In this first episode of Gradient Descent, hosts Vishnu Vettrivel (CTO of Wisecube AI) and Alex Thomas (Principal Data Scientist) discuss the rapid evolution of AI, the breakthroughs in LLMs, and the role of Natural Language Processing in shaping the future of artificial intelligence. They also share their experiences in AI development and explain why this podcast differs from other AI discussions.

Chapters:

00:00 – Introduction

01:56 – DeepSeek Overview

02:55 – Scaling Laws and Model Performance

04:36 – Peak Data: Are we running out of quality training data?

08:10 – Industry reaction to DeepSeek

09:05 – Jevons' Paradox: Why cheaper AI can drive more demand

11:04 – Supervised Fine-Tuning vs Reinforcement Learning (RLHF)

14:49 – Why Reinforcement Learning helps AI models generalize

20:29 – Distillation and Training Efficiency

25:01 – AI safety concerns: Toxicity, bias, and censorship

30:25 – Future Trends in LLMs: Cheaper, more specialized AI models?

37:30 – Final thoughts and upcoming topics

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

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: 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:

- Jevons’ Paradox: https://en.wikipedia.org/wiki/Jevons_paradox

- Scaling Laws for Neural Language Models: https://arxiv.org/abs/2001.08361

- Distilling the Knowledge in a Neural Network: https://arxiv.org/abs/1503.02531

- SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training: https://arxiv.org/abs/2501.17161

- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning: https://arxiv.org/abs/2501.12948

- Reinforcement Learning: An Introduction (Sutton & Barto): https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf