ODSC's Ai X Podcast

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ODSC's Ai X Podcast

With Ai X Podcast, Open Data Science Conference (ODSC) brings its vast experience in building community and its knowledge of the data science and AI fields to the podcast platform. The interests and challenges of the data science community are wide ranging. To reflect this Ai X Podcast will offer a similarly wide range of content, from one-on-one interviews with leading experts, to career talks, to educational interviews, to profiles of AI Startup Founders. Join us every two weeks to discover what’s going on in the data science community. Find more ODSC lightning interviews, webinars, live trainings, certifications, bootcamps here - https://aiplus.training/ Don't miss out on this exciting opportunity to expand your knowledge and stay ahead of the curve.

  1. Generative AI and the Need for Critical Thinking in Music, Business, and More with Yves Mulkers

    قبل ٣ أيام

    Generative AI and the Need for Critical Thinking in Music, Business, and More with Yves Mulkers

    In this episode of ODSC’s Ai X Podcast, we speak with Yves Mulkers, the founder of 7wData, about the power of using generative AI in music, business, and more, and the need for critical thinking to make the most out of AI applications. Prior to working at 7wData, Yves was a Data Architect at GSK, a Business Intelligence Analyst at Colruyt Group, and has plenty more experience in data management. 7wData, the company he founded, is a data management firm that offers unique solutions for software and hardware product-based businesses surrounding disruptive technologies. The main topics discussed are how to use generative AI in business, challenges his team has faced, novel applications in music, why humans are still needed in the AI development process, and how critical thinking is a skill that AI still can’t replicate. Topics: - How music led to an interest in data warehouses and artificial intelligence - The need for creativity when developing algorithms - AI as a tool to supplement human creativity - Using generative AI to optimize SEO, writing, newsletters, and websites - Getting the team onboard with using generative AI - Generative AI as a learning tool for customized learning and development - How to start small with generative AI implementation - Roadblocks experienced when using generative AI - Prompt engineering advice to get fine-tuned answers for your needs - Tools for ensuring ethical and responsible AI - His book, “Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies” Resources: - Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies: https://www.amazon.com/Generative-Business-Applications-Executive-TinyTechGuides-ebook/dp/B0CTRBVYYH - Yves’ LinkedIn: https://www.linkedin.com/in/yves-mulkers/ - Yves’ X/Twitter: https://x.com/yvesmulkers - His blogs: https://7wdata.be/author/yves-mulkers/ - More on 7wData: https://7wdata.be/ - Previous interview with DataManagementU: https://www.ewsolutions.com/thought-leaders/yves-mulkers/ This episode was sponsored by: Ai+ Training https://aiplus.training/ Home to hundreds of hours of on-demand, self-paced AI training, ODSC interviews, free webinars, and certifications in in-demand skills like LLMs and Prompt Engineering And created in partnership with ODSC https://odsc.com/ The Leading AI Training Conference, featuring expert-led, hands-on workshops, training sessions, and talks on cutting-edge AI topics and tools, from data science and machine learning to generative AI to LLMOps Never miss an episode, subscribe now!

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  2. Getting Hugs, Fine-Tuning, and Avoiding the AI API Dependency Trap with Hugging Face’s Jeff Boudier

    ١٢ ربيع الآخر

    Getting Hugs, Fine-Tuning, and Avoiding the AI API Dependency Trap with Hugging Face’s Jeff Boudier

