Games

Business magnate Elon Musk enters the Heavenly Bodies: Fashion & The Catholic Imagination Costume Institute Gala at The Metropolitan Museum on May 07, 2018 in New York City. Google DeepMind CEO Demis Hassabis and Tesla CEO Elon Musk were invited to sign a 350-year-old book in London last Friday. The Royal Society, which aims to promote excellence in science, is the world's oldest independent scientific academy. The Charter Book dates back to 1663 and contains the signature of every Royal Society fellow and member. Over the years, the book has been signed by scientists such as Isaac Newton, Charles Darwin, Alan Turing, David Attenborough, and Tim Berners-Lee.

There are many reasons England did not reach the World Cup final this year. There are myriad factors which contributed to Croatia stopping football coming home. I could probably spend a while talking about Harry Kane's missed opportunity, Modric's masterclass or general fatigue setting in during Extra Time. Instead, working at a technology company, I spoke with our AI Department Skunkworx and they took the opportunity to look at things from a different perspective. After using their machine-learning tools to analyse data from every world cup game ever, they presented me with multiple patterns.

In the late 18th century, Wolfgang von Kempelen amazed the world with his chess playing automaton, the Mechanical Turk, as it defeated worthy opponents including Napoleon Bonaparte and Benjamin Franklin. Onlookers were amazed at how a device of gears and sprockets had been able to master the complexities of chess and demanded to know what was inside. As it turned out, the chassis of the Mechanical Turk actually housed a human being – a simple yet highly-effective deceit made possible by the inability of onlookers to take a peek inside. While the story of the Mechanical Turk is more than 100 years old, it may be a useful parable for modern society's increasing use of artificial intelligence (AI), and how we are often relying on findings with little understanding of how they are reached. The opacity of AI systems has significant potential for abuse, particularly through the long-term impact of intentional or even unintentional biases in the data.

With an estimated market size of 7.35 billion US dollars, artificial intelligence is growing by leaps and bounds. McKinsey predicts that AI techniques (including deep learning and reinforcement learning) have the potential to create between $3.5T and $5.8T in value annually across nine business functions in 19 industries. Although machine learning is seen as a monolith, this cutting-edge technology is diversified, with various sub-types including machine learning, deep learning, and the state-of-art technology of deep reinforcement learning. Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment.

Blackboard, chalks, textbooks and ink pens are slowly becoming a thing of a past. It is time to embrace technology where digital facilities coupled with tech-savvy teachers are enriching students' learning experiences. A little glimpse into the dynamic digital world is indicative of how technology has given a whole new meaning to education. Education with the help of technology has crossed borders and has opened up a world of opportunities for students. From easy sharing of information to collaboration with the help of email and cloud applications to instant access to learning programs anytime, anywhere -- here is how technology will alter the education sector in 2018.

We all get a general idea: Artificial Intelligence (AI) is about machines that can "think". Or, at least, that can act as if they were thinking. This simulated thought-process includes the understanding of natural language, the capacity of learning by themselves, pattern recognition, data analytics among other abilities. We're witnessing AI become part of our everyday lives. Various software programs and devices already use technologies that include a certain grade of AI (for example, Google algorithms or Siri) or a very specialized mode of AI (like AlphaGo, the machine that not only defeated a Go champion, but also created a new way to face and play the game by itself).

Technology is automating out human error, democratizing the search for engineering talent, and speeding R&D times with implications across drug discovery, car manufacturing, and much more. Across industries, designers, chemists, and engineers are constantly hypothesis testing. Will this design look right? Does this compound fit our needs? Testing and iterating is the essence of research and development.

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.

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.

When IBM's Deep Blue supercomputer won its famous chess rematch with then world champion Garry Kasparov in May 1997, the victory was hailed far and wide as a triumph of artificial intelligence. But John McCarthy – the man who coined the term and pioneered the field of AI research – didn't see it that way. As far back as the mid-60s, chess was called the "Drosophila of artificial intelligence" – a reference to the fruit flies biologists used to uncover the secrets of genetics – and McCarthy believed his successors in AI research had taken the analogy too far. "Computer chess has developed much as genetics might have if the geneticists had concentrated their efforts starting in 1910 on breeding racing Drosophila," McCarthy wrote following Deep Blue's win. "We would have some science, but mainly we would have very fast fruit flies."