NLP (Natural Language Processing)

Deep Learning

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

In this session of the Super Data Science Podcast, I chat with Ruben Kogel who has done some amazing data analytics in the startup space.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/1

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SDS 001 : Self-serve Analytics, Data Science MBA and Managing a Team of Analysts with Ruben Kogel

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

Think of our inaugural episode of The AI Podcast as a guide for the perplexed.  Host Michael Copeland speaks with NVIDIA's Will Ramey about the history behind today's AI boom and the key concepts you need to know to get your head around a technology that's reshaping the world.

Ep. 1: Deep Learning 101 - Will Ramey, NVIDIA Senior Manager for GPU Computing

In many ways, AI and finance are made for each other. Machine learning and other techniques make it easier to identify patterns that might otherwise not be detected by the human eye, and finance is quantitative to begin with so that it’s hard not to find traction. Financial firms have also invested heavily in AI in the past, and more are starting to tap into the  financial applications of machine learning (ML) and deep learning. This week, we’re joined by CEO and Co-founder of Kavout&nbsp;Alex Lu, whose company offers AI trading applications for enterprises and individuals. Lu speaks today about the kinds of patterns that traders now have access to in finance, and he gives examples of ways Kavout and other institutions are using artificial intelligence in stock trading to build better and more personalized products and services. &nbsp;

Artificial Intelligence in Stock Trading - Future Trends and Applications

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

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: Natural language processing (NLP) has become popular in the past two years as more businesses processes implement this technology in different niches. In inviting our guest today, we want to know specifically which industries, businesses or processes NLP could be leveraged to learn from activity logs. For instance, we aim to understand how car companies can extract insights from the incident reports they receive from individual users or dealerships, whether it is a report related to manufacturing, service or weather. In the same manner, how can insights be gleaned from the banking or insurance industries based on activity logs? We speak with the University of Texas’s Dr. Bruce Porter to discover the current and future use-cases of NLP in customer feedback. &nbsp;Interested readers can listen to the full interview with Bruce here:  https://www.techemergence.com/using-nlp-customer-feedback-automotive-banking &nbsp;

Using NLP for Customer Feedback in Automotive, Banking, and More

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

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NLP for Customer Service - How Does it Work?

The AI in Business Podcast