When we talk about artificial intelligence, people always tend to think of the only spaces where this tech can replace humans. However, there is another vertical, which is one of those least expected, that AI has influenced in recent times -- Music composition. The music industry has also witnessed tremendous transformations done by AI over the past couple of years -- not only in terms of listening to music but also in terms of how music is made. American singer-songwriter, Taryn Southern in 2017 created a big, moody ballad song Break Free entirely produced by an AI. Not only the song but Southern also went on to create the entire album I AM AI which is the first LP to be entirely composed and produced using AI.

A group of scientists from the Massachusetts Institute of Technology (United States) has created a machine learning system that processes sounds like people. This model can understand the meaning of a word and classify a song according to its genre or style: classical, jazz, pop, rock, blues, soul, hip hop, techno, house, etc. It is the first invention of this type that mimics the way the brain works. As the experiments carried out at MIT show, it can compete in precision with humans. The research, published in the journal Neuron, is based on deep neural networks, that is, a structure inspired by brain cells that analyses information by layers.

In a hilarious turn of events last month, a Russian robot named Boris was unmasked as a man in a robot suit. Likewise, state-run media in China unveiled its AI reporter in November, and to this day it's not clear if this is an actual AI system boiling down news stories or just a synthesized voice with an avatar. More fabricated robotic theatrics appeared to be on display this week at the Consumer Electronics Show in Las Vegas, where LG CTO I.P. Park delivered the opening CES keynote address. Park was accompanied onstage for the hour-long presentation by CLOi, a conceptual robot line perhaps best known for failing during a live demo at CES a year ago. This year, however, CLOi did a bit of everything: The robot acted as co-host, cracked jokes, delivered some LG HomeBrew beer, and even helped some guy who hates blind dates find true love.

We address the problem of designing a conversational avatar capable of a sequence of casual conversations with older adults. Users at risk of loneliness, social anxiety or a sense of ennui may benefit from practicing such conversations in private, at their convenience. We describe an automatic spoken dialogue manager for LISSA, an on-screen virtual agent that can keep older users involved in conversations over several sessions, each lasting 10-20 minutes. The idea behind LISSA is to improve users' communication skills by providing feedback on their non-verbal behavior at certain points in the course of the conversations. In this paper, we analyze the dialogues collected from the first session between LISSA and each of 8 participants. We examine the quality of the conversations by comparing the transcripts with those collected in a WOZ setting. LISSA's contributions to the conversations were judged by research assistants who rated the extent to which the contributions were "natural", "on track", "encouraging", "understanding", "relevant", and "polite". The results show that the automatic dialogue manager was able to handle conversation with the users smoothly and naturally.

Sling TV is introducing an arguably overdue feature for insatiable viewers: personalized recommendations. As of January 17th, Apple TV users will receive custom-tailored suggestions for live and on-demand programs, whether it's a channel, a movie or a sports match. You can find it in the My TV section in a carousel alongside favorites. The show advice will be coming to other devices "in the future," and there are plans for both improved recommendations as well as more personalization going forward. The addition isn't a shock when rivals like YouTube TV have had recommendations, but it'll be a bit help if you like Sling TV's experience.

And even these engagement patterns are giving way to new and more seamless and natural methods of interaction. For example, images and video feeds can be used to track assets, authenticate individual identities, and understand context from surrounding environments. Advanced voice capabilities allow interaction with complex systems in natural, nuanced conversations. Moreover, by intuiting human gestures, head movements, and gazes, AI-based systems can respond to nonverbal user commands. Intelligent interfaces combine the latest in human-centered design techniques with leading-edge technologies such as computer vision, conversational voice, auditory analytics, and advanced augmented reality and virtual reality. Working in concert, these techniques and capabilities are transforming the way we engage with machines, data, and each other. At a dinner party, your spouse, across the table, raises an eyebrow ever so slightly. The gesture is so subtle that no one else notices, but you received the message loud and clear: "I'm bored. Most people recognize this kind of intuitive communication as a shared language that develops over time among people in intimate relationships. We accept it as perfectly natural--but only between humans.

I'm trying to create a toy poker hand evaluator. There are a little over 133 million possible 7 card combinations in a 52 card deck ( c(52,7)) but there there are less than 4 thousand possible resulting hands given that the best 5 cards are taken, equal flushes are ties, etc. Further, there exists a fast solution (two-plus-two) that uses a giant table where each card indexes to the next card and the final card is the index to the answer. So this is effectively a kind of "split up hash function" if you will, I believe there must be a hash function that could be used without using this large multi-index table strategy. So my question is, how would one use machine learning to go about solving this problem? I can uniquely encode all 133 million combinations and I can put them together with the answer they should produce.

This week, some of gaming's biggest franchise names are in some questionable places. We've got the trials and tribulations of Star Wars games, the questionable sexual politics of Assassin's Creed, and some weird advertising for Kingdom Hearts. I sense a disturbance in the Force--something has gone wrong with the Star Wars license at Electronic Arts. According to a report from Kotaku, EA has canceled an open-world Star Wars game in progress at EA Vancouver. In fact, you may remember mention of the game last year, when EA shut down Visceral Games, which was also developing a now-canceled Star Wars project; EA Vancouver took over the project.

No self-lacing sneakers today, but we do have a giant tractor. Less randomly, Google is buying Fossil's smartwatch tech arm, we take a closer look at the AIs that gamble and a robot dog picks itself up. NVIDIA is very aware...AMD is edging closer to breaking NVIDIA's graphic dominance After AMD released its seven-nanometer Radeon VII graphics card, NVIDIA CEO Jensen Huang responded by essentially trashing it. But he should be worried: According to a CES performance tease, the AMD's Radeon VII actually beat NVIDIA's RTX 2080 in several tasks. It also bested the RTX 2080 when playing Strange Brigade and other titles, especially at 4K resolution.

If you want to take your SEO to the next level – or even keep up – in 2018 and beyond, then you need to understand how artificial intelligence (AI) is shaping SEO, and how you can use this knowledge to boost your rankings. Up until recently, search engine algorithms were entirely hand-coded by engineers but this has its limitations, not least because of the sheer size of the task and the potential for human error. Artificial intelligence such as speech recognition and image classification software has helped to pave the way for integrating'machine learning' into search engine algorithms. Now new technologies are enabling engineers to push the boundaries even further. Artificial intelligence presents an opportunity to create an algorithm that learns from the behaviour of searchers and, ultimately, refines itself with minimal human input, if any. With the help of AI, search engines can consider factors such as your location, your search history, your favourite websites, and what other users click on for a similar query to give you the most appropriate search results for your individual needs. The AI can then analyse your behaviour in response to a particular search and how you interact with the results, and then improve what it offers the next time someone makes the same search.