    In this episode of ODSC’s Ai X Podcast, Jeff Boudier of Hugging Face joins us to discuss Hugging Face’s new “Hugs” service for deploying AI, among other new Hugging Face developments. He’ll also go into detail about fine-tuning for model performance, the evolution of AI agents, and the challenges faced when deploying AI models into production. Jeff Boudier is the Head of Product at Hugging Face, the #1 open platform for AI builders. Previously, Jeff was a co-founder of Stupeflix, acquired by GoPro, where he served as director of Product Management, Product Marketing, Business Development, and Corporate Development. Show Questions: - Details about Hugging Face’s new 'Hugs' service for deploying AI. - Hugging Face’s mission and how it is evolving with AI advancements. - How fine-tuning is helping enterprises improve model performance. - Gathering community feedback and managing fast-moving developments. - Staying ahead of rapid AI advancements in the open-source realm. - How Hugging Face is making it easier to fine-tune models. - Hugging Face’s support for Retrieval-Augmented Generation (RAG). - Challenges enterprises face when deploying AI models to production. - The evolution of AI agents alongside large language models. - Hugging Face’s integrations with other platforms and AI agents. - The importance of privacy in running AI models locally. - Concerns about models overfitting to academic benchmarks. - Shifting benchmarks toward real-world production performance. - Jeff’s upcoming session at ODSC West. - Where to follow Jeff Boudier and Hugging Face. Show Notes: - Jeff’s upcoming session at ODSC West, “How to Build Your Own AI with Open Source and Hugging Face”: ⁠https://odsc.com/speakers/how-to-build-your-own-ai-with-open-source-and-hugging-face/⁠ - Jeff’s Twitter/X: ⁠https://x.com/jeffboudier⁠ - LinkedIn: ⁠https://www.linkedin.com/in/jeffboudier/⁠ - HuggingChat: ⁠https://huggingface.co/chat/⁠ - PEFT: State-of-the-art Parameter-Efficient Fine-Tuning: ⁠https://github.com/huggingface/peft⁠ - Hugging Face Spaces: ⁠https://huggingface.co/spaces⁠ - LLM Evaluation Guide: ⁠https://github.com/huggingface/evaluation-guidebook?tab=readme-ov-file⁠ - LoRA: Low-Rank Adaptation of Large Language Models: ⁠https://arxiv.org/abs/2106.09685⁠ - DPO: Direct Preference Optimization: ⁠https://arxiv.org/abs/2305.18290⁠ - Open LLM Leaderboard: ⁠https://huggingface.co/open-llm-leaderboard⁠ - LLM Guardrails: ⁠https://github.com/dottxt-ai/outlines⁠ This episode was sponsored by:   Ai+ Training ⁠https://aiplus.training/⁠  Home to hundreds of hours of on-demand, self-paced AI training, ODSC interviews, free webinars, and certifications in in-demand skills like LLMs and Prompt Engineering And created in partnership with ODSC ⁠https://odsc.com/⁠  The Leading AI Training Conference, featuring expert-led, hands-on workshops, training sessions, and talks on cutting-edge AI topics and tools, from data science and machine learning to generative AI to LLMOps Never miss an episode, subscribe now!

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  3. From LLMs to AI Agents and RAG: Mastering GenAI Evaluations with Jason Lopatecki

