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Deloitte wins $106 million contract with the Pentagon's AI hub


The Defense Information Systems Agency awarded a $106 million contract to Deloitte Consulting to build the Pentagon's artificial intelligence hub's AI development platform, the U.S. Department of Defense announced Aug. 12. The company will "design and build" the Joint Artificial Intelligence Center's Joint Common Foundation, a capability that DoD AI leadership has stated will be integral in developing, testing and fielding AI capabilities. The contract has a one-year base period worth $31 million with three option years through August 2024. Work is scheduled to start Aug. 17, according to Lt. Cmdr. "The Joint Common Foundation will provide an AI development environment to test, validate and field AI capabilities at scale across the Department of Defense," Abrahamson said. "The impact of the JCF will come from enterprise‐wide access to AI tools and data for AI developers across the Department and its partners that will help synchronize AI projects, reduce development redundancy and enable the broad deployment of AI-enabled solutions to the tactical edge where front line operators can benefit from these capabilities."

Deloitte Awarded $106 Million for Joint Artificial Intelligence Center – Tech Check News


Defense contracts valued at $7 million and above ARMY Moderna TX Inc.,* Cambridge, Massachusetts, was awarded a $1,525,000,000 firm-fixed-price contract for 100 million filled drug production doses of a SARS-CoV-2 mRNA-1273 vaccine. Bids were solicited via the internet with one received. Work will be performed in Cambridge, Massachusetts, with an estimated completion date of March 31, 2022. Fiscal 2020 research, development, test and evaluation (Army) funds in the amount of $1,525,000,000 were obligated at the time of the award. U.S. Army Contracting Command, Aberdeen Proving Ground, Maryland, is the […]

Ibeo's LiDAR systems to provide higher autonomy to autonomous vehicles - Geospatial World


Germany's Ibeo Automotive Systems, which specializes in lidar systems for autonomous driving, has signed a contract to provide China's Great Wall Motor Company (GWM) with its latest solid-state design. Ibeo said that it has commissioned key partner ZF Friedrichschafen – which in 2016 acquired a major stake in Ibeo – to produce the sensors and control unit for the "Level 3" system, which will provide partial autonomy. GWM has contracted one of its own subsidiaries to develop the system, which will be based around vertical cavity surface-emitting lasers (VCSELs) produced by Austria's AMS. Ibeo points out that, after signing a letter of intent in 2019, it has already been in pre-development with GWM for a year. Officially, the project started with the signing of an additional contract by the two parties last month.

Study: Machine learning can predict market behavior


Machine learning can assess the effectiveness of mathematical tools used to predict the movements of financial markets, according to new Cornell research based on the largest dataset ever used in this area. The researchers' model could also predict future market movements, an extraordinarily difficult task because of markets' massive amounts of information and high volatility. "What we were trying to do is bring the power of machine learning techniques to not only evaluate how well our current methods and models work, but also to help us extend these in a way that we never could do without machine learning," said Maureen O'Hara, the Robert W. Purcell Professor of Management at the SC Johnson College of Business. O'Hara is co-author of "Microstructure in the Machine Age," published July 7 in The Review of Financial Studies. Other Cornell co-authors are: David Easley, the Henry Scarborough Professor of Social Science in the College of Arts and Sciences and professor of information science in Computing and Information Science; and Marcos Lopez de Prado, professor of practice in Operations Research and Information Engineering in the College of Engineering and chief information officer of True Positive Technologies.

Government paid Vote Leave AI firm to analyse UK citizens' tweets

The Guardian

Privacy campaigners have expressed alarm after the government revealed it had hired an artificial intelligence firm to collect and analyse the tweets of UK citizens as part of a coronavirus-related contract. Faculty, which was hired by Dominic Cummings to work for the Vote Leave campaign and counts two current and former Conservative ministers among its shareholders, was paid £400,000 by the Ministry of Housing, Communities and Local Government for the work, according to a copy of the contract published online. In June the Guardian reported Faculty had been awarded the contract, but that key passages in the published version of the document describing the work that the company would carry out had been redacted. In response to questions about the contract in the House of Lords, the government published an unredacted version of the contract, which describes the company's work as "topic analysis of social media to understand public perception and emerging issues of concern to HMG arising from the Covid-19 crisis". A further paragraph describes how machine learning will be applied to social media data.

Tamr Helps Air Force Wrangle Data


Data prepper Tamr Inc. will assist the U.S. Air Force in boosting utilization of its air assets under a five-year contract designed to use machine learning techniques to accelerate the flight certification process for new aircraft configurations. Those configurations include equipping front-line aircraft with new weapons, sensors and defenses such as electronic warfare pods. Tamr said the contract with the Air Force's Seek Eagle Office could be worth as much $60 million. The office based at Eglin Air Force Base, Fla., is responsible for integration new technologies into front-line aircraft. The Air Force office will use Tamr's machine learning platform to organize more than 30 years of aircraft performance studies dispersed across the organization.

5 reasons why Microsoft is even entertaining a Tik Tok purchase


Microsoft's talks to acquire Tik Tok don't make a whole lot of sense on the surface. In fact, nothing about this deal makes sense given you have a tech giant that is known for the enterprise, President Trump tweeting about Tik Tok, legislators chiming in and a 45-day deal deadline. Sure, I've read a few Wall Street analysts do some mental gymnastics to argue for the Microsoft purchase of Tik Tok. Depending on price ($10 billion too good to pass up and $50 billion crazy), Microsoft CEO Satya Nadella is going to have some explaining to do. With all that said, here is a bit of informed speculation about why this Microsoft-Tik Tok lunacy is happening. The Department of Defense's JEDI cloud contract is to be announced soon.

Don't Forget what 'Deep' & 'Learning' Actually Mean


Think critically about whether you need to apply deep-learning to your datasets. Deep Learning, one of the "hottest" things in AI, has a way of seeping into popular culture as this mysterious, software that can make seemingly amazing classifications at human-level accuracy in Computer Vision, speech recognition, or play games like Go, recommend our favorite movies, and the like. But deep learning has crucial pitfalls, when it drives cars that sadly, more than once, have injured or killed their drivers or pedestrians because of silly image-recognition mistakes. Or, when deep learning is used for face-recognition ––something that clearly has adverse effects on people of color, LGBT, and other marginalized groups –– and if deep learning's face-prediction is used by institutions of power with a history of racism, LGBT-phobia, and tossed back and forth between private companies and governments –– deep-learning's pitfalls become frighteningly magnified. Another example is when Facebook's deep-learning neural translation machine led to the illegal arrest of a Palestinian man because of a post he made, at the end of 2017.

Everything you need to become a self-taught Machine Learning Engineer


All of these books are 400–500 pages long, with the first two being about statistical ML and the last two being about deep learning. Grab these books and find your people. Look for places that other curious programmers are spending time. For me, that was Bradfield. The kind of person who spends 10–20 hours/week learning is exactly the kind of person I wanted to study with.

Matrix and the Blockchain Based Smart Hospital Program


As the virus continues to spread quickly in many parts of the world, the demand for touchless and contactless services have grown and have become increasingly popular. One point of caution many people find them selves at is in hospitals or other healthcare facilities as they are filled with at-risk people or even coronavirus patients. It is perfectly understandable to feel a sense of caution in any place filled with people, especially healthcare facilities in this day in age. The recent events that have unfolded have prompted some healthcare centers to launch remote doctor appointments for much of the general public, especially the elderly. As the pandemic continues to exploit vulnerabilities in healthcare systems around the world, do these contactless practices paint the future of a new normal?