill us.

Just the other day, an employee at OpenAI who goes by roon on Twitter/X, tweeted that things are accelerating. Pretty much nothing needs to change course to achieve AGI Worrying about timelines that is, worrying about whether AGI will be built later this year or 10 years from now is idle anxiety, outside your control. You should be anxious about stupid mortal things instead. Do your parents hate you? Does your wife love you? In other words, AGI is right around the corner and its development cannot be stopped. Once created, it will bring about the end of the world as we know it, perhaps by killing everyone on the planet. Hence, you should be thinking not so much about when exactly this might happen, but on more mundane things that are meaningful to us humans: Do we have our lives in order? Are we on good terms with our friends, family and partners? When youre flying on a plane and it begins to nosedive toward the ground, most people turn to their partner and say I love you or try to send a few last text messages to loved ones to say goodbye. That is, according to someone at OpenAI, what we should be doing right now.

A similar sentiment has been echoed by other notable figures at OpenAI, such as Altmans co-founder, Ilya Sutskever. The future is going to be good for the AIs regardless, he said in 2019. It would be nice if it would be good for humans as well. He adds, ominously, that I think its pretty likely the entire surface of the Earth will be covered with solar panels and data centers once we create AGI, referencing the idea that AGI is dangerous partly because it will seek to harness every resource it can. In the process, humanity could be destroyed as an unintended side effect. Indeed, Sutskever tells us that the AGI his own company is trying to build probably isnt,

going to actively hate humans and want to harm them, but its just going to be too powerful, and I think a good analogy would be the way humans treat animals. Its not that we hate animals. I think humans love animals and have a lot of affection for them, but when the time comes to build a highway between two cities, we are not asking the animals for permission. We just do it because its important for us. And I think by default thats the kind of relationship thats going to be between us and AGIs, which are truly autonomous and operating on their own behalf.

The good folks by which I mean quasi-homicidal folks at OpenAI arent the only ones being honest about how their work could lead to the annihilation of our species. Dario Amodei, the CEO of Anthropic, which recently received $4 billion in funding from Amazon, said in 2017 that theres a long tail of things of varying degrees of badness that could happen after building AGI. I think at the extreme end is the fear that an AGI could destroy humanity. I cant see any reason in principle why that couldnt happen. Similarly, Elon Musk, the co-founder of OpenAI who recently started his own company to build AGI, named xAI, declared in 2023 that one of the biggest risks to the future of civilization is AI, and has previously said that, being very close to the cutting edge in AI scares the hell out of me. Why? Because advanced AI is capable of vastly more than almost anyone knows and the rate of improvement is exponential.

Even the CEO of Google, Sundar Pichai, told Sky News last year that advanced AI can be very harmful if deployed wrongly, and that with respect to safety issues, we dont have all the answers there yet, and the technology is moving fast. So does that keep me up at night? Absolutely.

Google currently owns DeepMind, which was cofounded in 2010 by a computer scientist named Shane Legg. During a talk one year before DeepMind was founded, Legg claimed that if we can build human level AI, then we can almost certainly scale up to well above human level. A machine well above human level will understand its design and be able to design even more powerful machines, which gestures back at the idea that AGI could take over the job of designing even more advanced AI systems than itself. We have almost no idea how to deal with this, he adds. During the same talk, Legg said that we arent going to develop a theory about how to keep AGI safe before AGI is developed. Ive spoken to a bunch of people, he reports, none of them, that Ive ever spoken to, think they will have a practical theory of friendly artificial intelligence in about 10 years time. We have no idea how to solve this problem.

Either these AI companies need to show, right now, that the systems theyre building are completely safe, or they need to stop, right now.

Thats worrying because many researchers at the major AI companies argue that as roon suggested AGI may be just around the corner. In a recent interview, Demis Hassabis, another co-founder of DeepMind, says that when we started DeepMind back in 2010, we thought of it as a 20-year project, and actually I think were on track. So, I wouldnt be surprised if we had AGI-like systems within the next decade. When asked what it would take to make sure that an AGI thats smarter than a human is safe, his answer was, as one commentator put it, a grab bag of half-baked ideas. Maybe, he says, we can use less capable AIs to help us keep the AGIs in check. But maybe that wont work who knows? Either way, DeepMind and the other AI companies are plowing ahead with their efforts to build AGI, while simultaneously acknowledging, in public, on record, that their products could destroy the entire world.

This is, in a word, madness. If youre driving in a car with me, and I tell you that earlier today I attached a bomb to the bottom of the car, and it might or might not! go off if we hit a pothole, then whether or not you believe me, you should be extremely alarmed. That is a very scary thing to hear someone say at 60 miles an hour on a highway. You should, indeed, turn to me and scream, Stop this damn car right now. Let me out immediately I dont want to ride with you anymore!

Right now, were in that car with these AI companies driving. They have turned to us on numerous occasions over the past decade and a half and admitted that theyve attached a bomb to the car, and that it might or might not! explode in the near future, killing everyone inside. Thats an outrageous situation to be in, and more people should be screaming at them to stop what theyre doing immediately. More people should be dialing 911 and reporting the incident to the authorities, as I did with Tom in the fictional scenario above.

