The past decade, and particularly the past few years, has been transformative for artificial intelligence, not so much in terms of what we can do with this technology as what we are doing with it. Some place the advent of this era to 2007, with the introduction of smartphones. At its most essential, intelligence is just intelligence, whether artifact or animal. It is a form of computation, and as such, a transformation of information. The cornucopia of deeply personal information that resulted from the willful tethering of a huge portion of society to the internet has allowed us to pass immense explicit and implicit knowledge from human culture via human brains into digital form. Here we can not only use it to operate with human-like competence but also produce further knowledge and behavior by means of machine-based computation.
For decades—even prior to the inception of the term—AI has aroused both fear and excitement as humanity contemplates creating machines in our image. This expectation that intelligent artifacts should by necessity be human-like artifacts blinded most of us to the important fact that we have been achieving AI for some time. While the breakthroughs in surpassing human ability at human pursuits, such as chess, make headlines, AI has been a standard part of the industrial repertoire since at least the 1980s. Then production-rule or “expert” systems became a standard technology for checking circuit boards and detecting credit card fraud. Similarly, machine-learning (ML) strategies like genetic algorithms have long been used for intractable computational problems, such as scheduling, and neural networks not only to model and understand human learning, but also for basic industrial control and monitoring.
In the 1990s, probabilistic and Bayesian methods revolutionized ML and opened the door to some of the most pervasive AI technologies now available: searching through massive troves of data. This search capacity included the ability to do semantic analysis of raw text, astonishingly enabling web users to find the documents they seek out of trillions of webpages just by typing only a few words.
AI is core to some of the most successful companies in history in terms of market capitalization—Apple, Alphabet, Microsoft, and Amazon. Along with information and communication technology (ICT) more generally, AI has revolutionized the ease with which people from all over the world can access knowledge, credit, and other benefits of contemporary global society. Such access has helped lead to massive reduction of global inequality and extreme poverty, for example by allowing farmers to know fair prices, the best crops, and giving them access to accurate weather predictions.
Having said this, academics, technologists, and the general public have raised a number of concerns that may indicate a need for down-regulation or constraint. As Brad Smith, the president of Microsoft recently asserted, “Information technology raises issues that go to the heart of fundamental human-rights protections like privacy and freedom of expression. These issues heighten responsibility for tech companies that create these products. In our view, they also call for thoughtful government regulation and for the development of norms around acceptable uses.”
Artificial intelligence is already changing society at a faster pace than we realize, but at the same time it is not as novel or unique in human experience as we are often led to imagine. Other artifactual entities, such as language and writing, corporations and governments, telecommunications and oil, have previously extended our capacities, altered our economies, and disrupted our social order—generally though not universally for the better. The evidence assumption that we are on average better off for our progress is ironically perhaps the greatest hurdle we currently need to overcome: sustainable living and reversing the collapse of biodiversity.
AI and ICT more generally may well require radical innovations in the way we govern, and particularly in the way we raise revenue for redistribution. We are faced with transnational wealth transfers through business innovations that have outstripped our capacity to measure or even identify the level of income generated. Further, this new currency of unknowable value is often personal data, and personal data gives those who hold it the immense power of prediction over the individuals it references.
But beyond the economic and governance challenges, we need to remember that AI first and foremost extends and enhances what it means to be human, and in particular our problem-solving capacities. Given ongoing global challenges such as security, sustainability, and reversing the collapse of biodiversity, such enhancements promise to continue to be of significant benefit, assuming we can establish good mechanisms for their regulation. Through a sensible portfolio of regulatory policies and agencies, we should continue to expand—and also to limit, as appropriate—the scope of potential AI applications.
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