Data Scientists: In many organizations, machine learning engineers and data scientists tend to work alone. That makes some people think that keeping track of their experimentation process is not that important as long as they can deliver that one last model. This is true to an extent, but when you want to come back to an idea, re-run a model from a couple of months ago or simply compare and visualize the differences between runs, the need for a system or tool for tracking ML experiments becomes (painfully) apparent. Teams of Data Scientists: A specialized tool for tracking ML experiments is even more useful for the whole team of data scientists. It allows them to see what others are doing, share the ideas and insights, store experiment metadata, retrieve it at any time and analyze it whenever they need to.

Jyoti Bansal founded AppDynamics, a company that Cisco bought in 2017 for $3.7 billion. He might have been content to rest on that big win, but instead he went on to launch Harness and a venture capital arm, Unusual Ventures. Today, he announced his newest company called Traceable, which attacks security at the code level. Bansal says that security has traditionally looked at protecting the network and hardware, but today the attack surface is more at the software level, and that's why he decided to start another company. "The software is becoming the primary attack vector for a lot of things. If you look at most of the sophisticated data breaches […], they are happening in the code, not in the network or the infrastructure anymore," he explained.

Counterfeiters have leveraged consumer fear and uncertainty created by coronavirus (COVID-19) to flood the market with fakes, misinformation and counterfeits, taking advantage of demand and panic buying for essential goods and services. As one example, social media bot accounts are causing life-threatening coronavirus misinformation to spread across the internet. The Reuters Institute for the Study of Journalism and the Oxford Internet Institute recently released the results of a study that reviewed 225 pieces of COVID-19 misinformation rated false or misleading by fact-checkers. The research found that "false (COVID-19) information spread by politicians, celebrities, and other prominent public figures" accounted for 69% of total engagement on social media, even though their posts made up just 20% of the study's sample. Likewise, counterfeit N95 masks, test kits and ventilator parts have posed challenges for governments across the globe trying to keep their populations safe during COVID-19.

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South Korean tech giant Samsung has started working on the sixth next generation cellular technology and it expects completion of the 6G standard and its earliest commercialisation date could be as early as 2028. Mass commercialisation of 6G may occur around 2030, Samsung said on Tuesday, adding that both humans and machines will be the main users of 6G. Samsung, which released a white paper entitled "The Next Hyper-Connected Experience for All," said 6G will be characterised by provision of advanced services such as truly immersive extended reality (XR), high-fidelity mobile hologram and digital replica. The development comes even as the world is still far from realising the full potential of the fifth generation cellular technology, commonly known as 5G. Samsung said its vision for 6G is to bring the next hyper-connected experience to every corner of life.

Until recently, the use of artificial intelligence (AI) was only possible with great effort and construction of own neural networks. Today, the barrier to entering the world of AI through cloud computing services has fallen dramatically. Thus, one can immediately use current AI technology for the (partial) automation of the quality control of components without having to invest heavily in AI research. In this article, we show how such an AI system can be implemented exemplarily on the Google Cloud Platform (GCP). For this purpose, we train a model using AutoML and integrate it perspectively using Cloud Functions and App Engine into a process where manual corrections in quality control are possible.

MIT researchers have concluded that the well-known ImageNet data set has "systematic annotation issues" and is misaligned with ground truth or direct observation when used as a benchmark data set. "Our analysis pinpoints how a noisy data collection pipeline can lead to a systematic misalignment between the resulting benchmark and the real-world task it serves as a proxy for," the researchers write in a paper titled "From ImageNet to Image Classification: Contextualizing Progress on Benchmarks." "We believe that developing annotation pipelines that better capture the ground truth while remaining scalable is an important avenue for future research." When the Stanford University Vision Lab introduced ImageNet at the Conference on Computer Vision and Pattern Recognition (CVPR) in 2009, it was much larger than many previously existing image data sets. The ImageNet data set contains millions of photos and was assembled over the span of more than two years. ImageNet uses the WordNet hierarchy for data labels and is widely used as a benchmark for object recognition models.

"In today's environment of high trade volumes and increasing regulatory obligations, our customers must rely on solutions that not only identify suspicious activity, but can easily scale to changing trading behaviours without adding headcount," said Nigel Fonseca, Senior Vice President at Alithya. "By hosting AI-FI Trade Surveillance on Microsoft Azure, we have increased our flexibility in configuration, while decreasing implementation time and cost. The advanced security features of Azure ensure that our customer's data is highly secure." The deployment of AI-FI Trade Surveillance at a major Canadian bank has resulted in increased operational effectiveness due to the reduction of false positives by almost 95%. The solution has rules that cover equities, derivatives, fixed Income, and foreign exchange trading throughout Canada, US, APAC, EU and the UK.

AKA, an artificial intelligence development company, announced a function called "Academy Mode", developed for Pepper to serve in the classroom despite the current situation of COVID-19. Academy Mode is designed specifically for the Softbank Robotics Humanoid robot, Pepper, to fit classroom settings and will be released in Korea, Japan, and China's market first. Since entering Japan's market in 2015, AKA has been working together continuously with Softbank Robotics Japan. In May 2019, AKA became an official reseller of Softbank Robotics China for the Pepper robot. Since then, AKA has been actively developing different functions for Pepper to work better in the English education environment.

Since February of last year, tens of thousands of patients hospitalized at one of Minnesota's largest health systems have had their discharge planning decisions informed with help from an artificial intelligence model. But few if any of those patients has any idea about the AI involved in their care. That's because frontline clinicians at M Health Fairview generally don't mention the AI whirring behind the scenes in their conversations with patients. At a growing number of prominent hospitals and clinics around the country, clinicians are turning to AI-powered decision support tools -- many of them unproven -- to help predict whether hospitalized patients are likely to develop complications or deteriorate, whether they're at risk of readmission, and whether they're likely to die soon. But these patients and their family members are often not informed about or asked to consent to the use of these tools in their care, a STAT examination has found.