President Trump argues that too many US workers have lost their jobs to foreign peers as companies have offshored manufacturing. US companies need to bring those jobs back and, in doing so, restore America's industrial greatness. His diagnosis isn't altogether wrong: fewer companies manufacture cars, for instance, in the US than was previously the case. But this thesis is too simplistic. US companies are really just trying to allocate capital efficiently when they manufacture goods outside of the US.

With so much discussion over foreign policy and immigration concerns from Muslim-majority nations after the inauguration of Donald Trump, there might be some confusion about how the U.S. is fighting terrorism in the Middle East. The U.S. may not be in a direct war with anyone other than the Islamic State, also known as ISIS, but there is still a military presence in multiple countries carried over from previous administrations. The number of combat troops has dipped due to drone warfare, but President Trump, who campaigned on being tougher on ISIS, has said he would be willing to send up to 30,000 troops to Iraq and Syria. However, he inherited a military presence in not just those two countries, but other hotspots, as well. Just days after the Sept. 11 attacks, Congress and President George W. Bush authorized the use of military force to overthrow the Taliban.

The concept that a computer program can learn and adapt to new data without human interference. Machine learning is a field of artificial intelligence that keeps a computer's built-in algorithms current regardless of changes in the worldwide economy. Various sectors of the economy are dealing with huge amounts of data available in different formats from disparate sources. The enormous amount of data, known as Big Data, is becoming easily available and accessible due to the progressive use of technology. Companies and governments realize the huge insights that can be gained from tapping into big data but lack the resources and time required to comb through its wealth of information.

This is an image of the Zaamenkomst panel: one of the best remaining exemplars of rock art from the San people of Southern Africa. As soon as you see it, you are inevitably herded, like the eland in the scene, through a series of thoughts. Does it have a meaning? Why are the eland running? What do the white lines coming from the mouths of the humans and animals signify? What event is unfolding in this scene?

An Introduction to'Machine Learning' -- I came across this article and thought it was worth a share, the original article was surrounded in adverts and difficult to read, so I make no apologies for plagiarizing it! I have kept the original link at the bottom of the article, enjoy . . . The concept that a computer program can learn and adapt to new data without human interference. Machine learning is a field of artificial intelligence that keeps a computer's built-in algorithms current regardless of changes in the worldwide economy. Various sectors of the economy are dealing with huge amounts of data available in different formats from disparate sources.

If you think Americans have a love affair with cars now, just wait until people begin to treat them not merely as a means of transportation but as smartphones on wheels -- or robot servants. Automobiles are more wired than ever. But experts say the late-model cars on the road today are just a precursor to truly connected cars. In the near future this next generation of cars -- loaded with sensors and screens, feeding back to third parties huge amounts of data on vehicle performance and even driver behavior -- will communicate with other devices, be loaded with apps and even make their own decisions. While for now the smart car is lagging behind the smart home, automobile manufacturers, technology firms, ride-hailing companies and financial institutions are on course to integrate automobiles into the burgeoning Internet of Things. In the process they intend to revolutionize transportation and the fabric of people's daily lives. "The car is going to be an extension of who you are, just like the phone is," said Suresh Ramamurthi, the chairman and chief technology officer of CBW Bank, a small bank in Weir, Kan., that has attracted a national client base of fintech startups. In the more distant future, the car may become something greater still: an autonomous agent that can carry out tasks and authorize payments without requiring its owner's input.

The field of medical AI is buzzing. More and more companies set the purpose to disrupt healthcare with the help of artificial intelligence. Here, I collected the biggest names currently on the market ranging from start-ups to tech giants to keep an eye on in the future. No one doubts that artificial intelligence has unimaginable potential. Within the next couple of years, it will revolutionize every area of our life, including medicine. Although many have their fears and doubts about AI taking over the world, Stephen Hawking even said that the development of full artificial intelligence could spell the end of the human race.

You don't have to be a gambler to appreciate the complexities of the card game Texas Hold'Em. It involves a strategy that needs to evolve based on the players around the table, it takes a certain amount of intuition, and it doesn't require the player to win every hand. Just a few days ago, an artificial intelligence (AI) algorithm named Libratus beat four professional poker players at a no-limit Texas Hold'Em tournament played out over 20 days. If you have even the slightest understanding of how to write code, you would realize that it is impossible to actually code a software program to do that with such "imperfect information". The AI algorithm did exceptionally well and was utilizing strategies that humans had never used before.

We propose a new linear algebraic approach to the computation of Tarskian semantics in logic. We embed a finite model M in first-order logic with N entities in N-dimensional Euclidean space R^N by mapping entities of M to N dimensional one-hot vectors and k-ary relations to order-k adjacency tensors (multi-way arrays). Second given a logical formula F in prenex normal form, we compile F into a set Sigma_F of algebraic formulas in multi-linear algebra with a nonlinear operation. In this compilation, existential quantifiers are compiled into a specific type of tensors, e.g., identity matrices in the case of quantifying two occurrences of a variable. It is shown that a systematic evaluation of Sigma_F in R N gives the truth value, 1(true) or 0(false), of F in M. Based on this framework, we also propose an unprecedented way of computing the least models defined by Datalog programs in linear spaces via matrix equations and empirically show its effectiveness compared to state-of-the-art approaches.

In order to compare and analyse open data across cities, standard representations or ontologies have to be created. This paper defines a shelter ontology that includes concepts of shelters, slums, households and homelessness. The design of the ontology is based upon the data requirements of ISO 37120. ISO 37120 defines 100 indicators to measure and compare city performance. There are three shelter-themed indicators defined, namely 15.1 Percentage of city population living in slums, 15.2 Number of homeless per 100 000 population, and 15.3 Percentage of households that exist without registered legal titles. This ontology enables both the representation of the ISO 37120 Shelter theme indicators' definitions, and a city's indicator values and supporting data. This enables the analysis of city indicators by intelligent agents.