NLP (Natural Language Processing)

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Hey, sick of the election yet?  Fear not, there are algorithms that can automagically generate political-ish speech so that we never need to be without an endless supply of Congressional speeches and Donald Trump twitticisms!  

Relevant links:
http://arxiv.org/pdf/1601.03313v2.pdf
http://qz.com/631497/mit-built-a-donald-trump-ai-twitter-bot-that-sounds-scarily-like-him/
https://twitter.com/deepdrumpf

Congress Bots and DeepDrumpf

Closing Gaps in Natural Language Processing May Help Solve World’s Tough Problems – w/ Dr. Dan Roth

Ten years ago, it would have been difficult to talk into your phone and have anything meaningful happen. AI and natural language processing (NLP) have made large leaps in the last decade, and in this episode Dr. Catherine Havasi articulates why and how. Havasi talks about how NLP used to work, and how a focus on deep learning has helped transform the prevalence and capabilities of NLP in the industry. For the last 17 years, Havasi has been working on a project through the MIT Media Lab called ConceptNet, a common sense lexicon for machines. She is also Founder of Luminoso, which helps businesses make sense of text data and improve their business processes.

NLP Systems Have a Lot to Learn from Humans

Natural language processing (NLP) sounds cool in theory. We’re familiar with Siri and Echo of course, but where does it play a role in other companies? In today’s episode, we speak with Samuir Rahman from Mattermark, whose entire business model is predicated on organizing and making findable information about companies, and generating a platform to search by unique criterion. Doing so involves some conceptual work with NLP to make things findable. Samuir talks about what Mattermark is doing with this technology now and where he thinks the future may take the field, and interesting topic for investors and founders alike.

How Natural Language Processing Helps Mattermark Find Business Opps

This week's interview was recorded live at Nuance's Silicon Valley office with guest&nbsp;Charlie Ortiz, director of the AI and Natural Language (NL) Processing Lab for Nuance Communications in Silicon Valley. In this episode,&nbsp;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).

The Future of Chatbots and Personal Assistants at Nuance's AI Lab

I'm always a little shocked when I see how much venture investing goes into the healthcare space, which brings me to the subject of this week's episode: just how the healthcare industry&nbsp;is (and isn't) being impacted by innovations in AI technology. Guest Steve Gullans of Boston-Based Excel Venture Management&nbsp;talks&nbsp;about some of the various healthcare-related ML and AI applications that he sees being brought to light, and touches&nbsp;on which innovations have a better chance of getting blocked and redirected by parties of interest&nbsp;and those that&nbsp;have more promise in being accepted and rolled out sooner. By the end of this episode, listeners will have a&nbsp;more clear picture of practical considerations in&nbsp;healthcare technology adoption, reasons&nbsp;that are&nbsp;often less about quality or potential of the technology and more about&nbsp;clarity on ROI for investors.

AI Healthcare Applications – and Why Doctors Don't Want to Be Replaced

How an argument in a bar led Google's Ian Goodfellow to create Generative Adversarial Networks - deep learning systems that argue with each other - an AI breakthrough that promises to help researchers build systems that can learn with less human intervention.

Ep. 25: Google's Ian Goodfellow on How an Argument in a Bar Led to Generative Adversarial Networks

In recent years hedge funds have taken the lead in algorithmic investing - or robo-trading as it’s sometimes called. But there’s no reason the hedge fund world should have all the good stuff. In this episode of the AI Podcast, we speak with Gaurav Chakravorty, co-founder of qplum, a startup that’s working to bring that same machine learning investing approach to the rest of us.

Ep. 26: Deep Learning Promises to Bring Algorithmic Investing Smarts to the Rest of Us

Purple shirts, haircuts, and cats. How are these three all related? According to deep learning pioneer Andrew Ng, they all played a part in AI’s growing presence in our lives. Ng, formerly of Google and Baidu, and the founder of his new company, Deeplearning.ai, joined this week’s episode of the AI Podcast to share his thoughts on AI being the new electricity.

Deep Learning Pioneer Andrew Ng on AI as the New Electricity

Ep. 32: Deep Learning Pioneer Andrew Ng on AI as the New Electricity

The robots that have taken on tasks in the real world - which is to say the world where physics apply - are primarily programmed to do a specific job, such as welding a joint in a car or sweeping up cat hair. So what if robots could learn, and take it a step further - what if they could teach themselves, and pass on their knowledge to other robots? Where could that take machines, and the notion of machine intelligence? And how fast could we get there? Those are the questions our guest Sergey Levine,  an assistant professor at UC Berkeley's department of Electrical Engineering and Computer Sciences, is finding answers to.