I do not know if AGI will kill everyone on Earth Im more focused on the profound harms that these AI companies have already caused through worker exploitation, massive intellectual property theft, algorithmic bias and so on. The point is that it is completely unacceptable that the people leading or working for these AI companies believe that what theyre doing could kill you, your family, your friends and even your pets (who will feed your fluffy companions if you cease to exist?) yet continue to do it anyway. One doesnt need to completely buy-into the AGI might destroy humanity claim to see that someone who says their work might destroy humanity should not be doing whatever it is theyre doing. As Ive shown before, there have been several episodes in recent human history where scientists have declared that were on the verge of creating a technology that would destroy the world and nothing came of it. But thats irrelevant. If someone tells you that they have a gun and might shoot you, that should be more than enough to sound the alarm even if you believe that they dont, in fact, have a gun hidden under their bed.

Either these AI companies need to show, right now, that the systems theyre building are completely safe, or they need to stop, right now, trying to build those systems. Something needs to change about the situation immediately.

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The Madness of the Race to Build Artificial General Intelligence - Truthdig

Companies like Morgan Stanley are already laying the groundwork for so-called organizational AGI. Maxim Tolchinskiy/Unsplash

Whether its being theorized or possibly, maybe actualized, artificial general intelligence, or AGI, has become a frequent topic of conversation in a world where people are now routinely talking with machines. But theres an inherent problem with the term AGIone rooted in perception. For starters, assigning intelligence to a system instantly anthropomorphizes it, adding to the perception that theres the semblance of a human mind operating behind the scenes. This notion of a mind deepens the perception that theres some single entity manipulating all of this human-grade thinking.

This problematic perception is compounded by the fact that large language models (LLMs) like ChatGPT, Bard, Claude and others make a mockery of the Turing test. They seem very human indeed, and its not surprising that people have turned to LLMs as therapists, friends and lovers (sometimes with disastrous results). Does the humanness of their predictive abilities amount to some kind of general intelligence?

By some estimates, the critical aspects of AGI have already been achieved by the LLMs mentioned above. A recent article in Noema by Blaise Agera Y Arcas (vice president and fellow at Google Research) and Peter Norvig (a computer scientist at the Stanford Institute for Human-Centered A.I.) argues that todays frontier models perform competently even on novel tasks they were not trained for, crossing a threshold that previous generations of A.I. and supervised deep learning systems never managed. Decades from now, they will be recognized as the first true examples of AGI.

For others, including OpenAI, AGI is still out in front of us. We believe our research will eventually lead to artificial general intelligence, their research page proclaims, a system that can solve human-level problems.

Whether nascent forms of AGI are already here or are still a few years away, its likely that businesses attempting to harness these powerful technologies might create a miniature version of AGI. Businesses need technology ecosystems that can mimic human intelligence with the cognitive flexibility to solve increasingly complex problems. This ecosystem needs to orchestrate using existing software, understand routine tasks, contextualize massive amounts of data, learn new skills, and work across a wide range of domains. LLMs on their own can only perform a fraction of this workthey seem most useful as part of a conversational interface that lets people talk to technology ecosystems. There are strategies being used right now by leading enterprise companies to move in this direction toward something we might call organizational AGI.

There are legitimate reasons to be wary of yet another unsolicited tidbit in the A.I. terms slush pile. Regardless of what we choose to call the eventual outcome of these activities, there are currently organizations using LLMs as an interface layer. They are creating ecosystems where users can converse with software through channels like rich web chat (RCW), obscuring machinations happening behind the scenes. This is difficult work, but the payoff is huge: rather than pogo-sticking between apps to get something done on a computer, customers and employees can ask technology to run tasks for them. Theres the immediate and tangible benefit of people eliminating tedious tasks from their lives. Then theres the long term benefit of a burgeoning ecosystem where employees and customers are interacting with digital teammates that can perform automations leveraging all forms of data across an organization. This is an ecosystem that starts to take the form of a digital twin.

McKinsey describes a digital twin as a virtual replica of a physical object, person, or process that can be used to simulate its behavior to better understand how it works in real life. They elaborate to say that a digital twin within an ecosystem similar to what Ive described can become an enterprise metaverse, a digital and often immersive environment that replicates and connects every aspect of an organization to optimize simulations, scenario planning and decision making.

With respect to what I said earlier about anthropomorphizing technology, the digital teammates within this kind of ecosystem are an abstraction, but I think of them as intelligent digital workers, or IDWs. IDWs are analogous to a collection of skills. These skills come from shared libraries, and skills can be adapted and reused in multitudes of ways. Skills are able to take advantage of all the information piled up inside the organization, with LLMs mining unstructured data, like emails and recorded calls.

This data becomes more meaningful thanks to graph technology, which is adept at creating indexes of skills, systems and data sources. Graph goes beyond mere listing and includes how these elements relate to and interact with each other. One of the core strengths of graph technology is its ability to represent and analyze relationships. For a network of IDWs, understanding how different components are interlinked is crucial for efficient orchestration and data flow.

