Book
The myth of artificial intelligence : why computers can't think the way we do
Publication Information
Cambridge, Massachusetts : The Belknap Press of Harvard University Press, 2021.
Physical Description
viii, 312 pages ; 22 cm
Summary
"Futurists are certain that humanlike AI is on the horizon, but in fact engineers have no idea how to program human reasoning. AI reasons from statistical correlations across data sets, while common sense is based heavily on conjecture. Erik Larson argues that hyping existing methods will only hold us back from developing truly humanlike AI"--
Futurists insist that AI will soon eclipse the capacities of the most gifted human mind. What hope do we have against superintelligent machines? But we aren't really on the path to developing intelligent machines. In fact, we don't even know where that path might be. A tech entrepreneur and pioneering research scientist working at the forefront of natural language processing, Erik Larson takes us on a tour of the landscape of AI to show how far we are from superintelligence, and what it would take to get there. Ever since Alan Turing, AI enthusiasts have equated artificial intelligence with human intelligence. This is a profound mistake. AI works on inductive reasoning, crunching data sets to predict outcomes. But humans don't correlate data sets: we make conjectures informed by context and experience. Human intelligence is a web of best guesses, given what we know about the world. We haven't a clue how to program this kind of intuitive reasoning, known as abduction. Yet it is the heart of common sense. That's why Alexa can't understand what you are asking, and why AI can only take us so far. Larson argues that AI hype is both bad science and bad for science. A culture of invention thrives on exploring unknowns, not overselling existing methods. Inductive AI will continue to improve at narrow tasks, but if we want to make real progress, we will need to start by more fully appreciating the only true intelligence we know--our own."--
Contents
- Part One. The simplified world: The intelligence errors
- Turing at Bletchley
- The superintelligence error
- The singularity, then and now
- Natural language understanding
- AI as technological kitsch
- Simplifications and mysteries
- Part Two. The problem of inference: Don't calculate, analyze
- The puzzle of Peirce (and Peirce's Puzzle)
- Problems with deduction and induction
- Machine learning and big data
- Abductive inference
- Inference and language I
- Inference and language II
- Part Three. The future of the myth: Myths and heroes
- AI mythology invades neuroscience
- Neocortical theories of human intelligence
- The end of science?