Superdatascience

AI visionary and CEO of SingularityNET Dr. Ben Goertzel provides a deep dive into the possible realization of Artificial General Intelligence (AGI) within 3-7 years. Explore the intriguing connections between self-awareness, consciousness, and the future of Artificial Super Intelligence (ASI) and discover the transformative societal changes that could arise.This episode is brought to you by AWS Inferentia, by the AWS Insiders Podcast, and by Modelbit, for deploying models in seconds. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.In this episode you will learn:• Decentralized and benevolent AGI [03:13] • The SingularityNET ecosystem [13:10]• Dr. Goertzel's vision for realizing AGI - combining DL with neuro-symbolic systems, genetic algorithms and knowledge graphs [25:50]• How reaching AGI will trigger Artificial Super Intelligence [38:51]• Dr. Goertzel's approach to AGI using OpenCog Hyperon [42:34]• Why Dr. Goertzel believes AGI will be pos

Jon Krohn welcomes Professor Dr. Bob Knight to explore human intelligence, the prefrontal cortex, and the transformative potential of brain implants for data collection. Discover the pivotal role of machine learning in treating Parkinson's and delve into exciting future advancements.Additional materials: www.superdatascience.com/696Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

What are transformers in AI, and how do they help developers to run LLMs efficiently and accurately? This is a key question in this week’s episode, where Hugging Face’s ML Engineer Lewis Tunstall sits down with host Jon Krohn to discuss encoders and decoders, and the importance of continuing to foster democratic environments like GitHub for creating open-source models.This episode is brought to you by the AWS Insiders Podcast, by WithFeeling.ai, the company bringing humanity into AI, and by Modelbit, for deploying models in seconds. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.In this episode you will learn:• What a transformer is, and why it is so important for NLP [04:34]• Different types of transformers and how they vary [11:39]• Why it’s necessary to know how a transformer works [31:52]• Hugging Face’s role in the application of transformers [57:10]• Lewis Tunstall’s experience of working at Hugging Face [1:02:08]• How and where to sta

Modeling tabular data and spreadsheets doesn’t have to be tedious with CatBoost’s open-source tree-boosting algorithm. CatBoost does what it says on the tin, blending categories with boosting that allows you to train your models faster and handle large datasets for ML tasks across multiple GPUs. In this week’s Five-Minute Friday, host Jon Krohn gets to grips with the technical components of CatBoost that give it the speed and accuracy so acclaimed by its users.Additional materials: www.superdatascience.com/694Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

Harpreet Sahota, a data science expert and deep learning developer at Deci AI, joins Jon Krohn to explore the fascinating realm of object detection and the revolutionary YOLO-NAS model architecture. Discover how machine vision models have evolved and the techniques driving compute-efficient edge device applications..This episode is brought to you by AWS Inferentia, by WithFeeling.ai, the company bringing humanity into AI, and by Modelbit, for deploying models in seconds. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.In this episode you will learn:• What is machine vision? [07:02]• Object detection and YOLO architectures [13:00]• Deci's YOLO-NAS: Optimal object detection model architecture [23:39]• Developer Relations [1:00:16]• Harpreet's 'top-down' approach to learning Deep Learning [1:06:50]Additional materials: www.superdatascience.com/693

Join Jon as he navigates listeners through the innovative SpQR approach—a cutting-edge, lossless LLM weight compression technique that harnesses the power of quantization. Tune in as Jon delves into the four steps behind this groundbreaking method in this week's episode.Additional materials: www.superdatascience.com/692Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

GPUs vs CPUs, chip design and the importance of chips in AI research: This highly technical episode is for anyone who wants to learn what goes into chip development and how to get into the competitive industry of accelerator design. With advice from expert guest Ron Diamant, Senior Principal Engineer at AWS, you’ll get a breakdown of the need-to-know technical terms, what chip engineers need to think about during the design phase and what the future holds for processing hardware.This episode is brought to you by Posit, the open-source data science company, by the AWS Insiders Podcast, and by 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:• What CPUs and GPUs are [05:29]• The differences between accelerators used for deep learning [14:31]• Trainium and Inferentia: AWS's A.I. Accelerators [22:10]• If model optimizations will lead to lower demand for hardware

Krishna Gade, the founder and CEO of Fiddler.AI, discusses the challenges faced by Large Language Models (LLMs) in Generative AI, including inaccuracies, biases, and privacy risks. He emphasizes the importance of monitoring to build trust in AI and highlights Fiddler's explainability algorithms and pre-built bias detection tools as vital solutions.Additional materials: www.superdatascience.com/690Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

Arize's Amber Roberts and Xander Song join Jon Krohn this week, sharing invaluable insights into ML Observability, drift detection, retraining strategies, and the crucial task of ensuring fairness and ethical considerations in AI development.This episode is brought to you by Posit, the open-source data science company, by AWS Inferentia, and by Anaconda, the world's most popular Python distribution. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.In this episode you will learn:• What is ML Observability [05:07]• What is Drift [08:18]• The different kinds of model drift [15:31]• How frequently production models should be retrained? [25:15]• Arize's open-source product, Phoenix [30:49]• How ML Observability relates to discovering model biases [50:30]• Arize case studies [57:13]• What is a developer advocate [1:04:51]Additional materials: www.superdatascience.com/689

