Increasingly, AI programs reflect a closed loop where privacy goes up against truthfulness. Supposing you were concerned about a new technology, as far as the risks it might pose to individuals and to society. One thing you could do would be to form a committee composed of experts, practitioners of the technology, ethicists, philosophers and like-minded individuals. You might then propose policy and regulation and best practices to minimize harm. Another approach would be to just write computer programs, presuming that your enlightened engineering effort would eliminate harm by making technology do the right thing from the get-go.

More data was collected in the life sciences industry in 2017 than ever before. With the rise of modern information systems and digital technologies like artificial intelligence, data analytics and data science capabilities have become critical for research and decision making across industries and academia. In this talk, Mr Glen de Vries, President and Co-Founder of Medidata Solutions speaks about his career journey from being a lab scientist to President and Co-Founder of one of the largest public technology companies founded in New York and the lessons he has learnt. The role of digital transformation and artificial intelligence in the age of precision medicine, including how the industry can use it to improve and speed up clinical trial processes and decision making for accelerated outcomes for patients What the life sciences and clinical trials industry will look like in the next 10 years plus the skills and talent that the industry will need Mr Glen de Vries is the President and Co-founder of Medidata Solutions, the leading cloud platform for life sciences research. He has been driving Medidata's mission since the company's inception in 1999: Powering smarter treatments and healthier people.

According to an article that appeared in Fortune earlier this year: Automation could replace 40% of jobs in 15 years. This article joins countless others in sounding the warning bells of the forthcoming AI-style industrial revolution. As we've heard so often, AI will replace jobs by the thousands. Almost overnight, half the country will be out of work. Admittedly, it would be impossible to tackle this issue from every angle. However, we can offer our sense of where this industry is, what the effects might be, and where we might be headed within 15 years. Is it going to happen?

Helsinki, Finland - An audience of international peace brokers have gathered inside a room in the historic House of Estates. They have come from South Sudan, the Central African Republic, Ukraine, Colombia and elsewhere to hear a scientist speak. That scientist is Timo Honkela, and his keynote speech on the second day of April's National Dialogues conference is titled Peace from a Different Perspective - a Dialogue of a Million People. But 54-year-old Honkela is working on a machine that he hopes will facilitate world peace. "World peace would be a good goal to work for in my remaining days," he says, smiling over a cup of coffee during a break in the conference.

HOW can a patent office seriously assert that it is serious about innovation when everyone who meets the officials comes from law firms and rarely has any scientific background? If this system's inception truly dates back to need to advance science, shouldn't these officials focus on actual scientists? This may sound like a shallow observation, but it perfectly describes the pattern we've been seeing at the European Patent Office (EPO) under António Campinos and his predecessor Battistelli (neither of whom has any background in the sciences). Seeing how the U.S. Patent and Trademark Office (USPTO) wants to work around 35 U.S.C. § 101, we're nowadays witnessing a similar trend in America too. A resurgence of software patents in Europe poses risk to US (case)law as well. We hope that American readers understand that. The EPO openly brags about objectives like spreading software patents to the whole world.

Artificial Intelligence (AI), machine learning, and deep learning are taking the healthcare industry by storm. They are not pie in the sky technologies any longer; they are practical tools that can help companies optimize their service provision, improve the standard of care, generate more revenue, and decrease risk. Nearly all major companies in the healthcare space have already begun to use the technology in practice; here I present some of the important highlights of the implementation, and what they mean for other companies in healthcare. AI, machine learning, and deep learning are already increasing profits in the healthcare industry. For example, according to research firm Frost & Sullivan by 2021, AI systems will generate $6.7 billion in global healthcare industry revenue.

Truck driving is one of the most popular professions in the United States. However, a staggering 40 percent of the miles logged each year are done with an empty truck. Seattle-based Convoy is revolutionizing the industry by providing better matches for shippers and truckers, allowing them to move freight more efficiently. Using AWS Machine Learning, Convoy recommends the best matches by analyzing millions of shipping jobs along with trucker availability.

Watchdogs ask EC to delay repo haircut floors. It should come as no surprise that credit card companies supplement their revenues by selling real-time access to consumer transaction data – albeit aggregated and anonymised – and even less of a surprise that enterprising hedge funds have found a way to monetise it. This week, Risk.net reported how scrutinising data from millions of credit card transactions allowed a quant team to infer whether a company's sales are on the up or trending lower – without the need to wait for quarterly sales reports to be published. The analysis was delivered through a machine learning implementation of the random forest technique in which multitudes of decision trees combine to produce predictions. In this case, the algorithm enabled the quant shop to get an early warning on the health of companies whose options it held.

Generative AI models have a propensity for learning complex data distributions, which is why they're great at producing human-like speech and convincing images of burgers and faces. But training these models requires lots of labeled data, and depending on the task at hand, the necessary corpora are sometimes in short supply. The solution might lie in an approach proposed by researchers at Google and ETH Zurich. In a paper published on the preprint server Arxiv.org These self- and semi-supervised techniques together, they say, can outperform state-of-the-art methods on popular benchmarks like ImageNet.

Inspur has announced the open-source release of TF2, an FPGA-based efficient AI computing framework. The inference engine of this framework employs the world's first DNN shift computing technology, combined with a number of the latest optimization techniques, to achieve FPGA-based high-performance low-latency deployment of universal deep learning models. This is also the world's first open-sourced FPGA-based AI framework that contains comprehensive solutions ranging from model pruning, compression, quantization, and a general DNN inference computing architecture based on FPGA. The open source project can be found at https://github.com/TF2-Engine/TF2. Many companies and research institutions, such as Kuaishou, Shanghai University, and MGI, are said to have joined the TF2 open source community, which will jointly promote open-source cooperation and the development of AI technology based on customizable FPGAs, reducing the barriers to high-performance AI computing technology, and shortening development cycles for AI users and developers.