NLP (Natural Language Processing)

AI_in_Industry

new ai pod casts

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

One of the most memorable moments from this interview is when our guest mentioned that Larry Page hired him to solve intelligence; very few people can say this, and this says something about today’s guest, Dr. Nando de Freitas - a senior researcher at Google and professor at Oxford - as well as the gravity of his present work. Today, I speak with Nando about a topic well known through his research at Google, deep learning. de Freitas gives his perspective on the basics of deep learning, the applications in conversational interfaces and recognizing images and videos, and what the future of this technology might look like in the nearer future.

DeepMind's Nando de Freitas - Why Deep Learning is Like Building with Legos

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

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

Artificial intelligence is coming - should be worried about our jobs? Well, it depends. Our guest Dr. Kevin LaGrandeur spent the last two years researching the impacts of automation and artificial intelligence on society and the job market. In this interview on AI in Industry, we explore the near future of AI's impact on the world of work, and I ask Kevin some important questions, including:  What skills are least "automate-able" in the next decade? What middle class professions have the greatest risk of automation, and what should those professionals be doing now to hedge against job loss? What should business leaders be doing now to prepare for "phasing out" work while still taking care of their employees?  For more interviews with AI executives and researchers (and more insight on applying AI in your organization) - visit us online at: www.TechEmergence.com

Surviving the Machine Age - Technological Job Loss with Kevin LaGrandeur

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?

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.

STRATA DATA CONFERENCE


Strata Data Conference in San Jose, March 5-8, 2018
Registration is now open 
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

When we talk about natural language processing (NLP), applications like handling customer service or chatbots which can aid with questions, come to mind. Yet, in recent years, NLP platforms have been increasingly used in content marketing and content production applications. In this episode of AI in industry, we talk to Tomás Ratia García-Oliveros, the co-founder and CEO founder of Frase.io, a Boston based startup which focuses on NLP problems around content marketing and content creation. Tomas explores how NLP platforms are now able to summarise resources on the web, perform contextual search and language understanding applications related to this domain. See the full interview article with&nbsp;Tomás Ratia García-Oliveros live at:  www.techemergence.com/artificial-intelligence-content-marketing-content-creation

Artificial Intelligence for Content Marketing and Content Creation

Episode summary: Most NLP applications we hear about involve marketing, customer service, and other customer-facing functions - but that there are NLP-related opportunities in other back-end functions as well.&nbsp;&nbsp; In this episode of AI in industry, we speak with Talla's Chief Data Scientist, Byron Galbraith, about how businesses can leverage chatbots or other NLP applications for improving document search for internal company communication. Byron explores what is currently possible using AI to improve search operations using contextual awareness. Byron also paints a vision of what AI-enabled "knowledge sharing" and "knowledge discovery" might look like in the future. For the full article of this episode, visit:&nbsp;TechEmergence.com/artificial-intelligence-team-communication/

Artificial Intelligence for Team Communication

Episode summary: This week’s episode explores the current possibilities in applying natural language processing for legal contract review. We speak with Andrew Antos and Nischal Nadhamuni from Klaritylaw, a Boston-based startup focused on using natural language processing (NLP) based information extraction, from non-disclosure agreements (NDAs), in a live setting. We delve into the current and future roles of AI and lawyers with respect to legal contracts. AI is currently being applied in applications like retroactive analysis and information identification in legal documents. According to Andrew and Nishchal, in the future we will see on-the-fly legal content creation from AI tools and NLP being applied to most commercial contracting. Although, one restraint that AI companies presently face in the legal domain is the lack of access to huge amounts of publicly available data. You can listen to the full interview with Andrew and Nischal here:    https://www.techemergence.com/applying-ai-legal-contracts-whats-possible-now/&nbsp;‎

Applying AI to Legal Contracts - What's Possible Now

Episode Summary: Machine learning (ML) can be used to identify objects and pictures or help steer vehicles, but is not best suited for text-based AI applications says Robbie Allen, founder of Automated Insights. In this episode of AI in Industry, we speak with Robbie about what is possible in generating text with AI and why rules based processes are a big part of natural language generation (NLG). We also explore which industries are likely to adopt such NLG techniques and in what ways can NLG help in business intelligence applications in the near future. You can listen to the full interview with Robbie here:    https://www.techemergence.com/robbie-allen-from-automated-insights-the-use-cases-of-natural-language-generation   &nbsp;

Robbie Allen from Automated Insights - The Use-Cases of Natural Language Generation

Episode summary: In this week's interview on the AI in Industry podcast, we speak with Amir Konigsberg, the CEO of Twiggle, about the future of product search - and how eCommerce and retail brands can use natural language processing (NLP) to improve their user experience. Amir explains some of the factors that make eCommerce product search challenging, and the artificial intelligence approaches that can improve it today and within the next five years. Interested readers can learn more about present and future use-cases for artificial intelligence applications in retail in our full article on that topic. You can listen to the full interview with Amir Konigsberg from Twiggle here:  https://www.techemergence.com/nlp-for-ecommerce-search-current-challenges-and-future-potential

NLP for eCommerce Search - Current Challenges and Future Potential

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

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 Chatbots Work, and How They Evolve

The AI in Business Podcast