American Institute of Artificial Intelligence (AiAi) announces alliance with Leathwaite to help provide business executives with A.I. market insight and education. Al Naqvi, professor and CEO, said, "Our institute is dedicated to providing guidance and training to C-Level executives on the responsible and effective adoption of A.I. solutions, and we were honored when we were approached by Leathwaite and asked to help them deliver this invaluable insight to their clients." Max Woolger, Head of New Partnerships of Leathwaite said, "One of the most valuable services our company provides is market intelligence regarding industry trends, and A.I. is the most common topic of conversation lately. We are pleased to have American Institute of A.I. to provide such powerful information to our clients." The alliance constitutes research, solutions, and education.

American Institute of Artificial Intelligence is announcing the launch of several business courses in Artificial Intelligence. Al Naqvi, professor and researcher at the American Institute of A.I. said, "Technology firms are buying grocery stores. Tech firms are moving into the auto, financial, and healthcare sectors. They are not just offering products and services in these areas, they are becoming those sectors. Business leaders must train themselves on how to deal with these new competitive dynamics and remain relevant in the A.I. Economy."

Forgotten memories have been reawakened in mice with Alzheimer's disease, suggesting that the condition may not actually destroy our memories, but instead impair our ability to recall them. It has long been assumed that Alzheimer's disease completely erases memories. The condition involves clumps of proteins known as amyloid plaques and tau tangles accumulating in the brain, where they are thought to destroy the neurons that store our memories. But experiments by Christine Denny at Columbia University and her colleagues suggest that memories may not be wiped by Alzheimer's disease, but instead become harder to access. What's more, these memories can be reawakened by artificially activating the neurons they are stored in. The finding could be revolutionary, says Ralph Martins at Edith Cowan University in Australia.

If research and advisory firm Gartner Inc. is right in its forecast, Artificial Intelligence (AI) technologies will become pervasive in almost every new software product and service by the year 2020. The growth in AI, broadly a set of computational technologies and methodologies aimed at helping machines emulate human intelligence, is being driven primarily by sophisticated algorithms, the availability of huge data sets, greater computing power, and advances in machine learning as well as deep learning. Machine learning, a subset of AI, is broadly about teaching a computer how to spot patterns and use mountains of data to make connections without any programming to accomplish the specific task. A recommendation engine is a good example. Deep learning, an advanced machine learning technique, uses layered (hence "deep") neural networks (neural nets) that are loosely modelled on the human brain.

Digital technologies like the internet and smartphones are transforming our lives and society. They are proving to be powerful tools for liberating individuals' creative and entrepreneurial potential, as well as providing new educational opportunities and higher wages for marginalized people, both in the U.S. and around the globe. Unfortunately, in the U.S., outdated government regulations and weak consumer protections are undermining these opportunities. What's more, the Trump administration has not yet made significant moves to address this growing crisis: As of this writing, five key White House positions are vacant, without even acting directors or interim leaders to help the executive branch formulate U.S. science and technology policy. As the founder of both the Open Technology Institute and the X-Lab policy and innovation organization, I have spent years at the heart of many Washington, D.C. battles over technology policy, fighting for ideas that would best serve American workers and the general public.

MEPs have approved rules for keeping humans firmly in charge of Artificial Intelligence (AI). They include ways to establish liability in law, for example where driverless cars cause accidents. They have also called for ethical standards to be built in to AI algorithms and robots that work for humans, and standardisation across Member States to ensure a level playing field for technology companies. After a heated debate, the vote was passed on Thursday by a large majority, 369 to 123 with 85 abstentions. But clauses that would introduce a basic state allowance for people who lose their jobs to robotisation were defeated. A coalition of right-wing parties voted them out.

At Market Research Future (MRFR), we enable our customers to unravel the complexity of various industries through our Cooked Research Report (CRR), Half-Cooked Research Reports (HCRR), Raw Research Reports (3R), Continuous-Feed Research (CFR), and Market Research & Consulting Services.

Over the last few years we've seen many companies blindly follow various technology hypes like they're the ultimate answer to making businesses future proof. Artificial intelligence (AI) and machine learning are currently receiving huge attention, but are they really such a game changer? Companies often don't have any idea how disruptive technology, like AI, could impact their customers' experience and business, and they don't have a clear understanding of its potential or its implications. That's why we invited Igor Mikhalev from Firmshift, a data-driven technology development company, to answer a few questions about machine learning and AI. About Igor Mikhalev After leading multiple scientific software development initiatives for industry leaders in knowledge extraction, scientific big data management and publishing, Igor Mikhalev completed his MBA at the University of Amsterdam (Amsterdam Business School).

Consumer trends and behaviors are changing rapidly with advances in technology. The keyword "Beauty" is one of the most searched terms on the web with almost 4 billion queries received annually according to estimation by L'Oréal. They want beauty to be closely connected with technology.That's what is connected beauty all about. Nowadays, consumers interested in beauty care want to see how technology can provide them a better personal experience and make them feel connected. Beyond just improving your good looks, today's apps and devices can also empower the connected consumer to self-diagnose beauty issues, and respond accordingly to imperfections.

Conventionally, the questions on a test are assumed to be kept secret from test takers until the test. However, for tests that are taken on a large scale, particularly asynchronously, this is very hard to achieve. For example, TOEFL iBT and driver's license test questions are easily found online. This also appears likely to become an issue for Massive Open Online Courses (MOOCs, as offered for example by Coursera, Udacity, and edX). Specifically, the test result may not reflect the true ability of a test taker if questions are leaked beforehand. In this paper, we take the loss of confidentiality as a fact. Even so, not all hope is lost as the test taker can memorize only a limited set of questions' answers, and the tester can randomize which questions to let appear on the test. We model this as a Stackelberg game, where the tester commits to a mixed strategy and the follower responds. Informally, the goal of the tester is to best reveal the true ability of a test taker, while the test taker tries to maximize the test result (pass probability or score). We provide an exponential-size linear program formulation that computes the optimal test strategy, prove several NP-hardness results on computing optimal test strategies in general, and give efficient algorithms for special cases (scored tests and single-question tests). Experiments are also provided for those proposed algorithms to show their scalability and the increase of the tester's utility relative to that of the uniform-at-random strategy. The increase is quite significant when questions have some correlation---for example, when a test taker who can solve a harder question can always solve easier questions.