Estoy intentando leer algunos libros sobre aprendizaje de máquinas que no vayan en la línea habitual de hype y encantamiento o pensamiento mágico.
El primero es Atlas of AI de Kate Crawford del que copio un párrafo de la introducción:
The story of Clever Hans is compelling from many angles: the relationship between desire, illusion, and action, the business of spectacles, how we anthropomorphize the nonhuman, how biases emerge, and the politics of intelligence. Hans inspired a term in psychology for a particular type of conceptual trap, the Clever Hans Effect or observer-expectancy effect, to describe the influence of experimenters’ unintentional cues on their subjects. The relationship between Hans and von Osten points to the complex mechanisms by which biases find their ways into systems and how people become entangled with the phenomena they study. The story of Hans is now used in machine learning as a cautionary reminder that you can’t always be sure of what a model has learned from the data it has been given. Even a system that appears to perform spectacularly in training can make terrible predictions when presented with novel data in the world.
This opens a central question of this book: How is intelligence “made,” and what traps can that create? At first glance, the story of Clever Hans is a story of how one man constructed intelligence by training a horse to follow cues and emulate humanlike cognition. But at another level, we see that the practice of making intelligence was considerably broader. The endeavor required validation from multiple institutions, including academia, schools, science, the public, and the military. Then there was the market for von Osten and his remarkable horse—emotional and economic investments that drove the tours, the newspaper stories, and the lectures. Bureaucratic authorities were assembled to measure and test the horse’s abilities. A constellation of financial, cultural, and scientific interests had a part to play in the construction of Hans’s intelligence and a stake in whether it was truly remarkable.
We can see two distinct mythologies at work. The first myth is that nonhuman systems (be it computers or horses) are analogues for human minds. This perspective assumes that with sufficient training, or enough resources, humanlike intelligence can be created from scratch, without addressing the fundamental ways in which humans are embodied, relational, and set within wider ecologies. The second myth is that intelligence is something that exists independently, as though it were natural and distinct from social, cultural, historical, and political forces. In fact, the concept of intelligence has done inordinate harm over centuries and has been used to justify relations of domination from slavery to eugenics.
Otro párrafo de la introducción:
In the pages of this book you will read about the myth of artificial intelligence. The myth is not that true AI is possible. As to that, the future of AI is a scientific unknown. The myth of artificial intelligence is that its arrival is inevitable, and only a matter of time—that we have already embarked on the path that will lead to human-level AI, and then superintelligence. We have not. The path exists only in our imaginations. Yet the inevitability of AI is so ingrained in popular discussion—promoted by media pundits, thought leaders like Elon Musk, and even many AI scientists (though certainly not all)—that arguing against it is often taken as a form of Luddism, or at the very least a shortsighted view of the future of technology and a dangerous failure to prepare for a world of intelligent machines.
As I will show, the science of AI has uncovered a very large mystery at the heart of intelligence, which no one currently has a clue how to solve. Proponents of AI have huge incentives to minimize its known limitations. After all, AI is big business, and it’s increasingly dominant in culture. Yet the possibilities for future AI systems are limited by what we currently know about the nature of intelligence, whether we like it or not. And here we should say it directly: all evidence suggests that human and machine intelligence are radically different. The myth of AI insists that the differences are only temporary, and that more powerful systems will eventually erase them. Futurists like Ray Kurzweil and philosopher Nick Bostrom, prominent purveyors of the myth, talk not only as if human-level AI were inevitable, but as if, soon after its arrival, superintelligent machines would leave us far behind.
This book explains two important aspects of the AI myth, one scientific and one cultural. The scientific part of the myth assumes that we need only keep “chipping away” at the challenge of general intelligence by making progress on narrow feats of intelligence, like playing games or recognizing images. This is a profound mistake: success on narrow applications gets us not one step closer to general intelligence. The inferences that systems require for general intelligence—to read a newspaper, or hold a basic conversation, or become a helpmeet like Rosie the Robot in The Jetsons—cannot be programmed, learned, or engineered with our current knowledge of AI. As we successfully apply simpler, narrow versions of intelligence that benefit from faster computers and lots of data, we are not making incremental progress, but rather picking low-hanging fruit. The jump to general “common sense” is completely different, and there’s no known path from the one to the other. No algorithm exists for general intelligence. And we have good reason to be skeptical that such an algorithm will emerge through further efforts on deep learning systems or any other approach popular today. Much more likely, it will require a major scientific breakthrough, and no one currently has the slightest idea what such a breakthrough would even look like, let alone the details of getting to it.
Mythology about AI is bad, then, because it covers up a scientific mystery in endless talk of ongoing progress. The myth props up belief in inevitable success, but genuine respect for science should bring us back to the drawing board. This brings us to the second subject of these pages: the cultural consequences of the myth. Pursuing the myth is not a good way to follow “the smart money,” or even a neutral stance. It is bad for science, and it is bad for us. Why? One reason is that we are unlikely to get innovation if we choose to ignore a core mystery rather than face up to it. A healthy culture for innovation emphasizes exploring unknowns, not hyping extensions of existing methods—especially when these methods have been shown to be inadequate to take us much further. Mythology about inevitable success in AI tends to extinguish the very culture of invention necessary for real progress—with or without human-level AI. The myth also encourages resignation to the creep of a machine-land, where genuine invention is sidelined in favor of futuristic talk advocating current approaches, often from entrenched interests.
Who should read this book? Certainly, anyone should who is excited about AI but wonders why it is always ten or twenty years away. There is a scientific reason for this, which I explain. You should also read this book if you think AI’s advance toward superintelligence is inevitable and worry about what to do when it arrives. While I cannot prove that AI overlords will not one day appear, I can give you reason to seriously discount the prospects of that scenario. Most generally, you should read this book if you are simply curious yet confused about the widespread hype surrounding AI in our society. I will explain the origins of the myth of AI, what we know and don’t know about the prospects of actually achieving human-level AI, and why we need to better appreciate the only true intelligence we know—our own.
Efectivamente: una de las razones de mi preocupación con el aprendizaje de máquinas es el discurso irracional y determinista. Otra, esa especie de aura de magia y un «tú no lo entiendes pero la máquina sí» que se ha instalado en la sociedad (o se intenta instalar) como prólogo a una tiranía santurrona de unos pocos.