If you have a question about this talk, please contact Mohammad Taher Pilehvar. The efficacy of applying machine learning to real-world problems has benefitted from the affordable availability of computational resources (CPU cycles, RAM) in abundance. In this presentation, I aim to present how linguistic and machine learning can be usefully combined to solve customer problems. To this end, I will describe some example applications of machine learning to problems from the financial, legal & regulatory and pharmacology domains, all using different use cases and expectations yet all based on natural language processing aided by supervised learning. I conclude by contrasting academic research from scientific research conducted in an industry environment.

Google Play Music, Google's music streaming service, is getting smarter. On Monday, an update to the Google Play Music app will kick in a new feature that can predict what you want to hear next. In fact, Google is so confident in the app's accuracy, that it completely revamped the welcome screen on Play Music so you can just tap to start listening to a playlist suited for you based on the time, your location, and a variety of other factors. Google says it's able to accomplish this through machine learning, the technology that's starting to power a lot of intelligent services Google makes like Google Photos and the new Google Assistant. It also plays into Google's broader mission to use machine learning and artificial intelligence in all of its products.

I'd pick a universally accessible binary classification problem: learning which foods are yummy and which are yucky. 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"). For each labeled example, we also provide the computer with ways to describe the food ("features").

The next frontier for artificial intelligence may involve teaching computers to distinguish albacore tuna from its yellowfin cousin. The Nature Conservancy, an environmental non-profit, is working with several Pacific Island nations and a big tuna fishing company to more easily count and identify fish caught at sea using cutting edge technology. The goal is to use trendy artificial intelligence techniques like deep learning to help fishermen reduce the number of protected animals like sharks and turtles that are accidentally caught along with the tuna. The Nature Conservancy hopes that the program could prevent overfishing and help threatened and endangered sea life recover without putting fishermen out of work. "We have real optimism that data science community can help us differentiate a turtle from a tuna, and flag when a shark comes on board," said Mark Zimring, a project director for The Nature Conservancy.

How do you feel about conversations with bots so far? Are they engaging, disappointing or interesting? For bot makers, there are lots of challenges in crafting more engaging bots. Challenge 1: Getting inspiration for how to build your bot based on how others built theirs. You can't just search for bots based on the meta details in conversations, because there isn't such a database.

Just over five years ago, IBM's Watson supercomputer crushed opponents in the televised quiz show Jeopardy. It was hard to foresee then, but artificial intelligence is now permeating our daily lives. Since then, IBM has expanded the Watson brand to a cognitive computing package with hardware and software used to diagnose diseases, explore for oil and gas, run scientific computing models, and allow cars to drive autonomously. The company has now announced new AI hardware and software packages. The original Watson used advanced algorithms and natural language interfaces to find and narrate answers.

The labor market always seeks to remove inefficiencies, and it is true that whatever can be automated will be. This has been true since the 1770s, when textile factories became the first to succeed with mechanization, ushering in the industrial revolution and delivering advances across industries, building great fortunes and elevating the life of the average worker. Artificial intelligence (AI) will define the next industrial revolution, delivering similarly game-changing benefits to companies and workers through efficiency and improved human capital allocation. Investors recognize this, and the record-breaking capital flows into the AI market underscore the awaiting opportunity. However, with revolution comes uncertainty, and the consensus is that AI will lead to job loss.

Fujitsu Reports Boost to Todai Robot's College Exam Scores A revolutionary AI technique is about to transform the self-driving car. Here's how Google Play is using AI to improve search Nvidia Boom Thanks to Artificial Intelligence: Can the Chipmaker Keep It Up? Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

Google will buy London-based artificial intelligence company DeepMind. The Information reports that the acquisition price was more than $500 million, and that Facebook was also in talks to buy the startup late last year. DeepMind confirmed the acquisition to us, but couldn't disclose deal terms. The acquisition was originally confirmed by Google to Re/code. Google's hiring of DeepMind will help it compete against other major tech companies as they all try to gain business advantages by focusing on deep learning.

Google's streaming music service can now predict what you want to listen to next Why Google Bets Voice-Powered Devices Are The Future Of Computing? Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.