DETROIT (Reuters) - Nvidia is launching new technology to expand its reach in the automotive industry, including a virtual driving assistant that can park a standard car and a system to speed up the training of autonomous vehicles. As part of its fall new product event, centered around its "Omniverse" simulation technology, Nvidia Chief Executive Jensen Huang said the company will begin offering automakers a system called Drive Concierge. It uses Nvidia hardware and artificial intelligence software to provide a variety of services, including an autonomous driving function that allows a human-driven vehicle to park itself. Another extension of the Omniverse strategy will be a system called "Omniverse Replicator" that will simplify and speed up the tasks required to create a virtual world for training automated vehicles. Nvidia, the world's biggest maker of graphics and artificial intelligence chips, has been pushing for years to expand technology originally developed for computer gaming and graphics into the auto industry.

Using artificial intelligence (AI) to identify cancer is an emerging technology. Many research studies have leveraged the power of AI to detect cancer and have demonstrated how the integration of AI technology in cancer care could improve the accuracy and speed of diagnosis, and lead to better health outcomes. Now, researchers from Tulane, Central South University in China, the University of Oklahoma Health Sciences Center, Temple University, and Florida State University have collaborated to determine whether AI could be a tool to help pathologists detect colorectal cancer. Their new study is published in the journal Nature Communications in a paper titled, "Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images." "Colorectal cancer (CRC) is the second most common cause of cancer death in Europe and America," the researchers wrote.

Google is reportedly pursuing a Pentagon contract that plans to use Artificial Intelligence and cloud technology to gain an edge in the battlefield, but the move is almost certainly going to stir a furor among employees and the public. Tech giants are no stranger to government and military contracts. The defense and federal law enforcement agencies have worked with the likes of Amazon, Google, Microsoft, and IBM for years. However, Google's case is slightly different, and there's a lot of ugly history behind it, especially in the past three years. Following vocal protest from employees, the company announced in 2018 that it will not renew its artificial intelligence contract with the U.S. Department of Defense over Project Maven, formally known as the Algorithmic Warfare Cross-Functional Team (AWCFT).

The 18th International Conference for Artificial Intelligence and Law (ICAIL 2021) was organized at the University of São Paulo School of Law, Brazil. ICAIL is a biannual conference organized under the auspices of the International Association for Artificial Intelligence and Law (iaail.org) For the first time, the ICAIL conference was organized entirely online, due to the overall Covid-19 pandemic situation. Despite these unusual circumstances, the conference came out as a considerable success, attracting almost 1400 registered participants, the highest number ever. The conference talks were streamed publicly on the YouTube channel and the discussions and networking were enabled on the platforms accessible for the registered participants.

In July of last year, a DJI Mavic 2 drone approached a Pennsylvania power substation. Two 4-foot nylon ropes dangled from its rotors, a thick copper wire connected to the ends with electrical tape. The device had been stripped of any identifiable markings, as well as its onboard camera and memory card, in an apparent effort by its owner to avoid detection. Its likely goal, according to a joint security bulletin released by DHS, the FBI, and the National Counterterrorism Center, was to "disrupt operations by creating a short circuit." The drone crashed on the roof of an adjacent building before it reached its ostensible target, damaging a rotor in the process.

The most recent generation of predictive language models also appears to learn something about the underlying meaning of language. These models can not only predict the word that comes next, but also perform tasks that seem to require some degree of genuine understanding, such as question answering, document summarization, and story completion. Such models were designed to optimize performance for the specific function of predicting text, without attempting to mimic anything about how the human brain performs this task or understands language. But a new study from MIT neuroscientists suggests the underlying function of these models resembles the function of language-processing centers in the human brain. Computer models that perform well on other types of language tasks do not show this similarity to the human brain, offering evidence that the human brain may use next-word prediction to drive language processing.

The past decade has witnessed the unprecedented march of technology and the opportunities, dangers, and disruptions that accompany it. In the last 4-5 years, a synthetic media technology (that uses machine learning techniques and is created by generative adversarial networks – GANs) known as deep fakes, has revolutionised the ways that digital media can be altered. The ability of state and non-state actors to generate, forge, and manipulate media has created clickbait headlines and fake news, 'terrorised women' by substituting faces to create fake porn, and abetted the spread of misinformation and disinformation. An opinion piece in the Washington Post has called this worrying trend of mass-scale manipulation the "democratisation of forgery". In the last 4-5 years, a synthetic media technology known as deep fakes, has revolutionised the ways that digital media can be altered.

Enterprise deployment of AI and machine learning (ML) for cash flow forecasting is expected to increase 450% over the next two years, according to the recently released 2021 Cash Forecasting & Visibility Survey from GTreasury and Strategic Treasurer. The survey of nearly 250 enterprises across industries highlights a growing appetite for AI/ML modernization among finance and treasury teams seeking more accurate and more immediate cash flow forecasts. To sharpen forecasting capabilities (which are critical for determining business direction and priorities), today's enterprises are embracing new technology strategies and refining methods to introduce greater automation and efficiency. While just 6% of respondents currently use AI/ML technology to predict and understand their cash forecasting, enterprises' reported plans indicate that, within two years, that number will reach 27%. Respondents also indicate a similarly bright trajectory for regression analysis: 12% use it currently, but projected usage will grow to 29% in two years, and 43% use or expect to use it at some point in the future.

Meta is shutting down Facebook's controversial face recognition feature and deleting the face data collected from users through the social media network, citing "growing societal concerns". But privacy campaigners are concerned that the company hasn't been clear on whether the algorithms trained on that data will be deleted. Images uploaded to Facebook have been scanned by artificial intelligence (AI) tools since 2010, giving the uploader the option of "tagging" people in the image. Meta, then known as Facebook itself, attracted criticism when the feature first launched for failing to ask permission from users, and has since struggled to align it with local privacy laws. In 2012, the company switched off face recognition for people in the EU after a German data protection commissioner said that it violated European Union law – it returned in 2018 with an explicit opt-in requirement.