Towards Data Science

"By integrating Monte Carlo Tree Search (MCTS) with LLMs, LATS unifies reasoning, acting, and planning, creating a more deliberate and adaptive problem-solving framework." Ozgur Guler outlines the benefits of using Language Agent Tree Search (LATS) to tackle complex LLM decision-making tasks.

What does the future hold for human-computer interaction in the age of AI and advanced speech recognition and synthesis? Luciano Abriata examines the current state of things and unpacks the challenges that stand between today's tools and Star Trek-like magic.

"It’s usually the more abstract/soft-skill-related part of the job that most people don’t know how to navigate — things like how to break down an abstract business problem into smaller, clearly defined analyses that can eventually lead to concrete business impact." Tessa Xie summarizes her learnings from her days as a consultant to help data scientists excel at their jobs.

New to speculative decoding? Het Trivedi shows how this approach can help speed up LLM inference in a new, practical guide.

To gain a hands-on understanding of different reinforcement learning algorithms and how they perform, follow along Anand Majmudar's fascinating experiment, where simulated humanoid robots try to battle it out against the others based on their respective policies.

For a comprehensive overview of RAG systems, why they don't always work as expected, and how to approach them effectively, head right over to Ahmed BESBES's latest article.

"In this post I will show you how the Floyd-Warshall algorithm can be employed to compute bond distances between atoms in a molecule, or said in a different way, the minimal number of bonds separating every possible pair of atoms." Don't miss Luciano Abriata's new tutorial.

If you've been hearing the recent buzz around Mamba and state-space models and wanted to explore the topic in greater depth, don't miss Sascha Kirch's patiently explained series — part 2 dives deep into the inner workings of state space models.

If you're curious to learn about linear programming and its problem-solving potential, head right over to Jarom Hulet's latest guide, which shows how this technique can be used on a classic stock-cutting problem.

Don't miss Raúl Vizcarra Chirinos's latest deep dive, which covers "some challenges that emerge when discussing privacy in terms of AI, including a brief overview of Machine Unlearning and the SISA training approach (Sharded, Isolated, Sliced, and Aggregated training)."