Scientists Can Make Neural Networks 90% Smaller

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Artificial Intelligence

Researchers from MIT found a way to create neural networks that are 90% smaller but just as smart.

In a new paper, researchers from MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) have shown that neural networks contain subnetworks that are up to one-tenth the size yet capable of being trained to make equally accurate predictions — and sometimes can learn to do so even faster than the originals.

This article stood out to me because if neural networks can be smaller but just as smart, maybe it could encourage companies to keep machine learning locally on a device, like Apple does.

This Algorithm Could Erase Your Criminal Records

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This month, a California judge erased thousands of criminals records with the help of an algorithm. The creators of it say they’re just getting started.

It discards any record involving a violent crime, as such records do not qualify. For those that remain, the tool automatically fills out the necessary paperwork. In other words, the algorithm replaced the process being done manually at the expungement clinics.

Working with San Francisco’s raw data, Code For America was able to identify 8,132 eligible criminal records in a matter of minutes – in addition to the 1,230 found manually already. They dated as far back as 1975, the year in which the city started digitising its files.

AI, Machine Learning, and Deep Learning Explained

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Which AI is smarter, Siri or Alexa?

We hear the first two terms all the time from Apple. They can be confusing. So, in order to help differentiate between the terms, the TechRepublic has written up a short but helpful tutorial for business people.

The first step is communicating what the definitions are for AI, machine learning (ML), and deep learning. There is some argument that AI, ML, and deep learning are each individual technologies. I view AI/ML/deep learning as successive stages of computer automation and analytics that are built on a common platform.

A traffic planning example makes it clear.

Manage Faces in Apple’s Photos App

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David Murphy has a nice tip out on how to organize photos by Faces on iOS. It’s a great way to manage photos of people.

On the three platforms you’re most likely to use to store your smartphone pictures—Apple Photos, Amazon Photos, and Google Photos—machine learning can categorize your photos by the faces in them, rather than rudimentary details like when or where they were taken.

Introducing the Animal-AI Olympics to Test AI Smarts

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In the Animal-AI Olympics, AI will be given tests originally designed to test animal cognition in a US$10,000 competition.

The Animal-AI Olympics is the creation of a team of researchers at the Leverhulme Centre for the Future of Intelligence in Cambridge, England, along with GoodAI, a Prague-based research institute. The competition is part of a bigger project at the Leverhulme Centre called Kinds of Intelligence, which brings together an interdisciplinary team of animal cognition researchers, computer scientists, and philosophers to consider the differences and similarities between human, animal, and mechanical ways of thinking.

Apple Acquires Machine Learning Startup Laserlike

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Apple has acquired Laserlike, a young startup founded by three former Google engineers. It’s a machine learning startup that could help Apple improve its recommendation algorithms in News, TV, Apple Music, etc (paywall).

An Apple spokesperson confirmed the acquisition of the four-year-old startup, which was founded by three former Google engineers, Anand Shukla, Srinivasan Venkatachary and Steven Baker, and had raised more than $24 million from Redpoint Ventures and Sutter Hill Ventures, according to CrunchBase. Terms of the deal could not be learned.

I look forward to getting better recommendations.

Human War Follows a Universal Mathematical Law

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Here’s something you don’t read every day. A new study of human war over the past 600 years that it appears to follow power law distribution.

The thinking goes like this. Society is a complex web of social, political, and economic forces that depend on the network of links between individuals and the countries they represent. These links are constantly rearranging, sometimes because of violence and death. When the level of rearrangement and associated violence rises above a threshold level, we describe the resulting pattern as war.

The second step is building a machine learning system that can predict when the next large-scale conflict is likely to occur. Or maybe we’ll have dystopian war AIs that will use this information against us.