The 2nd SBMI Healthcare Machine Learning Hackathon is calling capable and motivated undergraduate and graduate students from Gulf Coast Consortia institutions and other Houston area universities. Come join us for this great opportunity to challenge your coding skills, meet new people, and enjoy the gathering of young hackers. This 24-hour Hackathon is organized by the Center for Secure HEalthcare Machine Learning (SHEL) at the School of Biomedical Informatics in UTHealth. The event is sponsored by Vir Biotechnology, for a prize of $1,200 for the winner. Undergraduate, master, and doctoral (only 1st and 2nd year) students from the institutes within the Gulf Coast Consortia (include UTHealth, MDACC, UH, Rice, TAMU, UTMB, IBT, and Baylor) and colleges in the vicinity of TMC are highly encouraged to apply.

Deep learning is a complicated process that's fairly simple to explain. A subset of machine learning, which is itself a subset of artificial intelligence, DL is one way of implementing machine learning (automated data analysis) via what are called artificial neural networks -- algorithms that effectively mimic the human brain's structure and function. And while it remains a work in progress, there is unfathomable potential. In this article, we'll briefly explain how deep learning works and introduce the best companies in 2020. Deep learning is a subcategory of machine learning methods powered by artificial intelligence technologies.

The Las Vegas self-driving shuttle is one of many smart cities projects. Welcome to Las Vegas, city of smart lights, self-driving shuttles and startups. Away from the glittering, casino-strewn area known as the Strip is a far more pedestrian-looking area. It's just a 15-minute drive from Las Vegas Boulevard, but it feels like a different world. It's quiet downtown, because while the Strip was thronging with 200,000 extra visitors for CES 2020 last week, the streets here were cold and empty.

Companies of all sizes are implementing AI, ML, and cognitive technology projects for a wide range of reasons in a disparate array of industries and customer sectors. Some AI efforts are focused on the development of intelligent devices and vehicles, which incorporate three simultaneous development streams of software, hardware, and constantly evolving machine learning models. Other efforts are internally-focused enterprise predictive analytics, fraud management, or other process-oriented activities that aim to provide an additional layer of insight or automation on top of existing data and tooling. Yet other initiatives are focused on conversational interfaces that are distributed across an array of devices and systems. And others have AI & ML project development goals for public or private sector applications that differ in more significant ways than these.

Dear C-Suite member, I have to ask: How do you define digital transformation, and what does it look like once you've digitally transformed? The truth is that everyone defines digital transformation differently, and as such, the end states are difficult to define. But what if I told you that most digital transformation roadmaps were no longer enough to compete for the future? In reality, digital transformation has become foundational. It's necessary that every organization embrace digital transformation to modernize infrastructure, operations and most importantly, performance.

In the future, is it conceivable that a firm would be charged with legal malpractice if they didn't use artificial intelligence (AI)? Today, artificial intelligence offers a solution to solve or at least make the access-to-justice issue better and completely transform our traditional legal system. Here's what you need to know about how AI, big data, and online courts will change the legal system. When I sat down in conversation with Richard Susskind, OBE, the world's most-cited author on the future of legal services, to discuss the future of law and lawyers, it became apparent just how much change the legal system will face over the next decade thanks to innovation brought about by artificial intelligence and big data. In Richard's book The Future of Law, published in 1996, he predicted that in the future, lawyers and clients would communicate via email.

It is Brands that prove something in reality like that artificial intelligence is impossible. Let's be clear about what is referred to as artificial intelligence and what is artificial intelligence. Neither definitions are static though. Nothing in memetic world is static. Each new sensemaking activity makes the object of that activity slightly different.

Yesterday, The New York Times ran an alarming piece by Kashmir Hill about Clearview AI, a startup that allows third parties to quickly learn many details about you based on only seeing your face; The New York Times further reported that Clearview's technology is already in use by government agencies across the United States. Today, therefore, I am sharing some tips on how to prevent yourself from being recognized by facial recognition systems. I have personally utilized some of these techniques in test environments – and they worked. Others I have seen demonstrated. Keep in mind that not all of the tips that I provide below apply in all environments – normally, people seeking not to be recognized also do not want to stand out.

British company Exscientia has been working with several pharmaceutical companies (including Sanofi, GlaxoSmithKline, and Roche), offering its artificial intelligence system to aid the drug discovery process. With the new announcement, Bayer are to back the project with €240 million ($266 million) over the course of three years. The focus of this digital transformation of the medication development process will be on the application of artificial intelligence to speed up the discovery of small molecule drug candidates. The drug candidates will have targets linked to oncology and cardiovascular disease. The deal between the two companies, as PharmaPorum reports, will see Bayer owning the rights to the compounds and Exscientia will receive royalties relating to future sales.

The benefits that arose from mass production and specialization during that earlier period were constrained by two key factors, they argue. First, the advantages of size are subject to practical limitations. Growth at traditional organizations eventually hits a point beyond which the firm will "suffer from diseconomies of scale, scope and learning." By contrast, "algorithm-driven operating models" of the A.I. economy are "almost infinitely scalable." Indeed, the "self-reinforcing loops" of network and learning effects that digital environments facilitate can actually accelerate returns with scale.