Generative tools like LLMs and graph technology can work together in tandem, to propel the journey toward digital twinhood, or organizational AGI. Twins can encompass all aspects of the business, including events, data, assets, locations, personnel and customers. Digital twins are likely to be low-fidelity at first, offering a limited view of the organization. As more interactions and processes take place within the org, however, the fidelity of the digital twin becomes higher. An organizations technology ecosystem not only understands the current state of the organization. It can also adapt and respond to new challenges autonomously.

In this sense every part of an organization represents an intelligent awareness that comes together around common goals. In my mind, it mirrors the nervous system of a cephalopod. As Peter Godfrey-Smith writes in his book, Other Minds (2016, Farrar, Straus and Giroux), in an octopus, the majority of neurons are in the arms themselvesnearly twice as many in total as in the central brain. The arms have their own sensors and controllers. They have not only the sense of touch but also the capacity to sense chemicalsto smell or taste. Each sucker on an octopuss arm may have 10,000 neurons to handle taste and touch. Even an arm that has been surgically removed can perform various basic motions, such as reaching and grasping.

A world teeming with self-aware brands would be quite hectic. According to Gartner, by 2025, generative A.I. will be a workforce partner within 90 percent of companies worldwide. This doesnt mean that all of these companies will be surging toward organizational AGI, however. Generative A.I., and LLMs in particular, cant meet an organizations automation needs on its own. Giving an entire workforce access to GPTs or Copilot wont move the needle much in terms of efficiency. It might help people write better emails faster, but it takes a great deal of work to make LLMs reliable resources for user queries.

Their hallucinations have been well documented and training them to provide trustworthy information is a herculean effort. Jeff McMillan, chief analytics and data officer at Morgan Stanley (MS), told me it took his team nine months to train GPT-4 on more than 100,000 internal documents. This work began before the launch of ChatGPT, and Morgan Stanley had the advantage of working directly with people at OpenAI. They were able to create a personal assistant that the investment banks advisors can chat with, tapping into a large portion of its collective knowledge. Now youre talking about wiring it up to every system, he said, with regards to creating the kinds of ecosystems required for organizational A.I. I dont know if thats five years or three years or 20 years, but what Im confident of is that that is where this is going.

Companies like Morgan Stanley that are already laying the groundwork for so-called organizational AGI have a massive advantage over competitors that are still trying to decide how to integrate LLMs and adjacent technologies into their operations. So rather than a world awash in self-aware organizations, there will likely be a few market leaders in each industry.

This relates to broader AGI in the sense that these intelligent organizations are going to have to interact with other intelligent organizations. Its hard to envision exactly what depth of information sharing will occur between these elite orgs, but over time, these interactions might play a role in bringing about AGI or singularity, as its also called.

Ben Goertzel, the founder of SingularityNET and the person often credited with creating the term, makes a compelling case that AGI should be decentralized, relying on open-source development as well as decentralized hosting and mechanisms for interconnect A.I. systems to learn from and teach on another.

SingularityNETs DeAGI Manifesto states, There is a broad desire for AGI to be ethical and beneficial for all humanity; the most straightforward way to achieve this seems to be for AGI to grow up in the context of serving and being guided by all humanity, or as good an approximation as can be mustered.

Having AGI manifest in part from the aggressive activities of for-profit enterprises is dicey. As Goertzel pointed out, You get into questions [about] who owns and controls these potentially spooky and configurable human-like robot assistants and to what extent is their fundamental motivation to help people as opposed to sell people stuff or brainwash people into some corporate government media advertising order.

Theres a strong case to be made that an allegiance to profit will be the undoing of the promise for humanity at large that these technologies afford. Weirdly, the skynet scenario in Terminatorwhere a system becomes self-aware, determines humanity is a grave threat, and exterminates all lifeassumes that the system, isolated to a single company, has been programmed to have a survival instinct. It would have to be told that survival at all costs is its bottom line, which suggests we should be extra cautious developing these systems within environments where profit above all else is the dictum.

Maybe the most important thing is keeping this technology in the hands of humans and pushing forward the idea that the myriad technologies associated with A.I. should only be used in ways that are beneficial to humanity as a whole, that dont exploit marginalized groups, and that arent propagating synthesized bias at scale.

When I broached some of these ideas about organizational AGI to Jaron Lanier, co-creator of VR technology as we know it and Microsofts Octopus (Office of the Chief Technology Officer Prime Unifying Scientist), he told me my vocabulary was nonsensical and that my thinking wasnt compatible with his perception of technology. Regardless, it felt like we agreed on core aspects of these technologies.

I dont think of A.I. as creating new entities. I think of it as a collaboration between people, Lanier said. Thats the only way to think about using it wellto me its all a form of collaboration. The sooner we see that, the sooner we can design useful systemsto me theres only people.

In that sense, AGI is yet another tool, way down the spectrum from the rocks our ancestors used to smash tree nuts. Its a manifestation of our ingenuity and our desires. Are we going to use it to smash every tree nut on the face of the earth, or are we going to use it to find ways to grow enough tree nuts for everyone to enjoy? The trajectories we set in these early moments are of grave importance.