Ep. 37: Sergey Levine on How Deep Learning Will Unleash a Robotics Revolution

Natural language processing has gained more and more attention with the raise of (or rather, the "fad" of) chatbots. Despite the flurry of press releases from companies about their conversational agents (only a few of which seem to be delivering real business value), few business leaders understand the value of NLP for customer service, sales enablement, or eCommerce. In this week's episode of AI in Industry we interview Narjes Boufaden, computational linguistics PhD and CEO of Keatext, an NLP company based in Montreal. Narjes explores the possible business applications of NLP - specifically for customer service and customer experience - and she also explains (in layman's terms) how NLP systems are trained and integrated into businesses today. The ROI on this episode (in my opinion), is a firm understanding of what NLP can and cannot do, and what business applications it can realistically solve today.&nbsp;I was fortunate to meet Narjes in person during my Montreal trip, and I'm glad we were able to bring her on the program shortly thereafter. For more expert interviews on the business applications of AI, visit: www.TechEmergence.com

NLP for Customer Service - How Does it Work?

This week on AI in Industry, we speak to Paul Barba (Chief Scientist at Lexalytics) about what how companies are using natural language processing, and what it takes (in terms of expertise, time, and training) to get these systems working. From sentiment analysis to categorization, Paul walks us through interesting and fruitful use-cases and sheds light on the back-end "tweaking" required to keep NLP productive in a changing business environment. For more interviews on the applications of AI in business, visit: www.TechEmergence.com

How Can Businesses Get NLP to Work?

In this episode, discover how chatbots and conversational agents can provide you an advantage in the realms of customer support, product, support, lead engagement, and more, and learn the theory behind creating useful chatbots you can use in your own business. Right now, if we intend to find a piece of information or purchase something on the Internet, we might use a search engine that provides us with a list of sites we can browse in order to find ourselves a resolution for that intent. This week’s guest, Chief Scientist at Conversica, Dr. Sid J Reddy, talks about how AI and ML can usher in the next a new era of search software, one that will bring you a faster, more accurate resolution to your intent. Most importantly, Dr. Reddy discusses how chatbot technology can be integrated into areas such as customer service, product support, and lead engagement. By the end of the episode, listeners will have a better idea of the importance of collecting data and how they can use that data to &nbsp;to build chatbot templates they can use in multiple domains and applications. For more interviews on the business applications of AI, visit: www.TechEmergence.com

What Chatbots Can Do, and Cannot Do

When I first discovered and started using Apache Spark, a majority of the use cases I used it for involved unstructured text. The absence of libraries meant rolling my own NLP utilities, and, in many cases, implementing a machine learning library (this was pre deep learning, and MLlib was much smaller). I’d always wondered why no one bothered to create an NLP library for Spark when many people were using Spark to process large amounts of text. The recent, early success of BigDL confirms that users like the option of having native libraries.

In this episode of the Data Show, I spoke with David Talby of Pacific.AI, a consulting company that specializes in data science, analytics, and big data. A couple of years ago I mentioned the need for an NLP library within Spark to Talby; he not only agreed, he rounded up collaborators to build such a library. They eventually carved out time to build the newly released Spark NLP library. Judging by the reception received by BigDL and the number of Spark users faced with large-scale text processing tasks, I suspect Spark NLP will be a standard tool among Spark users.

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Talby and I also discussed his work helping companies build, deploy, and monitor machine learning models. Tools and best practices for model development and deployment are just beginning to emerge—I summarized some of them in a recent post, and, in this episode, I discussed these topics with a leading practitioner.

Building a Natural Language Processing Library for Apache Spark

There’s a lot of hype out there about conversational AI. Although according to our guest, we’re nowhere near the day when AI can generate accurate conversations for the average business to integrate into their customer service, chatbots still have practical applications. In this episode, we interview the head of research at Digital Genius, Yoram Bachrach. Yoram succinctly outlines the current applications of chatbots—what they can and can’t do—and details how business can best prepare to automate their customer service. For more interviews about the applications of AI in industry, visit us online: www.TechEmergence.com

How Chatbots Work, and How They Evolve

In this episode of AI in Industry, we explore how artificial intelligence can be use to manipulate human behavior - in gaming and in business. We explore how game designers use psychology and machine learning to drive their own desired outcomes, leaving users to "feel" in control. Dr. Charles Isbell teaches machine learning at Georgia Tech. He explores the manipulative elements of game design, and how some of the same AI approaches are likely being used at tech giants like Amazon and Facebook. In this episode you learn how businesses leverage the "illusion of choice" with subtly influential AI techniques. Charles also helps us understand which businesses will be most able to use AI to guide user behavior in the years ahead. For more interviews about the applications of AI in industry, visit: www.TechEmergence.com

AI for Social Influence and Behavior Manipulation with Dr. Charles Isbell

This week, AI in Industry features Dr. David Franke, Chief Scientist at Vast. David talks about how AI can work with scarce transaction data to derive meaningful analytics for big purchases, such as cars and houses. He elaborates on how the AI can glean information from user interaction and marketplace data to provide customers with the relevant product fit, deals and recommendations on big purchases. He also discusses the future trends and business benefits for early adopters of AI for purchase recommendations of high-cost items.&nbsp; For more insights on this topic, go to&nbsp;www.techemergence.com &nbsp;

AI Recommendation Engines for Big Purchases - Will You Buy Your Home or Car Using AI?

