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Artificial intelligence (AI) breakthroughs are accelerating. AI technology can already be found in a wide range of applications, from combating climate change and space exploration to developing cancer therapies and providing real-world navigation for robots. The amount of research papers focused on AI has grown so rapidly in recent years that it seems almost exponential. While we are still some way from widespread AI adoption in all areas of human endeavor, it is safe to say that the technology has now bridged the gap between early adopters of new and little-known products and mass adoption by mainstream users .
The most exciting AI breakthrough of the year is the new category of generative AI based on large language models. A variety of imaging tools appeared almost overnight, including OpenAI’s DALL-E, Google’s Imagen, Stability.ai’s Stable Diffusion, and Midjourney. I wrote a few months ago about the disruptive impact these tools are having on creative professions ranging from digital artists to programmers.
As dramatic as these developments are, perhaps more significant is the new conversational text bot ChatGPT, also from OpenAI and based on GPT-3.5. It was trained on a huge amount of textual data from a variety of online sources. Among other things, it can chat, answer questions, create plays and articles, write and debug code, run tests, manipulate data, give advice, and tutor.
ChatGPT has been widely discussed online, including by veteran reporters Kevin Roose at The New York Times and Derek Thompson at The Atlantic. Thompson calls this and other recent generative AI tools a “second mind for the creative class.” Roose wrote that ChatGPT “is already being compared the iPhone in terms of their potential impact on society.”
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However, ChatGPT is still in its infancy. Almost everyone, including OpenAI, recognizes that the technology is far from a perfect product, as evidenced by the “occasionally incorrect information” it generates. Still, as Jack Clark notes in his Import AI newsletter, “In a few years these systems could be better than humans, which will have wild repercussions.”
AI: New master of strategy
While these notable highlights of generative AI are of tremendous importance, several other recent AI developments could ultimately have even greater impacts on the world. One example is the recent AI defeat of human experts in Stratego, a two-player strategic war game that requires long-term thinking, bluffing, and strategizing. Deep Mind’s DeepNash algorithm, a trained autonomous agent capable of developing human-level expertise, underpins the AI playing Stratego. DeepNash is based on a completely new approach to algorithms using game theory and model-free deep reinforcement learning.
Unlike Chess and Go, Stratego is a game of imperfect information: players cannot directly observe the identity of their opponent’s pieces. It is considered one of the most difficult games due to its seemingly infinite number of possible moves (an amazing 10535), more so than even the notoriously complex Go (10th360). To win, DeepNash blended both long-term strategy and short-term decision making like bluffing and risk-taking, a unique skill for an AI.
As reported by Singularity Hub, the researchers stated, “By creating a generalizable AI system that is robust in the face of uncertainty, we hope to further bring the problem-solving capabilities of AI into our inherently unpredictable world.”
The Art of Diplomacy
Speaking of unpredictability, Meta recently unveiled “Cicero” – an AI system named after the classic statesman and scholar who witnessed the fall of the Roman Republic – that defeated the humans in another strategic wargame, diplomacy.
Unlike Stratego, Chess, or Go – all of which are zero-sum competitions – Diplomacy is cooperative and competitive at the same time. Up to seven players compete against each other, negotiating using deception and cooperation, trust and treachery to form and break alliances for total supremacy. In other words, diplomacy is like real strategic negotiations between multiple competing entities, be they players, corporations or countries. As reported by Gizmodo, “to ‘win’ at Diplomacy [the AI] must both understand the rules of the game efficiently [and] fundamentally understand human interactions, deception and collaboration.”
This rich capability encapsulates what Meta wanted to develop: “Can we build more effective and flexible agents that can use language to negotiate, persuade, and collaborate with humans to achieve strategic goals, much like humans do.” do it?” The company claims that Cicero has achieved more than double the average score of people who play on webDiplomacy.net and is in the top 10% of participants who have played more than one game.
Meta positions Cicero as a research breakthrough that combines two distinct areas of AI: strategic thinking and natural language processing. Three-time world diplomacy champion Andrew Goff says, “Cicero is resilient, ruthless and patient.” He adds, “It makes the best decisions, not just for itself but for the people it works with.”
“Narrow AI” includes algorithms that do just one thing, albeit very well – like making a recommendation for a book you might like based on books you’ve previously viewed on an e-commerce site. A narrow AI algorithm cannot effectively transfer everything it has learned to another algorithm designed to serve a different specific purpose.
The other end of the AI spectrum is referred to as “strong AI” or alternatively artificial general intelligence (AGI). Probably every AI expert would agree that this does not exist today and remains in the realm of science fiction. If AGI is achieved, it would be a single AI system – or possibly a group of interconnected systems – that could be applied to any task or problem, since it can act and think similarly to humans.
Murray Shanahan, professor of cognitive robotics at Imperial College London, said in the Exponential View podcast that AGI is “in some ways as smart as humans and capable of the same degree of generalization that humans are capable of, and share common traits sense that humans have.” That sounds a lot like the capabilities of this new wave of strategy algorithms.
However, there is no uniform AGI definition. For example, Elon Musk doesn’t think ChatGPT is suitable as it didn’t invent anything amazing:
On the way to artificial general intelligence (AGI)
At least by these criteria, ChatGPT is not AGI, and neither are DeepNash or Cicero. What they all have in common, however, is significant progress in this direction. As Stuart Russell, a professor of computer science at the University of California and a leading researcher in the field of artificial intelligence, observes: “You will not be able to pinpoint the actual arrival date of general-purpose AI; it’s not just one day. It’s not all or nothing either. The impact [of AI] will increase. With each advancement of the AI, it expands the range of tasks considerably.”
With each passing year, we can expect much greater capabilities on the road to AGI as these models become more sophisticated and new systems emerge.
Given the pace and scope of recent AI breakthroughs, and the tremendous increase in the body of research, we can expect developments to accelerate and have a profound impact on work and life. For example, within a few years, ChatGPT or a similar system could become an app similar to Samantha in the movie you. ChatGPT is already doing some of what Samantha did: an AI that remembers previous conversations, develops insights based on those discussions, provides useful guidance and therapy, and can do it with thousands of users simultaneously. Or imagine NATO using tools like DeepNash or Cicero with its members or in negotiations with rivals.
We are seeing increasing momentum towards AGI, although experts estimate the timing of its appearance at 2045. AGI or not, AI technology is becoming more sophisticated and ingrained in our lives.
Gary Grossman is Senior VP of Technology Practice at Edelman and Global Head of the Edelman AI Center of Excellence.
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