New machine learning method could supercharge battery development for electric vehicles

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Battery performance can make or break the electric vehicle experience, from driving range to charging time to the lifetime of the car. Now, artificial intelligence has made dreams like recharging an EV in the time it takes to stop at a gas station a more likely reality, and could help improve other aspects of battery technology. For decades, advances in electric vehicle batteries have been limited by a major bottleneck: evaluation times. At every stage of the battery development process, new technologies must be tested for months or even years to determine how long they will last. But now, a team led by Stanford professors Stefano Ermon and William Chueh has developed a machine learning-based method that slashes these testing times by 98 percent.


In BJP's Deepfake Video Shared On WhatsApp, Leader Speaks In 2 Languages

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The BJP used artificial intelligence technology to create two deepfake videos of party leader Manoj Tiwari, in which he has been morphed realistically to show him speaking in two languages to appeal to different voter groups ahead of the Delhi assembly election earlier this month. Deepfake uses AI (artificial intelligence) to create morphed videos that seem real; it can even put words in the mouth of an another person. NDTV is one of the leaders in the production and broadcasting of un-biased and comprehensive news and entertainment programmes in India and abroad. NDTV delivers reliable information across all platforms: TV, Internet and Mobile. Follow us on Twitter: https://twitter.com/ndtv


5 priorities for AI

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What do we mean by'Boring AI' and how can your business benefit from it? AI can solve a number of problems but the biggest area keeping the C-Suite awake is how to use automation and AI to generate business value, or new sources of revenue. Our report discusses how'boring' business as usual applications of AI can add more value than deeply technical solutions.


FCA Insight on Twitter

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ICYMI Artificial Intelligence systems need to be transparent, but it's about more than just the technology.


Overcoming the trade-off between quality, speed and cost in software development with AI

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This is usually as true for the delivery of software as it is for anything else, but mounting pressure to digitally transform and continuously deliver updates has made speed a default requirement for most organisations. This leaves a choice between quality and cost, which often comes down to a decision about testing. Testing--especially unit testing--has been an underappreciated stage in the software delivery lifecycle (SDLC) for decades. It's historically been slow, resource-intensive, and less interesting than the development of new features, which may be why the primary motivation to write unit tests for many developers is external pressures, e.g. Within organisations that enforce code coverage targets, mandated manual testing can feel a lot like being told to eat your vegetables because they're good for you.


Artificial Intelligence and Intellectual Property - CEIPI - University of Strasbourg

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CEIPI is pleased to announce the offering of the 3rd edition of the Advanced Training Program on "Artificial Intelligence and Intellectual Property" that will take place in Strasbourg from 23 to 25 April 2020. This new training follows the very successful editions of past years, gathering a high number of professionals coming from almost all the European countries, and as far as Brazil, Canada, United States, China, India, Malaysia and Japan, and including senior officials from renowned institutions. Artificial Intelligence (AI) and robots have been the subject of science fiction for some time. That fictional future is now a present reality. The regulation of AI's activities is set to become a primary policy issue.


Publications

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Inceoglu I, Thomas G, Chu C, Plans D, Gerbasi A (2018). Leadership behavior and employee well-being: an integrated review and a future research agenda. Lopez D, Brown AW, Plans D. (2019). Modelling and simulation of operation and maintenance strategy for offshore wind farms based on multiagent system. Murphy J, Brewer R, Coll M-P, Plans D, Hall M, Shiu SS, Catmur C, Bird G. (2019).


The Big Discussion: Artificial Intelligence - Part 3

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Field Service organizations need to take a proactive approach to build a platform infrastructure that is capable of capitalizing on AI. This selection is critical to set up early and helps pave the way for the future. Field Service Management platforms that are purpose-built on the cloud and leverage a robust, open API communication structure are best poised to help organizations take advantage of the growing use cases for AI. Anticipating the future needs of the organization and investing appropriately in the current systems is critical. We are just starting to scratch the surface of the potential use cases for AI.


Levels And Limits Of AI

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I recently spoke with the innovation team of a Fortune 50 company about their 2020 initiatives, one of which was artificial intelligence. When I asked what specifically they want to use AI for, an executive replied, "Everything." I pushed a little more asking, "Are there any specific problems that you're seeking AI vendors for?" The reply was something like "We want to use AI in all of our financial services groups." This was particularly unsatisfying considering that the company is a financial services company.


An ASP semantics for Constraints involving Conditional Aggregates

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We elaborate upon the formal foundations of hybrid Answer Set Programming (ASP) and extend its underlying logical framework with aggregate functions over constraint values and variables. This is achieved by introducing the construct of conditional expressions, which allow for considering two alternatives while evaluating constraints. Which alternative is considered is interpretation-dependent and chosen according to an associated condition. We put some emphasis on logic programs with linear constraints and show how common ASP aggregates can be regarded as particular cases of so-called conditional linear constraints. Finally, we introduce a polynomial-size, modular and faithful translation from our framework into regular (condition-free) Constraint ASP, outlining an implementation of conditional aggregates on top of existing hybrid ASP solvers.