Runway Gen-2: Generative AI for Video Creation with Anastasis Germanidis - #622

Runway Gen-2: Generative AI for Video Creation with Anastasis Germanidis - #622

Today we’re joined by Anastasis Germanidis, Co-Founder and CTO of RunwayML. Amongst all the product and model releases over the past few months, Runway threw its hat into the ring with Gen-1, a model that can take still images or video and transform them into completely stylized videos. They followed that up just a few weeks later with the release of Gen-2, a multimodal model that can produce a video from text prompts. We had the pleasure of chatting with Anastasis about both models, exploring the challenges of generating video, the importance of alignment in model deployment, the potential use of RLHF, the deployment of models as APIs, and much more! The complete show notes for this episode can be found at twimlai.com/go/622.

Jaksot(776)

(3/5) Cambrian Intelligence - Using AI to Simplify the Programming of Robots - TWiML Talk #18

(3/5) Cambrian Intelligence - Using AI to Simplify the Programming of Robots - TWiML Talk #18

This week I'm on location at NYU/ffVC AI NexusLab startup accelerator, speaking with founders from the 5 companies in the program's inaugural batch. This interview is with Cambrian Intelligence, a company using AI to simplify the programming of industrial robots for the automotive industry. The notes for this series can be found at twimlai.com/nexuslab. Thanks to Future Labs at NYU Tandon and ffVenture Capital for sponsoring the series!

7 Huhti 201723min

(2/5) Klustera - Location-Based Intelligence for Smarter Marketing - TWiML Talk #18

(2/5) Klustera - Location-Based Intelligence for Smarter Marketing - TWiML Talk #18

This week I'm on location at NYU/ffVC AI NexusLab startup accelerator, speaking with founders from the 5 companies in the program's inaugural batch. This interview is with Klustera, a company applying location-based intelligence and machine learning to help brands execute smarter marketing campaigns. The notes for this series can be found at twimlai.com/nexuslab. Thanks to Future Labs at NYU Tandon and ffVenture Capital for sponsoring the series!

7 Huhti 201722min

(1/5) HelloVera - AI-Powered Customer Support  - TWiML Talk #18

(1/5) HelloVera - AI-Powered Customer Support - TWiML Talk #18

This week I'm on location at NYU/ffVC AI NexusLab startup accelerator, speaking with founders from the 5 companies in the program's inaugural batch. This interview is with HelloVera, a company applying artificial intelligence to the challenge of automating customer support experiences. The notes for this series can be found at https://twimlai.com/nexuslab. Thanks to Future Labs at NYU Tandon and ffVenture Capital for sponsoring the series!

7 Huhti 201725min

Interactive Machine Learning Systems with Alekh Agarwal - TWiML Talk #17

Interactive Machine Learning Systems with Alekh Agarwal - TWiML Talk #17

This week my guest is Alekh Agarwal. Alekh is a researcher with Microsoft Research whose research is focused on Interactive Machine Learning. In our discussion, Alekh and I discuss various aspects of this exciting area of research such as active learning, reinforcement learning, contextual bandits and more.

31 Maalis 201730min

Machine Learning in Cybersecurity with Evan Wright - TWiML Talk #16

Machine Learning in Cybersecurity with Evan Wright - TWiML Talk #16

This week my guest is Evan Wright, principal data scientist at cybersecurity startup Anomali. In my interview with Evan, he and I discussed about a number of topics surrounding the use of machine learning in cybersecurity. If Evan’s name sounds familiar, it’s because Evan was the winner of the O’Reilly Strata+Hadoop World ticket giveaway earlier this month. We met up at the conference last week and took advantage of the opportunity to record this show. Our conversation covers, among other topics, the three big problems in cybersecurity that ML can help out with, the challenges of acquiring ground truth in cybersecurity and some ways to accomplish it, and the use of decision trees, generative adversarial networks, and other algorithms in the field. The show notes can be found at twimlai.com/talk/16.

24 Maalis 20171h 4min

Domain Knowledge in Machine Learning Models for Sustainability with Stefano Ermon - TWiML Talk #15

Domain Knowledge in Machine Learning Models for Sustainability with Stefano Ermon - TWiML Talk #15

My guest this week is Stefano Ermon, Assistant Professor of Computer Science at Stanford University, and Fellow at Stanford’s Woods Institute for the Environment. Stefano and I met at the Re-Work Deep Learning Summit earlier this year, where he gave a presentation on Machine Learning for Sustainability. Stefano and I spoke about a wide range of topics, including the relationship between fundamental and applied machine learning research, incorporating domain knowledge in machine learning models, dimensionality reduction, and his interest in applying ML & AI to addressing sustainability issues such as poverty, food security and the environment. The show notes can be found at twimlai.com/talk/15.

17 Maalis 201754min

Scaling Deep Learning: Systems Challenges & More with Shubho Sengupta — TWiML Talk #14

Scaling Deep Learning: Systems Challenges & More with Shubho Sengupta — TWiML Talk #14

This week my guest is Shubho Sengupta, Research Scientist at Baidu. I had the pleasure of meeting Shubho at the Rework Deep Learning Summit earlier this year, where he delivered a presentation on Systems Challenges for Deep Learning. We dig into this topic in the interview, and discuss a variety of issues including network architecture, productionalization, operationalization and hardware. The show notes can be found at twimlai.com/talk/14.

10 Maalis 20171h 12min

Understanding Deep Neural Nets with Dr. James McCaffrey - TWiML Talk #13

Understanding Deep Neural Nets with Dr. James McCaffrey - TWiML Talk #13

My guest this week is Dr. James McCaffrey, research engineer at Microsoft Research. James and I cover a ton of ground in this conversation, including recurrent neural nets (RNNs), convolutional neural nets (CNNs), long short term memory (LSTM) networks, residual networks (ResNets), generative adversarial networks (GANs), and more. We also discuss neural network architecture and promising alternative approaches such as symbolic computation and particle swarm optimization. The show notes can be found at twimlai.com/talk/13.

3 Maalis 20171h 16min

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