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Why I'm Devoting My Life to Machine Learning

Huffington Post - Tech news and opinion

This is the shortest path I see towards machine intelligence: first, we develop ways to allow specialized AIs to manipulate formal concepts, write programs, run experiments, and at the same time develop mathematical intuition (even creativity) about the concepts they are manipulating. Then, we use our findings to develop an AI scientist that would assist us in AI research, as well as other fields. It would be a specialized superhuman artificial intelligence to be applied to scientific research. This would tremendously speed up the development of AI. At first we would apply it to solve well-scoped problems: for instance, developing agents to solve increasingly complex and open-ended games.


Google wants to put machine learning right into your phone

#artificialintelligence

"What Google has been able to achieve with neural networks is providing us with the building blocks for machine intelligence, laying the groundwork for the next decade of how technology will enhance the way people interact with the world," said Blaise Ag?era y Arcas, head of Google's machine intelligence group in Seattle. "By working with Movidius, we're able to expand this technology beyond the data center and out into the real world, giving people the benefits of machine intelligence on their personal devices."


What humans need to learn about machine learning

#artificialintelligence

Artificial intelligence, machine intelligence, cognitive computing -- whatever you want to call machines that are capable of understanding and acting upon their environment -- is no longer solely the purview of highly credentialed lab directors and deep-thinking computer scientists. It has entered mainstream consciousness, and the public expects IT to play a leadership role as machine learning enters our workplaces, our living spaces and our lives. Chances are that you are not. Most executives, in the opinion of New York Timestechnology columnist John Markoff, are "ill prepared for this new world in the making." People have been thinking about automated work forever.


What humans need to learn about machine learning

#artificialintelligence

Artificial intelligence, machine intelligence, cognitive computing -- whatever you want to call machines that are capable of understanding and acting upon their environment -- is no longer solely the purview of highly credentialed lab directors and deep-thinking computer scientists. It has entered mainstream consciousness, and the public expects IT to play a leadership role as machine learning enters our workplaces, our living spaces and our lives. Chances are that you are not. Most executives, in the opinion of New York Timestechnology columnist John Markoff, are "ill prepared for this new world in the making." People have been thinking about automated work forever.


Silicon smarts

AITopics Original Links

When a select band of computer scientists met at Dartmouth College in Hanover, New Hampshire, in 1956 to begin work on a field they called'artificial intelligence', they were optimistic, to say the least. Their founding principle of developing machine intelligence was based on an assumption that human intelligence could itself be well characterized. They argued that: "Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." Ask ten people to define human intelligence and you will get at least eleven answers. To a philosopher, intelligence is the absence of a lack of intelligence.