Kuhn's framework of scientific progress (Kuhn, 1962) provides a useful framing of the paradigm shifts that have occurred in Artificial Intelligence over the last 60 years. The framework is also useful in understanding what is arguably a new paradigm shift in AI, signaled by the emergence of large pre-trained systems such as GPT-3, on which conversational agents such as ChatGPT are based. Such systems make intelligence a commoditized general purpose technology that is configurable to applications. In this paper, I summarize the forces that led to the rise and fall of each paradigm, and discuss the pressing issues and risks associated with the current paradigm shift in AI.

With a low electrical hum, a small team of boxy, wheeled robots called "ants" criss-cross the top of a giant 3D grid of grey storage crates – 60,000 of them - ceaselessly arranging and rearranging them to order. Just one man, jokingly known as the robot whisperer, walks among them with a laptop. It would be hard to conceive of a more vivid example of robots taking on human jobs. "As robot technology advances, we can use them more and more, together with humans, to do useful work, and I think this is the future," says Jeroen Dekker, co-founder of Active Ants, the Dutch firm behind this newly opened e-commerce warehouse outside Northampton. "Yes, some jobs are disappearing, but that's the nasty jobs, for which we cannot find enough people."

While the field of artificial intelligence (AI) has made steady progress over the past few decades, only recently did progress rapidly accelerate, allowing scientific achievements to be translated into real-world use cases. In the last few years, AI has been developing at a consistently rapid pace and has achieved an inflection point. But before we can examine just how AI is revolutionizing our way of life, we must first look at how it got to where it is today. AI had already entered the minds of prominent scientists by the 1950s, as evidenced by Alan Turing's 1950 paper, "Computing Machinery and Intelligence." Between 1957 and 1974, computing capacity advanced to the point where people were able to improve machine learning algorithms and put AI to use.

Much of the U.S. artificial intelligence (AI) discussion revolves around futuristic dreams of both utopia and dystopia. However, it bears remembering that AI is also becoming a real-world economic fact with major implications for national and regional economic development as the U.S. crawls out of the COVID-19 pandemic. Based on advanced uses of statistics, algorithms, and fast computer processing, AI has become a focal point of U.S. innovation debates. Even more, AI is increasingly viewed as the next great "general purpose technology"--one that has the power to boost the productivity of sector after sector of the economy. All of which is why state and city leaders are increasingly assessing AI for its potential to spur economic growth.

Perhaps in no other technology has there been so many decades of large year-over-year improvements as in computing. It is estimated that a third of all productivity increases in the U.S. since 1974 have come from information technology,a,4 making it one of the largest contributors to national prosperity. The rise of computers is due to technical successes, but also to the economics forces that financed them. Bresnahan and Trajtenberg3 coined the term general purpose technology (GPT) for products, like computers, that have broad technical applicability and where product improvement and market growth could fuel each other for many decades. But, they also predicted that GPTs could run into challenges at the end of their life cycle: as progress slows, other technologies can displace the GPT in particular niches and undermine this economically reinforcing cycle. We are observing such a transition today as improvements in central processing units (CPUs) slow, and so applications move to specialized processors, for example, graphics processing units (GPUs), which can do fewer things than traditional universal processors, but perform those functions better. Many high profile applications are already following this trend, including deep learning (a form of machine learning) and Bitcoin mining. With this background, we can now be more precise about our thesis: "The Decline of Computers as a General Purpose Technology." We do not mean that computers, taken together, will lose technical abilities and thus'forget' how to do some calculations.

Artificial intelligence requires carefully considered regulation to ensure technologies balance cooperation and competition for the greater good, according to expert speakers at the AI Summit 2020. As a general purpose technology, artificial intelligence (AI) can be used in a staggering array of contexts, with many advocates framing its rapid development as a cooperative endeavour for the benefit of all humanity. The United Nations, for example, launched it's AI for Good initiative in 2017, while the French and Chinese governments talk of "AI for Humanity" and "AI for the benefit of mankind" respectively – rhetoric echoed by many other governments and supra-national bodies across the world. On the other hand, these same advocates also use language and rhetoric that emphasises the competitive advantages AI could bring in the more narrow pursuit of national interest. "Just as in international politics, there's a tension between an agreed aspiration to build AI for humanity, and for the common good, and the more selfish and narrow drive to compete to have advantage," said Allan Dafoe, director of the Centre for the Governance of AI at Oxford University, speaking at the AI Summit, which took place online this week.

AI’s true potential emerges from its ability to drive transformation across multiple sectors through diverse range of applications. Research suggests that we can best understand the implications of AI by viewing AI as a General Purpose Technology (GPT). AI as a GPT implies, that AI led innovations will be reflected not only as direct contribution in the sectors it is applied to, but also drive complementary innovations and spillover benefits across the economy. Source: Implications of AI on Indian Economy “For more than 250 years the fundamental drivers of economic growth have been technological innovations…… The most important general-purpose technology of our era is artificial intelligence, particularly machine learning.” —Erik Brynjolfsson and Andrew McAfee, 2018 Three critical features characterizing GPTs that AI fulfils: GPTs are pervasive, evolve and improve with time, and play role in enabling innovation. Pervasiveness: AI pans across various “application sectors” including automotive, banking, consumer goods, healthcare, insurance, pharmaceuticals, retail, telecommunications, and transport and logistics sectors, etc., making it pervasive Technical Improvements: Field of AI continues to undergo significant transformations, not just in terms of performance and applicability but also changing trends in various AI techniques like the rise of game theory, machine learning and natural language processing Enabling Innovations: Diffusion of AI has enabled a wide range of activities that were unimaginable before. AI’s predictive capabilities are reducing costs and altering organizational costs across verticals Like other GPTs in the past, the effects of AI will not be fully realized until waves of complementary innovative solutions are developed, implemented and deploying. GPTs have unlocked the growth potential and played a significant role in driving economies. AI fulfils certain fundamental characteristics of GPTS as highlighted above, and thus to is expected to validate the future promise of AI-driven economic growth. When one thinks of AI as a GPT, the implications for output, welfare and productivity gains are huge. Conceptualising AI as a GPT and its adoption will: Drive innovation across sectors Generate social benefits and improve welfare/productivity Result in spillover benefits throughout economy NASSCOM, ICRIER and Google conducted a joint study on Implications of AI on Indian Economy. The report traces the impact of AI on the Indian economy using an econometric model to estimate the impact of GPTs, such as AI, on firm productivity. Watch out for more interesting articles on AI ! References [1] Implications of AI on Indian Economy [2] Facebook AI Research [3] Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics [4] http://ide.mit.edu/sites/default/files/publications/2019-04JCurvebrief.final2_.pdf

What the near future of Artificial Intelligence could be.

To kick off the Future Development blog in 2020, we present the fourth in a four-part series on the future of development. A couple of years ago, Vladimir Putin warned Russians that the country that led in technologies using artificial intelligence will dominate the globe. He was right to be worried. Russia is now a minor player, and the race seems now to be mainly between the United States and China. But don't count out the European Union just yet; the EU is still a fifth of the world economy, and it has underappreciated strengths.

A couple of years ago, Vladimir Putin warned Russians that the country that led in technologies using artificial intelligence will dominate the globe. He was right to be worried. Russia is now a minor player, and the race seems now to be mainly between the United States and China. But don't count out the European Union just yet; the EU is still a fifth of the world economy, and it has underappreciated strengths. Technological leadership will require big digital investments, rapid business process innovation, and efficient tax and transfer systems.