Buenisimos

NLP (Natural Language Processing)

data stuff

The O'Reilly Data Show explores the opportunities and techniques driving big data and data science. Through interviews and analysis, we highlight the people putting data to work.

The O'Reilly Data Show Podcast

Evaluación de la situación fiscal argentina, a la que el economista atribuyó durante todo el año el desenlance recesivo e inflacionario que estamos viviendo.

Carlos Maslatón entrevista a José Luis Espert, Realpolitik 20/12/2013 FM Identidad

Entrevistamos al economista José Luis Espert, super preciso con los números fiscales y cuasifiscales del estado argentino. También hablamos con el diputado nacional por Santa Fé de la Coalición Cívica-UNEN, Pablo Javkin, que no se reputa chavista y sí como un socialdemócrata. Javkin, de notable carrera, fue activista político en el secundario, en la Universidad a nivel de Centros y de federaciones regionales y FUA, luego concejal de Rosario, diputado provincial y ahora nacional, casi un cursus honorum de la república romana de la antigüedad. 

Realpolitik, 19/03/2014, imperdible por José Luis Espert y por el diputado Pablo Javkin

Nuevamente conversamos con el valioso periodista de Mar del Plata Claudio Ceraso sobre la desaparición del avión malayo del vuelo MH370. Luego, gran conversación con Horacio Liendo, autor de la Ley de Convertibilidad de 1991, donde analizamos primero la esencia de la moneda incluído el Bitcoin como creación humana descentralizada, para desembocar a continuación en la deuda pública argentina y en los juicios contra Argentina en Nueva York. Además, nuestros comentarios políticos y económicos como siempre. 

Realpolitik 27/03/2014 con Horacio Liendo y con Claudio Ceraso de Radio Mitre Mar del Plata

Emilio Apud ha sido Secretario de Energía de la Nación y nos actualiza sobre YPF, Vaca Muerta y el shale gas. 

Realpolitik 03/04/2014, editorial sobre seguridad, Malvinas, CFK y conversación con Emilio Apud

Un enfoque social de la reconversión de Buenos Aires de ciudad europea a ciudad tercermundista, más una revisión del actual escenario de la gastronomía porteña con la periodista gastronómica Nieves Otero. 

Realpolitik 04/04/2014; transformación de Buenos Aires y estado de la gastronomía; Nieves Otero

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

In this episode of the Data Show, I spoke with Aurélien Géron, a serial entrepreneur, data scientist, and author of a popular, new book entitled "Hands-on Machine Learning with Scikit-Learn and TensorFlow." Géron’s book is aimed at software engineers who want to learn machine learning and start deploying machine learning models in real-world products.

Becoming a Machine Learning Engineer

In this episode of the Data Show, I spoke with Jeremy Stanley, VP of data science at Instacart, a popular grocery delivery service that is expanding rapidly. As Stanley describes it, Instacart operates a four-sided marketplace comprised of retail stores, products within the stores, shoppers assigned to the stores, and customers who order from Instacart. The objective is to get fresh groceries from popular retailers delivered to customers in a timely fashion. Instacart’s goals land them in the center of the many opportunities and challenges involved in building high-impact data products.

Data Science and Deep Learning in Retail

In this episode of the Data Show, I speak with Naveen Rao, VP and GM of the Artificial Intelligence Products Group at Intel. In an earlier episode, we learned that scaling current deep learning models requires innovations in both software and hardware. Through his startup Nervana (since acquired by Intel), Rao has been at the forefront of building a next generation platform for deep learning and AI.

I wanted to get his thoughts on what the future infrastructure for machine learning would look like. At least for now, we’re seeing a variety of approaches, and many companies are using heterogeneous processors (even specialized ones) and proprietary interconnects for deep learning. Nvidia and Intel Nervana are set to release processors that excel at both training and inference, but as Rao pointed out, at large-scale there are many considerations—including utilization, power consumption, and convenience—that come into play.

Building a Next-Generation Platform for Deep Learning

In this episode of the Data Show, I spoke with Ken Stanley, founding member of Uber AI Labs and associate professor at the University of Central Florida. Stanley is an AI researcher and a leading pioneer in the field of neuroevolution—a method for evolving and learning neural networks through evolutionary algorithms. In a recent survey article, Stanley went through the history of neuroevolution and listed recent developments, including its applications to reinforcement learning problems.