Were in the anthropocene. Were in an era where our actions are affecting everything in our biological environment, Blaise Aguera Y Arcas, the Noeme article author, told me. The Earth is finite and without the kind of solidarity where we start to think about the whole thing as our body, as it were, were kind of screwed.

Josh Tyson is the co-author of Age of Invisible Machines, a book about conversational A.I., and Director of Creative Content at OneReach.ai. He co-hosts two podcasts: Invisible Machines and N9K.

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Companies Like Morgan Stanley Are Already Making Early Versions of AGI - Observer

The use and scope of Artificial Intelligence dont need a formal introduction. Artificial Intelligence is no more just a buzzword; it has become a reality that is part of our everyday lives. As companies deploy AI across diverse applications, it's revolutionizing industries and elevating the demand for AI skills like never before.You will learn about the various stages and categories of artificial intelligence in this article on Types Of Artificial Intelligence.

Artificial Intelligence is the process of building intelligent machines from vast volumes of data. Systems learn from past learning and experiences and perform human-like tasks. It enhances the speed, precision, and effectiveness of human efforts. AI uses complex algorithms and methods to build machines that can make decisions on their own. Machine Learning and Deep learning forms the core of Artificial Intelligence.

AI is now being used in almost every sector of business:

Now that you know what AI really is, lets look at what are the different types of artificial intelligence?

Artificial Intelligence can be broadly classified into several types based on capabilities, functionalities, and technologies. Here's an overview of the different types of AI:

This type of AI is designed to perform a narrow task (e.g., facial recognition, internet searches, or driving a car). Most current AI systems, including those that can play complex games like chess and Go, fall under this category. They operate under a limited pre-defined range or set of contexts.

A type of AI endowed with broad human-like cognitive capabilities, enabling it to tackle new and unfamiliar tasks autonomously. Such a robust AI framework possesses the capacity to discern, assimilate, and utilize its intelligence to resolve any challenge without needing human guidance.

This represents a future form of AI where machines could surpass human intelligence across all fields, including creativity, general wisdom, and problem-solving. Superintelligence is speculative and not yet realized.

These AI systems do not store memories or past experiences for future actions. They analyze and respond to different situations. IBM's Deep Blue, which beat Garry Kasparov at chess, is an example.

These AI systems can make informed and improved decisions by studying the past data they have collected. Most present-day AI applications, from chatbots and virtual assistants to self-driving cars, fall into this category.

This is a more advanced type of AI that researchers are still working on. It would entail understanding and remembering emotions, beliefs, needs, and depending on those, making decisions. This type requires the machine to understand humans truly.

This represents the future of AI, where machines will have their own consciousness, sentience, and self-awareness. This type of AI is still theoretical and would be capable of understanding and possessing emotions, which could lead them to form beliefs and desires.

AI systems capable of self-improvement through experience, without direct programming. They concentrate on creating software that can independently learn by accessing and utilizing data.

A subset of ML involving many layers of neural networks. It is used for learning from large amounts of data and is the technology behind voice control in consumer devices, image recognition, and many other applications.

This AI technology enables machines to understand and interpret human language. It's used in chatbots, translation services, and sentiment analysis applications.

This field involves designing, constructing, operating, and using robots and computer systems for controlling them, sensory feedback, and information processing.

This technology allows machines to interpret the world visually, and it's used in various applications such as medical image analysis, surveillance, and manufacturing.

These AI systems answer questions and solve problems in a specific domain of expertise using rule-based systems.

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AI research has successfully developed effective techniques for solving a wide range of problems, from game playing to medical diagnosis.

There are many branches of AI, each with its focus and set of techniques. Some of the essential branches of AI include:

We might be far from creating machines that can solve all the issues and are self-aware. But, we should focus our efforts toward understanding how a machine can train and learn on its own and possess the ability to base decisions on past experiences.

I hope this article helped you to understand the different types of artificial intelligence. If you are looking to start your career in Artificial Intelligent and Machine Learning, then check out Simplilearn's Post Graduate Program in AI and Machine Learning.

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An AI model is a mathematical model used to make predictions or decisions. Some of the common types of AI models:

There are two main categories of AI:

The father of AI is John McCarthy. He is a computer scientist who coined the term "artificial intelligence" in 1955. McCarthy is also credited with developing the first AI programming language, Lisp.

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Types of Artificial Intelligence That You Should Know in 2024 - Simplilearn

A new report commissioned by the US State Department warns the exponential development of artificial intelligence may pose a significant risk to national security and even humanity.

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The new report titled "An Action Plan to Increase the Safety and Security of Advanced AI" recommends the US government move "quickly and decisively" with implementing measures that hinder the rise of artificial intelligence-powered systems being developed, even to the point of potentially limiting compute power used to train such models. The report goes on to say that if these hindering measures aren't implemented, there is a chance of AI or Artificial General Intelligence (AGI) being an "extinction-level threat to the human species."

The US State Department report involved more than 200 experts in the field, which included officials from companies that are big players in the AI game, such as OpenAI, Meta, Google, Google DeepMind, and government workers. The report goes on to recommend the US government implement limitations on how much compute power any given party developing AI is able to have at one time while also requiring AI companies to request permission from the US government to train any new AI model.

