Machine learning has now entered its business heyday. Almost half of CIOs were predicted to have implemented AI by 2020, a number that is expected to grow significantly in the next five years. Because creating a machine learning model and putting it into operation in an enterprise environment are two very different things. The biggest challenge for companies looking to use AI is operationalizing machine learning, the same way DevOps operationalized software development in the 2000's. Simplifying the data science workflow by providing necessary architecture and automating feature serving with feature stores are two of the most important ways to make machine learning easy, accurate, and fast at scale.

The Food and Drug Administration on Friday issued an emergency authorization for a new test to detect Covid-19 infections -- one that stands apart from the hundreds already authorized. Unlike tests that detect bits of SARS-CoV-2 or antibodies to it, the new test, called T-Detect COVID, looks for signals of past infections in the body's adaptive immune system -- in particular, the T cells that help the body remember what its viral enemies look like. Developed by Seattle-based Adaptive Biotechnologies, it is the first test of its kind. Adaptive's approach involves mapping antigens to their matching receptors on the surface of T cells. They and other researchers had already shown that the cast of T cells floating around in an individual's blood reflects the diseases they've encountered, in many cases years later.

When Krish Ashok, a technologist and food science enthusiast based out of Chennai, shared pictures of his father and great-grandmother, animated through a new artificial intelligence (AI) tool with the senior members in his family, they were deeply moved. "They were shaken by what they saw," Ashok told Zenger News. Deep Nostalgia, which animates old photos, is the latest AI tool that has taken the Internet by storm. But, it has also sparked a conversation about the ethical implications of such tools -- and not everyone is comfortable with it. The video reenactment technology's web-based version was overrun by eager users within a week of its launch on Feb. 25, and the site had to undergo maintenance to keep up.

We've seen widespread disruption, change and uncertainty in every sphere of business. Yet, chaotic, unstable times tend to also bring great leaps forward in terms of technology and innovation. In 2020, I've seen numerous enterprises discover just how much AI and ML tools can help their organization remain stable and even continue to grow despite the turmoil rolling through the markets. But this growth comes with the necessity to assure the health of ML models in production to avoid drifts, biases and anomalies. While AI adoption has taken a giant leap forward, we've learned that ML models need to be adaptable and robust.

NASA's Perseverance took its first drive across the surface of Mars. It was a short jaunt for the car-sized rover, which went about .01 NASA engineers called the drive a success and a'real milestone' for the mission. The debut drive was one of several'firsts' for Perseverance the agency announced Friday, two weeks after the rover landed on the Red Planet. Engineers successfully also tested Perseverance's robotic arm, deployed scientific instruments and completed vital flight software updates. NASA shared images from the Mars Perseverance rover's first drive over the surface of the Red Planet.

UPDATED A new machine learning technique could make it easier for penetration testers to find SQL injection exploits in web applications. Introduced in a recently published paper by researchers at the University of Oslo, the method uses reinforcement learning to automate the process of exploiting a known SQL injection vulnerability. While the technique comes with quite a few caveats and assumptions, it provides a promising path toward developing machine learning models that can assist in penetration testing and security assessment tasks. Reinforcement learning is a branch of machine learning in which an AI model is given the possible actions and rewards of an environment and is left to find the best ways to apply those actions to maximize the reward. "It's inevitable that AI and machine learning are also applied in offensive security," Laszlo Erdodi, lead author of the paper and postdoctoral fellow at the department of informatics at the University of Oslo, told The Daily Swig.

The company sells Medicare Advantage plans, focusing on customer experience and leveraging machine learning and artificial intelligence to …

AI can assist with cybersecurity and aiding financial institutions in terms of fraud prevention. Complex algorithms allow computers to solve problems that were previously only solvable by humans, such as detecting unusual fraud patterns or strange spending fluctuations or with questionable invoices. However, it remains that AI for cybersecurity companies need to be careful to not overly rely on it for their security, or they could be setting themselves up for problems. As an example, firms needs to pay attention to proper implementation of the cognitive capabilities of AI to ensure that it can really detect a threat. However, artificial intelligence does not always work and there remains a learning curve, according to Mike Cutlip, who is the Chief Executive Officer at Authoriti.

China is looking to boost research into what it calls "frontier technology" including quantum computing and semiconductors, as it competes with the U.S. for supremacy in the latest innovations. In its five-year development plan, the 14th of its kind, Beijing said it would make "science and technology self-reliance and self-improvement a strategic pillar for national development," according to a CNBC translation. Premier Li Keqiang said on Friday that China would increase research and development spending by more than 7% per year between 2021 and 2025, in pursuit of "major breakthroughs" in technology. China's technology champions such as Huawei and SMIC have been targeted by U.S. sanctions as tensions between Beijing and Washington have ramped up in the past few years. As such, China has concentrated on boosting its domestic expertise in areas it sees as strategically important, such as semiconductors.

This week the American National Security Commission on artificial intelligence released its final report. Cursory inspection of its 756 pages suggests that it's just another standard product of the military-industrial complex that so worried President Eisenhower at the end of his term of office. On closer examination, however, it turns out to be a set of case notes on a tragic case of what we psychologists call "hegemonic anxiety" – the fear of losing global dominance. The report is the work of 15 bigwigs, led by Dr Eric Schmidt, the former CEO of Alphabet (and before that the adult supervisor imposed by venture capitalists on the young co-founders of Google). Of the 15 members of the commission only four are female.