Last week, we discussed Multi-class SVM loss; specifically, the hinge loss and squared hinge loss functions. A loss function, in the context of Machine Learning and Deep Learning, allows us to quantify how "good" or "bad" a given classification function (also called a "scoring function") is at correctly classifying data points in our dataset. In fact, if you have done previous work in Deep Learning, you have likely heard of this function before -- do the terms Softmax classifier and cross-entropy loss sound familiar? I'll go as far to say that if you do any work in Deep Learning (especially Convolutional Neural Networks) that you'll run into the term "Softmax": it's the final layer at the end of the network that yields your actual probability scores for each class label. To learn more about Softmax classifiers and the cross-entropy loss function, keep reading.

Artificial intelligence is a big deal. It will change our society, and the way we do things. Just maybe not immediately, and in law it might be even longer. The function of artificial intelligence is directly connected to the concept of big data. The superior functioning of artificial intelligence over current processes is based in part on the superior ability of computing large amounts of information, data sets that are so large and so complex that the traditional means of processing this information simply isn't adequate enough when compared to techniques like predictive analytics.

While it's a bit premature to start fearing for your job just yet, robots are on the rise in the restaurant sector. The US' influential National Restaurant Association show in Chicago featured three robots this year: a sushi bot that can make 3,600 pieces of nigiri per hour; a vending-machine-style robot that makes bespoke salads and a robotic fry cook. More impressively, over in Japan, Pizza Hut is trialling a robotic waiter called Pepper. If the promotional video is to be believed, the 3ft humanoid is extremely sophisticated, verbally interacting with diners in much the same way as a human being. It can even respond to questions about dietary requirements, giving info on calorie counts and fat content.

Robots and artificial intelligence were once just the stuff of Hollywood fantasy, from the Star Wars and Terminator movies to Iron Man's JARVIS butler. However, intelligent machines that can simulate reasoning and develop knowledge to mimic human thought processes are now part of the real world -- and with that comes the risk of bias and cyber attack. Investment in artificial intelligence (AI) in the U.S. more than doubled to 587 million last year, and machine learning is expected to drive an increase in the use of robotics and software automation in coming years. AI algorithms are already used in investment, healthcare, programming, law, music, book and film recommendation services, games, and language learning -- to name but a few sectors. These uses often produce outcomes that would be unattainable by humans or do so at a far lower cost.

Making a good impression with a prospective employer often requires little more than a great résumé and congenial personality. But how do you impress an algorithm? That's the question facing applicants of Facebook, IBM, and a spate of other companies that are starting to incorporate artificial intelligence into their hiring practices. They're using machines to scan work samples, parse social media posts, and analyze facial expressions on behalf of HR managers. Such practices raise questions about accuracy and privacy, but proponents argue that harnessing AI for hiring could lead to more diverse, empathetic, and dynamic workplaces.

Rachel Law, the 20-something co-founder of a New York startup called Kip, is sitting next to me at a café, tapping her phone screen to show how the company's service based on artificial intelligence allows users to shop while on Slack by communicating not in typed or spoken words but in cartoonish emoji. If Law were doing this demo for either Donald Trump or Hillary Clinton and used the terms artificial intelligence, Slack and emoji in the same sentence, each candidate's brain would no doubt seize up like an engine that had run out of oil. We have a problem, folks. Over the next four-year presidential term, a swarm of fantastic new technologies, such as artificial intelligence, virtual reality, blockchain, personal genomics and drones, will profoundly alter society, business and geopolitics in ways we've never seen. And our two major-party presidential candidates don't have a clue.

One of the big games coming out this week is Event[0]--available for Windows and Mac on September 14th. It's a sci-fi game set in an alternate retrofuture reality in which humanity built a starship in 1985 and has since embraced artificial intelligence even more than we have now. The events depicted in the game take place in 2012, on board a starship, where you, the player, are left alone with an insecure AI known as Kaizen. The idea is to explore the ship (which requires completing some hacking puzzles) in order to gather items and information. With this, you can then talk to Kaizen through the terminals located across the ship, in order to make it trust you a little more.

Scientists at IBM Research have collaborated with 20th Century Fox to create the first-ever cognitive movie trailer for the movie Morgan. Utilizing experimental Watson APIs and machine learning techniques, the IBM Research system analyzed hundreds of horror/thriller movie trailers. After learning what keeps audiences on the edge of their seats, the AI system suggested the top 10 best candidate moments for a trailer from the movie Morgan, which an IBM filmmaker then edited and arranged together.

Unexpected convergent consequences… this is what happens when eight different exponential technologies all explode onto the scene at once. This blog (the second of seven) is a look at artificial intelligence. Future blogs will look at other tech areas. An expert might be reasonably good at predicting the growth of a single exponential technology (e.g. the Internet of Things), but try to predict the future when A.I., Robotics, VR, Synthetic Biology and Computation are all doubling, morphing and recombining… You have a very exciting (read: unpredictable) future. This year at my Abundance 360 Summit I decided to explore this concept in sessions I called "Convergence Catalyzers."

The rise of cloud computing brings with it the promise of infinite computing power. The rise of Big Data brings with it the possibility of ingesting all the world's log files. The combination of the two has sparked widespread interest in data science as truly the "one ring to rule them all." When we speculate about such a future, we tend to use two phrases to describe this new kind of analytics--artificial intelligence (AI) and machine learning. Most people use them interchangeably. AI develops conceptual models of the world that are underpinned by set theory and natural language.