Subcommittee on Research and Technology and Subcommittee on Energy Hearing - Artificial Intelligence - June 26th, 2018 Dr. Tim Persons, chief scientist, GAO Mr. Greg Brockman, co-founder and chief technology officer, OpenAI Dr. Fei-Fei Li, chairperson of the board and co-founder, AI4ALL OpenAI was founded by Elon Musk and Sam Altman

Finally, they tested the systems. In some cases, they used test problems with the same abstract factors as the training set -- like both training and testing the AI on problems that required it to consider the number of shapes in each image. In other cases, they used test problems incorporating different abstract factors than those in the training set. For example, they might train the AI on problems that required it to consider the number of shapes in each image, but then test it on ones that required it to consider the shapes' positions to figure out the right answer.

Hundreds of organisations and thousands of techies, including Elon Musk, Demis Hassabis from Google's DeepMind, and the head of the Chocolate Factory's AI lab Jeff Dean have promised never to support the development of autonomous weapons. The pledge was organised by the Future of Life Institute, an outreach geroup focused on tackling existential risks. It was co-founded by a group of researchers, including Max Tegmark, a physics professor at the Massachusetts Institute of Technology, Viktoriya Krakovna, a scientist at DeepMind, and Jann Tallinn, co-founder of Skype. "We will neither participate in nor support the development, manufacture, trade, or use of lethal autonomous weapons," it reads. The promise is based on a "moral component" that machines should be forbidden from making "life-taking decisions."

Today during the Joint Conference on Artificial Intelligence, the Future of Life Institute announced that more than 2,400 individuals and 160 companies and organizations have signed a pledge, declaring that they will "neither participate in nor support the development, manufacture, trade or use of lethal autonomous weapons." The signatories, representing 90 countries, also call on governments to pass laws against such weapons. Google DeepMind and the Xprize Foundation are among the groups who've signed on while Elon Musk and DeepMind co-founders Demis Hassabis, Shane Legg and Mustafa Suleyman have made the pledge as well. The pledge comes as a handful of companies are facing backlash over their technologies and how they're providing them to government agencies and law enforcement groups. Google has come under fire for its Project Maven Pentagon contract, which is providing AI technology to the military in order to help them flag drone images that require additional human review. Similarly, Amazon is facing criticism for sharing its facial recognition technology with law enforcement agencies while Microsoft has been called out for providing services to Immigration and Customs Enforcement (ICE).

One popular test, called Raven's Progressive Matrices, features several rows of images with the final row missing its final image. It's up to the test taker to choose the image that should come next based on the pattern of the completed rows. The test doesn't outright tell the test taker what to look for in the images -- maybe the progression has to do with the number of objects within each image, their color, or their placement. It's up to them to figure that out for themselves using their ability to reason abstractly. To apply this test to AIs, the DeepMind researchers created a program that could generate unique matrix problems.

One popular test, called Raven's Progressive Matrices, features several rows of images with the final row missing its final image. It's up to the test taker to choose the image that should come next based on the pattern of the completed rows. The test doesn't outright tell the test taker what to look for in the images -- maybe the progression has to do with the number of objects within each image, their color, or their placement. It's up to them to figure that out for themselves using their ability to reason abstractly. To apply this test to AIs, the DeepMind researchers created a program that could generate unique matrix problems.

Energy Saving: In 2014, Google acquired an AI startup, DeepMind, to slash costs and improve efficiencies in its data centers. The AI engine automatically managed power usage by discovering and reporting inefficiencies across 120 data center variables - fans, cooling systems, windows etc. The results have been positive. Google has been able to reduce its total data center power consumption by 15 percent, saving the company millions over the next several years. Additionally, the company saved 40 percent alone on power consumed for cooling purposes.

Will artificial intelligences ever be able to match humans in abstract thought, or are they just very fancy number crunchers? Researchers at Google DeepMind are trying to find out by challenging AIs to solve abstract reasoning puzzles similar to those found in IQ tests. If you have ever taken an IQ test, you'll know that one kind of question involves looking at sets of abstract shapes and choosing which should come next in a given the sequence. These puzzles are known as Raven's progressive …

One popular test, called Raven's Progressive Matrices, features several rows of images with the final row missing its final image. It's up to the test taker to choose the image that should come next based on the pattern of the completed rows. The test doesn't outright tell the test taker what to look for in the images -- maybe the progression has to do with the number of objects within each image, their color, or their placement. It's up to them to figure that out for themselves using their ability to reason abstractly. To apply this test to AIs, the DeepMind researchers created a program that could generate unique matrix problems.

Raven's Progressive Matrices are one of the widely used tests in evaluating the human test taker's fluid intelligence. Analogously, this paper introduces geometric generalization based zero-shot learning tests to measure the rapid learning ability and the internal consistency of deep generative models. Our empirical research analysis on state-of-the-art generative models discern their ability to generalize concepts across classes. In the process, we introduce Infinite World, an evaluable, scalable, multi-modal, light-weight dataset and Zero-Shot Intelligence Metric ZSI. The proposed tests condenses human-level spatial and numerical reasoning tasks to its simplistic geometric forms. The dataset is scalable to a theoretical limit of infinity, in numerical features of the generated geometric figures, image size and in quantity. We systematically analyze state-of-the-art model's internal consistency, identify their bottlenecks and propose a pro-active optimization method for few-shot and zero-shot learning.