YouTube has figured out a way to get deeper inside your head. The Internet's biggest video site on Tuesday rolled out a smarter machine learning engine on its iOS and Android mobile apps, allowing it to serve up better recommendations for viewers. The gussied-up recommendation system is based on deep neural network technology -- the same type parent Google uses for search results -- which will help YouTube find patterns and learn more about what a viewer wants with each visit. "Delivering a personal recommendation engine that shows YouTube really understand you is our goal," Johanna Wright, vice president of YouTube product management, said in an interview. "We're able to do this because Google has some of the best machine learning in the world."

Machine learning is the process of building analytical models to automatically discover previously unknown patterns from data that indicate associations, sequences, anomalies (outliers), classifications, and clusters and segments. These patterns reveal hidden rules as to why an event happened--for example, rules that predict likely customer churn. The widely used Cross Industry Standard Process for Data Mining (CRISP-DM) methodology is used to develop predictive analytical models. CRISP-DM includes six phases: business understanding, data understanding, data preparation, model development using supervised and unsupervised learning, model evaluation and model deployment. The business understanding phase involves defining the business problem or use case, the business objectives and the business questions that need to be answered.

Finance disruptors and creative minds are invited to consider the role of AI in the future of banking in this, our second Crowdstorm with Deutsche Bank. The digital revolution has altered nearly every aspect of our daily lives and the financial industry is no exception. Deutsche Bank has recognised this shift and is committed to opening up their innovation process and jovoto creatives are playing an important role in this process. It is not often that IBM, Google, Facebook and Tesla agree on anything. But when it comes to AI, they all say it is THE decisive step towards industry 4.0. For this reason, the leading tech companies, as well as an increasing number of more typical industrial companies, are assigning the majority of their Research and Development budgets to AI applications.

Artificial intelligence: students were surprised to learn they had been dealing with a bot all semester. To help with his class this year, a Georgia Tech professor hired Jill Watson, a teaching assistant unlike any other in the world. Throughout the semester, she answered questions online for students, relieving the professor's overworked teaching staff. But, in fact, Jill Watson was an artificial intelligence bot. Ashok Goel, a computer science professor, did not reveal Watson's true identity to students until after they'd turned in their final exams.

One of the largest law firms in the US, Baker & Hostetler has hired Ross, according to a report at Futurism. Ross is a natural language legal assistant for lawyers, that is based on IBM Watson Artificial Intelligence. Ross can sift through mountains of legal data, to give succinct and direct answer to questions. It works like a search engine, but instead of giving a list of answers that again puts the onus of shifting through the data on the user, Ross just gives one most appropriate answer. Ross is different from other digital assistants that do this, because instead of basing the results on keyword indexing, Ross actually has cognitive capabilities.

PDF: We made a fancy PDF of this post for printing and offline viewing. Note: The reason this post took three weeks to finish is that as I dug into research on Artificial Intelligence, I could not believe what I was reading. It hit me pretty quickly that what's happening in the world of AI is not just an important topic, but by far THE most important topic for our future. So I wanted to learn as much as I could about it, and once I did that, I wanted to make sure I wrote a post that really explained this whole situation and why it matters so much. Not shockingly, that became outrageously long, so I broke it into two parts. This is Part 1--Part 2 is here. We are on the edge of change comparable to the rise of human life on Earth. It seems like a pretty intense place to be standing--but then you have to remember something about what it's like to stand on a time graph: you can't see what's to your right. So here's how it actually feels to stand there: Imagine taking a time machine back to 1750--a time when the world was in a permanent power outage, long-distance communication meant either yelling loudly or firing a cannon in the air, and all transportation ran on hay. When you get there, you retrieve a dude, bring him to 2015, and then walk him around and watch him react to everything. It's impossible for us to understand what it would be like for him to see shiny capsules racing by on a highway, talk to people who had been on the other side of the ocean earlier in the day, watch sports that were being played 1,000 miles away, hear a musical performance that happened 50 years ago, and play with my magical wizard rectangle that he could use to capture a real-life image or record a living moment, generate a map with a paranormal moving blue dot that shows him where he is, look at someone's face and chat with them even though they're on the other side of the country, and worlds of other inconceivable sorcery.

Google has a big gift for anyone trying to fulfill the promise of artificial intelligence: software that helps computers understand human speech and text. The parser software, called SyntaxNet, breaks sentences down into components to better understand the meaning of words -- a boon to AI developers trying to get computers to grok natural language. SyntaxNet is now open-source software so anybody can use it for free and modify it how they want. Google did the same in 2015 with another AI technology, TensorFlow, which lets anyone link computers into a neural network that can process data in a way analogous to our own biological brains. But neural networks aren't useful until they've been trained on massive quantities of real-world data, for example by processing millions of images or listening to thousands of hours of speech.

In this presentation we will look at implant technology, both in terms of new forms of identification and also in bringing humans and technology together, particularly with regard to the security issues that this presents. We will also see how good the latest artificial intelligence is at posing as a human. The audience will be given the opportunity to try for themselves and see if they can tell the difference between human and machine. Kevin Warwick is Emeritus Professor at Reading and Coventry Universities. His research areas are artificial intelligence, biomedical systems and...

Google has revealed the most powerful computer for understanding the English language in the world – and called it Parsey McParseFace. The technology, which is built on the more sensibly named TensorFlow and SyntaxNet frameworks, is a powerful tool that uses new artificial intelligence technology to be able to analyse the linguistic structure of language, and understand what each part of a sentence does to its meaning. Google is making the tool open source, so that anybody can use it for free. But it will probably go down in history because of its silly name. Google said that the name – a reference to the controversial Boaty McBoatface – was a suggestion that came while it was trying to name the new technology, and that it didn't have any better alternatives.

The amount of data produced every day grows tremendously in most real-life domains, including medical imaging, genomics, text categorisation, computational biology, and many others. Although it appears beneficial at the first glance (more data could mean more possibilities of extracting and revealing useful underlying knowledge), handling massively large datasets became a challenging issue and attracts research attention, especially in the era of big data. This big data revolution affected many research fields, including statistics, machine learning, parallel computing, and computer systems in general [1]. Storing and analysing the acquired historical information should allow predicting the label of an incoming (unseen) feature vector, containing some quantified features of a given data example. If the labels are categorical, then we are to tackle the classification task (it's regression otherwise).