In yet another episode of man versus machine, an artificial intelligence developed by Carnegie Melon University has been absolutely dismantling a team of professional poker players, accumulating a staggering lead of almost $800,000. The showdown takes place as part of the "Brains vs. Artificial Intelligence" competition which pits a group of four poker pros against the crafty supercomputer Libratus in a heads-up game of No-Limit Texas Hold'em slated to continue for 120,000 hands. Gary Vaynerchuk was so impressed with TNW Conference 2016 he paused mid-talk to applaud us. Since January 11 when the contest initially kicked off, the players have now passed the midway point of the the race, having completed almost 65,000 hands in total. What is more intriguing is that so far Libratus has managed to keep an impressive lead over its human opponents, stacking up a profit of $794,392.

Patrick Stobbs recently sat in a conference room here playing songs from his smartphone, attempting to show how his start-up, Jukedeck, is at the cutting edge of music. The tune sounded like the soundtrack to a 1980s video game. "This is where we were two years ago," he said, looking slightly embarrassed. "And this is where we are now," he continued. He then played a gentle piano piece.

The history of artificial intelligence is littered with false starts, but Goldman Sachs Research's Heath Terry says we've reached a turning point that will put the technology within reach across sectors. He explains how a growing web of interconnected devices, systems and sensors is helping to fuel development and delves into AI's potential to impact "every industry."

The next iPhone is going to entirely do away with the home button at the bottom of the screen. The much-rumoured – and potentially already previewed – removal of the button has long been thought to be one of the major changes coming to the next phone. But a new report sheds light on how potentially the biggest design change ever to come to the iPhone will actually work. In the place of the characteristic round button will come a special sensor embedded in the screen. And that will be the beginning of a range of new additions that could eventually become an entire biometric system for checking who is using the phone.

Last year I wrote an overview post which defines a number of key concepts related to data science -- including data science itself -- and attempts to explain how these pieces fit together into a so-called "data science puzzle." As a new year begins, and a previous year worth of advances, insights, and accomplishments get rolled into our collective professional outlook, I thought it would be prudent to revisit this puzzle, noting and incorporating any changes and updates which may contribute to rearranging the puzzle for the foreseeable future, and to provide some addition commentary where warranted. Big Data is still important to data science. Take your pick of metaphors, but any way you look at it, Big Data is the raw material that has continues to fuel the data science revolution. As relates to Big Data, I believe that justification of data-acquisition and -retention from a business point of view, expectations that Big Data projects start providing actual financial returns, and the challenges related to data privacy and security will become the big Big Data stories not only of 2017 but moving forward in general.

Google and other tech companies have been building artificial intelligence for some time and recently Google Deep Mind research teams believe they have developed the first version of a General Artificial Intelligence that is capable of learning on its own without assistance in different situations. The term'general' in this case means that the AI can adapt to learning different situations and problems without additional training. In Google's case, the team used an algorithm and sensors to detect play on the game board of the ancient game Go. In the game of Go, black and white stones are placed successively on a grid. When you surround another players stones, you take the stones as prisoners.

Artificial Intelligence proved to be a very unstable field, its 60 year history is a chain of periods filled with excitement and anticipation of the singularity, taking turns with times of total ignorance known as AI winters, when donors lose hope in replacing people by machines, when the general public is satiated with chat- and chess- and fridge-bots. But this time* everything is different. The decade started with a lavish AI spring, flourished with Big Data and the Internet of Things, doped the world with an almost forgotten scent of smart homes and smart cities. This set the stage for something bigger, a new AI summer, a hot and deep summer of Neural Networks: NN based AI, or ANN, also referred today as "real" and "strong" AI: manufactured systems are imitating a network of brain neurons, algorithms are rewriting themselves, computers learning from their mistakes, and showing off with the first successes in science, business, security ... and art. In July 2015 Google released their Deep Dream, a restless algorithm that learned to stare at an image until it sees the dog inside.

Instant messaging apps have taken over. WhatsApp, iMessage, WeChat, Signal, Slack, Facebook Messenger, Snapchat -- billions of users exchange information in bite-sized chunks on any or all of these platforms on a daily basis. In fact, as of mid-2015, people were spending more time on messaging apps than on social networks, and as messaging apps become increasingly more sophisticated, this trend shows no sign of reversing. Messaging platforms have expanded far beyond simply enabling users to send and receive text messages, photos, and videos. Many of them allow users to exchange documents and files, voice memos, location information, and sometimes even cash.

A Northwestern University team developed a new computational model that performs at human levels on a standard intelligence test. This work is an important step toward making artificial intelligence systems that see and understand the world as humans do. "The model performs in the 75th percentile for American adults, making it better than average," said Northwestern Engineering's Ken Forbus. "The problems that are hard for people are also hard for the model, providing additional evidence that its operation is capturing some important properties of human cognition." The new computational model is built on CogSketch, an artificial intelligence platform previously developed in Forbus' laboratory.

Join Entrepreneur's The Goal Standard Challenge and make 2017 yours. Artificial intelligence (AI) gets more advanced every year, but there are still some major limitations keeping us from seeing a futuristic reality that includes robot butlers and near-complete societal automation. Fortunately, some of these limitations are on the verge of being overcome, and if you watch and plan carefully, you'll be able to take advantage of those improvements for your business. Right now, most AI systems "learn" new information through a kind of structured force-feeding, relying on information given to those systems by humans. However, this form of "supervised learning" isn't scalable, and doesn't mimic the way that human beings naturally learn.