    ٥ ربيع الآخر

    From LLMs to AI Agents and RAG: Mastering GenAI Evaluations with Jason Lopatecki

    In this episode of ODSC’s Ai X Podcast, our guest today, Jason Lopatecki, co-founder and CEO of Arize AI, joins us to discuss GenAI evaluations. Arize AI is a startup that is one of the leaders in AI observability and LLM evaluation. It's the same company behind the very popular open-source evaluation project, Phoenix. Prior to Arize, Jason was the co-founder and chief innovation officer at TubeMogul where he scaled the business into a public company that was eventually acquired by Adobe. SHOW TOPICS: Jason’s background and key moments in his career Arize AI's founding journey and focus on observability and evaluation Primary challenges of evaluating GenAI and foundational models Using LLM / AI as-a-judge Common mistakes to avoid when evaluating LLMs Evaluation-driven development. AI agents, agentic AI, and challenge for evaluation Breaking down AI agents into manageable components. Agent Control Flow and assessing how agents make correct decisions at each step Evaluating individual actions performed by AI agents Retrieval Augmented Generation (RAG) evaluation Ensuring RAG retrieved information is accurate and relevant Risks and benefits of using open-source models vs. proprietary models, Large Language Model evaluation metrics The drawbacks of public benchmarks Practical considerations for creating an effective evaluation pipeline, and how it differs between experimentation and production The advantages of SLMs (Small language Models) Building an LLM task evaluation from scratch, the steps involved SHOW NOTES - Jason Lopatecki, CEO and Co-Founder of Arize AI: https://www.linkedin.com/in/jason-lopatecki-9509941 https://twitter.com/jason_lopatecki Arize AI: https://twitter.com/arizeai - Arize AI blogs https://arize.com/blog/ - Jason’s Talk at ODSC West - Demystifying LLM Evaluation - https://odsc.com/speakers/demystifying-llm-evaluation/ - Foundational Models https://en.wikipedia.org/wiki/Foundation_model - AI Agents https://en.wikipedia.org/wiki/Intelligent_agent - Agentic AI https://venturebeat.com/ai/agentic-ai-a-deep-dive-into-the-future-of-automation/ - Prometheus: Inducing Fine-grained Evaluation Capability in Language Models https://arxiv.org/abs/2310.08491 - Open LLM Leaderboard https://huggingface.co/open-llm-leaderboard - OpenAI o1 https://openai.com/o1/ - Mistral LLMs https://docs.mistral.ai/getting-started/models/models_overview/ - Llama 3.2 https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/ - Evaluation Prompts: https://arize.com/blog-course/evaluating-prompt-playground/ Phoenix - Open Source AI Observability & Evaluation -https://github.com/Arize-ai/phoenix This episode was sponsored by: Ai+ Training ⁠https://aiplus.training/⁠ Home to 600+ hours of on-demand, self-paced AI training, live virtual training, and certifications in in-demand skills like LLMs and prompt engineering. And created in partnership with ODSC ⁠https://odsc.com/⁠ The Leading AI Training Conference, featuring expert-led, hands-on workshops, training sessions, and talks on cutting-edge AI topics and tools, from data science and machine learning to generative AI to LLMOps Join us at our upcoming and highly anticipated conference ODSC West in South San Francisco October 29-31.

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  4. Google's AI-Powered Tools for Data Scientists: Building the Automated Future of Data Science with Paige Bailey

    ٢٧ ربيع الأول

    Google's AI-Powered Tools for Data Scientists: Building the Automated Future of Data Science with Paige Bailey

