AI Articles and Analysis about Customer service

Episode Summary: This week's interview was recorded live at Nuance's Silicon Valley office with guest Charles Ortiz, director of the AI and Natural Language (NL) Processing Lab for Nuance Communications in Silicon Valley. In this episode, Ortiz speaks about what he sees as the most important developments in natural language processing (NLP) over the last few years, what advancements brought us to where we are today, and where progress might take NLP in the coming years ahead (both at Nuance and beyond).

Episode Summary: In this episode, we speak with Dr. Matteo Berlucchi, the founder of Your.MD, which uses artificial intelligence to create one of the first personal health assistant platforms in 70+ countries. Berlucchi talks about the challenges in making an AI do what you want, specifically helping people self diagnose and seek proper treatment. He discusses the multiple approaches to AI that are blended together in order to yield optimal results, and touches on the sometimes stark differences between what AI can do in the lab versus the functional application for tens of thousands of people. If you're interested in the diverse applications of AI and the challenges in running a startup, Dr. Berlucchi's makes for an interesting episode.

Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chatbots, or search engines. Given the high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google's Tensorflow).

It's been over a month since our last major artificial intelligence consensus (which covered 33 AI researcher perspectives on the 20-year risks of AI), and we decided that this time around, we'd speak with AI executives directly about the future of artificial intelligence and machine learning in consumer tech.
The media is awash with buzz-stories about autonomous vehicles, speech recognition, robotics, and more, but it seems difficult to glean a perspective on which consumer AI tech trends are likely to make the biggest impact in the coming 5 years.
While there's certainly no crystal ball, our preference as a market research firm is to combine research and news analysis with a strong consensus from dozens of experts in the field. When it comes to AI for consumer tech, we decided to ask executives and founders of artificial intelligence companies what they believe to be the most important AI consumer tech trends in the next half decade.
You can see a full list of the answers to our "AI Consumer Tech Trends" below in our large infographic.

Episode Summary: This week's in-person interview is with Dr. Adam Coates, who spent 12 years at Stanford studying artificial intelligence before accepting his current position of Director of Baidu's Silicon-Valley based artificial intelligence lab. We speak about his ideas around consumer artificial intelligence applications and impact and what he's excited about, as well as what he thinks may be more 'hype' than reality. He gives a an idea about applications that Baidu is working, to potentially influence billions of mobile and computer users worldwide. If you're interested in the developments of speech recognition and natural language processing, this is an episode you won't want to miss.

Despite what the media tends to depict, artificial intelligence is being put to better use than winning video games and board games. In fact, two of the world’s leading tech giants have begun using AI to help the blind perceive the world in helpful new ways.

Episode Summary: We’ve featured a number of artificial intelligence researchers on the show, but today we switch gears and dive into the business side of the industry. In this episode, Dr. Mazin Gilbert (who earned his PhD in Engineering) breaks down AT&T’s efforts to make more intelligent systems large-scale. How do they train their network to route traffic through the right nodes on holidays, when certain areas of traffic are overloaded? How can a system know, based on signals from hardware, which pieces might be going bad and need replacing and send out a message to alert the company? Making a network ‘aware’ is a large challenge, but Mazin gives an insider’s perspective as to how AT&T uses machine learning technologies in order to remain profitable.

Facebook's entry into the personal assistant game with it's "M" assistant is actually an interesting view to Facebook's big bet on the future of consumer AI.