InstaDeep’s Karim Beguir joins host Jake Smith to talk about using AI to drive autonomous decision making at the enterprise level. Karim discusses utilizing reinforcement learning (RL) made popular by DeepMind in industrial environments and why the company’s RL relies on Intel CPUs. Karim also talks about the complexity of applying RL to specific real-world use cases, such as scheduling trains in Germany, supply chain and logistics, and vaccine and biotechnology. Jake and Karim discuss the future of AI in Europe and the need to protect personal data through homomorphic encryption. 

Follow Karim on Twitter at: https://twitter.com/kbeguir
Follow Jake on Twitter at: https://twitter.com/jakesmithintel

Applying Reinforcement Learning in the Real World - CitC Episode 234

Autoencoders, transformers, latent space: Learn the elements of generative AI and hear what data scientist David Foster has to say about the potential for generative AI in music, as well as the role that world models play in blending generative AI with reinforcement learning.

This episode is brought to you by Posit, the open-source data science company (https://posit.co), by Anaconda, the world's most popular Python distribution (superdatascience.com/anaconda), and by https://WithFeeling.ai, the company bringing humanity into AI. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• Generative modeling vs discriminative modeling [04:21]
• Generative AI for Music [13:12]
• On the threats of AI [23:15]
• Autoencoders Explained [38:36]
• Noise in Generative AI [48:11]
• What CLIP models are (Contrastive Language-Image Pre-training) [54:07]
• What World Models are [1:00:40]
• What a Transformer is [1:11:14]
• How to use transformers for music generation [1:19:50]

Additional materials: www.superdatascience.com/687

The Super Data Science Podcast: Machine Learning | AI | Success

687: Generative Deep Learning, with David Foster

Reinforcement learning through human feedback (RLHF) has come a long way. In this episode, research scientist Nathan Lambert talks to Jon Krohn about the technique’s origins of the technique. He also walks through other ways to fine-tune LLMs, and how he believes generative AI might democratize education.

This episode is brought to you by AWS Inferentia (https://go.aws/3zWS0au) and AWS Trainium (https://go.aws/3ycV6K0), and Crawlbase (https://crawlbase.com), the ultimate data crawling platform. Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

In this episode you will learn:
• Why it is important that AI is open [03:13]
• The efficacy and scalability of direct preference optimization [07:32]
• Robotics and LLMs [14:32]
• The challenges to aligning reward models with human preferences [23:00]
• How to make sure AI’s decision making on preferences reflect desirable behavior [28:52]
• Why Nathan believes AI is closer to alchemy than science [37:38]

Additional materials: www.superdatascience.com/791

791: Reinforcement Learning from Human Feedback (RLHF), with Dr. Nathan Lambert

In this podcast, Roland and Anthony discuss the use of simulation of both musical instruments and robots. They explore how software and sampling techniques allow musicians to replicate the sounds of real instruments and to design better pianos before manufacturing. They discuss how robot simulations allow testing code safely in virtual environments, avoiding costly or dangerous real-world consequences. The conversation extends to frameworks like Gazebo, Unity, and reinforcement learning platforms, and also how simulations cannot always bridge the gap between training in virtual spaces and real-world execution.

Read a transcript of this interview: https://bit.ly/4ff74TB

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Transcript

Generally AI - Season 2 - Episode 5: Do Robots Dream of Electric Pianos?

Good morning! It's January 20th, 2025, and welcome to AI Tech News Today. Here are the latest headlines in the world of Artificial Intelligence!

Man builds nuclear fusion reactor in kitchen using Claude AI. This might sound like science fiction, but user HudZah took on this unconventional project by successfully building a neutron-producing nuclear fusor right in his kitchen. Streamed over a thirty-six-hour live session, he utilized Claude A-I as his co-pilot for every step, from obtaining components to piecing it all together safely. With parts like a thirty thousand-volt electrostatic precipitator and a highly efficient vacuum system, HudZah turned dreams of fusion into reality. Just don’t try this one at home!

OpenAI’s "Ph.D.-caliber super-agents" set to debut, but Altman warns against hype. OpenAI is preparing to unveil advanced AI systems called “Ph.D.-level super-agents,” designed to tackle complex challenges with expert-level insight. However, CEO Sam Altman advised everyone to “chill and cut expectations a hundred times.” While potential exists for these powerful agents, Altman cautions against assuming we’re just a stone’s throw from true artificial general intelligence. Expect incremental improvements instead of miracles!

Runway’s new AI image generator is here, and it looks fittingly cinematic. Runway has released "Frames," a text-to-image generator focused on creating high-quality images for professional use. With the ability to style images across nineteen presets—from “Vivid Warm” to “Terracotta”—it’s aimed at creative professionals who want consistent visuals. The model even allows for quick transformations from still images to videos. Pretty cool, right?

Humanoid robot masters the waltz by mimicking human movement. Researchers at UC San Diego developed ExBody2, an AI system that enables humanoid robots to learn and replicate human movements smoothly, including the classic waltz. By using motion capture data and reinforcement learning, the robots can adapt to real-world scenarios, opening the door for greater coordination and flexibility in humanoid robotics. Who knew dancing robots could be a thing?

That’s all for today’s AI news! Stay tuned and continue exploring the dynamic world of Artificial Intelligence with us. Keep those inquisitive minds engaged, and we will see you next time!

Kitchen Fusion: DIY Enthusiast Constructs Nuclear Reactor with Claude AI's Help

In this episode of the Data Show, I spoke with Robert Nishihara and Philipp Moritz, graduate students at UC Berkeley and members of RISE Lab. I wanted to get an update on Ray, an open source distributed execution framework that makes it easy for machine learning engineers and data scientists to scale reinforcement learning and other related continuous learning algorithms. Many AI applications involve an agent (for example a robot or a self-driving car) interacting with an environment. In such a scenario, an agent will need to continuously learn the right course of action to take for a specific state of the environment.

How Ray Makes Continuous Learning Accessible and Easy to Scale

Machine learning is transforming business from every angle. Learn how machine learning is driving operational efficiency, improving customer experience and helping to solve business problems.

TechTrends

The Age of Decisioning

This is an interview with Sudharshan Ravichandiran as part of AI Expert Corner Series
Sudharshan Ravichandiran is a Data Scientist and author of Best Selling books in the Areas of Machine Learning
In this podcast we talk about Reinforcement Learning, MetaLearning and Data science in general. Hope you'll enjoy!

AI Expert Corner Series

In this episode of the SuperDataScience Podcast, I chat with the Machine Learning Research Scientist, John Langford. You will hear about unsupervised, supervised learning and reinforcement learning, and the differences between the three. You will learn about applications of contextual bandits and reinforcement learning in general, YOLO style algorithms versus simulator algorithms, technics for avoiding local optimums. You will also learn about the balance between exploration and exploitation, learning to search and active learning.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/275

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 275: Machine Learning Through Reinforcement & Contextual Bandits

In this episode of the SuperDataScience Podcast, I chat with Data Scientist, Peyman Hesami. You will find out what reinforcement learning is and how it works on an intuitive level. You will hear about the differences between reinforcement learning versus classification, or other supervised learning methods, and how it's used for personalization specifically. You will learn about six distinct advantages of reinforcement learning, what role reinforcement learning is going to play in the future of machine learning and why. Also, you will find out how and why Peyman made a career transition to work for a startup, how he's using reinforcement learning, and what is the biggest mistake he has made with reinforcement learning.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/293

SDS 293: True Personalization Through Reinforcement Learning

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As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact.

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We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.

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.

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