AI Research Posts

FOD#42: Debunking Moravec's Paradox

Is 2024 the year of advanced robotics?

AI Chips Are Taking Over: Seven Specialized Processors from Tech Giants

Google, Microsoft, Amazon, and other giants race to develop specialized processors for artificial intelligence

Token 1.21: Vulnerabilities in LLMs and how to deal with them

Explore the most common LLM vulnerabilities, from prompt injection to retrieval attacks, and learn how to defend AI systems effectively.

FOD#41: GPU's rival? What is Language Processing Unit (LPU)

we explore how it makes inference 10x faster plus we offer the best curated list of the freshest ML news and papers

Key Insights from Dr. Andrew Ng's Stanford Talk on AI Opportunities

The Unhyped Importance of Capable AI

The four levels of AI explained - ANI, ACI, AGI, ASI - and why the Capable phase, not AGI, is where the real impact is happening right now.

Token 1.20: Explainable AI techniques and tools for LLMs

A guide to Explainable AI (XAI) for LLMs: techniques like surrogate models and attention visualization, plus tools such as SHAP, LIME, and Captum.

FOD#40: Trust and Responsibility in the Age of AI

we explore AI's dual role in reshaping quality journalism's future and offer the best curated list of the freshest ML news and papers

China Opens Up AI: Top 5 Large Language Models to Know Now

Dive into the cutting-edge of Chinese AI with these powerful language models

Databricks: the Future of Generative AI in the Enterprise Arena

Navigating the Shift: Databricks' Journey from Open Source to AI Powerhouse

Token 1.19: How to optimize LLM Inference

How to optimize LLM inference: TensorRT, GPU vs TPU, AWS accelerators, model serialization, and deployment best practices for large language models.

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