Microsoft got where it is by ensuring that Windows ran on many different types of hardware. Monday, the company said its cloud computing platform will soon offer access to the most exotic hardware of all: quantum computers.
Microsoft is one of several tech giants investing in quantum computing, which by crunching data using strange quantum mechanical processes promises unprecedented computational power. The company is now preparing its Azure cloud computing service to offer select customers access to three prototype quantum computers, from engineering conglomerate Honeywell and two startups, IonQ, which emerged from the University of Maryland, and QCI, spun out of Yale. Read More
Daily Archives: December 4, 2019
NLP Picks Bestsellers – A Lesson in Using NLP for Hidden Feature Extraction
Summary: 99% of our application of NLP has to do with chatbots or translation. This is a very interesting story about expanding the bounds of NLP and feature creation to predict bestselling novels. The authors created over 20,000 NLP features, about 2,700 of which proved to be predictive with a 90% accuracy rate in predicting NYT bestsellers. Read More
Artificial intelligence: How to measure the “I” in AI
This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI.
Last week, Lee Se-dol, the South Korean Go champion who lost in a historical matchup against DeepMind’s artificial intelligence algorithm AlphaGo in 2016, declared his retirement from professional play.
“With the debut of AI in Go games, I’ve realized that I’m not at the top even if I become the number one through frantic efforts,” Lee told the Yonhap news agency. “Even if I become the number one, there is an entity that cannot be defeated.” Read More
On the Measure of Intelligence
To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate intelligence in a way that enables comparisons between two systems,as well as comparisons with humans. Over the past hundred years, there has been an abundance of attempts to define and measure intelligence, across both the fields of psychology and AI. We summarize and critically assess these definitions and evaluation approaches,while making apparent the two historical conceptions of intelligence that have implicitly guided them. We note that in practice, the contemporary AI community still gravitates to-wards benchmarking intelligence by comparing the skill exhibited by AIs and humans at specific tasks, such as board games and video games. We argue that solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience: unlimited priors or unlimited training data allow experimenters to “buy” arbitrary levels of skills for a system, in a way that masks the system’s own generalization power. We then articulate a new formal definition of intelligence based on Algorithmic Information Theory, describing intelligence as skill-acquisition efficiency and highlighting the concepts of scope,generalization difficulty,priors, and experience, as critical pieces to be accounted for in characterizing intelligent systems. Using this definition, we propose a set of guidelines for what a general AI benchmark should look like.Finally, we present a new benchmark closely following these guidelines, the Abstraction and Reasoning Corpus (ARC), built upon an explicit set of priors designed to be as close as possible to innate human priors. We argue that ARC can be used to measure a human-like form of general fluid intelligence and that it enables fair general intelligence comparisons between AI systems and humans. Read More
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