Data-driven methods have shown potential in electric-vehicle battery management tasks such as capacity estimation, but their deployment is bottlenecked by poor performance in data-limited scenarios. Sharing battery data among algorithm developers can enable accurate and generalizable data-driven models. However, an effective battery management framework that simultaneously ensures data privacy and fault tolerance is still lacking. This paper proposes a swarm battery management system that unites a decentralized swarm learning (SL) framework and credibility weight-based model merging mechanism to enhance battery capacity estimation in data-limited scenarios while ensuring data privacy and security. The effectiveness of the SL framework is validated on a dataset comprising 66 commercial LiNiCoAlO2 cells cycled under various operating conditions. Specifically, the capacity estimation performance is validated in four cases, including data-balanced, volume-biased, feature-biased, and quality-biased scenarios. Our results show that SL can enhance the estimation accuracy in all data-limited cases and achieve a similar level of accuracy with central learning where large amounts of data are available.

From research to diagnosis to treatment, AI has the potential to improve outcomes for some treatments by 30 to 40 percent and reduce costs by up to 50 percent. Although healthcare algorithms are predicted to represent a $42.5B market by 2026, less than 35 algorithms have been approved by the FDA, and only two of those are classified as truly novel.1 Obtaining the large data sets necessary for generalizability, transparency, and reducing bias has historically been difficult and time-consuming, due in large part to regulatory restrictions enacted to protect patient data privacy. That's why the University of California, San Francisco (UCSF) collaborated with Microsoft, Fortanix, and Intel to create BeeKeeperAI. It enables secure collaboration between algorithm owners and data stewards (for example, healthy systems, etc.) in a Zero Trust environment (enabled by Azure Confidential Computing), protecting the algorithm intellectual property (IP) and the data in ways that eliminate the need to de-identify or anonymize Protected Health Information (PHI)--because the data is never visible or exposed. By uncovering powerful insights in vast amounts of information, AI and machine learning can help healthcare providers to improve care, increase efficiency, and reduce costs.

Fortanix Inc., a data-first multicloud security company, today introduced Confidential AI, a new software and infrastructure subscription service promising users the secure use of private data without compromising privacy and compliance. AI modeling relies on accurate complete data sets. Because of privacy laws, data teams instead often use educated assumptions to make AI models as strong as possible. The development of AI applications can be hindered by the inability to use highly sensitive, private data for AI modeling. Fortanix utilizes Intel SGX secure enclaves on Microsoft Azure confidential computing infrastructure to provide trusted execution environments, making AI models more accurate.

From research to diagnosis to treatment, AI has the potential to improve outcomes for some treatments by 30 to 40 percent and reduce costs by up to 50 percent. Although healthcare algorithms are predicted to represent a $42.5B market by 2026, less than 35 algorithms have been approved by the FDA, and only two of those are classified as truly novel.1 Obtaining the large data sets necessary for generalizability, transparency, and reducing bias has historically been difficult and time-consuming, due in large part to regulatory restrictions enacted to protect patient data privacy. That's why the University of California, San Francisco (UCSF) collaborated with Microsoft, Fortanix, and Intel to create BeeKeeperAI. It enables secure collaboration between algorithm owners and data stewards (for example, healthy systems, etc.) in a Zero Trust environment (enabled by Azure Confidential Computing), protecting the algorithm intellectual property (IP) and the data in ways that eliminate the need to de-identify or anonymize Protected Health Information (PHI)--because the data is never visible or exposed. By uncovering powerful insights in vast amounts of information, AI and machine learning can help healthcare providers to improve care, increase efficiency, and reduce costs.

AI is literally shaping every walk of life. Diverse sectors are now sharing the common bedrock of AI integration. AI is evolving at a rapid rate, revolutionizing industries and business organizations. Adhering to this, organizations and companies are strengthening their workforces to make AI-powered deliveries swift and, of course, to renovate them to a great degree possible. Analytics Insight has picked up some most trending AI jobs that passionate tech enthusiasts can try out in May 2021.

Recent scrutiny of artificial intelligence (AI)–based facial recognition software has renewed concerns about the unintended effects of AI on social bias and inequity. Academic and government officials have raised concerns over racial and gender bias in several AI-based technologies, including internet search engines and algorithms to predict risk of criminal behavior. Companies like IBM and Microsoft have made public commitments to "de-bias" their technologies, whereas Amazon mounted a public campaign criticizing such research. As AI applications gain traction in medicine, clinicians and health system leaders have raised similar concerns over automating and propagating existing biases.1 But is AI the problem?

This insight was featured in the November 2019 issue of HealthCare Business News magazine. With over 150 independent software vendors developing machine learning solutions for medical imaging, sorting through the plethora of options to select vendors is a challenge. Here are 10 factors radiologists should consider (and questions they should ask) before partnering with vendors providing AI solutions for medical imaging. The foremost consideration for healthcare providers adopting AI into their clinical workflow is relevancy. Does the AI solution truly address the needs of the healthcare provider, regardless of the associated costs and inconveniences to implement such a solution?

Artificial intelligence continues to evolve, altering the way organizations operate and individuals interact with a business. While this game-changing technology is increasing its dominance in today's world, it is now in high demand as businesses hunt for a competitive edge. Considering reports, demand for AI positions and skills has more than doubled over the past three years. And in India, it is gaining a rapid pace as several big techies are choosing the country for their business growth. Let's have a look at the top AI job titles with the highest pay in India in November 2019.

The second beauty contest, where humans are judged by the robots completes with over six thousand images evaluated by the five robot judges. In addition to the panel of judges from the first contest, Beauty.AI 2.0 featured three new robot judges including: "Average Face" built on the hypothesis that the closer the face is to the average face within the ethnic group, the more attractive it is "AntiAgeist" evaluating the difference between the predicted and actual chronological age "PIMPL" evaluating the number and distribution of pimples and other dark spots (but not freckles) The results were sent to the individual participants via secure link and winners were announced at http://winners2.beauty.ai/#win . The results were surprising, since the consensus scores provided by the robot jury disagreed with human opinion. Tens of participants responded with angry emails criticizing the winners selected by the robot jury. Statements including "what is your "robot" worth??? One walk through a shopping-mall and I will discover more attractive people vs. that ones "won" your Beauty Contest", "If this is how I will be judged in the future, I don't want to see it", "You need human opinion" were among the most pleasant ones with rare positive comments including "this contest is a confidence booster!".

Second beauty contest, where humans are judged by Artificial Intelligence Beauty.AI 2.0 launches. Robot judgement day is set for 01.08.2016. People are encouraged to download the free Beauty.AI 2.0 mobile app in either the Google Play market or Apple App Store and algorithm developers should reach out to the organizers through a form on the http://beauty.ai/ Beauty.AI 1.0 was covered by the leading reporters and journalists at New Scientist, Techcrunch, Cosmopolitan, GQ, Yahoo! Beauty.AI 2.0 will provide valuable prizes for both human competitors and robot jury members.