    In this Episode of ODSC’s Ai X Podcast, we speak with Paige Bailey, the engineering lead for GenAI Developer Experience at Google, about Google’s AI-powered tools for data scientists.  Paige has a deep understanding of the generative AI landscape, having previously served as an applied machine learning engineer at Microsoft and GitHub, and a product lead for Google's PaLM v2 and Gemini models. She is also passionate about making cutting-edge AI technology accessible, and empowering developers to build the next generation of innovative applications. If you enjoy this episode, you can hear from Paige in person at ODSC West 2024 in South San Francisco, where she will be giving the talk, “Data Science in the Age of Generative AI”. Learn more here: https://odsc.com/speakers/data-science-in-the-age-of-generative-ai/ Show Topics: - Guest background and key career moments - The evolution of data science as a field - The history, evolution, and current state of Google Colab - AI code assistants and how Google Colab's AI compares to other coding systems - The importance of data visualization - Overview of Gemini 1.5 Pro and 1.5 Flash capabilities  - Dense models vs. Mixture of Experts (MoE) models - Google's open-source family of dense models and their capabilities - Overview of Retrieval Augmented Generation (RAG) - The challenges and considerations of fine-tuning open-source models - Google AI Studio for LLMS, code execution, function calling, and fine-tuning - Google’s new data science agent Show Notes:  - ODSC West 2024 Talk, “Data Science in the Age of Generative AI”: https://odsc.com/speakers/data-science-in-the-age-of-generative-ai/ - Paige’s LinkedIn: ⁠https://www.linkedin.com/in/dynamicwebpaige/⁠ - Paige’s webpage: ⁠http://webpaige.dev⁠ - Paige's X:⁠@dynamicwebpaige⁠ - Google Colab: https://colab.research.google.com/ - Colab AI Coding Features:  ⁠https://blog.google/technology/developers/google-colab-ai-coding-features/⁠ - Gemma Open Models: https://ai.google.dev/gemma - Gemini 1.5 Flash: ⁠https://deepmind.google/technologies/gemini/flash/⁠ - Gemini 1.5 Pro: ⁠https://deepmind.google/technologies/gemini/pro/⁠ - Mixture of Experts (MOE): https://huggingface.co/blog/moe - Dense Neural Network: ⁠https://paperswithcode.com/method/dense-connections⁠ - Google AI Studio: ⁠https://aistudio.google.com/⁠ - Google AI Studio X: @googleaistudio - Google Data Science Agent: ⁠https://labs.google.com/code/dsa⁠ - Retrieval-augmented generation: https://cloud.google.com/use-cases/retrieval-augmented-generation?hl=en - RAG Evaluation: ⁠https://huggingface.co/learn/cookbook/en/rag_evaluation⁠ - Anna Goldie’s ODSC’s Ai X Podcast Episode #41–“Deep Reinforcement Learning in the Real World”: https://podcasts.apple.com/us/podcast/deep-reinforcement-learning-in-the-real-world-with/id1721516836?i=1000650492206 This episode was sponsored by:   Ai+ Training⁠ ⁠https://aiplus.training/⁠⁠  Home to 600+ hours of on-demand, self-paced AI training, live virtual training, and certifications in in-demand skills like LLMs and prompt engineering. And created in partnership with ODSC⁠ ⁠https://odsc.com/⁠⁠  The Leading AI Training Conference, featuring expert-led, hands-on workshops, training sessions, and talks on cutting-edge AI topics and tools, from data science and machine learning to generative AI to LLMOps Join us at our upcoming and highly anticipated conference ODSC West in South San Francisco October 29-31.

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  5. AI for Robotics and Autonomy with Francis X. Govers III

    ٢١ ربيع الأول

    AI for Robotics and Autonomy with Francis X. Govers III

    In this episode of ODSC’s Ai X Podcast, we are joined by Francis Govers, a contributor to the design of over 30 manned and unmanned land, sea, air, and space vehicles, and an expert in robotics and autonomy. Francis is an Air Force veteran, spent 10 years at NASA, was a lead engineer for the International Space Station, was Deputy Chief Engineer for the US Army Future Combat Systems, participated in the DARPA Grand Challenge, and managed a Zeppelin airship. He worked on the "Yellow Line" for football and designed telemetry systems for NASCAR and IndyCar. He designed RAMSEE, the robot security guard, as CTO of Gamma 2 Robotics. As a commercial pilot, writer, artist, musician, engineer, race car nut, and designer, Francis has a serious addiction to building things that frequently get him into trouble. He has published 48 magazine articles and contributed to five books. He received five outstanding achievement awards from NASA, the Explorer Award by the National Space Society, recognition from Scientific American for “World Changing Ideas”, and was recognized by the Vertical Flight Society as a "Titan of Autonomy". SHOW TOPICS: - The second edition of “Artificial Intelligence for Robotics” which came out in February - Tell us about your background and key career moments - Tell us about your book - what’s the motivation and audience? - The growing industry of robotics startups and their appeal to investors - What is the fundamental difference between an AI-enabled robot and a traditional robot? - How does autonomy differ from pre-programmed robotic behaviors? - Why is supervised learning important in AI robotics, and how is it used in this book? - You show the reader how to build a robot in your book. What are the primary technical components (hardware and software) needed to build an AI robot? - Tell us about Robotic Operating Systems aka ROS - How can deep learning be used for robotics and is transfer learning applicable? - How can we teach a robot to listen and what are the main challenges in developing a robot capable of understanding human speech? - What is Spectral Analysis? - Beyond just listening, how do we give a robot a personality, known as artificial personality (AP)? - What is the purpose of Monte Carlo modeling in creating human-like interactions for the robot? - How does the robot’s personality system enable it to hold simple conversations with children? - Why is it important for the robot to understand the mood of its human user during interactions? - How do robots traditionally navigate unstructured envoirnments and what new methods does AI introduce? SHOW NOTES: Francis Govers: https://www.linkedin.com/in/francisxgoversiii/ Artificial Intelligence for Robotics: https://www.packtpub.com/en-us/product/artificial-intelligence-for-robotics-9781788835442 NASA Space Station: https://www.nasa.gov/international-space-station/ Q-Learning: https://en.wikipedia.org/wiki/Q-learning This episode was sponsored by:   Ai+ Training⁠ ⁠https://aiplus.training/⁠⁠  And created in partnership with ODSC⁠ ⁠https://odsc.com/⁠⁠  Join us at our upcoming and highly anticipated conference ODSC West in South San Francisco October 29-31.

