Today Microsoft president Brad Smith called for federal regulation of facial recognition software. "In a democratic republic, there is no substitute for decision making by our elected representatives regarding the issues that require the balancing of public safety with the essence of our democratic freedoms. Facial recognition will require the public and private sectors alike to step up -- and to act," Smith wrote in a blog post. Recent events explain why Smith is speaking out now. Last month, while the majority of U.S. citizens was outraged about the idea of separating families who unlawfully entered the United States, Microsoft was criticized by the public and hundreds of its own employees for its contract with Immigration and Customs Enforcement (ICE).

Could behavioural economics and machine learning help to better understand consumers' movie preferences? A team of researchers from the University of Cambridge, the University of West England, and the Alan Turing Institute dove deeper into this question, in a fascinating study that combines behavioural economics, business and AI. Marco Del Vecchio, Alexander Kharlamov, Glenn Parry, and Ganna Pogrebna used their diverse skillsets to develop tools that could help the media industry to better understand what content viewers really want to see. Currently, the motion picture, media and entertainment industry selects content offerings based on top-down decisions, typically informed by expertise, experience, surveys and focus groups. "Our main motivation was to understand whether and to what extent we can put viewer perceptions at the heart of the equation," the researchers said.

I got excellent feedback after taking part in the panel discussion, and since it was not recorded I thought I would write a blog post on some of what I discussed, my response to the core questions and some other thoughts I had on the topic. Artificial Intelligence (AI) is often thought of as being new areas that are currently hard to automate, difficult problems to solve using computers, and ultimately replacing humans jobs. Yet we have been using a form of AI or Machine Learning (ML) since the 1950's as Artificial neural network, later on adopted by businesses in 1970's initially as decision support systems, and later evolving into data mining, business intelligence, analytics & insights, and more recently data science. What has changed is that the sales and marketing teams are now involved, and sometimes even overpromising on what is possible! Yet there has also been an increase in computing power, storage capacity with massive datasets collected from a larger number of sources, and open source data science code, packages and tutorials that are readily available.

A basic concept of computer science is the algorithm. The technical term is named after the Persian mathematician Muhammad Ibn Musa al-Khwarizmi, author of a work on calculation rules (who lived around 780 to 850 AD). Examples from everyday life are recipes, handicraft instructions, rules of the game, instructions for use, score, pattern. The first known written algorithms were created around 2000 BC in Mesopotamia (see Donald E. Knuth; Luis Trabb Pardo: The early development of programming languages, in: Donald E. Knuth (ed.): A widespread calculation method is recorded in the Papyrus Rhind (around 1550 B.C.): Egyptian multiplication.

Since man hunted and got a taste for the meat of the Auroch, later domesticated into the ancestors of modern cattle breeds, the market for beef has grown steadily. The last 10 years have not been so kind, with plummeting beef consumption and higher prices. There is some light, as meat intense diets like paleo and keto have turned some consumers back to beef, but just at the point when the cattle industry has become more consolidated, sophisticated and consumer focused it is ironically facing some of the greatest existential threats to its 10,000 years existence. Touted as sustainable, welfare friendly or conversely dismissed as'fake meat' the clear intent of growing meat on petri dishes is to displace the consumption of red-meat. Despite concerns of how'friendly' the technology really is, meat producers such as Cargill and Tyson foods have invested in startups in this market. Environmentalists advocating'Meatless Mondays' and other initiatives at consumer level have been unremitting in their attacks on the meat industry. These action groups have used sometimes dubious data to support their contention that cattle, and specifically beef uses more water, more resources and emits more greenhouses gases then other human choices. Their relentless attack appears to be having an effect on red meat consumption in the US and Europe.

Slime molds are among the world's strangest organisms. Long mistaken for fungi, they are now classed as a type of amoeba. As single-celled organisms, they have neither neurons nor brains. Yet for about a decade, scientists have debated whether slime molds have the capacity to learn about their environments and adjust their behavior accordingly. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.

In this tutorial, we will demonstrate how to implement the state of the art End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF paper for Named Entity Recognition using Pytorch. The main aim of the tutorial is to make the audience comfortable with Pytorch using this tutorial and give a step-by-step walkthrough of the Bi-LSTM-CNN-CRF architecture for Named-Entity-Recognition.

AI, machine learning, and deep learning - these terms overlap and are easily confused, so let's start with some short definitions. AI means getting a computer to mimic human behavior in some way. Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications. Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems. Those descriptions are correct, but they are a little concise.

Probably the biggest single driver of change in the industry is the increasing advance of technology. Everyone has read about the perceived threat of artificial intelligence (AI) and how it's set to take lawyers' jobs – and although Michael Skapinker of the Financial Times wrote recently that, like plumbers, lawyers are not yet approaching their'Uber' moment and remain largely a "disruption-free profession", other commentators take a slightly different view. Richard Susskind, for one, might disagree – having written in his book Tomorrow's Lawyers: An Introduction to Your Future: "AI will disrupt not just the world of practising lawyers but also our common perception of the legal process." It's important to note that artificial intelligence isn't something to be afraid of; adopted in the right way, it will enable lawyers to perform their work more effectively. It's clearly crucial in any law firm or corporate legal department for work to be resourced appropriately – and if some of this work can be done by a machine more quickly and more efficiently than by a human, then of course that option should be considered.

In the past, machine learning was only capable of interpolating data -- making predictions about situations that are "between" other, known situations. It was incapable of extrapolating -- making predictions about situations outside of the known -- because it learns to fit the known data as closely as possible locally, regardless of how it performs outside of these situations. In addition, collecting sufficient data for effective interpolation is both time- and resource-intensive, and requires data from extreme or dangerous situations. But now, Georg Martius, former ISTFELLOW and IST Austria postdoc, and since 2017 a group leader at MPI for Intelligent Systems in Tübingen, Subham S. Sahoo, a PhD student also at MPI for Intelligent Systems, and Christoph Lampert, professor at IST Austria, developed a new machine learning method that addresses these problems, and is the first machine learning method to accurately extrapolate to unseen situations. The key feature of the new method is that it strives to reveal the true dynamics of the situation: it takes in data and returns the equations that describe the underlying physics. "If you know those equations," says Georg Martius, "then you can say what will happen in all situations, even if you haven't seen them."