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

As a human, we can often understand the mood, intention, and future action of another person just by looking at them. We see their posture, their facial expression, where their eyes are focused, and we can get a decent understanding of what they might do next. The problem of computer vision for body language is a much harder problem to solve, but we are indeed making progress. Our guest this week is Paul Kruszewski, an computer science PhD who's spent nearly the last 20 years focused on 3D modeling and artificial intelligence. Today, he's CEO of Wrnch, a Montreal-based AI company focused on reading and understanding human body language. Paul explains how advances in 3D modeling and computer vision have allowed researchers to get machines to "understand" the posture, movements, and intentions of human being - and he also helps explore the future applications that this technology might have in security, retail, sports, and more. For more interviews on the applications of AI in business, visit: https://TechEmergence.com

Computer Vision for Body Language - How it Works and How it Could be Used

This week on AI in Industry, we explore IoT security with Bob Baxley (Chief Engineer at Bastille). This includes information on how different IoT security is compared to infosec, the unique challenges IoT security presents (for detecting and scanning wireless network traffic that runs on various protocols and for classifying types of cyberthreats), what the future of IoT security might look like, and how deep learning and machine learning tools can be used to better classify and detect threats and attacks in the cyberspace. For more insight on the applications of AI in industry, visit: TechEmergence.com

AI for IoT Security - with Dr. Bob Baxley of Bastille

Sarcasm? On the Internet? You're kidding. But sarcasm is no joke. Long before today's sentiment analysis systems struggled to accurately understand human communication, people struggled to understand one another's sarcasm. Now, thanks to the work of Dr. Pushpak Bhattacharyya and his team computers are beginning to understand one of humanity's most challenging, and amusing, modes of communication. Dr. Bhattacharyya, director of IIT Patna, and a professor at the Computer Science and Engineering Department at IIT Bombay has spent the past few years using GPU-powered deep learning to detect sarcasm.

Ep. 48: Sarcasm Detector Uses AI to Understand People at Their Funniest, Meanest

In this episode of the Data Show, I spoke Jesse Anderson, managing director of the Big Data Institute, and my colleague Paco Nathan, who recently became co-chair of Jupytercon. This conversation grew out of a recent email thread the three of us had on machine learning engineers, a new job role that LinkedIn recently pegged as the fastest growing job in the U.S. In our email discussion, there was some disagreement on whether such a specialized job role/title was needed in the first place. As Eric Colson pointed out in his beautiful keynote at Strata Data San Jose, when done too soon, creating specialized roles can slow down your data team.

We recorded this conversation at Strata San Jose, while Anderson was in the middle of teaching his very popular two-day training course on real-time systems. We closed the conversation with Anderson’s take on Apache Pulsar, a very impressive new messaging system that is starting to gain fans among data engineers.

What Machine Learning Engineers Need to Know

Convolutional nets are great for image classification... if this were 2016. But it's 2018 and Canada's greatest neural networker Geoff Hinton has some new ideas, namely capsule networks. Capsule nets are a completely new type of neural net architecture designed to do image classification on far fewer training cases than convolutional nets, and they're posting results that are competitive with much more mature technologies.

In this episode, we'll give a light conceptual introduction to capsule nets and get geared up for a future episode that will do a deeper technical dive.

A Conceptual Introduction to Capsule Networks

Last episode we talked conceptually about capsule networks, the latest and greatest computer vision innovation to come out of Geoff Hinton's lab. This week we're getting a little more into the technical details, for those of you ready to have your mind stretched.

A Technical Introduction to Capsule Networks

Episode summary: In this week’s episode of AI in Industry we speak with Rodney Brooks, Founder and CTO of Rethink Robotics, a collaborative robot manufacturers founded in Boston in 2008. Rodney explores robotic safety an regulations and he also paints a picture of what robots might be capable of in the next five years. Executives in the logistics and manufacturing sectors considering adopting robots will find Rodney’s insights most valuable. &nbsp;Rodney explores what applications will move into the realm of robotics and what application won't in the near future and delves into what business executives need to know about human robot collaboration before considering their adoption. Interested readers can see the full interview with Rodney Brooks from Rethink Robotics here:&nbsp;https://www.techemergence.com/improving-robot-safety-capability-artificial-intelligence-rodney-brooks/

Improving Robot Safety and Capability with Artificial Intelligence - with Rodney Brooks

A huge part of the ascent of deep learning in the last few years is related to advances in computer hardware that makes it possible to do the computational heavy lifting required to build models with thousands or even millions of tunable parameters. This week we'll pretend to be electrical engineers and talk about how modern machine learning is enabled by hardware.

CPUs, GPUs, TPUs: Hardware for Deep Learning

Episode summary: In this week’s episode of AI in Industry we speak with DataRobot CEO Jeremy Achin about the future of AI applications for people without a data science background. We specifically discuss how future AI tools might bypass the complexity of machine learning programming and make intuitive interfaces that function more like today’s everyday software. Our business leader listeners will be interested in Jeremy’s predictions about how the UX for AI-related tools might become more simplified and code-less in the coming 5 years. Interested readers can listen to the full interview with Jermy here:&nbsp;https://www.techemergence.com/will-artificial-intelligence-become-easier-use/

Will Artificial Intelligence Become Easier to Use?

This week's episode features Bryan Catanzaro, vice president of applied deep learning research at NVIDIA, and if you've been following the podcast for a while, you know that an earlier episode featuring Bryan is one of the most popular podcasts we've done. Bryan is going to walk us through some of the latest developments at NVIDIA research... as well as share a story that involves Andrew Ng and cats.

