Image has been cropped for size. Docbot doesn't exactly make the sci-fi physician-replacement robots that its name may imply, but it is making strides towards building more advanced artificial intelligence (AI) tools for the clinical setting. The California startup today unveiled an AI workflow suite for colonoscopies--and it believes it can develop a validated, commercially available version in the next few months. Put simply, Qualoscopy--as the system is called--is part diagnostic support tool, part automated scribe. It applies AI to polyp detection but also integrates in electronic health records (EHR) systems to auto-document procedures and associated quality measures.

NITI Aayog has identified five sectors that includes healthcare, agriculture, education, infrastructure and transportation that can benefit from adoption of artificial intelligence. In its discussion paper on national strategy for artificial intelligence (AI) released on Monday, the Aayog has proposed a two-tier structure to address research in India. According to the Aayog, application of AI to healthcare could increase access and affordability of quality healthcare while it can enhance farmers income, increase productivity and reduce wastage when used in agriculture. "In areas of education AI can improve access and quality of education while improving connectivity and safer modes of transportation when put to infrastructure and transportation sectors," the Aayog said. Aiming to put AI to use for all and across sectors, the Aayog has identified barriers that needs to be addressed to achieve success in the use of AI.

In this year's Financial Budget, Finance Minister had mandated NITI Aayog, a policy think tank of the Government of India, to establish the National Program on AI to guide R&D in frontier technologies. In a result of this mandate, NITI Aayog today presented its discussion paper on National Strategy for Artificial Intelligence (NSAI). In its discussion paper on national strategy for artificial intelligence released today, NITI aayog has identified sectors -- healthcare, agriculture, education, infrastructure and transportation as key five sectors that can benefit from AI adoption. The NSAI paper gives many disruptive suggestion including one to utilize market place models for data, annotation, and deployable solutions in AI. We need to democratise access to and development of this technology.

Japan is aiming to launch self-driving vehicle services on public roads by 2020 when Tokyo hosts the Olympics and Paralympics, according to a draft growth strategy presented Monday. The government set a goal of increasing the number of locations where such services are available to over 100 across the country by 2030, reflecting its ambition to spread the use of autonomous driving technology. The draft strategy, presented to a government panel on future investment, pointed to the need for Japan to make better use of advanced technology such as artificial intelligence and big data. A slow response will leave the country lagging behind in the intensifying global technology race, the strategy document said, adding that advances can be utilized to solve Japan's dual challenges of its aging society and falling birthrate. Automakers and other companies are competing to develop self-driving technology that the government hopes to show off in autonomous vehicles when the Olympics and Paralympics are held in Tokyo.

News reports of May 20, 2018 have reported the government has embarked upon an ambitious defence project for incorporating artificial intelligence (AI) to enhance operational preparedness of the armed forces in a significant way. The project is to include equipping the military with unmanned tanks, vessels, aerial vehicles and robotic weaponry. The DRDO has been talking of preparing the military for robotic warfare for past several years but without much to show on ground other than few applications. DRDO's Centre for Artificial Intelligence & Robotics (CAIR) has developed a range of robots with varied applications, and is also developing: man portable Unmanned Ground Vehicle (UGV) for low intensity conflicts and surveillance in urban scenario; wall climbing and flapping wing robot; walking robot with four and six legs for logistics support; Network Traffic Analysis (NETRA) which can monitor internet traffic. But considering the pace at which developments are taking place, particularly in China in combining robotics and AI, our slow progress in this field is liable to leave us at huge asymmetric disadvantage.

Grab your beverage of choice and enjoy the read! Do hit reply if you're up for a brainstorming session on use cases, new research or ways to future proof your SaaS or enterprise product by implementing ML where it makes sense. On the current "AI revolution": In a lovely piece, Prof. Michael Jordan of Berkeley explores many of the central tenets driving the excitement around AI today. He makes the case for a new engineering discipline, defines the differences between human-imitative AI (i.e. "The current focus on doing AI research via the gathering of data, the deployment of "deep learning" infrastructure, and the demonstration of systems that mimic certain narrowly-defined human skills -- with little in the way of emerging explanatory principles -- tends to deflect attention from major open problems in classical AI. These problems include the need to bring meaning and reasoning into systems that perform natural language processing, the need to infer and represent causality, the need to develop computationally-tractable representations of uncertainty and the need to develop systems that formulate and pursue long-term goals. These are classical goals in human-imitative AI, but in the current hubbub over the "AI revolution," it is easy to forget that they are not yet solved."

In 1997, he was the world's number one-ranked chess grandmaster, and considered to be among the best players of all time. But in May that year, he had a match against a powerful artificial intelligence (AI) system designed by IBM. Most experts assumed that Kasparov would win --chess had long been presumed to be one of those complex intellectual pursuits where humans would always be able to beat machines. But over the course of six hard-fought games, and in front of millions watching on television, IBM's AI software came out on top. I find this moment fascinating -- and I'm slightly ashamed to admit that I've spent hours watching each of the games on YouTube. The reason I'm a bit obsessed is that IBM's victory was something of a watershed moment -- a prelude to the AI era we're entering today, in which software is increasingly capable of carrying out tasks that previously required human-level intelligence to do.

In this April 12, 2018, photo, a visitor watches a Chinese company displaying a Chinese-made industrial robot demonstration on processing soybean at the International soybean exhibition in Shanghai. China says it will narrow its trade surplus with the United States but rejects pressure to change technology development tactics seen as a path to prosperity and its rightful place as a global leader. Beijing highlighted the sensitivity of the issue with its threat to scrap deals aimed at settling a sprawling trade dispute with Washington if President Donald Trump's threatened tariff hike on $50 billion of Chinese technology goes ahead.

A top officer at the technology company IBM gave a presentation at the Elliott School of International Affairs Monday discussing the future of artificial intelligence in the workforce. The event, which featured Martin Fleming, the chief analytics officer and a chief economist at IBM, was the last installation of the Institute for International Economic Policy's 10th-anniversary speaker series, which ran throughout this academic year. Previous speakers included Louise Fox, a chief economist at the United States Agency for International Development and Bob Koopman, a chief economist at the World Trade Organization. Fleming began the event by describing artificial intelligence's increasingly important role in modern society – and its ability to structure typically unstructured data and make predictions about the future. He added that many forms of AI can improve productivity without killing human jobs – one of the most common fears about expanding robotics in the workforce.

Tony Frazier, president of Radiant Solutions, has said the company looks at the potential use of geospatial data to support artificial intelligence-based training efforts in the public sector, SpaceNews reported Saturday. He told the publication in an interview increasing the access to public data can help developers create open-source machine learning algorithms designed to recognize objects present in radar and optical imagery. "Our goal is to make data openly available to facilitate the creation of great algorithms that we can then apply at scale against commercial and government sources," added Frazier, a 2018 Wash100 recipient. Radiant, a business unit of Maxar Technologies, seeks to recruit professionals who possess data science, software development and geospatial analysis skills over the next year in a push to help address a demand for intelligence and mapping services in the defense market. SpaceNews estimates the Herndon, Va.-based contractor's work with the Defense Department, intelligence agencies and the U.S. Special Operations Command accounts for 90 percent of its annual $300 million revenue.