Susan Etlinger, an Industry Analyst at Altimeter, a Prophet company, joins us to discuss The AI Maturity Playbook: Five Pillars of Enterprise Success. This playbook covers trends affecting AI, and offers a maturity model that practitioners can use within their own organizations - addressing everything from strategy and product development, to culture and ethics.
Fully Connected – a series where Chris and Daniel keep you up to date with everything that’s happening in the AI community.
This week we discuss all things inference, which involves utilizing an already trained AI model and integrating it into the software stack. First, we focus on some new hardware from Amazon for inference and NVIDIA’s open sourcing of TensorRT for GPU-optimized inference. Then we talk about performing inference at the edge and in the browser with things like the recently announced ONNX JS.
Joe Doliner (JD) joined the show to talk about productionizing ML/AI with Pachyderm, an open source data science platform built on Kubernetes (k8s). We talked through the origins of Pachyderm, challenges associated with creating infrastructure for machine learning, and data and model versioning/provenance. He also walked us through a process for going from a Jupyter notebook to a production data pipeline.
Fully Connected – a series where Chris and Daniel keep you up to date with everything that’s happening in the AI community.
This week we discuss BERT, a new method of pre-training language representations from Google for natural language processing (NLP) tasks. Then we tackle Facebook’s Horizon, the first open source reinforcement learning platform for large-scale products and services. We also address synthetic data, and suggest a few learning resources.
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.
Brett Gaylor joins Chris and Daniel to chat about the recently announced winners of Mozilla’s creative media awards, which focuses on exposing the impact of AI on society. These winners include a film that responds to the audience (via AI recognized emotions) and an interesting chatbot called Wanda.
Himani Agrawal joins Daniel and Chris to talk about how she got into data science and artificial intelligence, and offers advice to others getting into these fields. She goes on to describe the role of artificial intelligence and machine learning within AT&T and telecom in general.
In this new and updates show, Daniel and Chris discuss, among other things, efforts to use AI in art and efforts to make AI interfaces look human. They also discuss some learning resources related to neural nets, AI fairness, and reinforcement learning.
Lindsey Zuloaga joins us to discuss bias in hiring, bias in AI, and how we can fight bias in hiring with AI. Lindsey tells us about her experiences fighting bias at HireVue, where she is director of data science, and she gives some practical advice to AI practitioners about fairness in models and data.
Chris and Daniel are back together in another news/updates show. They discuss PyTorch v1.0, some disturbing uses of AI for tracking social credit, and learning resources to get you started with machine learning.
NVIDIA Chief Scientist Bill Dally joins Daniel Whitenack and Chris Benson for an in-depth conversation about ‘everything AI’ at NVIDIA. As the leader of NVIDIA Research, Bill schools us on GPUs, and then goes on to address everything from AI-enabled robots and self-driving vehicles, to new AI research innovations in algorithm development and model architectures. This episode is so packed with information, you may want to listen to it multiple times.
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.
Chris and Daniel discuss new advances in AI research (including a creepy dancing video), how AI is creating opportunity for new chip startups, and uses of deep learning in healthcare. They also share some great learning resources, including one of Chris’s favorite online courses.
Chris DeBellis, a lead AI data scientist at Honeywell, helps us understand what Mask R-CNN is and why it’s useful for robot perception. We also explore how this method compares with other convolutional neural network approaches and how you can get started with Mask R-CNN.
This week, Daniel and Chris talk about playing Dota at OpenAI, O’Reilly’s machine learning survey, AI-oriented open source (Julia, AutoKeras, Netron, PyTorch), robotics, and even the impact AI strategy has on corporate and national interests. Don’t miss it!
Mike Bugembe teaches us how to build a culture of data-driven decision making within a company, leverage behavioral economics, and identify high value use cases for AI.
Chris and Daniel help us wade through the week’s AI news, including open ML challenges from Intel and National Geographic, Henry Kissinger’s views on AI, and a model that can detect personality based on eye movements. They also point out some useful resources to learn more about pandas, the vim editor, and AI algorithms.
Jared Lander, the organizer of NYHackR and general data science guru, joined us to talk about the landscape of AI techniques, how deep learning fits into that landscape, and why you might consider using R for ML/AI.
In this episode, Chris and Daniel discuss the latest news, including an article about Google’s AI principles, and they highlight some useful resources to help you level up.
