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Artificial Intelligence Robots: Why Human Baby Brains Are Smarter Than AI

#artificialintelligence

Machines are capable of understanding speech, recognizing faces and driving cars safely, making recent technological advancements seem impressively powerful. But if the field of artificial intelligence is going to make the transformative leap into building human-like machines, it'll first have to master the way babies learn. "Relatively recently in AI there's been a shift from thinking about designing systems that can do the sort of things that adults can do, to realizing if you want to have systems that are as flexible and powerful and do the kinds of things that adults do, you need to have systems that can learn the way babies and children do," developmental psychologist Alison Gopnik, a researcher at the University of California at Berkeley, told International Business Times. "If you compare what computers can do now to what they could do 10 years ago, they've certainly made a lot of progress, but if you compare them to what a four year old can do, there's still a pretty enormous gap." Babies and children construct theories about the world around them using the same approach scientists use to construct scientific theories.


Artificial Intelligence Robots: Why Human Baby Brains Are Smarter Than AI

International Business Times

Machines are capable of understanding speech, recognizing faces and driving cars safely, making recent technological advancements seem impressively powerful. But if the field of artificial intelligence is going to make the transformative leap into building human-like machines, it'll first have to master the way babies learn. "Relatively recently in AI there's been a shift from thinking about designing systems that can do the sort of things that adults can do, to realizing if you want to have systems that are as flexible and powerful and do the kinds of things that adults do, you need to have systems that can learn the way babies and children do," developmental psychologist Alison Gopnik, a researcher at the University of California at Berkeley, told International Business Times. "If you compare what computers can do now to what they could do 10 years ago, they've certainly made a lot of progress, but if you compare them to what a four year old can do, there's still a pretty enormous gap." Babies and children construct theories about the world around them using the same approach scientists use to construct scientific theories.


Why the future of AI is open source

#artificialintelligence

Artificial general intelligence (AGI), which is the next phase of artificial intelligence, where computers meet and exceed human intelligence, will almost certainly be open source. AGI seeks to solve the broad spectrum of problems that intelligent human beings can solve. This is in direct contrast with narrow AI (encompassing most of today's AI), which seeks to exceed human abilities at a specific problem. Put simply, AGI is all the expectations of AI come true. At a fundamental level, we don't really know what intelligence is and whether there might be types of intelligence that are different from human intelligence.


A Roadmap towards Machine Intelligence

arXiv.org Artificial Intelligence

The development of intelligent machines is one of the biggest unsolved challenges in computer science. In this paper, we propose some fundamental properties these machines should have, focusing in particular on communication and learning. We discuss a simple environment that could be used to incrementally teach a machine the basics of natural-language-based communication, as a prerequisite to more complex interaction with human users. We also present some conjectures on the sort of algorithms the machine should support in order to profitably learn from the environment.


Machine Common Sense Concept Paper

arXiv.org Artificial Intelligence

This paper summarizes some of the technical background, research ideas, and possible development strategies for achieving machine common sense. Machine common sense has long been a critical-but-missing component of Artificial Intelligence (AI). Recent advances in machine learning have resulted in new AI capabilities, but in all of these applications, machine reasoning is narrow and highly specialized. Developers must carefully train or program systems for every situation. General commonsense reasoning remains elusive. The absence of common sense prevents intelligent systems from understanding their world, behaving reasonably in unforeseen situations, communicating naturally with people, and learning from new experiences. Its absence is perhaps the most significant barrier between the narrowly focused AI applications we have today and the more general, human-like AI systems we would like to build in the future. Machine common sense remains a broad, potentially unbounded problem in AI. There are a wide range of strategies that could be employed to make progress on this difficult challenge. This paper discusses two diverse strategies for focusing development on two different machine commonsense services: (1) a service that learns from experience, like a child, to construct computational models that mimic the core domains of child cognition for objects (intuitive physics), agents (intentional actors), and places (spatial navigation); and (2) service that learns from reading the Web, like a research librarian, to construct a commonsense knowledge repository capable of answering natural language and image-based questions about commonsense phenomena.