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  6. Reinforcement Learning for Finance with Dr. Yves J. Hilpisch

    ١٤ ربيع الأول

    Reinforcement Learning for Finance with Dr. Yves J. Hilpisch

    In this episode of ODSC’s Ai X Podcast, Dr. Yves J. Hilpisch, founder and CEO of The Python Quants (http://tpq.io), and founder and CEO of The AI Machine (http://aimachine.io), joins us to discuss reinforcement learning for finance. Yves is also the author of the book "Reinforcement Learning for Finance” and has a diploma in Business Administration and a Ph.D. in Mathematical Finance. Yves is also an adjunct professor for Computational Finance at the Miami Herbert Business School. Show Topics: Overview of The Python Quants The speaker's new book, “Reinforcement Learning for Finance” and why the focus on reinforcement learning Dynamic time problems Markov decision processes Key types of reinforcement learning models Deep Q-Learning (DQL) and how it relates to Q-Learning How deep Q-Learning be applied to financial contexts, such as trading strategies or portfolio management Issues associated with using static historical time series data for training DQL agents in finance End-of-day data vs tick data Adding white noise to historical time series data to improve the training of DQL agents Key differences between the noisy time series data and the simulated time series data approaches Generative Adversarial Networks (GANs) utility for generating synthetic financial time series data GANs’ advantages over traditional Monte Carlo simulations in generating financial data How to check the quality of synthetic data The role of Kolmogorov-Smirnov (KS) test in evaluating the synthetic data generated by GANs How the chapter compare the effectiveness of GAN-generated data to real financial data The primary goal of the trading agent The role of buy bots The role of agentic AI Topic analysis and sentiment analysis Overview of the “Researchers Find AI Model Outperforms Human Stock Forecasters ‘Financial Statement Analysis with Large Language Models’” paper Yves’ session at ODSC Europe SHOW NOTES Monte Carlo Simulation in Finance: https://www.investopedia.com/articles/investing/112514/monte-carlo-simulation-basics.asp Python Quants: https://home.tpq.io/ Certificate in Python for Finance: https://home.tpq.io/certificate/ Markov decision process: https://en.wikipedia.org/wiki/Markov_decision_process Black Scholes model: https://www.investopedia.com/terms/b/blackscholes.asp Deep Q Learning: https://www.tensorflow.org/agents/tutorials/0_intro_rl Backtesting: https://www.investopedia.com/terms/b/backtesting.asp Model collapse: https://en.wikipedia.org/wiki/Model_collapse GANS: https://en.wikipedia.org/wiki/Generative_adversarial_network Black Swan Events: https://www.investopedia.com/terms/b/blackswan.asp Kamograve Smirnov test: https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test Delta Hedging: https://www.investopedia.com/terms/d/deltahedging.asp Hedging strategies: https://www.investopedia.com/trading/hedging-beginners-guide/ Option Replication: https://www.cfainstitute.org/en/membership/professional-development/refresher-readings/option-replication-put-call-parity Geometric Brownian motion: https://en.wikipedia.org/wiki/Geometric_Brownian_motion Jump Diffusion: https://en.wikipedia.org/wiki/Jump_diffusion Heston model: https://en.wikipedia.org/wiki/Heston_model Bates Mode: https://en.wikipedia.org/wiki/Stochastic_volatility_jump Gain Fallacy (A loss of 70% requires a 300% gain to break even): https://www.rgbcapitalgroup.com/preserving-capital Prime Brokers: https://www.investopedia.com/terms/p/primebrokerage.asp Algorithmic trading: https://www.investopedia.com/articles/active-trading/101014/basics-algorithmic-trading-concepts-and-examples.asp Financial statement analysis, with large language models: https://arxiv.org/pdf/2407.17866

