The description of what the US military wants from the future software is outlined in a request for the submission of white papers published on the Federal Business Opportunities website on Wednesday. The self-improving AI tool is meant to work with text, voice, images and other content on social media in Arabic, French, Pashtu, Farsi, Urdu, Russian and Korean. It should understand colloquial phrasing, spelling variations, social media brevity codes and emojis, and also recognize various dialects. The content will be automatically analyzed for sentiment – at minimum distinguish positive, neutral and negative emotions and preferably tell anger, pleasure, sadness and excitement. It should also have the "capability to suggest whether specific audiences could be influenced based on derived sentiment."

The present global verve about artificial intelligence (AI) and machine learning technologies has resonated in China as much as anywhere on earth. With the State Council's issuance of the "New Generation Artificial Intelligence Development Plan" (新一代人工智能发展规划) on July 20, China's government set out an ambitious roadmap including targets through 2030. Meanwhile, in China's leading cities, flashy conferences on AI have become commonplace. It seems every mid-sized tech company wants to show off its self-driving car efforts, while numerous financial tech start-ups tout an AI-driven approach. Chatbot startups clog investors' date books, and Shanghai metro ads pitch AI-taught English language learning.

With the omnipresence of the term artificial intelligence (AI) and the increased popularity of deep learning, a lot of security practitioners are being lured into believing that these approaches are the magic silver bullet we have been waiting for to solve all of our security challenges. But deep learning -- or any other machine learning (ML) approach -- is just a tool. And it's not a tool we should use on its own. We need to incorporate expert knowledge for the algorithms to reveal actual security insights. Before continuing this post, I will stop using the term artificial intelligence and revert back to using the term machine learning.

The mystery surrounding the fate of a secret military satellite deepened today when the Pentagon refused to answer even simple questions about whether the mission to launch it had gone awry. On Sunday, private space firm SpaceX blasted a Falcon 9 rocket from Cape Canaveral, Florida carrying the secret government satellite, known as Zuma. US media this week reported that the billion-dollar payload did not make it into orbit and was presumed to have been lost. SpaceX said Tuesday that the rocket worked fine, but its statement left open the possibility that something could have gone wrong after the launch. A top secret billion-dollar spy satellite plummeted into the Indian Ocean after a botched SpaceX mission over the weekend, but Elon Musk's company has insisted they are not to blame.

Self-driving cars may be getting all the attention, but the big impact of artificial intelligence and machine learning in the enterprise is in cybersecurity, and especially in securing data center networks. And given all the threats data centers are facing this year the help is much needed. According to a recent survey of 400 security professionals by Wakefield Research and Webroot, a cybersecurity vendor, 99 percent of US respondents believe AI overall could improve their organizations' cybersecurity. And 87 percent report their organizations are already using AI as part of their cybersecurity strategy. In fact, 74 percent of cybersecurity professionals in the US believe that within the next three years their companies will not be able to safeguard digital assets without AI.

Artificial Intelligence: Arterys AI has not had a bad year yet. Between breakthrough technologies and soaring funding rounds, there was no shortage of strong candidates to choose from in 2017. Ambra Health CEO Morris Panner, JD, gave the nod to Arterys. The 10-year-old San Francisco, California, company both started and ended 2017 in style. In January, it received a first-of-its-kind FDA approval for its cloud-based technology, which applies AI and deep learning to medical imaging analysis.

As part of a regular series powered by IBM, BetaKit interviews Canadian tech leaders using innovation for the greater good. In the environmental tech space, common missions revolve around reducing pollution, fixing destroyed habitats, or making the environment better. Less commonly will you find a startup that wants the world to spend less money on solutions, but that's exactly what Ambience Data hopes to do. The company's internet-of-things (IoT) sensors provide hyperlocal air quality data and, through machine-learning algorithms on the cloud, aid in urban development strategy. Instead of trying to fix environmental problems, co-founder and CEO Nisha Sarveswaran hopes that Ambience Data can turn the conversation to investing in prevention.

There has been quite a bit of activity – if not outright hype – regarding the promise of big data and data science. In October, I had a chance to participate in a panel discussion on the promises and perils of data science, in which fellow panelist and digital thought leader Dion Hinchliffe predicted the eventual emergence of "data science as a service." The time is coming when analytics and accompanying insights would be available online, without the need to hire a roomful of PhDs to help make sense of things. This has interesting implications for the insurance industry. Along these lines, I recently heard from Mike de Waal, president and founder of Global IQX, who sees data science as one of the most important developments yet for the industry – particularly an industry that increasingly is looking to data to better serve policyholders in new and innovative ways. Consider the many areas where data is playing a role these days: "telemetry, IoT, wearables, AI, chatbots and drones are tools that help group insurers better engage with customers and improve business processes," according to de Waal.

Decades in the making, Canadian research in fields such as neural networking and machine learning is now coming into its own, as centres like the Montreal Institute for Learning Algorithms and Toronto's Vector Institute for Artificial Intelligence are propelling vibrant ecosystems and startup communities, while government is doing its part through immigration policies geared towards attracting new talent and programs like the Pan-Canadian Artificial Intelligence Strategy, which will devote $125 million in federal dollars to AI research over the next five years. We've seen significant investment from venture capital, too, with the current boom in VC funding for Canadian tech companies having a lot to do with movements in the AI space. And as more and more companies start to incorporate AI technologies, the field keeps growing. But where are the AI investment winners, you ask? Here are five stocks that analysts say have significant upsides.

Drones used to attack two Russian military bases in Syria were so high-tech they were designed to offset jamming technology, were capable of launching precision strikes and could not have been made without foreign assistance, the defence ministry in Moscow has said . The ministry's drone department head Gen Alexander Novikov said the drones used in the weekend's raids differed from the rudimentary craft earlier used by rebels in Syria. The attacks required satellite navigation data that are not available on the internet, complex engineering works and elaborate tests, Gen Novikov said. Analysts say the drones present the biggest military challenge so far to Russia's role in Syria'The creation of drones of such class is impossible in makeshift conditions,' Novikov said. 'Their development and use requires the involvement of experts with special training in the countries that manufacture and use drones.' The ministry said Saturday's raid on the Hemeimeem air base in the province of Lattakia and Russia's naval facility in the port of Tartus involved 13 drones.