Machine Learning is a set of techniques by which a computer system learns how to perform a task through recognizing patterns in data and inferring decision rules, rather than through explicit instructions. Machine learning also refers to the subfield of computer science and statistics studying how to advance those techniques.
Machine learning has led to most of the recent advances in artificial intelligence. These advances have been incorporated into systems used by millions of people every day, such as Google’s search and translate tools, Amazon’s digital assistant Alexa and Netflix’s movie recommendation algorithm. It also includes specialized systems such as AlphaGo, text generators like BERT and GPT-2 and game-playing systems like OpenAI Five, AlphaStar and poker-playing Pluribus.
The essence of machine learning is a system recognizing patterns of relationships between inputs and outputs. For example, the U.S. Postal Service used machine learning to train a system to recognize handwritten zip codes on mail. The system was fed images of handwritten examples paired with the corresponding numbers as typed by humans, so it learned to identify what features were common in handwritten digits and how they varied. Once trained, the system could correctly identify previously unseen examples of handwritten digits.
Not all computing is machine learning and not all artificial intelligence systems that use computing use machine learning. Many computer programs, including most commonly used software, use rule-based systems where programmers set the actions the system should take. However, machine learning is useful for applications where it is difficult for human designers to specify the correct actions to take. For example, IBM’s Deep Blue used a rule-based, exhaustive-search approach to beat the world chess champion. Deep Blue is therefore an example of an AI system not based on machine learning. On the other hand, DeepMind used machine learning to create AlphaGo, an AI system capable of out-performing humans in Go. While it is theoretically possible to solve Go with rule-based algorithms, the search space is so large that winning against a human would have been impossible. Machine learning allowed AlphaGo to infer strategies not yet discovered by humans, leading it to beat the world champion.
—CSET Glossary, “Machine Learning“
The Catholic Church is one of the world’s largest landowners. Using public data and GIS, Molly Burhans is trying to make it a better steward.
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The machine-learning model takes less than a minute to predict future weather worldwide more precisely than other approaches.
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We see a major need for future research regarding functional capabilities and realization technologies for an enterprise data marketplace.
One of the biggest sources of climate uncertainty is how clouds will behave. Caltech physicist Tapio Schneider is trying to give us some answers.
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In this work, we briefly survey the first decade of research in social bot detection. Via a longitudinal analysis, we discuss the main trends of research in the fight against bots, the major results that were achieved, and the factors that make this never-ending battle so challenging. Capitalizing on lessons learned from our extensive analysis, we suggest possible innovations that could give us the upper hand against deception and manipulation. Studying a decade of endeavors in social bot detection can also inform strategies for detecting and mitigating the effects of other—more recent—forms of online deception, such as strategic information operations and political trolls.
A Decade of Social Bot Detection Read More
There have been previous attempts to use machine learning to detect patterns that make one molecule smell like garlic and another like jasmine… Several other teams applied AI to that data and made successful predictions. But Wiltschko’s team took a different approach. They used something called a graph neural network, or GNN.
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Scientists are trying to crack the code of how smell works—and create robots that can sniff out the world’s secrets like a dog.
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Cloud platforms, such as Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform, are tremendously complex. Cloud platforms are also extremely expensive to build and operate, so providers have a strong incentive to optimize their use. A nascent approach is to leverage machine learning (ML) in the platforms’ resource management using supervised learning techniques.
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Communications of the ACM, March 2019
By Judea Pearl
“Unlike the rules of geometry, mechanics, optics, or probabilities, the rules of cause and effect have been denied the benefits of mathematical analysis.” “…the art of automated reasoning.”
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Communications of the ACM, January 2019
By Yolanda Gil, Suzanne A. Pierce, et al.
“Many aspects of geosciences pose novel problems for intelligent systems research… A recently launched Research Coordination Network on Intelligent Systems for Geosciences followed a workshop at the National Science Foundation on this topic. This expanding network builds on the momentum of the NSF EarthCube initiative for geosciences, and is driven by practical problems in Earth, ocean, atmospheric, polar, and geospace sciences. Based on discussions and activities within this network, this article presents a research agenda for intelligent systems inspired by geosciences challenges.”
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Communications of the ACM, June 2018, Vol. 61 No. 6, Pages 13-14
By Chris Edwards
“The secret to deep learning’s success in avoiding the traps of poor local minima may lie in a decision taken primarily to reduce computation time.”
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MIT Technology Review, Feb. 15, 2019
By David Rotman
“In other words, AI’s chief legacy might not be driverless cars or image search or even Alexa’s ability to take orders, but its ability to come up with new ideas to fuel innovation itself.”
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