NVIDIA's Bryan Catanzaro on the Latest from NVIDIA Research - Ep. 57

Think of it as like a USB port for your body. Emil Hewage is the co-founder and CEO at Cambridge Bio-Augmentation Systems, a neural engineering startup. They UK startup is building interfaces that use AI to help plug medical devices int our nervous systems.  CBAS was named one of the top startups at Y Combinator’s Winter ‘17 cohort by TechCrunch and won the top prize with accelerator MassChallenge UK 2015.

A USB Port for Your Body? Startup Uses AI to Connect Medical Devices to Nervous System - Ep. 59

Episode summary: In the last two or three years we at TechEmergence have witnessed a definite uptick in AI applications like predictive maintenance and heavy industry. Many exciting  business intelligence and sensor data applications are making their way into “stodgy” industries like transportation,  oil and gas, and  telecom - where machine vision has countless applications. We had caught up with Massimiliano Versace, CEO of Neurala over 4 years ago in an interview about the ethical implications of AI. In this week’s episode of AI in Industry, Max speaks with us about how machine vision and drones can be used together to automate the process of facilities and heavy asset upkeep. Max walks us through potential applications in telecom and rail transportation and explains where he thinks machine vision has the strongest potential to impact the bottom line. Business leaders who manage heavy assets or physical infrastructure should find this interview insightful, as Max explains both current and near-future applications for machine vision for maintenance and upkeep. Interested readers can listen to the full interview with Max here:&nbsp;https://www.techemergence.com/applications-of-machine-vision-in-heavy-industry/

Applications of Machine Vision in Heavy Industry

NetFlix has changed the way we watch television for the better. The streaming video pioneer is much more than just an entertainment giant for the 21st century — it’s also a pioneer when it comes to using machine learning. While Justin Basilico, a research director with NetFlix, can’t share all the spoilers, he knows better than anyone how entertainment and machine learning intersect.

Netflix's Justin Basilico on How Entertainment and AI Intersect - Ep. 60

In this episode of AI in Industry, we speak with Brendan Kohler, the CTO of Co-founder of Boston-based startup Sentenai. Brendan speaks about how businesses today can effectively leverage the data being collected by their existing IoT and automation systems. Though example applications and use-cases, Brendan elaborates on what kinds businesses might use this technology today and what they can expect in the near future. 

Business leaders who manage large amounts of sensor data will gain an idea of current AI use-cases, as well as how the future trends towards self-diagnosing systems (in HVAC, in building automation, etc) might develop in the years ahead.

For more business insight on the applications of artificial intelligence, visit:
www.emerj.com

AI in Industry Podcast

Achieving Intelligent Automation - Leveraging IoT Data from Automated Systems

Episode summary: Here on the AI in Industry podcast, we’ve heard AI experts explain how high-performance computing (HPC) has enabled everything from  machine vision to  fraud detection. In this week’s episode, we speak with Paul Martino, Managing Partner at Bullpen Capital, about which industries and AI applications will require high-performance computing most. Paul also adds some useful tips for business leaders on how to prepare for the coming AI-related developments in hardware and software. Interested readers can listen to our full interview with Paul here: https://www.techemergence.com/?p=12779&amp;preview=true &nbsp; &nbsp;

High Performance Computing in Artificial Intelligence Applications with Paul Martino from Bullpen Capital

Episode summary: In this episode of AI in Industry, we speak with Khalifeh Al Jadda, Lead Data Scientist at CareerBuilder, about the applications of machine learning in improving a user’s search experience. Khalifeh also talks about what the future of search might look like and how AI will continue to make the search experience more intuitive (for search engines, platforms, eCommerce stores, and more). Business leaders listening in will get a sneak peak into the future of online search - and an understanding of how and where improvements in search features could impact their business. Interested readers can listen to the full interview with Khalifeh here: https://www.techemergence.com/better-than-elasticsearch-machine-learning-search/

Better Than Elasticsearch? How Machine Learning is Improving Online Search

I explore the nature of probability, and my approach to thinking about facts and belief. I talk about problem solving a lot; here I go into the fundamentals and work to justify how I think about it.

What problem does probability solve? Should we always think in terms of probability? What's the difference between an artificial language (code, math) and natural language? How many water molecules are in the Atlantic Ocean? Sometimes we have vague questions and make vague statements - should that be ok?

This episode is a little different, but I hope this is the beginning of a back-and-forth conversation with the Local Maximum listenership to try to get to the bottom of these deep issues.

Ep. 21 - Probability, Belief, and The Truth

So the overall theme of the show today is applying what we learned last time. Bayesian Probability is good. P-Value hacking - is where we get into trouble. Death to P-hacking!

We start by following up on my monologue from last week about Beliefs and Probability. This leads us into an article about the Reverend Thomas Bayes himself, and how his ideas transformed our thinking on probability and belief. 

And finally, are most of the scientific studies that you hear about wrong? Worry about p-hacking, and think like a Bayesian, and maybe we won't be misled as often; let’s begin to attempt to figure out how to make sense of it all.

Ep. 22 - Bayes Rulez, Death to P-Hacking!

Kris Conception talks about Natural Language Processing, Natural Language Understanding, and machine translation. We then go into how we build the taste ontology at Foursquare to supercharge the best city guide in the world, and how it connects concepts through languages and cultures.

ML

YUKUN CHEN