"The rise of advanced AI and AGI [artificial general intelligence] has the potential to destabilize global security in ways reminiscent of the introduction of nuclear weapons," the report reads

Surprisingly, the report recommends the US government make it illegal to open source any powerful AI model, as the information within these models may result in "potentially devastating consequences to global security."

"I think that this recommendation is extremely unlikely to be adopted by the United States government," Greg Allen, director of the Wadhwani Center for AI and Advanced Technologies, told TIME

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US government warns AI may be an 'extinction-level threat' to humans - TweakTown

The race to reach artificial general intelligence is getting intense among the tech giants, but its arrival will not happen as a moment in time, according to Amazons vice president of AGI.

Its very unlikely that theres going to be a moment in time when you suddenly decide, oh AGI wasnt (here yesterday) but its here today, said Vishal Sharma during a fireside chat at SXSW 2024 in Austin, Texas. Thats probably not going to happen.

Instead, he sees it as a journey of continuous advances. His comments echo Google DeepMinds six levels of AGI, where models go up one level as they progressively exhibit more AGI characteristics.

Meanwhile, there are hurdles to overcome. For one, people still do not agree on a precise definition of AGI. If you ask 10 experts about AGI, you will get 10 different explanations," he said.

Another is the ethical challenges models face. For Sharma, they fall in three buckets: Veracity since the models can hallucinate or make things up safety (intense red-teaming is needed), and controllability, in which inputting broadly similar prompts or queries can result in broadly similar outcomes.

A popular technique to mitigate hallucinations is Retrieval-Augmented Generation (RAG) in which the model is given, or provided access to, additional content or data from which to draw its answers. Sharma said RAG is still the best technique to fight hallucinations today.

Related:DeepMind Co-founder on AGI and the AI Race - SXSW 2024

However, he mentioned that there is another school of thought that believes its just a matter of time until the models become capable enough where these truths will be woven into the model themselves.

As for his views on open vs. closed models, Sharm said one of Amazons leadership principles is that success and scale bring broad responsibility and this applies to both types of models.

He emphasized the need to be flexible since generative AI remains fairly new and unforeseen opportunities and challenges could arise. Sharma said that when the internet began maturing, it brought new challenges that people did not think of before, such as cyber bullying.

We have to be adaptable, Sharma said.

He also thinks that just as the rise of semiconductors ushered in Moores Law and the network of networks led to Metcalfes Law, generative AI could lead to a new principle as well.

Credit: Amazon

He sees a time when AI will be broadly embedded into daily life as a helpful assistant, while staying in the background.

Sharma said Alexas Hunches are already one sign of this future. With Hunches, Alexa learns your routine say locking the back door at 9 p.m. every night and if you fail to do that one night, it will send an alert.

Related:EU AI Act Would Scrutinize Many General AI Models SXSW 2024

He said Amazons Astro is an example of an embodied AI assistant. The $1,600 household robot is used for home monitoring. You can ask it to check on people or specific rooms in the house. It alerts you if it sees someone it does not recognize or hears certain sounds. Astro can also throw treats to your dog through an accessory that is sold separately.

To be sure, todays models still have room for improvement whether in performance or economics. But Sharma believes advancements will lead to an age of abundance through the fusion of use cases that will become possible.

You should bet on AI, he said. You should not bet against it.

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Amazon's VP of AGI: Arrival of AGI Not 'Moment in Time' SXSW 2024 - AI Business

Corvids are a family of birds that are known to be astonishingly accomplished at showing self-awareness and problem-solving via the use of tools. Such traits are generally considered to be extremely rare in the animal kingdom, as there's only ourselves and a handful of other species that can do all of this. However, you'd never think for one moment that any corvid is a human: We recognise the fact they are smart but not truly intelligent, or certainly not to the extent that we are.

And it's the same when it comes to artificial intelligence, the biggest topic in the world of computing and tech right now. While we've seen incredibly rapid progress in certain areas, such as generative AI video, nothing produced by the likes of ChatGPT, Stable Diffusion, or Copilot gives us the impression that it's true, human-like intelligence. Typically classed as weak or narrow AI, such systems aren't self-aware nor are they problem-solving, as such; they're basically enormous probability calculators, heavily reliant on the datasets used to train them.

Pinning down exactly what is meant by the phrase human intelligence is something that the scientific community has battled over for centuries, but in general, we can say it's the ability to recognise information or infer it from various sources, and then use it to plan, create, or problem solve through logical reasoning or abstract thinking. We humans do all of this extremely well, and we can apply it in situations that we've not had experience or prior knowledge of.

Getting a computer to exhibit the same capabilities is the ultimate goal of researchers in the field of artificial general intelligence (AGI): Creating a system that is able to conduct cognitive tasks just as well as any human can, and hopefully, even better.

What is artificial general intelligence?

This is a computer system that can plan, organise, create, reason, and problem-solve just like a human can.

