An Iranian scientist named by the West as the leader of the Islamic Republic's disbanded military nuclear program was killed Friday in an ambush on the outskirts of Tehran, authorities said. Iran's foreign minister alleged the killing of Mohsen Fakhrizadeh bore "serious indications" of an Israeli role, but did not elaborate. Israel, long suspected of killing several Iranian nuclear scientists a decade ago, declined to immediately comment. Israeli Prime Minister Benjamin Netanyahu once told the public to "remember that name" when talking about Fakhrizadeh. The killing risks further raising tensions across the Mideast, nearly a year after Iran and the U.S. stood on the brink of war when an American drone strike killed a top Iranian general in Baghdad.

In the original "Austin Powers" film, the character Dr. Evil asked for "sharks with frickin' laser beams attached to their heads" in order to kill Austin Powers. While this scene is ridiculous and funny, the concept of using "laser beams" as a weapon is not new. However, with the rise in technology of robotics and AI, The Drive reports that the US government has been working on the'weapons of the future', which seem to be energy-directed weapons such as laser beams. All the microelectronics and iPhones and so forth, were starting to be drilled and the touch screens were being scribed, and all this whole industrial space opened up, and the reason was that these fiber lasers were very efficient at converting electrical power to optical power. The beam quality that came out of the fiber-laser, meaning the ability for that beam to be focusable, to provide a high-intensity spot to do things, like melt metal and drill holes, the beam quality was very high.

ENGLAND - 1958: English Electric developed several notable pioneering computers during the 1950s. The DEUCE took up a huge space compared to modern computers and worked from 1450 thermionic valves which grew hot, blow outs were frequent. However the DEUCE proved a popular innovation and some models were working in to the 1970s. Photograph by Walter Nurnberg who transformed industrial photography after WWII using film studio lighting techniques. When computers were still in the nascent stages, Alan Turing published his legendary paper, "Computing Machinery And Intelligence," in the Mind journal in 1950.

Kubeflow is an open-source project, dedicated to making deployments of ML projects simpler, portable and scalable. The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow. But how do we get started? Do we need a Kubernetes cluster?

TL;DR -- Amidst intentions of generating brilliant statistical analyses and breakthroughs in machine learning, don't get tripped up by these five common mistakes in the Data Science planning process. As a Federal consultant, I work with U.S. government agencies that conduct scientific research, support veterans, offer medical services, and maintain healthcare supply chains. Data Science can be a very important tool to help these teams advance their mission-driven work. I'm deeply invested in making sure we don't waste time and energy on Data Science models that: Based on my experience, I'm sharing hard-won lessons about five missteps in the Data Science planning process -- shortfalls that you can avoid if you follow these recommendations. Just like the visible light spectrum, the work we do as Data Scientists constitutes a small portion of a broader range.

AI Policy Matters is a regular column in the ACM SIGAI AI Matters newsletter featuring summaries and commentary based on postings that appear twice a month in the AI Matters blog. Confusion in the popular media about terms such as algorithm and what constitutes AI technology cause critical misunderstandings among the public and policymakers. More importantly, the role of data is often ignored in ethical and operational considerations. Even if AI systems are perfectly built, low quality and biased data cause unintentional and even intentional hazards. A generative pre-trained transformer GPT-3 is currently in the news.

This piece was originally published in Wired and appears here as part of our Climate Desk Partnership. In early October, a dead Soviet satellite and the abandoned upper stage of a Chinese rocket narrowly avoided a collision in low Earth orbit. If the objects had crashed, the impact would have blown them to bits and created thousands of new pieces of dangerous space debris. Only a few days prior, the European Space Agency had published its annual space environment report, which highlighted abandoned rocket bodies as one of the biggest threats to spacecraft. The best way to mitigate this risk is for launch providers to deorbit their rockets after they've delivered their payload. But if you ask Jeffrey Manber, that's a waste of a perfectly good giant metal tube.

As even the most inattentive observer of contemporary international politics will attest, technological competition – mostly, but not always, between the U.S. and its allies on one hand, and China and Russia on the other – has once again risen to the fore. Analysts, so far, have approached this issue from various angles: what it means in terms of military balances, the possibility of international cooperation, what a technological edge implies for domestic policies, and so on. The outgoing Trump administration has made technological contestation with China a cornerstone of its strategic policy, emphasizing the need for the United States to maintain its edge when it comes to artificial intelligence (AI), quantum information science, and aerospace and other critical technologies, among others. Other Indo-Pacific powers, such as Australia, India, and Japan, have also joined the fray in pushing both new and emerging tech at home as well as promoting collaboration around it between "like-minded countries." In June this year, a Global Partnership on Artificial Intelligence of 14 states along with the European Union was launched, to facilitate collective AI research as well as implementation.

Startups and venture capital are pouring into what might seem an unlikely place: India's vast, outdated agriculture industry. Seizing on controversial new deregulation, entrepreneurs are selling farmers apps to connect them to big buyers nationwide and using artificial intelligence (AI) to improve the rickety supply chains that lose one-fourth of India's produce to wastage. Enormous amounts of India's grain, fruit and vegetables rot between farm and table because of manual handling, repeated loading and unloading, poor inventory management, lack of adequate storage and slow movement of goods. This rate of wastage from faulty supply chains is four to five times that of most large economies, experts say. Prime Minister Narendra Modi's government introduced changes it calls a watershed that will "remove middlemen and let farmers sell their produce directly to buyers," improving their prospects, especially in far flung areas.

A revision to the postal law was enacted Friday, allowing Japan Post Co. to scrap Saturday and next-day deliveries of ordinary mail as early as autumn next year. The bill to amend the postal law was approved at a plenary session of the House of Councilors. The move reflects a decline in mail volumes and is also aimed at improving post office personnel's work conditions. The government had delayed the submission of the bill to the Diet in order to allow the Japan Post Holdings Co. group to prioritize dealing with the issue of irregularities in sales of Kampo postal life insurance products at post offices. The revised law will reduce the frequency of general mail deliveries from at least six days a week to at least five days a week.