Thanks to smartphones and their downsized keyboards, autocomplete has become a nearly ubiquitous feature of how we write these days. To save us precious seconds composing and (at least in my fat-thumbed case) correcting words, our keyboards now prompt us with suggestions of what we're trying to write to get the job done a little bit more easily. But email and messaging composing isn't the only area where artificial intelligence and semantic analytics are being used in this way. Today, a startup that has built a platform that applies the concept to the world of coding is announcing a round of funding to expand its business. Codota, an Israeli startup that provides an AI tool to developers to let them autocomplete strings of code that they are writing -- intended both to speed up their work (it claims to "boost productivity by 25%") and to make sure that it's using the right syntax and'spelled' correctly -- has picked up $12 million, a Series A led by e.ventures, with participation also from previous backer Khosla Ventures, along with new investors TPY Capital and Hetz Ventures.

As deep learning algorithms and artificial intelligence applications become more widely used, concern has been growing about their ethical implications. In particular, questions have emerged both about the trustworthiness of AI algorithms and the risks of relying on them for automated decision-making. How can technologists, regulators, and the general public be confident that AI algorithms are designed and used in ways that are safe and consistent with our values? Although numerous studies have proposed ethical considerations for artificial intelligence, there is currently no standardized framework for putting those values into effect. A new working paper from the AI Ethics Impact Group (AIEIG), however, seeks to change that.

Our 2020 Security Study shows that, when set up and monitored properly, AI and machine learning tools can increase cybersecurity effectiveness for companies both large and small. Organizations adopt AI for many different reasons, primarily to help with cybersecurity analysis or security orchestration, automation, and response (SOAR). Three-quarters of organizations using AI reported a significant improvement of cybersecurity effectiveness, however, due to the complexities of process automation and orchestration in enterprise-scale IT environments, AI adoption often gets impeded. But despite a lack of technological resources when compared to Enterprise organizations, Small and Medium organizations have a strong adoption rate of AI-based SOAR tools. Of the organizations that work with AI tools, 66 percent said they find the tools challenging to configure and use correctly.

AI.Reverie - AI.Reverie is a simulation platform that trains AI to understand the world. They offer a suite of synthetic data and vision APIs to help businesses across different industries train their machine learning algorithms and improve their AI accuracy and repeatability. Key industries AI.Reverie has solutions for including Agriculture, Industrial, including managing construction sites, Smart Cities, and Smart Homes. AI.Reverie has raised a total of $5.6M in funding over three rounds. Their latest funding was raised on Apr 14, 2020.

From detecting illegal stock market activity to increasing safety in self-driving vehicles to improving facial ID recognition, machine learning systems promise to revolutionize a range of security applications--and now a team of University of Maryland computing experts is working to ensure adversaries can't penetrate them. A new $3.1 million grant from the Defense Advanced Research Projects Agency (DARPA) will support efforts to develop safeguards against deception attacks on machine learning algorithms, which let systems learn from data and make decisions with a minimum of human programming. Attacks against these systems are an emerging security threat as artificial intelligence (AI) is further applied to industrial settings, medicine, information analysis and more. "We want to understand the vulnerabilities of these systems, craft defenses that would make it difficult to attack them, and improve the overall security of these systems," said Tom Goldstein, an associate professor of computer science, who is principal investigator of the four-year project. "As far as I'm aware, no one has actually looked at the security of machine learning for those applications," said Goldstein.

Do you remember watching crime shows where investigating teams used to hire sketch artists to draw the image/face of criminal described by witnesses? And they would then hunt for the person to lock him up. But one might wonder today, are these tactics still common in detecting crime or criminals? With the rise in Artificial Intelligence enabled Face and Image Recognition technologies, the days of sketching criminal are long gone. The process of identifying or verifying the identity of a person using their face has made investigations a lot easier today.

A little-known band of doctors and hi-tech wizards say they were able to find the vital speed needed to attack the coronavirus: the computing power of artificial intelligence. They call their new weapon "outbreak science." It could change the way we fight another contagion. Already it has led to calls for an overhaul of how the federal government does things. It's a digital early warning system, and it was among the first to raise alarms about this lethal outbreak.

In the U.S., at least 5,400 nurses, doctors, and other healthcare workers treating COVID-19 (coronavirus) patients have contracted the disease themselves. Of those medical professionals, dozens have died--and that's a conservative estimate. Treating patients with a virus that can live on surfaces for days and propagate through the air in respiratory droplets is dangerous, life-threatening work no matter how you slice it. That's why some Boston-area hospitals have turned to an unlikely assistant: a robotic dog named Spot. "Starting in early March, [we] started receiving inquiries from hospitals asking if our robots could help minimize their staff's exposure to COVID-19," Boston Dynamics, the maker of the robot, said in a blog post Thursday.

Tokyo stocks turned sharply higher Monday as sentiment improved thanks to continued advances on Wall Street. The Nikkei average of 225 selected issues on the first section of the Tokyo Stock Exchange soared 521.22 points, or 2.71 percent, to end at 19,783.22, The Topix index of all first-section issues closed up 25.96 points, or 1.83 percent, at 1,447.25. It lost 4.69 points the previous trading day. From the outset, market participants rushed to buy so-called cyclical stocks vulnerable to economic conditions, particularly technology issues such as industrial robot producer Fanuc and chip-testing device manufacturer Advantest.

As cancer cells spread in a culture dish, Guillaume Jacquemet is watching. The cell movements hold clues to how drugs or gene variants might affect the spread of tumours in the body, and he is tracking the nucleus of each cell in frame after frame of time-lapse microscopy films. But because he has generated about 500 films, each with 120 frames and 200–300 cells per frame, that analysis is challenging to say the least. "If I had to do the tracking manually, it would be impossible," says Jacquemet, a cell biologist at Åbo Akademi University in Turku, Finland. So he has trained a machine to spot the nuclei instead.