Prompt injection, prompt engineering, context windows, and more: In this week’s Five-Minute Friday, Jon explains why anyone looking to build their own product leveraging LLMs should stop to consider these and three more issues before jumping in. Phillip Carter first outlined these six issues in his article “All the Hard Stuff Nobody Talks About when Building Products with LLMs”.Additional materials: www.superdatascience.com/688Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

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, by Anaconda, the world's most popular Python distribution, and by 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: ww

Mircosoft’s Ruth Yakubu joins Jon Krohn to discuss Responsible AI principles and the open-source Responsible AI Toolbox, allowing users to assess their models for fairness, inclusiveness, privacy, explainability, accountability, and reliability before deployment.Additional materials: www.superdatascience.com/686Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

Richmond Alake, a Machine Learning Architect at Slalom Build, sits down with Jon to share real-time ML insights, tools and career experiences for a high-energy and high impact episode. From his work at Slalom Build to his two AI startups, discover the software choices, ML tools, and front-end development techniques used by a leader in the field.This episode is brought to you by Posit, the open-source data science company, by AWS Inferentia, and by 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:• What is a Machine Learning Architect? [03:09]• Richmond's startups [12:07]• Why Richmond started a podcast [29:51]• Richmond's new course on feature stores [38:05]• Why Richmond produces data science content [43:25]• Why All Data Scientists Should Write [51:30]Additional materials: www.superdatascience.com/685

Open-source LLMs, FlashAttention and generative AI terminology: Host Jon Krohn gives us the lift we need to explore the next big steps in generative AI. Listen to the specific way in which Stanford University’s “exact attention” algorithm, FlashAttention, could become a competitor for GPT-4’s capabilities.Additional materials: www.superdatascience.com/684Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

Monitoring malicious, user-generated content; contextual AI; adapting to novel evasion attempts: Matar Haller speaks to Jon Krohn about the challenges of identifying, analyzing and flagging malicious information online. In this episode, Matar explains how contextual AI and a “database of evil” can help resolve the multiple challenges of blocking dangerous content across a range of media, even those that are live-streamed.This episode is brought to you by Posit, the open-source data science company, by Anaconda, the world's most popular Python distribution, and by 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:• How ActiveFence helps its customers to moderate platform content [05:36]• How ActiveFence finds extreme social media users trying to evade detection [16:32]• How to monitor live-streaming content and analyze it for dangerous material [29:13]• The tec

In this week's episode, Mico Yuk, host of 'Analytics on Fire', joins Jon Krohn to share her effective business intelligence and analytics framework, BIDS, for persuading key decision makers. She crowns one "power" tool as the analytics king and discusses emerging tools that could challenge its dominance. Tune in for unapologetic insights on future and current BI trends and happenings from the world of BI and analytics.Additional materials: www.superdatascience.com/682Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

Unlock the power of XGBoost by learning how to fine-tune its hyperparameters and discover its optimal modeling situations. This and more, when best-selling author and leading Python consultant Matt Harrison teams up with Jon Krohn for yet another jam-packed technical episode! Are you ready to upgrade your data science toolkit in just one hour? Tune-in now!This episode is brought to you by Pathway, the reactive data processing framework, by Posit, the open-source data science company, and by Anaconda, the world's most popular Python distribution. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.In this episode you will learn:• Matt's book ‘Effective XGBoost’ [07:05]• What is XGBoost [09:09]• XGBoost's key model hyperparameters [19:01]• XGBoost's secret sauce [29:57]• When to use XGBoost [34:45]• When not to use XGBoost [41:42]• Matt’s recommended Python libraries [47:36]• Matt's production tips [57:57]Additional materials: www.superdatascience.

Industrial machinery’s dependence on data science, tech stacks to build IoT platforms, and transitioning from data science to product: This week’s Friday episode with Allegra Alessi explores the minutiae of product ownership for the Internet of Things at packaging company Bobst. Join host Jon Krohn and his guest as they unpack how the IoT is leading factory production.Additional materials: www.superdatascience.com/680Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

Generative AI, MLOps, and making smart investments in AI: This week’s episode is critical listening for AI investors and generative AI creators. AI investor George Mathew talks with host Jon Krohn about the emerging generative AI stack, the critical elements of MLOps to ensure a scalable model, and the tools developers can use for a saleable product.This episode is brought to you by Posit, the open-source data science company, by AWS Inferentia, and by Anaconda, the world's most popular Python distribution. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.In this episode you will learn:• Venture capital’s role in the technology startup ecosystem [05:59]• How RLHF helps UI become more intuitive [12:53]• The four layers of the generative AI stack [34:16]• The risks for generative AI business founders and investors [46:50]• How MLOps drive best practices and help implementation [56:33]• The importance of PLG (Product Lead Growth) [1:04:15]• How g

StableLM, the new family of open-source language models from the brilliant minds behind Stable Diffusion is out! Small, but mighty, these models have been trained on an unprecedented amount of data for single GPU LLMs. This week, Jon breaks down the mechanics of this model–see you there! Additional materials: www.superdatascience.com/678 Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.