Wednesday, AI 101: What is Whiteboard-of-Thought?

Friday, Guest post: The Elusive Definition of AGI

Strategies to prioritize AI initiatives that deliver real returns

Lessons from real AI success stories and case studies

How to achieve ROI from productivity gains to secure investor support

New research on the state of AI proficiency in today’s workforce, and how to apply it to drive more ROI from your own team

Mistral AI celebrates a year of Mistral 7B with its new les Ministraux models: Ministral 3B and 8B. These models target edge applications like smart assistants and robotics with low-latency, privacy-focused AI for local use cases. Efficiency meets elegance with up to 128k context lengths. Read about Mistral AI’s Bold Journey here.

NVIDIA’s quietly launched Nemotron. Optimized for NVIDIA hardware, the 70B model fine-tunes output quality, setting a new standard in efficient and effective language models for practical AI assistance. But there are some other opinions on that. The problem - IMO - too many benchmarks, one can choose whichever to demonstrate their model from the best side.

Meta teams up with horror giant Blumhouse to launch Movie Gen, AI models generating HD video and sound. Directors Aneesh Chaganty and Casey Affleck are experimenting with the tech, showing a glimpse of AI-driven filmmaking. A wider release is set for 2025; until then, it's all about perfecting the scares.

CodeGPT, powered by Meta’s latest Llama update, claims to boost coding productivity by 30%. Offering code suggestions, debugging help, and onboarding automation, it's a new developer's best friend. With Llama 3.2, Meta positions itself as the bridge between code and creativity.

Moonshot AI’s Kimi Chat Explore now rivals OpenAI, with expanded search and problem-solving skills. Backed by Tencent and Alibaba, the Chinese startup aims to automate complex tasks like investment analysis. It’s a strategic move in the AI arms race, and they’re not holding back. Read how Moonshot Revolutionizing Long-Context AI here.

Anthropic explores AI sabotage risks, evaluating threats like code tampering with models like Claude 3 Opus. While current sabotage capabilities are limited, the research underscores the need for proactive defenses to keep AI on the straight and narrow. Developers are called to refine and innovate safeguards.

Lightmatter gets $400 million in Series D, boosting its valuation to $4.4B. The photonics leader aims to expand its Passage engine, optimizing AI data centers with ultra-low latency and lightning speed. With heavyweights like Google Ventures backing, Lightmatter's redefining the future of AI infrastructure.

Microsoft’s bitnet.cpp is a game-changer for 1-bit LLMs, offering over 6x speed improvements and 80% energy savings on x86 CPUs. Capable of running 100B models on a single CPU, it's designed for scalability, making local AI as efficient as it gets—keeping performance and photonics hand-in-hand.
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NotebookLM adds customizable audio summaries, blending advanced visuals and insights for 200 countries. It leverages Gemini AI, now piloting team collaboration for businesses and universities. Google’s upgraded document analysis tool inches closer to knowledge synthesis dominance.
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With Nick Fox now leading search, Google repositions Prabhakar Raghavan as chief technologist. Gemini AI shifts under DeepMind, reinforcing product-research synergy. The reshuffle comes amid rising antitrust heat and revenue concerns – will a new team reverse Google’s fortunes?

Boston Dynamics and Toyota Research Institute team up to refine humanoid robotics. Combining Toyota’s AI and Large Behavior Models with the Atlas platform, the goal is to revolutionize automation and human-robot interaction. The future of dexterous, multi-tasking robots looks promising.

Super interesting about geoengineering from Andrew Ng

What would you do with an abundance of computing power? by Exponential View

Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation explores enhancing multimodal models by decoupling visual encoders for different tasks, improving flexibility and performance.

OMCAT: Omni Context Aware Transformer enhances cross-modal temporal understanding using a new dataset, improving multimodal model performance.

γ-MoD: Exploring Mixture-of-Depth Adaptation for Multimodal Large Language Models focuses on optimizing multimodal LLMs by adapting layers dynamically, reducing computation time significantly.

Web Agents with World Models develops a web agent that uses a world-model approach to improve long-horizon web tasks and decision-making efficiency.

Retrospective Learning from Interactions proposes a method for LLMs to learn from user interactions without external annotation, enhancing performance over time.

Looking Inward: Language Models Can Learn About Themselves by Introspection explores the ability of LLMs to introspect and predict their behavior, suggesting models can have privileged internal access.

Insights from the Inverse: Reconstructing LLM Training Goals Through Inverse RL applies IRL to uncover reward models guiding LLM behavior, improving alignment and interpretability.

A Common Pitfall of Margin-based Language Model Alignment: Gradient Entanglement identifies gradient entanglement issues in RLHF and proposes strategies for improved safety and alignment.

Toward General Instruction-Following Alignment for Retrieval-Augmented Generation proposes a pipeline to enhance instruction-following capabilities in retrieval-augmented generation models.

Controllable Safety Alignment: Inference-Time Adaptation to Diverse Safety Requirements introduces a framework for adapting LLM safety requirements without retraining, enhancing flexibility.

PopAlign: Diversifying Contrasting Patterns for a More Comprehensive Alignment proposes a new framework for aligning LLMs with diverse strategies, enhancing safety and model alignment comprehensively.

DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads optimizes attention mechanisms in LLMs, improving efficiency and reducing memory usage.

What Matters in Transformers? Not All Attention is Needed explores redundancy in transformer models, showing the efficiency benefits of pruning attention layers.

Thinking LLMs: General Instruction Following with Thought Generation proposes a training method for generating internal thoughts, improving LLM instruction-following capabilities.

Your Mixture-of-Expert LLM is Secretly an Embedding for Free demonstrates how MoE models can function as effective embedding models without additional training.

Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence proposes using swarm intelligence for collaborative model adaptation, optimizing LLMs across tasks.

Taming Overconfidence in LLMs: Reward Calibration in RLHF introduces new methods for calibrating LLM confidence during RLHF, reducing errors while maintaining accuracy.