Effective Mechanisms for Searching the Space of Machine Learning Algorithms

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

In this episode of the Data Show, I spoke with Roger Chen, co-founder and CEO of Computable Labs, a startup focused on building tools for the creation of data networks and data exchanges. Chen has also served as co-chair of O'Reilly's Artificial Intelligence Conference since its inception in 2016. This conversation took place the day after Chen and his collaborators released an interesting new white paper, "Fair value and decentralized governance of data." Current-generation AI and machine learning technologies rely on large amounts of data, and to the extent they can use their large user bases to create “data silos,” large companies in large countries (like the U.S. and China) enjoy a competitive advantage. With that said, we are awash in articles about the dangers posed by these data silos. Privacy and security, disinformation, bias, and a lack of transparency and control are just some of the issues that have plagued the perceived owners of “data monopolies.”

Acquiring and Sharing High-Quality Data

In this episode of the Data Show, I speak with Cassie Kozyrkov, technical director and chief decision scientist at Google Cloud. She describes "decision intelligence" as an interdisciplinary field concerned with all aspects of decision-making, and which combines data science with the behavioral sciences. Most recently she has been focused on developing best practices that can help practitioners make safe, effective use of AI and data. Kozyrkov uses her platform to help data scientists develop skills that will enable them to connect data and AI with their organizations' core businesses.

We had a great conversation spanning many topics, including:

How data science can be more useful

The importance of the human side of data

The leadership talent shortage in data science

Is data science a bubble?

Make Data Science More Useful

In this episode of the Data Show, I speak with Alex Ratner, project lead for Stanford’s Snorkel open source project; Ratner also recently garnered a faculty position at the University of Washington and is currently working on a company supporting and extending the Snorkel project. Snorkel is a framework for building and managing training data. Based on our survey from earlier this year, labeled data remains a key bottleneck for organizations building machine learning applications and services.

Ratner was a guest on the podcast a little over two years ago when Snorkel was a relatively new project. Since then, Snorkel has added more features, expanded into computer vision use cases, and now boasts many users, including Google, Intel, IBM, and other organizations. Along with his thesis advisor professor Chris Ré of Stanford, Ratner and his collaborators have long championed the importance of building tools aimed squarely at helping teams build and manage training data. With today’s release of Snorkel version 0.9, we are a step closer to having a framework that enables the programmatic creation of training data sets.

Labeling, Transforming, and Structuring Training Data Sets for Machine Learning

In this episode of the Data Show, I speak with Kesha Williams, technical instructor at A Cloud Guru, a training company focused on cloud computing. As a full stack web developer, Williams became intrigued by machine learning and started teaching herself the ML tools on Amazon Web Services. Fast forward to today, Williams has built some well-regarded Alexa skills, mastered ML services on AWS, and has now firmly added machine learning to her developer toolkit.

Becoming a Machine Learning Practitioner

In this episode of the Data Show, I speak with Michael Mahoney, a member of RISELab, the International Computer Science Institute, and the Department of Statistics at UC Berkeley. A physicist by training, Mahoney has been at the forefront of many important problems in large-scale data analysis. On the theoretical side, his works spans algorithmic and statistical methods for matrices, graphs, regression, optimization, and related problems. On the applications side, he has contributed to systems used for internet and social media analysis, social network analysis, as well as for a host of applications in the physical and life sciences. Most recently, he has been working on deep neural networks, specifically developing theoretical methods and practical diagnostic tools that should be helpful to practitioners who use deep learning.

Understanding Deep Neural Networks

O'Reilly Media spreads the knowledge of innovators. At O’Reilly, a big part of our business is paying attention to what’s new and interesting in the world of technology. We have a pretty good record at having anticipated some of the big technology developments in recent history. For instance, we launched the first commercial Web site, GNN, in 1993; we organized the meeting at which the term “open source” was first adopted; we were early investors in Blogger, which helped launch the blogging revolution; and more recently, our Web 2.0 conference launched a world-wide meme. We call this predictive sense the “O’Reilly Radar.” And while we’re certainly not always right, we are, at least, good at making interesting guesses.

Our methodology is simple: we draw from the wisdom of the alpha geeks in our midst, paying attention to what’s interesting to them, amplifying these weak signals, and seeing where they fit into the innovation ecology. Add to that the original research conducted by our Research team, and you start to get a good picture of what the technology world is thinking about.

Building a Natural Language Processing Library for Apache Spark

O'Reilly Radar