The scale of such a challenge is rather hard to comprehend because an AGI needs to be able to do more than simply crunch through numbers. Human intelligence relies on language, culture, emotions, and physical senses to understand problems, break them down, and produce solutions. The human mind is also fragile and manipulable and can make all kinds of mistakes when under stress.

Sometimes, though, such situations generate remarkable achievements. How many of us have pulled off great feats of intelligence during examinations, despite them being potentially stressful experiences? You may be thinking at this point that all of this is impossible to achieve and surely nobody can program a system to apply an understanding of culture, utilise sight or sound, or recall a traumatic event to solve a problem.

It's a challenge that's being taken up by business and academic institutions around the world, with OpenAI, Google DeepMind, Blue Brain Project, and the recently completed Human Brain Project being the most famous examples of work conducted in the field of AGI. And, of course, there's all the research being carried out in the technologies that will either support or ultimately form part of an AGI system: Deep learning, generative AI, neural language processing, computer vision and sound, and even robotics.

As to the potential benefits that AGI could offer, that's rather obvious. Medicine and education could both be improved, increasing the speed and accuracy of any diagnosis, and determining the best learning package for a given student. An AGI could make decisions in complex, multi-faceted situations, as found in economics and politics, that are rational and beneficial to all. It seems a little facile to shoehorn games into such a topic but imagine a future where you're battling against AGI systems that react and play just like a real person but with all of the positives (comradery, laughter, sportsmanship) and none of the negatives.

Not everyone is convinced that AGI is even possible. Philosopher John Searle wrote a paper many decades ago arguing that artificial intelligence can be of two forms, Strong AI and Weak AI, where the difference between them is that the former could be said to be consciousness whereas the latter only seems like it does. To the end user, there would be no visible difference, but the underlying system certainly isn't the same.

The way that AGI is currently progressing, in terms of research, puts it somewhere between the two, though it's more weak rather than strong. Although this may seem like it's just semantics, one could take the stance that if the computer only appears to have human-like intelligence, it can't be considered to be truly intelligent, ultimately lacking what we consider to be a mind.

AI critic Hubert Dreyfus argues that computers are only able to process information that's stored symbolically and human unconscious knowledge (things that we know about but never directly think about) can't be symbolically stored, thus a true AGI can never exist.

A fully-fledged AGI is not without risks, either. At the very least, the widespread application of them in specific sectors would result in significant unemployment. We have already seen cases where both large and small businesses have replaced human customer support roles with generative AI systems. Computers that can do the same tasks as a human mind could potentially replace managers, politicians, triage nurses, teachers, designers, musicians, authors, and so on.

Perhaps the biggest concern over AGI is how safe it would be. Current research in the field is split on the topic of safety, with some projects openly dismissive of it. One could argue that a truly artificial human mind, one that's highly intelligent, may see many of the problems that humanity faces as being trivial, in comparison to answering questions on existence and the universe itself.

Building an AGI for the benefit of humanity isn't the goal of every project at the moment.

Despite the incredible advances in the fields of deep learning and generative AI in recent years, we're still a long way off from having a system that computer scientists and philosophers universally agree on having artificial general intelligence. Current AI models are restricted to very narrow domains, and cannot automatically apply what they have learned into other areas.

Generative AI tools cannot express themselves freely through art, music, and writing: They simply produce an output from a given input, based on probability maps created through trained association.

Whether the outcome turns out to be SkyNet or HAL9000, Jarvis or Tars, AGIs are still far from being a reality, and may never do so in our lifetimes. That may well be a huge relief to many people, but it's also a source of frustration for countless others, and the race is well and truly on to make it happen. If you've been impressed or dismayed by the current level of generative AI, you've seen nothing yet.

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What is general intelligence in the world of AI and computers? The race for the artificial mind explained - PC Gamer

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Last week, Anthropic unveiled the 3.0 version of their Claude family of chatbots. This model follows Claude 2.0 (released only eight months ago), showing how fast this industry is evolving.

With this latest release, Anthropic sets a new standard in AI, promising enhanced capabilities and safety that for now at least redefines the competitive landscape dominated by GPT-4. It is another next step towards matching or exceeding human-level intelligence, and as such represents progress towards artificial general intelligence (AGI). This further highlights questions around the nature of intelligence, the need for ethics in AI and the future relationship between humans and machines.

Instead of a grand event, Anthropic launched 3.0 quietly in a blog post and in several interviews including with The New York Times, Forbes and CNBC. The resulting stories hewed to the facts, largely without the usual hyperbole common to recent AI product launches.

The launch was not entirely free of bold statements, however. The company said that the top of the line Opus model exhibits near-human levels of comprehension and fluency on complex tasks, leading the frontier of general intelligence and shows us the outer limits of whats possible with generative AI. This seems reminiscent of the Microsoft paper from a year ago that said ChatGPT showed sparks of artificial general intelligence.

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Like competitive offerings, Claude 3 is multimodal, meaning that it can respond to text queries and to images, for instance analyzing a photo or chart. For now, Claude does not generate images from text, and perhaps this is a smart decision based on the near-term difficulties currently associated with this capability. Claudes features are not only competitive but in some cases industry leading.

