Podcasts about o reilly ai

In this episode, I talk about a couple of recent features, share some extra resources, and point you to pretty cool computer vision demo.⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️News* Amazon Comprehend - multi-label classification: https://aws.amazon.com/about-aws/whats-new/2020/01/amazon-comprehend-launches-multi-label-custom-classification/ * Amazon Translate - batch translation: https://aws.amazon.com/about-aws/whats-new/2020/01/amazon-translate-introduces-batch-translation/ Extra resources* O’Reilly AI 2018 talk (SageMaker built-in algos): https://youtu.be/dGn1HAHbLWM* O’Reilly AI 2019 talk (Introduction to NLP models): https://youtu.be/kwHGlTi27LY * Sagify demo: https://youtu.be/cWv8zR2Qu94Detectron2 demohttps://youtu.be/7R8-VAk0ruk This podcast is also available in video at https://youtu.be/RE3gD1ti_0sFor more content, follow me on:* Medium: https://medium.com/@julsimon* Twitter: https://twitter.com/julsimon

We recently met up with Cormac Brick (Intel) and Mike Del Balso (Uber) at O’Reilly AI in SF. As the director of machine intelligence in Intel’s Movidius group, Cormac is an expert in porting deep learning models to all sorts of embedded devices (cameras, robots, drones, etc.). He helped us understand some of the techniques for developing portable networks to maximize performance on different compute architectures. In our discussion with Mike, we talked about the ins and outs of Michelangelo, Uber’s machine learning platform, which he manages. He also described why it was necessary for Uber to build out a machine learning platform and some of the new features they are exploring.

We recently met up with Cormac Brick (Intel) and Mike Del Balso (Uber) at O’Reilly AI in SF. As the director of machine intelligence in Intel’s Movidius group, Cormac is an expert in porting deep learning models to all sorts of embedded devices (cameras, robots, drones, etc.). He helped us understand some of the techniques for developing portable networks to maximize performance on different compute architectures. In our discussion with Mike, we talked about the ins and outs of Michelangelo, Uber’s machine learning platform, which he manages. He also described why it was necessary for Uber to build out a machine learning platform and some of the new features they are exploring.

We met up with Wojciech Zaremba at the O’Reilly AI conference in SF. He took some time to talk to us about some of his recent research related to reinforcement learning and robots. We also discussed AI safety and the hype around OpenAI.

We met up with Wojciech Zaremba at the O’Reilly AI conference in SF. He took some time to talk to us about some of his recent research related to reinforcement learning and robots. We also discussed AI safety and the hype around OpenAI.

An amazing panel of AI innovators joined us at the O’Reilly AI conference to answer the most pressing AI questions from Quora. We also discussed trends in the industry and some exciting new advances in FPGA hardware.

An amazing panel of AI innovators joined us at the O’Reilly AI conference to answer the most pressing AI questions from Quora. We also discussed trends in the industry and some exciting new advances in FPGA hardware.

Data is an increasingly sought after raw material for business in the modern economy. One of the factors driving this trend is the increase in applications for machine learning and AI which require large quantities of information to work from. As the demand for data becomes more widespread the market for providing it will begin transform the ways that information is collected and shared among and between organizations. With his experience as a chair for the O'Reilly AI conference and an investor for data driven businesses Roger Chen is well versed in the challenges and solutions being facing us. In this episode he shares his perspective on the ways that businesses can work together to create shared data resources that will allow them to reduce the redundancy of their foundational data and improve their overall effectiveness in collecting useful training sets for their particular products.

My guest for this show is Rana el Kaliouby. Rana is co-founder and CEO of Affectiva. Affectiva, as Rana puts it, "is on a mission to humanize technology by bringing in artificial emotional intelligence". If you liked my conversation about Emotional AI with Pascale Fung from last year’s O’Reilly AI conference, you’re going to love this one. My conversation with Rana kind of picks up where the previous one left off, with a focus on how her company is bringing Artificial Emotional Intelligence services to market. Rana and her team have developed a machine learning / computer vision platform that can use the camera on any device to read your facial expressions in real time, then maps it to an emotional state. Using data science to mine the world’s largest emotion repository, Affectiva has collected over 5.5 million pieces of emotional expression data to date, from laptop, driving, cellular interactions. Understanding the importance of personal privacy, Rana and her Co-Founder Rosalind Wright Picard have vowed to shy away from partnerships that would subject consumers to unknowing surveillance, a commendable effort. The notes for this show can be found at https://twimlai.com/talk/35

