That awe-inspiring quality was part of what Turkish new media artist Refik Anadol sought to spell out--in a medium of appropriately grand scale--with his new exhibition. The installation, put on by experiential art organization Artechouse, is a two-story empty room in Lower Manhattan bathed from floor to ceiling in high-resolution, laser-projected video. Through reality-bending graphics that ripple across the walls, Machine Hallucinations traces Anadol's own process of training a machine learning system, from data collection to image recognition to a point where the neural network can create its own art, of sorts. "We use this algorithm to narrate the story," Anadol told Adweek. "My personal challenge was, 'How can we learn what machines learn?' So this was a way of putting a camera in the mind of a machine and finding the memory points and connecting them to create a dream."

This presentation was recorded at GOTO Amsterdam 2019. Nell Watson - Founder of QuantaCorp, Engineer, Entrepreneur & Tech Philosopher ABSTRACT The emerging field of machine ethics offers a revolution in how ethical analyses and transactions can occur. By making ethical decisions computable, we can give a sense of morality to machine intelligence, such as autonomous vehicles and personal assistants. Learn how we can best program these fuzzy aspects of'humanity' into machine intelligence, in a way which respects differences of opinion and creeds, and yet provides adequate [...] Download slides and read the full abstract here: https://gotoams.nl/2019/sessions/782 Get your ticket at http://gotocon.com

Artificial intelligence developer DeepMind has just announced its latest milestone: a bot called AlphaStar that plays the popular real-time strategy game StarCraft II at Grandmaster level. This isn't the first time a bot has outplayed humans in a strategy war game. In 1981, a program called Eurisko, developed by artificial intelligence (AI) pioneer Doug Lenat, won the US championship of Traveller, a highly complex strategy war game in which players design a fleet of 100 ships. Eurisko was consequently made an honorary Admiral in the Traveller navy. The following year, the tournament rules were overhauled in an attempt to thwart computers.

I did a double-take last week when Micron Technology, one of the world's largest memory chip makers, acquired artificial intelligence hardware and software startup Fwdnxt. The move could be very interesting. If it bears fruit, Fwdnxt could bring Micron into direct competition with partners such as Intel and Nvidia, as Micron believes that memory and AI computing are converging into the same architecture. But it's no accident that one of the people at Micron in charge of this project is Steve Pawlowski, a former Intel chip architect who holds dozens of patents. Pawlowski is now vice president of advanced computing solutions at Micron.

Just as dawn is breaking on an AI-driven era of unprecedented opportunity, a worldwide recoupling from the American-led strategic and economic architecture is underway. While this is a major shift touching many vital sectors, right now it is most critical in defense technology. This was acutely apparent this summer, as longtime American allies like Turkey forged their own paths with military weapons procurements. In this moment of apparent uncertainty, there is an opportunity to reboot the global "operating system" for global competition in the 21st century. To further draw upon tech-industry parlance, holistic economic and technological development models, and the value chains with which they integrate, are the new geopolitical "killer app" for the coming decades.

The Science Research Council has been receiving an increasing number of applications for research support in the rather broad field with mathematical, engineering and biological aspects which often goes under the general description Artificial Intelligence (AI). The research support applied for is sufficient in volume, and in variety of discipline involved, to demand that a general view of the field be taken by the Council itself. In forming such a view the Council has available to it a great deal of specialist information through its structure of Boards and Committees; particularly from the Engineering Board and its Computing Science Committee and from the Science Board and its Biological Sciences Committee. These include specialised reports on the contribution of AI to practical aims on the one hand and to basic neurobiology on the other, as well as a large volume of detailed recommendations on grant applications. To supplement the important mass of specialist and detailed information available to the Science Research Council, its Chairman decided to commission an independent report by someone outside the AI field but with substantial general experience of research work in multidisciplinary fields including fields with mathematical, engineering and biological aspects. I undertook to make such an independent report, on the understanding that it would simply describe how AI appears to a lay person after two months spent looking through the literature of the subject and discussing it orally and by letter with a variety of workers in the field and in closely related areas of research. Such a personal view of the subject might be helpful to other lay persons such as Council members in the process of preparing to study specialist reports and recommendations and working towards detailed policy formation and decision taking. The report which follows must certainly not be viewed as more than such a highly personal view of the AI field. The author is grateful for the large amount of help and advice readily given in reply to his many requests. He must emphasize, however, that none but himself is responsible for the opinions expressed in this report. They represent mere!y the broad overall view of the subject which he reached after such limited studies as he was able to make in the course of two months. Readers might possibly have expected that the report would include a summary, but the author decided against this partly because considerable material is summarised already in almost every paragraph.

The human brain is often described in the language of tipping points: It toes a careful line between high and low activity, between dense and sparse networks, between order and disorder. Now, by analyzing firing patterns from a record number of neurons, researchers have uncovered yet another tipping point -- this time, in the neural code, the mathematical relationship between incoming sensory information and the brain's neural representation of that information. Their findings, published in Nature in June, suggest that the brain strikes a balance between encoding as much information as possible and responding flexibly to noise, which allows it to prioritize the most significant features of a stimulus rather than endlessly cataloging smaller details. The way it accomplishes this feat could offer fresh insights into how artificial intelligence systems might work, too. A balancing act is not what the scientists initially set out to find. Their work began with a simpler question: Does the visual cortex represent various stimuli with many different response patterns, or does it use similar patterns over and over again?

A report from the Defense Innovation Board recommends artificial intelligence ethic principles and actions for the Defense Department in its research, development and deployment of AI technologies. The report includes five principles and 12 recommendations, which the board created after 15 months of researching and discussing with various DOD officials, academic experts and industry partners. The board, which consists of 16 technologists who advise DOD, voted in favor of the recommendations at a public meeting in Washington, D.C., Thursday. To devise these principles, the board drew from existing laws including the Law of War, international law, the U.S. Constitution and Title 10 of the U.S. Code. Although DOD has other technology ethics codes, its AI Strategy in 2018 called for the creation of an individual set of ethics rules around AI. To realize the proposed principles, DIB formed 12 recommended actions.

Artificial intelligence can share our natural ability to make numeric snap judgments. Researchers observed this knack for numbers in a computer model composed of virtual brain cells, or neurons, called an artificial neural network. After being trained merely to identify objects in images -- a common task for AI -- the network developed virtual neurons that respond to specific quantities. These artificial neurons are reminiscent of the "number neurons" thought to give humans, birds, bees and other creatures the innate ability to estimate the number of items in a set (SN: 7/7/18, p. 7). This intuition is known as number sense.