• Gradient Descent - Podcast about AI and Data

  • By: Wisecube AI
  • Podcast

Gradient Descent - Podcast about AI and Data

By: Wisecube AI
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  • Summary

  • “Gradient Descent" is a podcast that delves into the depths of artificial intelligence and data science. Hosted by Vishnu Vettrivel (Founder of Wisecube AI) and Alex Thomas (Principal Data Scientist), the show explores the latest trends, innovations, and practical applications in AI and data science. Join us to learn more about how these technologies are shaping our future.
    Wisecube AI
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Episodes
  • The Future of Prompt Engineering: Prompts to Programs

    The Future of Prompt Engineering: Prompts to Programs
    Apr 29 2025

    Explore the evolution of prompt engineering in this episode of Gradient Descent. Manual prompt tuning — slow, brittle, and hard to scale — is giving way to DSPy, a framework that turns LLM prompting into a structured, programmable, and optimizable process.

    Learn how DSPy’s modular approach — with Signatures, Modules, and Optimizers — enables LLMs to tackle complex tasks like multi-hop reasoning and math problem solving, achieving accuracy comparable to much larger models. We also dive into real-world examples, optimization strategies, and why the future of prompting looks a lot more like programming.


    Listen to our podcast on these platforms:

    • YouTube: https://youtube.com/@WisecubeAI/podcasts

    • Apple Podcasts: https://apple.co/4kPMxZf

    • Spotify: https://open.spotify.com/show/1nG58pwg2Dv6oAhCTzab55

    • Amazon Music: https://bit.ly/4izpdO2


    Mentioned Materials:

    • DSPy Paper - https://arxiv.org/abs/2310.03714

    • DSPy official site - https://dspy.ai/

    • DSPy GitHub - https://github.com/stanfordnlp/dspy

    • LLM abstractions guide - https://www.twosigma.com/articles/a-guide-to-large-language-model-abstractions/


    Our solutions:

    - https://askpythia.ai/ - LLM Hallucination Detection Tool

    - https://www.wisecube.ai - Wisecube AI platform for large-scale biomedical knowledge analysis


    Follow us:

    - Pythia Website: https://askpythia.ai/

    - Wisecube Website: https://www.wisecube.ai

    - LinkedIn: https://www.linkedin.com/company/wisecube/

    - Facebook: https://www.facebook.com/wisecubeai

    - Twitter: https://x.com/wisecubeai

    - Reddit: https://www.reddit.com/r/pythia/

    - GitHub: https://github.com/wisecubeai


    #AI #PromptEngineering #DSPy #MachineLearning #LLM #ArtificialIntelligence #AIdevelopment
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    36 mins
  • Agentic AI – Hype or the Next Step in AI Evolution?

    Agentic AI – Hype or the Next Step in AI Evolution?
    Apr 12 2025

    Let’s dive into Agentic AI, guided by the "Cognitive Architectures for Language Agents" (CoALA) paper. What defines an agentic system? How does it plan, leverage memory, and execute tasks? We explore semantic, episodic, and procedural memory, discuss decision-making loops, and examine how agents integrate with external APIs (think LangGraph). Learn how AI tackles complex automation — from code generation to playing Minecraft — and why designing robust action spaces is key to scaling systems. We also touch on challenges like memory updates and the ethics of agentic AI. Get actionable insight…

    🔗 Links to the CoALA paper, LangGraph, and more in the description.

    🔔 Subscribe to stay updated with Gradient Descent!


    Listen on:

    • ⁠YouTube⁠: https://youtube.com/@WisecubeAI/podcasts

    • ⁠Apple Podcast⁠: https://apple.co/4kPMxZf

    • ⁠Spotify⁠: https://open.spotify.com/show/1nG58pwg2Dv6oAhCTzab55

    • ⁠Amazon Music⁠: https://bit.ly/4izpdO2


    Mentioned Materials:

    • Cognitive Architectures for Language Agents (CoALA) - https://arxiv.org/abs/2309.02427

    • Memory for agents - https://blog.langchain.dev/memory-for-agents/

    • LangChain - https://python.langchain.com/docs/introduction/

    • LangGraph - https://langchain-ai.github.io/langgraph/


    Our solutions:

    - https://askpythia.ai/ - LLM Hallucination Detection Tool

    - https://www.wisecube.ai - Wisecube AI platform can analyze millions of biomedical publications, clinical trials, protein and chemical databases.


    Follow us:

    - Pythia Website: https://askpythia.ai/

    - Wisecube Website: https://www.wisecube.ai

    - LinkedIn: https://www.linkedin.com/company/wisecube/

    - Facebook: https://www.facebook.com/wisecubeai

    - X: https://x.com/wisecubeai

    - Reddit: https://www.reddit.com/r/pythia/

    - GitHub: https://github.com/wisecubeai


    #AgenticAI #FutureOfAI #AIInnovation #ArtificialIntelligence #MachineLearning #DeepLearning #LLM
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    41 mins
  • LLM as a Judge: Can AI Evaluate Itself?

    LLM as a Judge: Can AI Evaluate Itself?
    Mar 22 2025
    In the second episode of Gradient Descent, Vishnu Vettrivel (CTO of Wisecube) and Alex Thomas (Principal Data Scientist) explore the innovative yet controversial idea of using LLMs to judge and evaluate other AI systems. They discuss the hidden human role in AI training, limitations of traditional benchmarks, automated evaluation strengths and weaknesses, and best practices for building reliable AI judgment systems.Timestamps:00:00 – Introduction & Context 01:00 – The Role of Humans in AI 03:58 – Why Is Evaluating LLMs So Difficult? 09:00 – Pros and Cons of LLM-as-a-Judge 14:30 – How to Make LLM-as-a-Judge More Reliable? 19:30 – Trust and Reliability Issues 25:00 – The Future of LLM-as-a-Judge 30:00 – Final Thoughts and Takeaways Listen on:• ⁠YouTube⁠: https://youtube.com/@WisecubeAI/podcasts• ⁠Apple Podcast⁠: https://apple.co/4kPMxZf• ⁠Spotify⁠: https://open.spotify.com/show/1nG58pwg2Dv6oAhCTzab55• ⁠Amazon Music⁠: https://bit.ly/4izpdO2 Follow us: • ⁠Pythia Website⁠: www.askpythia.ai• ⁠Wisecube Website⁠: www.wisecube.ai• ⁠Linkedin⁠: www.linkedin.com/company/wisecube• ⁠Facebook⁠: www.facebook.com/wisecubeai• ⁠Reddit⁠: www.reddit.com/r/pythia/Mentioned Materials:- Best Practices for LLM-as-a-Judge: https://www.databricks.com/blog/LLM-auto-eval-best-practices-RAG - LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods: https://arxiv.org/pdf/2412.05579v2- Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena: https://arxiv.org/abs/2306.05685- Guide to LLM-as-a-Judge: https://www.evidentlyai.com/llm-guide/llm-as-a-judge - Preference Leakage: A Contamination Problem in LLM-as-a-Judge: https://arxiv.org/pdf/2502.01534- Large Language Models Are Not Fair Evaluators: https://arxiv.org/pdf/2305.17926- Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment: https://arxiv.org/pdf/2402.14016v2- Optimization-based Prompt Injection Attack to LLM-as-a-Judge: https://arxiv.org/pdf/2403.17710v4- AWS Bedrock: Model Evaluation: https://aws.amazon.com/blogs/machine-learning/llm-as-a-judge-on-amazon-bedrock-model-evaluation/ - Hugging Face: LLM Judge Cookbook: https://huggingface.co/learn/cookbook/en/llm_judge
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    32 mins
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