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  7. From Automated Prompt Engineering to Deepfake Speech: AI Advancements and Ethical Challenges with Dr. Julie Wall

    ٨ ربيع الأول

    From Automated Prompt Engineering to Deepfake Speech: AI Advancements and Ethical Challenges with Dr. Julie Wall

    In this episode of ODSC’s Ai X Podcast, we speak with Dr. Julie Wall about recent AI advancements and their ethical implications, particularly automated prompt engineering and deepfake speech. Julie is a Professor of AI and Advanced Computing at the University of West London, where she researches designing intelligent systems to process and model temporal data, focusing on speech and language applications. She is also a member of the British Standards Institution (BSI) and serves as an expert in the field of AI. Show Note Topics: Guest introduction and key moments in their career - Addressing the limitations of manual prompting - Automated prompt engineering - Leveraging advanced techniques for automated prompt engineering including reinforcement learning, genetic algorithms, and gradient-based methods - Practical applications of automated prompt engineering - How automated prompt engineering improves the performance of NLP models - The potential challenges or considerations when implementing automated prompt engineering in real-world applications - How the feedback loop in automated systems help refine prompts over time, and what metrics are used to assess prompt effectiveness - Deepfake speech as misinformation - The rapid evolution of deepfake speech - Deepfake creation tools - The use of deepfake speech for political disinformation - The latest developments in legislation aimed at combating deepfake speech, and how different countries are approaching this issue - Deepfake detection methods and models Show Notes: - Dr. Julie Wall’s Bio: https://www.uwl.ac.uk/staff/julie-wall - Dr. Julie Wall’s Citations: https://scholar.google.co.uk/citations?user=EUlLEm4AAAAJ&hl=en - ODSC Europe: https://odsc.com/europe/ - ODSC West: https://odsc.com/california/ - A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications: https://arxiv.org/html/2402.07927v1 - Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review: https://arxiv.org/abs/2310.14735 - Prompt Engineering a Prompt Engineer: https://arxiv.org/html/2311.05661v3 - Prompt Engineering or Fine Tuning: An Empirical Assessment of Large Language Models in Automated Software Engineering Tasks: https://arxiv.org/pdf/2310.10508 - OpenAI Change Log: https://platform.openai.com/docs/changelog - Reinforcement learning human feedback: https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback - Generic algorithms: https://en.wikipedia.org/wiki/Genetic_algorithm - Gradient-based methods: https://en.wikipedia.org/wiki/Gradient_method - Deep Live Cam: https://github.com/hacksider/deep-live-cam - GANS: https://developers.google.com/machine-learning/gan/gan_structure - Neural Text to Speech: https://www.aane.in/research/neural-text-to-speech - GitHub topic on deepfakes https://github.com/topics/deepfake - GitHub topic on voice cloning: https://github.com/topics/voice-cloning Follow Sheamus McGovern on X (https://x.com/sheamusmcgov) or LinkedIn (https://www.linkedin.com/in/sheamus/) for podcast updates This episode was sponsored by: Ai+ Training ⁠https://aiplus.training/⁠ Home to 600+ hours of on-demand, self-paced AI training, live virtual training, and certifications in in-demand skills like LLMs and prompt engineering. And created in partnership with ODSC ⁠https://odsc.com/⁠ The Leading AI Training Conference, featuring expert-led, hands-on workshops, training sessions, and talks on cutting-edge AI topics and tools, from data science and machine learning to generative AI to LLMOps Join us at our upcoming and highly anticipated conference ODSC West in South San Francisco October 29-31. Never miss an episode, subscribe now!

