Within the next decade, the world will see a major disruption of the workforce due to advances in artificial intelligence (AI) technology. According to a McKinsey Global Institute report, 375 million workers, or about 14 percent of the global workforce, may be required to shift occupations as digitization, automation, and AI technologies start to take over the workspace. In a separate 2018 report by the Organization for Economic Cooperation and Development (OECD), half of the global workforce is expected to be impacted one way or another by machine-learning technologies. AI technology will be at the forefront of the Fourth Industrial Revolution, and it will prove to be a far greater challenge than the ones that preceded it. If the world does not prepare, robots and technology could cause mass unemployment.

The Indian Ministry of Science and Technology said this unique approach uses the predictive power of AI to identify road hazards and a collision warning system to communicate timely alerts to drivers, to make various safety-related improvements. Artificial intelligence (AI)-powered solutions may soon make roads in India a safer place to drive. The Indian government announced on Tuesday that an AI-powered technology could reduce the risk of road accidents in the country, which have killed more than a lakh people in 2020. In a bid to prevent this from happening, the Indian government said the AI approach will use a first-of-its-kind dataset consisting of 10,000 images. He said that this dataset is finely annotated with 34 classes collected from 182 driving sequences on Indian roads obtained from a front-facing camera attached to a car driving through the cities of Hyderabad, Bangalore and their outskirts.

Transfer learning is a promising approach to creating predictive models that incorporate simulation and experimental data into a common framework. In this technique, a neural network is first trained on a large database of simulations, then partially retrained on sparse sets of experimental data to adjust predictions to be more consistent with reality. Previously, this technique has been used to create predictive models of Omega and NIF inertial confinement fusion (ICF) experiments that are more accurate than simulations alone. In this work, we conduct a transfer learning driven hypothetical ICF campaign in which the goal is to maximize experimental neutron yield via Bayesian optimization. The transfer learning model achieves yields within 5% of the maximum achievable yield in a modest-sized design space in fewer than 20 experiments. Furthermore, we demonstrate that this method is more efficient at optimizing designs than traditional model calibration techniques commonly employed in ICF design. Such an approach to ICF design could enable robust optimization of experimental performance under uncertainty.

RapidAI's newly cleared artificial intelligence tool automatically analyzes CT pulmonary angiograms to detect signs of pulmonary embolism, …

Artificial Intelligence (AI) Patent Application filings continue their explosive growth trend at the U.S. Patent Office (USPTO). At the end of 2020, the USPTO published a report finding an exponential increase in the number of patent application filings from 2002 to 2018. In addition, current data shows that AI-related application filings pertaining to graphics and imaging are taking the lead over AI modeling and simulation applications. In the last quarter of 2020, the United States Patent and Trademark Office (USPTO) reported that patent filings for Artificial Intelligence (AI) related inventions more than doubled from 2002 to 2018. See Office of the Chief Economist, Inventing AI: Tracking The Diffusion Of Artificial Intelligence With Patents, IP DATA HIGHLIGHTS No. 5 (Oct.

A new artificial intelligence sleep app has been developed that might be able to replace sleeping pills for insomnia sufferers. Sleepio uses an AI algorithm to provide individuals with tailored cognitive behavioural therapy for insomnia (CBT-I). The National Institute for Health and Care Excellence (Nice) said it would save the NHS money as well as reduce prescriptions of medicines such as zolpidem and zopiclone, which can be dependency forming. Its economic analysis found healthcare costs were lower after one year of using Sleepio, mostly because of fewer GP appointments and sleeping pills prescribed. The app provides a digital six-week self-help programme involving a sleep test, weekly interactive CBT-I sessions and keeping a diary about sleeping patterns.

Artificial intelligence is not like us. For all of AI's diverse applications, human intelligence is not at risk of losing its most distinctive characteristics to its artificial creations. Yet, when AI applications are brought to bear on matters of national security, they are often subjected to an anthropomorphizing tendency that inappropriately associates human intellectual abilities with AI-enabled machines. A rigorous AI military education should recognize that this anthropomorphizing is irrational and problematic, reflecting a poor understanding of both human and artificial intelligence. The most effective way to mitigate this anthropomorphic bias is through engagement with the study of human cognition -- cognitive science.

"Data is the new oil." Originally coined in 2006 by the British mathematician Clive Humby, this phrase is arguably more apt today than it was then, as smartphones rival automobiles for relevance and the technology giants know more about us than we would like to admit. Just as it does for the financial services industry, the hyper-digitization of the economy presents both opportunity and potential peril for financial regulators. On the upside, reams of information are newly within their reach, filled with signals about financial system risks that regulators spend their days trying to understand. The explosion of data sheds light on global money movement, economic trends, customer onboarding decisions, quality of loan underwriting, noncompliance with regulations, financial institutions' efforts to reach the underserved, and much more. Importantly, it also contains the answers to regulators' questions about the risks of new technology itself. Digitization of finance generates novel kinds of hazards and accelerates their development. Problems can flare up between scheduled regulatory examinations and can accumulate imperceptibly beneath the surface of information reflected in traditional reports. Thanks to digitization, regulators today have a chance to gather and analyze much more data and to see much of it in something close to real time. The potential for peril arises from the concern that the regulators' current technology framework lacks the capacity to synthesize the data. The irony is that this flood of information is too much for them to handle.

Another month, another flood of weird, wonderful and cute images generated by an artificial intelligence. In April, OpenAI showed off its new picture-making neural network, DALL-E 2, which could produce remarkable high-res images of almost anything it was asked to. Now, just a few weeks later, Google Brain has revealed its own image-making AI, called Imagen. And it performs even better than DALL-E 2: it scores higher on a standard measure for rating the quality of computer-generated images and the pictures it produced were preferred by a group of human judges. But like OpenAI did with DALL-E, Google is going all in on cuteness.

"The attacker is poking at the memory controller, the library door, to say, 'is it busy now?' "We were motivated to undertake this work as there was nothing available that could do this kind of automated detecting and killing on a user's machine in real-time." Existing products, known as endpoint detection and response (EDR), are used to protect end-user devices such as desktops, laptops, and mobile devices and are designed to quickly detect, analyse, block, and contain attacks that are in progress. The main problem with these products is that the collected data needs to be sent to administrators in order for a response to be implemented, by which time a piece of malware may already have caused damage. To test the new detection method, the team set up a virtual computing environment to represent a group of commonly used laptops, each running up to 35 applications at the same time to simulate normal behaviour. The AI-based detection method was then tested using thousands of samples of malware.