This week, AI in Industry features Jeremy Barnes, Chief Architect at Element AI. Jeremy talks about the common mistakes some businesses might make while adopting AI to solve broad business problems. He also sheds light on the problem areas that could raise the market value of businesses through AI adoption, hiring the right talent with the right combination of subject matter expertise and business experience, and the business and technical aspects executives should consider before contemplating the adoption of AI. For more insights on the B2B applications of AI, go to techemergence.com

Avoiding Common Mistakes in Applying AI to Business Problems - with Jeremy Barnes of Element AI

Episode Summary: In this episode of the podcast, we interview AIG’s Chief Data Science Officer, Dr. Nishant Chandra, about natural language processing (NLP) for internal and team communication. Dr. Chandra talks about how NLP can help with sharing documents with specific team members whose roles warrant viewing those documents. Instead of a broad memo that would go out across the company, a document could be transformed to a tailored message depending on the individual receiving it. For instance, a document could be presented in a digestible way to the executive team, but be distilled to contain fewer details for the technology team to make it relevant to them. How might NLP serve this summarization role for internal communications in the next 5 years? See the full interview article here: www.techemergence.com/nlp-text-summarization-team-communication

NLP for Text Summarization and Team Communication

Robots following coded instructions to complete a task? Old school. Robots learning to do things by watching how humans do it? That’s the future. Earlier this year, Stanford’s Animesh Garg and Marynel Vázquez shared their research in a talk on “Generalizable Autonomy for Robotic Mobility and Manipulation” at the GPU Technology Conference last week.  We caught up with them to learn more about generalizable autonomy - the idea that a robot should be able to observe human behavior, and learn to imitate it in a way that’s applicable to a variety of tasks and situations. Like learning to cook by watching YouTube videos, or figuring out how to cross a crowded room for another.

Teaching Bots Learn by Watching Human Behavior - Ep. 67

Episode Summary: In this episode of the AI in Industry podcast, we interview Rajat Mishra, VP of Customer Experience at Cisco, about the best practices for adopting AI in the enterprise and how business leaders should think about the man-machine balance at their companies. Mishra talks with us about&nbsp;how the executive team should be able to imagine the future of specific work roles that might integrate AI technology or envision how those roles will shift in the short-term. In other words, how will AI affect workflows?

Balancing Machines and Human Employees When Adopting AI in the Enterprise

What makes a&nbsp;chatbot&nbsp;or a&nbsp;conversational interface&nbsp;actually work? What kind of work does one need to do to get a chatbot to do what one wants it to do? These are pivotal questions and questions that for most business leaders are still somewhat mysterious, but that’s exactly what we’re aiming to answer on this episode of the&nbsp;AI in Industry&nbsp;Podcast. This week we speak with Madhu Mathihalli, CTO and co-founder of Passage AI. We speak specifically about&nbsp;what kinds of tasks conversational interfaces are best at, what kinds of word tracks, what kind of questions and answer are they suited for and which are a bit beyond their grasp right now.&nbsp;In addition, we speak about what it takes to train these machines. In other words, how do we define the particular word tracks that we want to be able to automate and determine which of them might be lower hanging fruit for applying a chatbot or which of them might not? Read or listen to the full podcast here:&nbsp;https://www.techemergence.com/how-to-get-a-chatbot-to-do-what-one-wants-in-business/

How to Get a Chatbot to do What One Wants in Business

Dr. Kai-Fu Lee has been at the center of artificial intelligence for decades. 

dr. Lee developed the world's first speaker independent continuous speech recognition system, selected as the most important innovation of the year by BusinessWeek, and that was back in 1988. 

In the three decades since, Dr. Lee has led teams at Apple, Silicon Graphics, Microsoft and Google. 

In 2009 Dr. Lee left Google to start Sinovation Ventures, which now manages a $2 billion fund focusing on technology startups in China and the United States. 

His latest book, "AI Superpowers: China, Silicon Valley, and the New World Order," ranks number six on the New York Times Business Books best sellers list.

Kai-Fu Lee on the Future of AI in the US, China - Ep. 72

Investor, AI Pioneer Kai-Fu Lee on the Future of AI in the US, China - Ep. 72

Emerj.com is where business leaders turn to understand how AI is impacting their business and industry - and what to do about it.

Our objective, jargon-free research and industry overviews are designed to give executives and decision-makers exactly what they need for competitive insight, informed AI technology procurement and strategic planning around AI.

AI disruption will open up new vistas for opportunity, creating new business paradigms and making others extinct. 

Emerj market research helps leaders survive and thrive in an era of AI disruption.

www.emerj.com

How Can Businesses Get NLP to Work?

The AI in Business Podcast