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  8. What Gaming Teaches Us About Generative AI: A Conversation with Hilary Mason

    ٢٤ صفر

    What Gaming Teaches Us About Generative AI: A Conversation with Hilary Mason

    In this episode of ODSC’s Ai X Podcast, we speak with Hilary Mason about the intersection of gaming and generative AI. Hilary Mason is the co-founder and CEO of Hidden Door. Prior to her work at Hidden Door, she was the General Manager of the Machine Learning business unit at Cloudera. Before that, she was a Data Scientist in Residence at Accel Partners, Chief Scientist at bitly, and plenty more. She’s also an experienced entrepreneur and founded Fast Forward Labs, an applied machine learning research and consulting startup. SHOW NOTE TOPICS: Hillary’s journey and the key decisions that shaped her career Where are we with generative AI? How generative AI impacts creative domains such as story games Discussion on Hillary’s latest startup, Hidden Door NPC (Non-player characters) in gaming and the challenges of scripting What gaming can teach us about AI and generative AI Building ML products that don’t have a single objective function to optimize Why evaluating generative AI has become much harder than training generative AI Has the gaming industry embraced narrative AI and generative AI in general? How Hidden Door’s gaming models were trained How the role of data scientists is evolving in the era of generative AI Key lessons learned as an entrepreneur How do you get access to Hidden Door? Tell us about the vision behind your new startup Hidden Door which is essentially a generative AI game studio. How was Hidden Door trained? What advice do you have about generative AI’s impact on game developers? In the age of generative AI, how do you see the relationship between traditional machine learning methods and generative models evolving? Where do you think traditional ML continues to hold value? How do you see the role of data scientists evolving in the era of generative AI? What are some of the key lessons you’ve learned from your journey as a tech entrepreneur, and how have they shaped your leadership style? How do you build ethical businesses in a moment of technical change and uncertainty? How can people follow your work? SHOW NOTES: - Fast Forward Labs: https://github.com/fastforwardlabs - Hidden Door:https://www.hiddendoor.co/ - Hidden Door Discord: https://discord.com/invite/hiddendoor This episode was sponsored by: Ai+ Training ⁠https://aiplus.training/⁠ Home to hundreds of hours of on-demand, self-paced AI training, ODSC interviews, free webinars, and certifications in in-demand skills like LLMs and Prompt Engineering And created in partnership with ODSC ⁠https://odsc.com/⁠ The Leading AI Training Conference, featuring expert-led, hands-on workshops, training sessions, and talks on cutting-edge AI topics and tools, from data science and machine learning to generative AI to LLMOps Never miss an episode, subscribe now!

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With Ai X Podcast, Open Data Science Conference (ODSC) brings its vast experience in building community and its knowledge of the data science and AI fields to the podcast platform. The interests and challenges of the data science community are wide ranging. To reflect this Ai X Podcast will offer a similarly wide range of content, from one-on-one interviews with leading experts, to career talks, to educational interviews, to profiles of AI Startup Founders. Join us every two weeks to discover what’s going on in the data science community. Find more ODSC lightning interviews, webinars, live trainings, certifications, bootcamps here - https://aiplus.training/ Don't miss out on this exciting opportunity to expand your knowledge and stay ahead of the curve.

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