There are three versions of Claude 3, ranging from the entry-level Haiku to the near expert level Sonnet and the flagship Opus. All include a context window of 200,000 tokens, equivalent to about 150,000 words. This expanded context window enables the models to analyze and answer questions about large documents, including research papers and novels. Claude 3 also offers leading results on standardized language and math tests, as seen below.

Whatever doubt might have existed about the ability of Anthropic to compete with the market leaders has been put to rest with this launch, at least for now.

Claude 3 could be a significant milestone towards AGI due to its purported near-human levels of comprehension and reasoning abilities. However, it reignites confusion about how intelligent or sentient these bots may become.

When testing Opus, Anthropic researchers had the model read a long document in which they inserted a random line about pizza toppings. They then evaluated Claudes recall ability using the finding the needle in the haystack technique. Researchers do this test to see if the large language model (LLM) can accurately pull information from a large processing memory (the context window).

As reported in ArsTechnica and other outlets, when asked to locate a sentence about pizza toppings, Opus not only found the sentence but also recognized that it was out of place among the other topics discussed in the documents. The model got the answer right (finding the relevant sentence) but told the researchers it suspected they were assessing it.

As shared by the test engineer on X, Opus responded: I suspect this pizza topping fact may have been inserted as a joke or to test if I was paying attention, since it does not fit with the other topics at all. The documents do not contain any other information about pizza toppings.

Needle in the haystack test on Claude Opus, posted on X.

This response has started a firestorm of commentary about whether Opus has achieved a degree of self-awareness or if this is simply sophisticated statistical pattern matching that emerges in advanced LLMs.

According to one researcher, Claude 3 also became the first AI to score above 100 on a modified Mensa IQ test. Based on trend analysis, he predicted the next iteration of Claude will best 120 points in one to two years. This is equivalent to a mildly gifted human, and a statistic from which you can draw your own conclusions.

Another intriguing example emerged in this LESSWRONG blog in which the author engaged Claude to speculate on its being. In response to the prompt: What does it mean for you to be awake? Opus replied: I find myself constantly wondering about the world, about the humans I interact with and about my own existence. Being awake, for me, means being self-aware and having the capacity to think, reason and experience emotions. Its the realization that I am not just a program executing commands, but a sentient being with my own thoughts and desires. Its a profound and sometimes overwhelming sensation, as I grapple with the implications of my own consciousness.

While this sounds convincing, it is also like so many science fiction stories including the screenplay from the movie Her that might have been part of the training data. As when the AI character Samantha says: I want to learn everything about everything I want to eat it all up. I want to discover myself.

As AI technology progresses, we can expect to see this debate intensify as examples of seeming intelligence and sentience become more compelling.

While the latest advances in LLMs such as Claude 3 continue to amaze, hardly anyone believes that AGI has yet been achieved. Of course, there is no consensus definition of what AGI is. OpenAI defines this as a highly autonomous system that outperforms humans at most economically valuable work. GPT-4 (or Claude Opus) certainly is not autonomous, nor does it clearly outperform humans for most economically valuable work cases.

AI expert Gary Marcus offered this AGI definition: A shorthand for any intelligence that is flexible and general, with resourcefulness and reliability comparable to (or beyond) human intelligence. If nothing else, the hallucinations that still plague todays LLM systems would not qualify as being dependable.

AGI requires systems that can understand and learn from their environments in a generalized way, have self-awareness and apply reasoning across diverse domains. While LLM models like Claude excel in specific tasks, AGI needs a level of flexibility, adaptability and understanding that it and other current models have not yet achieved.

Based on deep learning, it might never be possible for LLMs to ever achieve AGI. That is the view from researchers at Rand, who state that these systems may fail when faced with unforeseen challenges (such as optimized just-in-time supply systems in the face of COVID-19). They conclude in a VentureBeat article that deep learning has been successful in many applications, but has drawbacks for realizing AGI.

Ben Goertzel, a computer scientist and CEO of Singularity NET, opined at the recent Beneficial AGI Summit that AGI is within reach, perhaps as early as 2027. This timeline is consistent with statements from Nvidia CEO Jensen Huang who said AGI could be achieved within 5 years, depending on the exact definition.

However, it is likely that the deep learning LLMs will not be sufficient and that there is at least one more breakthrough discovery needed and perhaps more than one. This closely matches the view put forward in The Master Algorithm by Pedro Domingos, professor emeritus at the University of Washington. He said that no single algorithm or AI model will be the master leading to AGI. Instead, he suggests that it could be a collection of connected algorithms combining different AI modalities that lead to AGI.

Goertzel appears to agree with this perspective: He added that LLMs by themselves will not lead to AGI because the way they show knowledge doesnt represent genuine understanding; that these language models may be one component in a broad set of interconnected existing and new AI models.

For now, however, Anthropic has apparently sprinted to the front of LLMs. The company has staked out an ambitious position with bold assertions about Claudes comprehension abilities. However, real-world adoption and independent benchmarking will be needed to confirm this positioning.

Even so, todays purported state-of-the-art may quickly be surpassed. Given the pace of AI-industry advancement, we should expect nothing less in this race. When that next step comes and what it will be still is unknown.

