Parag Mital is director of machine learning at Kadenze, as well as an artist and interdisciplinary researcher obsessed with the nature of information, representation and attention. Artificial intelligence allows machines to reason and interact with the world, and it's evolving at a breakneck pace. Many advances in AI can be attributed to machine learning, which works by tapping massive computing power to crunch through enormous amounts of digitized data. Now consider that most of our data, the best minds in the business and more computing power than you could ever imagine sit with just a handful of companies. For these reasons, only a few companies in the world are best situated to understand the true potential -- and the current limits -- of AI.

A tutorial given at NAACL HLT 2013. Machine learning is everywhere in today's NLP, but by and large machine learning amounts to numerical optimization of weights for human designed representations and features. The goal of deep learning is to explore how computers can take advantage of data to develop features and representations appropriate for complex interpretation tasks. This tutorial aims to cover the basic motivation, ideas, models and learning algorithms in deep learning for natural language processing. Recently, these methods have been shown to perform very well on various NLP tasks such as language modeling, POS tagging, named entity recognition, sentiment analysis and paraphrase detection, among others.

How do you explain machine learning to a child? Answer by Daniel Tunkelang, data scientist, search/discovery expert, led teams at LinkedIn and Google, on Quora. We want to teach a computer to recognize which foods are yummy and which foods are yucky. But the computer doesn't have a mouth or any way of tasting the food. Instead, we need to teach it by showing it examples of foods ("labeled training data"), some of which are yummy foods ("positive examples") and some of which are yucky foods ("negative examples").

During the 2016 Collision Conference held in New Orleans, our Content Strategist Cecilia Haynes interviewed conference speaker Dr. Tyrone Grandison. At the time of the interview, he was the Deputy Chief Data Officer at the U.S. Department of Commerce. Tyrone is currently the Chief Information Officer for the Institute for Health Metrics and Evaluation. Coming fresh off his talk on "Data science, apps and civic responsibility", Cecilia was thrilled to chat with Tyrone all about the democratization of data and how open data can help anyone build innovative products and services. I saw your talk and I thought you would be the the perfect person to reach out to.

In the aftermath of the Brexit vote to leave, one of the tweets that caught my eye was from founder and CEO of analyst firm HfS Research, Phil Fersht (@pfersht), which simply said, "at least the British can stop worrying about robots taking their jobs. Just get rid of the jobs altogether..." While that may not be amusing as it plays out against our globalised economy, it demonstrates how this topic has taken hold of so many of us. About a year ago, I was asked to sit on a panel at the annual Constellation Research Connected Enterprise 2015 called'The robots are here! The future of HR tech', to debate whether we're entering a dystopian existence where humans are the bottleneck to productivity and innovation or becoming a world of augmented humanity and digital humanisation. It's a fact that major economic shifts have led to both marginalisation, the downside, as well as great opportunity, clearly the upside.

We may have just seen a major player in the drive towards autonomous cars apply screeching brakes. Apple has reportedly abandoned its plans to build its own self-driving electric vehicle and is instead going to focus on the underlying autonomous software. A similar initiative to produce a fully autonomous car by Google also appeared to run out of steam. Building self-driving cars clearly poses a challenge that even the world's top technology giants can't yet meet. So what is it about building autonomous cars that is proving to be such a challenge? The high-value consumer electronics and software industry is used to very different margins than the cut-throat automotive sector, which has tough market entry conditions and tribal supply-chain relationships.

Heart of the Tesla's new autonomous driving hardware, that some day will enable fully self-driving cars, is the latest NVIDIA DRIVE PX 2 AI computing platform (see live presentation of it in action below). NVIDIA DRIVE PX 2 is the open AI car computing platform that enables automakers and their tier 1 suppliers to accelerate production of automated and autonomous vehicles. For NVIDIA, DRIVE PX 2 is now in full production as Tesla requires thousands of units each month for manufacturing of the Model S and Model X, soon that number could be tens of thousands per month when the Model 3 assembly starts later next year. Tesla Motors has announced that all Tesla vehicles -- Model S, Model X, and the upcoming Model 3 -- will now be equipped with an on-board "supercomputer" that can provide full self-driving capability. The computer delivers more than 40 times the processing power of the previous system. It runs a Tesla-developed neural net for vision, sonar, and radar processing.

"As digitization and machine intelligence continues to proliferate and get pervasive, the need for customer intimacy, transparency, and security of data are becoming essential to next generation business networks," said Nij Chawla, Chief Product Officer of CognitiveScale. "CognitiveScale is one of the first companies to combine Blockchain, big data, and machine learning for industry-specific outcomes that power the next-generation Internet of Trust." CognitiveScale provides two products called ENGAGE and AMPLIFY that use machine intelligence to transform customer experience at the edge of the business and deploy self-learning, self-assuring business processes at the core. The company's product portfolio has been enhanced in the past year to address an evolving market, where trust, relevance, and assurance are becoming increasingly integrated and business critical. CognitiveScale will use Blockchain technology to underpin its products to put additional "smarts" in Blockchain smart contracts for multiple industries and processes, including financial services, healthcare, and procurement.

"THE development of full artificial intelligence could spell the end of the human race," Stephen Hawking warns. Elon Musk fears that the development of artificial intelligence, or AI, may be the biggest existential threat humanity faces. Bill Gates urges people to beware of it. Dread that the abominations people create will become their masters, or their executioners, is hardly new. But voiced by a renowned cosmologist, a Silicon Valley entrepreneur and the founder of Microsoft--hardly Luddites--and set against the vast investment in AI by big firms like Google and Microsoft, such fears have taken on new weight.

In the last twelve months, we've witnessed a huge surge in the development and adoption of chatbots, artificial intelligence (A.I.), and machine learning. Many startups, including my own (ReplyYes), are utilizing A.I. and chatbots to help consumers engage with brands through their mobile devices in interesting and creative ways. Examples include the Domino's chatbot, which enables customers to order a pizza through Facebook Messenger, the Burberry chatbot for London Fashion Week that helps customers order products they see on the runway, and Lowercase Alpha that helps founders and friends of Chris Sacca's venture capital firm Lowercase discover some of the best new apps in the world. Given the increasing interest and venture capital dollars being spent to build creative chatbot and A.I. solutions, we've developed eight predictions that outline where we think things will evolve in the next 18-24 months. We're sure there are many additional trends that we're not predicting that will come to fruition.