Amazon has revealed a new tool it's using to ensure warehouse employees keep socially distant during the coronavirus pandemic. The new "Distance Assistant" is a combination television screen, depth sensor, and artificially-intelligent camera that can track workers' movements. As workers move past the camera, a monitor gives visual information to show if employees are within six feet of one another. If they are at a safe distance, they will be enclosed within a green circle on the screen. If they are not, they will be in a red circle.

Mozilla and the Electronic Freedom Foundation (EFF) have presented an open letter to Zoom after it said it would require customers to pay for end-to-end encryption. The letter, signed by over 19,000 internet users, says that "best-in-class security should not be something that only the wealthy or businesses can afford." The video conferencing software saw use boom during the coronavirus pandemic, as did other video calling applications such as Microsoft Teams and Houseparty. However, comments from its CEO Eric Yuan that the company would not encrypt conversations for free users so it can work better with law enforcement raised concerns for user security. The company had also shut down the account of a Tiananmen Square activist, who had a paid account, at the behest of the Chinese government.

Instagram is likely to overtake Twitter for news over the next year, as young people turn to social media for the latest stories, a new report has found. The latest release of the Reuters Institute Digital News Report reveals the rapid changes in the way people are engaging with news stories and the organisations that research and report them. It shows that trust and engagement with traditional news sources is falling, particularly among young people. More and more, they are moving to various platforms, including social networks, it found. It noted that many people were finding their news through these social media sites in entirely different ways, with apps such as Instagram promoting new formats like "stories" and short videos.

Health care languishes in data dissonance. A fundamental imbalance between collection and use persists across systems and geopolitical boundaries. Data collection has been an all-consuming effort with good intent but insufficient results in turning data into action. After a strong decade, the sentiment is that the data is inconsistent, messy, and untrustworthy. The most advanced health systems in the world remain confused by what they've amassed: reams of data without a clear path toward impact.

ALISO VIEJO, Calif., June 15, 2020 -- BrainChip Holdings Ltd announced an Early Access Program (EAP) for the Akida neural processor System-on-Chip (SoC). The EAP has been targeted at specific customers in a diversity of end markets for early adoption of the Akida device. Since announcing the start of wafer fabrication in April 2020, the demand for evaluation systems, including engineering prototypes, has been significant. In response to this demand, BrainChip has established an EAP for select partners to ensure availability of initial devices and evaluation systems for key applications. Multiple customers have committed to the advanced purchase of evaluation systems for a range of strategic Edge applications including ADAS/AV, Unmanned Aerial Vehicles (UAV), Edge vision systems and factory automation.

Have you ever wondered how self-driving cars are running on roads or how Netflix recommends the movies which you may like or how Amazon recommends you products or how Google search gives you such an accurate results or how speech recognition in your smartphone works or how the world champion was beaten at the game of Go? Machine learning is behind these innovations. In the recent times, it has been proven that machine learning and deep learning approach to solving a problem gives far better accuracy than other approaches. This has led to a Tsunami in the area of Machine Learning. Most of the domains that were considered specializations are now being merged into Machine Learning. Every domain of computing such as data analysis, software engineering, and artificial intelligence is going to be impacted by Machine Learning.

The racial profiling and police brutality in the George Floyd incident and #BlackLivesMatter protests and rioting unfolded debate on many levels. One of them is flaws in Artificial Intelligence that end up creating a racial bias in technology which is deemed as an instrumental force to bring digital age. However, all hope is not lost, especially in the Australian start-up sector. Presenting, Akin and Unleash Live, AI-backed companies founded by Liesl Yearsley and Hanno Blankenstein respectively. While Akin, uses AI to build bots that can converse with humans in a lifelike way, Unleash Live employs AI for real-time analysis of video footage coming from security cameras and drones.

Salt Security, a cybersecurity startup developing a threat protection solution that discovers APIs and detects vulnerabilities, has raised $20 million. It plans to use the new capital to renew its investments in product development and expand its sales and marketing teams. APIs (application programming languages) dictate the interactions between software intermediaries. They define the kinds of calls or requests that can be made, how they're made, the data formats that should be used, and the conventions to follow. And as over 80% of web traffic becomes API traffic, they're coming under threat. Gartner predicts that by 2021, 90% of web apps will have more surface area for attacks in the form of exposed APIs than frontends.

Region-based free energy was originally proposed for generalized belief propagation (GBP) to improve loopy belief propagation (loopy BP). In this paper, we propose a neural network based energy model for inference in general Markov random fields (MRFs), which directly minimizes the region-based free energy defined on region graphs. We term our model Region-based Energy Neural Network (RENN). Unlike message-passing algorithms, RENN avoids iterative message propagation and is faster. Also different from recent deep neural network based models, inference by RENN does not require sampling, and RENN works on general MRFs. RENN can also be employed for MRF learning. Our experiments on marginal distribution estimation, partition function estimation, and learning of MRFs show that RENN outperforms the mean field method, loopy BP, GBP, and the state-of-the-art neural network based model.

The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume, variety, and velocity of water-related data are increasing due to large-scale sensor networks and increased attention to topics such as disaster response, water resources management, and climate change. Combined with the growing availability of computational resources and popularity of deep learning, these data are transformed into actionable and practical knowledge, revolutionizing the water industry. In this article, a systematic review of literature is conducted to identify existing research which incorporates deep learning methods in the water sector, with regard to monitoring, management, governance and communication of water resources. The study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry for generation, prediction, enhancement, and classification tasks, and serves as a guide for how to utilize available deep learning methods for future water resources challenges. Key issues and challenges in the application of these techniques in the water domain are discussed, including the ethics of these technologies for decision-making in water resources management and governance. Finally, we provide recommendations and future directions for the application of deep learning models in hydrology and water resources.