According to the new market research report "Artificial Intelligence Market by Offering (Hardware, Software, Services), Technology (Machine Learning, Natural Language Processing, Context-Aware Computing, Computer Vision), End-User Industry, and Geography - Global Forecast to 2025", published by MarketsandMarkets, the Artificial Intelligence Market is expected to be valued at USD 21.5 billion in 2018 and is likely to reach USD 190.6 billion by 2025, at a CAGR of 36.6% during the forecast period. Major drivers for the market are growing big data, the increasing adoption of cloud-based applications and services, and an increase in demand for intelligent virtual assistants. The major restraint for the market is the limited number of AI technology experts. Critical challenges facing the AI market include concerns regarding data privacy and the unreliability of AI algorithms. Underlying opportunities in the artificial intelligence market include improving operational efficiency in the manufacturing industry and the adoption of AI to improve customer service.

Companies and public sector organisations say they have no choice but to automate their cyber defences as hacking become increasingly sophisticated. Security professionals can no longer keep pace with the volume and sophistication of attacks on computer systems. In a study of 850 security professionals across 10 countries, more than half said their organisations are overwhelmed with data. So they are turning to machine-learning technologies that can identify cyber attacks by analysing huge quantities of network data and have the potential to block attacks automatically. By 2020, two out of three companies plan to deploy cyber security defences incorporating machine learning and other forms of artificial intelligence (AI), according to the Capgemini study, Reinventing cyber security with artificial intelligence.

#Megvii, a Chinese #AI #startup that supplies facial recognition software for the Chinese government's Wsurveillance program, is expanding its technology beyond humans to recognize different faces of pets.

Elon Musk, the futurist billionaire behind SpaceX and Tesla, outlined his plans to connect humans' brains directly to computers on Tuesday night, describing a campaign to create "symbiosis with artificial intelligence." He said the first prototype could be implanted in a person by the end of next year. Arriving at that goal "will take a long time," Musk said in a presentation at the California Academy of Sciences in San Francisco, noting that securing federal approval for implanted neural devices is difficult. But testing on animals is already underway, and "a monkey has been able to control the computer with his brain," he said. Musk founded Neuralink Corp. in July 2016 to create "ultra-high bandwidth brain-machine interfaces to connect humans and computers."

Inherently biased artificial intelligence programs can pose serious problems for cybersecurity at a time when hackers are becoming more sophisticated in their attacks, experts told CNBC. Bias can occur in three areas -- the program, the data and the people who design those AI systems, according to Aarti Borkar, a vice president at IBM Security. "One is the algorithm itself," she told CNBC, referring to the lines of codes that teach an AI program to carry out specific tasks. "Is it biased in the way it's approached, and the outcome it's trying to solve?" A biased program may end up focusing on the wrong priorities and could miss the real threats, she explained.

Thiel's criticism appears to refer to Google's 2018 decision not to renew its contract with the Department of Defense, which allowed the agency to review drone footage with the company's artificial intelligence tools. The same year, Google faced backlash for working on "Dragonfly," a project to create a censored search engine for China. However, in December, CEO Sundar Pichai announced there were no plans to launch it.

Drone delivery service Wing is launching its own air-traffic control app to keep its craft safe in the skies. The company, owned by Google-parent Alphabet, recently started making deliveries in parts of Australia and Finland. Wing's new iOS and Android app aims to'help users comply with rules and plan flights more safely and effectively,' providing a rundown of airspace restrictions and hazards as well as events nearby that could interfere. The new app, Open Sky, is being released to drone flyers in Australia this month according to Wing. 'The design of our software has required a detailed understanding of flight rules -- along with buildings, roads, trees, and other terrain -- that allow aircraft to navigate safely at low altitudes, and we've used it to complete tens of thousands of flights on three continents,' Wing said in a blog post.

According to the United Nations World Water Assessment Programme, every day, 2 million tons of sewage and industrial and agricultural waste are discharged into the worlds water. In order to address this pervasive issue of increasing water pollution, while ensuring that the global population has an efficient, accurate, and low cost method to assess whether the water they drink is contaminated, we propose AquaSight, a novel mobile application that utilizes deep learning methods, specifically Convolutional Neural Networks, for automated water impurity detection. After comprehensive training with a dataset of 105 images representing varying magnitudes of contamination, the deep learning algorithm achieved a 96 percent accuracy and loss of 0.108. Furthermore, the machine learning model uses efficient analysis of the turbidity and transparency levels of water to estimate a particular sample of waters level of contamination. When deployed, the AquaSight system will provide an efficient way for individuals to secure an estimation of water quality, alerting local and national government to take action and potentially saving millions of lives worldwide.

Statistical models usually require vector representations of categorical variables, using for instance one-hot encoding. This strategy breaks down when the number of categories grows, as it creates high-dimensional feature vectors. Additionally, for string entries, one-hot encoding does not capture information in their representation.Here, we seek low-dimensional encoding of high-cardinality string categorical variables. Ideally, these should be: scalable to many categories; interpretable to end users; and facilitate statistical analysis. We introduce two encoding approaches for string categories: Gamma-Poisson matrix factorization on substring counts, and the min-hash encoder, for fast approximation of string similarities. We show that min-hash turns set inclusions into inequality relations that are easier to learn. Both approaches are scalable and streamable. Experiments on real and simulated data show that these methods improve supervised learning with high-cardinality categorical variables. We recommend the following: if scalability is central, the min-hash encoder is the best option as it does not require any data fit; if interpretability is important, the Gamma-Poisson factorization is the best alternative, as it can be interpreted as one-hot encoding on inferred categories with informative feature names. Both models enable autoML on the original string entries as they remove the need for feature engineering or data cleaning.

According to a new study from the Institute for Public Policy Research (IPPR), nearly 10% of women work in jobs with a high potential for automation, compared with only 4% of men. So what, I hear you say. Substitute "robots" for "austerity", "the demise of unionisation", "public-sector pay freezes", "modern life" – pick any of these and women will always come off worst. Except maybe this time the pointy heads are on to something: perhaps better understanding what the risks are will give us all some agency, and even allow us to change the future. As Carys Roberts, the author of the IPPR report, tells me: "We don't even talk about risks in this area, because there are so many different factors.