**Date Created** — 20230821- **Author | Presenter** — **Recommended by** — Resource - [How researchers are teaching AI to learn like a child](https://www.science.org/content/article/how-researchers-are-teaching-ai-learn-child) ## My Notes ## Quotes - “In the past few years, AI has shown that it can translate speech, diagnose cancer, and beat humans at poker. But for every win, there is a blunder. **Image recognition algorithms can now distinguish dog breeds better than you can, yet they sometimes mistake a chihuahua for a blueberry muffin**. AIs can play classic Atari video games such as *Space Invaders* with superhuman skill, but when you remove all the aliens but one, the AI falters inexplicably. Machine learning—one type of AI—is responsible for those successes and failures. Broadly, AI has moved from software that relies on many programmed rules (also known as Good Old-Fashioned AI, or GOFAI) to systems that learn through trial and error. Machine learning has taken off thanks to powerful computers, big data, and advances in algorithms called neural networks. Those networks are collections of simple computing elements, loosely modeled on neurons in the brain, that create stronger or weaker links as they ingest training data.” - “Yet systems such as Alpha clearly are not extracting the lessons that lead to common sense. To play Go on a 21-by-21 board instead of the standard 19-by-19 board, the AI would have to learn the game anew. In the late 1990s, Marcus trained a network to take an input number and spit it back out—about the simplest task imaginable. But he trained it only on even numbers. When tested with odd numbers, the network floundered. It couldn't apply learning from one domain to another, the way Chloe had when she began to build her Lego sideways.” - “The answer is not to go back to rule-based GOFAIs. A child does not recognize a dog with explicit rules such as "if number of legs=4, and tail=true, and size>cat." **Recognition is more nuanced—a chihuahua with three legs won't slip past a 3-year-old**. Humans are not blank slates, nor are we hardwired. Instead, the evidence suggests we have predispositions that help us learn and reason about the world. Nature doesn't give us a library of skills, just the scaffolding to build one.” --- **Tags** — [[literature-notes]], [[artificial-intelligence]] , [[ai-problems]] ,