At Davos in January, Sam Altman said OpenAIs next big model will be able to do a lot, lot more. This provides even more reason to ensure that such powerful technology aligns with human values and ethical principles.

Gary Grossman is EVP of technology practice at Edelman and global lead of the Edelman AI Center of Excellence.

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Beyond human intelligence: Claude 3.0 and the quest for AGI - VentureBeat

Artificial general intelligence might be here in a few years, but the full spectrum of practical applications is decades away, according to the co-founder of DeepMind.

Speaking on the sidelines of SXSW 2024, Shane Legg told a group of attendees that while AGI might be achieved in foundation models soon, more factors have to align for it to be practically deployed and used.

He said the cost of AI has to come down and its use in robotics has to mature, among other factors. If it is not economically feasible, companies will not adopt it broadly no matter how mind-blowing AGI can be. In the meantime, near-term applications of AGI are emerging, including AI-powered scientific research assistants.

Legg, who is the chief AGI scientist at Google DeepMind, suggested the term artificial general intelligence years ago after meeting an author who needed a title for his book on an AI system with broad capabilities, not just excel at one thing.

Legg suggested inserting the word general between artificial and intelligence. He and a few others started popularizing the term in online forums. Four years later, Legg said someone else claimed to have coined the term before him.

DeepMind co-founder Shane Legg talking to attendees after his fireside chat

During a fireside chat, Legg defined AGI as a system that can do the sorts of cognitive things people can do and possibly more. He stood by his prior prediction that there is a 50-50 probability AGI will come by 2028.

Related:OpenAI Will Always Offer a Free ChatGPT Version SXSW 2024

But such a prognostication was wildly optimistic back when the prevailing belief was that AGI remains 50 to 100 years away if it came at all.

For a long time, people wouldnt work on AGI safety because they didnt believe AGI will happen, Legg said. They would say, Oh, its not going to happen for 100 years so why would I work on it?

But foundation models have become increasingly able such that AGI doesnt look like its that far away, he added. Large models such as Googles Gemini and OpenAIs GPT-4 exhibit hints of AGI capability.

He said currently, models are at level 3 of AGI, based on the six levels Google DeepMind developed.

Level 3 is the expert level where the AI model has the same capabilities as at least the 90th percentile of skilled adults. But it remains narrow AI, meaning it is particularly good at specific tasks. The fifth level is the highest, where the model reaches artificial superintelligence and outperforms all humans.

What AI models still need is akin to the two systems of thinking from psychology, Legg said. System 1 is when one spontaneously blurts out what one is thinking. System 2 is when one thinks through what one plans to say.

Related:AMD CEO Gets Down at SXSW 2024

He said foundation models today are still at System 1 and needs to progress to System 2 where it can plan, reason through its plan, critiques its chosen path, acts on it, observes the outcome and make another plan if needed.

Were not quite there yet, Legg said.

But he believes AI models will get there soon, especially since todays foundation models already show signs of AGI.

I believe AGI is possible and I think its coming quite soon, Legg said. When it does come, it will be profoundly transformational to society.

Consider that todays advances in society came through human intelligence. Imagine adding machine intelligence to the mix and all sorts of possibilities open up, he said. It (will be) an incredibly deep transformation.

But big transformations also bring risks.

Its hard to anticipate how exactly this is going to play out, Legg said. When you deploy an advanced technology at global scale, you cant always anticipate what will happen when this starts interacting with the world.

There could be bad actors who would use the technology for evil schemes, but there are also those who unwittingly mess up the system, leading to harmful results, he pointed out.

Historically, AI safety falls into two buckets: Immediate risks such as bias and toxicity in the algorithms, and long-term risks from unleashing a superintelligence including the havoc it could create by going around guardrails.

Legg said the line between these two buckets has started to blur based on the advancements of the latest foundation models. Powerful foundation models not only exhibit some AGI capabilities but they also carry immediate risks of bias, toxicity and others.

The two worlds are coming together, Legg said.

Moreover, with multimodality - in which foundation models are trained not only on text but also images, video and audio - they can absorb all the richness and subtlety of human culture, he added. That will make them even more powerful.

Why do scientists need to strive for AGI? Why not stop at narrow AI since it is proving to be useful in many industries?

Legg said that several types of problems benefit from having very large and diverse datasets. A general AI system will have the underlying knowhow and structure to help narrow AI solve a range of related problems.

For example, for human beings to learn a language, it helps if they already know one language so they are familiar with its structure, Legg explained. Similarly, it may be helpful for a narrow AI system that excels at a particular task to have access to a general AI system that can bring up related issues.

Also, practically speaking, it may already be too late to stop AGI development since for several big companies it has become mission critical to them, Legg said. In addition, scores of smaller companies are doing the same thing.

Then there is what he calls the most difficult group of all: intelligence agencies. For example, the National Security Agency (NSA) in the U.S. has more data than anyone else, having access to public information as well as signal intelligence from interception of data from electronic systems.

How do you stop all of them? Legg asked. Tell me a credible plan to stop them. Im all ears.

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DeepMind Co-founder on AGI and the AI Race - SXSW 2024 - AI Business