I talked about Intel’s acquisition of Nervana Systems on the podcast when it happened almost a year ago, so I was super excited to have an opportunity to sit down with Nervana co-founder Naveen Rao, who now leads Intel’s newly formed AI Products Group, for the first show in our O'Reilly AI series. We talked about how Intel plans to extend its leadership position in general purpose compute into the AI realm by delivering silicon designed specifically for AI, end-to-end solutions including the cloud, enterprise data center, and the edge; and tools that let customers quickly productize and scale AI-based solutions. I also spoke with Hanlin Tang, an algorithms engineer at Intel’s AIPG, about two tools announced at the conference: version 2.0 of Intel Nervana’s deep learning framework Neon and Nervana Graph, a new toolset for expressing and running deep learning applications as framework and hardware-independent computational graphs. Nervana Graph in particular sounds like a very interesting project, not to mention a smart move for Intel, and I’d encourage folks to take a look at their Github repo. The show notes for this page can be found at https://twimlai.com/talk/31

My guest for this second show in our O’Reilly AI series is Doug Eck of Google Brain. Doug did a keynote at the O’Reilly conference on Magenta, Google’s project for melding machine learning and the arts. Magenta’s goal is to produce open-source tools and models that help people in their personal creative processes. Doug’s research starts with using so-called “generative” machine learning models to create engaging media. Additionally, he is working on how to bring other aspects of the creative process into play. We talk about the newly announced Performance RNN project, which uses neural networks to create expressive, AI-generated music. We also touch on QuickDraw, a project by Google AI Experiments, in which users as Doug describes it, “play Pictionary” with a visual classifier. We dig into what he foresees as possibilities for Magenta, machine learning models eventually developing storylines, generative models for media and creative coding. The notes for this episode can be found at https://twimlai.com/talk/32.

My guest for this third episode in the O'Reilly AI series is Ben Vigoda. Ben is the founder and CEO of Gamalon, a DARPA-funded startup working on Bayesian Program Synthesis. We dive into what exactly this means and how it enables what Ben calls idea learning in the show. Gamalon's first application structures unstructured data — input a paragraph or phrase of unstructured text and output a structured spreadsheet/database row or API call. This can be applicable to a wide range of data challenges, including enterprise product and customer information, AI or digital assistant, and many others. Before Gamalon, Ben was co-founder and CEO of Lyric Semiconductor, Inc., which created the first microprocessor architectures dedicated for statistical machine learning. The company was based on his PhD thesis at MIT and acquired by Analog Devices. In today’s talk we are discussing probabilistic programming, his new approach to deep learning, posterior distribution, and the difference between sampling methods and variational methods and how solvers work in the system. Nerd alert: We go pretty deep in this discussion. The notes for this show can be found at https://twimlai.com/talk/33

My guest this time is Hilary Mason. Hilary was one of the first “famous” data scientists. I remember hearing her speak back in 2011 at the Strange Loop conference in St. Louis. At the time she was Chief Scientist for bit.ly. Nowadays she’s running Fast Forward Labs, which helps organizations accelerate their data science and machine intelligence capabilities through a variety of research and consulting offerings. Hilary presented at the O'Reilly AI conference on “practical AI product development” and she shares a lot of wisdom on that topic in our discussion. The show notes can be found at twimlai.com/talk/11.

My guest this time is Francisco Webber, founder and General Manager of artificial intelligence startup Cortical.io. Francisco presented at the O’Reilly AI conference on an approach to natural language understanding based on semantic representations of speech. His talk was called “AI is not a matter of strength but of intelligence.” My conversation with Francisco was a bit technical and abstract, but also super interesting. The show notes can be found at twimlai.com/talk/10.

My guest this time is Pascale Fung, professor of electrical & computer engineering at Hong Kong University of Science and Technology. Pascale delivered a presentation at the recent O'Reilly AI conference titled "How to make robots empathetic to human feelings in real time," and I caught up with her after her talk to discuss teaching computers to understand and respond to human emotions. We also spend some time talking about the (information) theoretical foundations of modern approaches to speech understanding. The notes for this show can be found at twimlai.com/talk/9.

My guest this time is Diogo Almeida, senior data scientist at healthcare startup Enlitic. Diogo and I met at the O'Reilly AI conference, where he delivered a great presentation on in-the-trenches deep learning titled “Deep Learning: Modular in theory, inflexible in practice,” which we discuss in this interview. Diogo is also a past 1st place Kaggle competition winner, and we spend some time discussing the competition he competed in and the approach he took as well. The notes for this show can be found at twimlai.com/talk/8.