A NewsGuard audit exposes “Pravda,” (which means Truth) a Moscow-based disinformation network, for flooding AI training data with pro-Kremlin falsehoods—3.6 million articles in 2024 alone. Leading AI chatbots echoed these narratives 33% of the time, distorting global AI-generated news. American fugitive-turned-propagandist John Mark Dougan even boasted that Russian narratives could “change worldwide AI,” validating concerns over AI’s susceptibility to manipulation – another indication to push for AI literacy.

The future of Quantum and AI – an interview with Satya Nadella 

Give a try to these two video-generation models: Character-3 from Hedra Studio – an omnimodal AI, integrating text, image, and audio to simplify content creation. I played with it for 5 min (that’s my way to say who far the models get):

Luma made it “Dream Machine” available this week also

Perplexity wants to break out of the browser, with growing signs of partnerships with hardware companies to integrate its AI into everyday devices. Deutsche Telekom’s AI Phone, featuring Perplexity’s assistant, debuts this year, blending AI seamlessly into voice interactions. Phones for now, then TVs? Where next?

China’s AI ambitions get a new face with Manus, a high-performing AI agent (Built on Anthropic Claude Sonnet) from Monica.ai, reportedly outpacing OpenAI and Anthropic on key benchmarks. Founded by Xiao Hong, Manus started as a browser plugin and is now a $100M startup targeting international markets – strategically dodging China’s AI regulations. Unlike AGI purists, Xiao is focused on business, leveraging user data for monetization. Exclusive and invite-only, Manus could redefine China’s AI playbook abroad.

Apple faced setbacks and successes in AI this week. Siri’s much-touted AI enhancements, promised for 2024, have been delayed. Security risks, particularly prompt injection vulnerabilities (as noted by Simon Willison), may be a factor. But on the hardware side, Apple flexes its muscle with the M3 Ultra chip, cementing its status as the leader in AI silicon. Ben Thompson argues Apple should open its AI models to developers, shifting from an aggregator to a true AI platform, leveraging its hardware to create new ecosystems.

A glimpse of the future: Cortical Labs’ CL1 computer fuses human brain cells with silicon (!) to create an adaptive, low-energy AI system. Kept alive with pumps and temperature controls, the system has already taught itself to play Pong. With implications for AI, robotics, and neuroscience, it raises big ethical questions about machine consciousness. At $35,000 per unit, it ships in June 2025 – ushering in an era of living computers.

Mistral unveils a cutting-edge document understanding API, excelling in text, table, and equation extraction at scale. A new benchmark in AI-powered OCR.

Andrew Barto and Richard Sutton, pioneers of reinforcement learning, take home the 2024 Turing Award. Their work from the 1980s underpins everything from AlphaGo to modern AI assistants, fulfilling Turing’s vision of machines that learn from experience. From robotics to targeted ads, their impact is everywhere. A well-earned recognition.

Differentiable Logic Cellular Automata (Google Research) – Integrates Neural Cellular Automata with Differentiable Logic Gate Networks to enable self-healing, pattern generation, and robust computational architectures ​→read their blog

Phi-4-Mini Technical Report (Microsoft) – Introduces a 3.8B parameter multimodal model using Mixture-of-LoRAs, excelling in math, coding, and reasoning while maintaining efficiency ​→read the paper

Babel: Open Multilingual Large Language Models (Alibaba) – Develops an open-source LLM serving 90% of global speakers across 25 languages, excelling in underrepresented linguistic benchmarks ​→read the paper

Aya Vision: Expanding the Worlds AI Can See (Cohere) – Introduces an open-weight vision model outperforming larger competitors in multilingual and multimodal benchmarks ​→read the paper

LLMVoX: Autoregressive Streaming Text-to-Speech Model – Proposes a lightweight, LLM-agnostic TTS system with low latency, high accuracy, and seamless integration into multimodal AI ​→read the paper

The Best of Both Worlds: Integrating Language Models and Diffusion Models for Video Generation (Moonshot AI) – Proposes LanDiff, a hybrid text-to-video model combining LLMs and diffusion techniques, surpassing existing models like Hunyuan Video and Sora →read the paper

Dedicated Feedback and Edit Models Empower Inference-Time Scaling – Improves LLM inference by layering critique and refinement steps, enabling superior performance and zero-shot distillation benefits.

From Hours to Minutes: Lossless Acceleration of Ultra Long Sequence Generation – Speeds up ultra-long text generation (100K tokens) by optimizing KV cache updates, cutting processing time from hours to minutes.

HybridNorm: Towards Stable and Efficient Transformer Training – Enhances transformer training stability by combining normalization strategies, improving loss reduction and benchmark performance.

Liger: Linearizing Large Language Models to Gated Recurrent Structures – Converts LLMs into efficient recurrent structures, preserving accuracy while reducing inference costs.

Union of Experts: Adapting Hierarchical Routing to Decomposed Transformers – Reduces computation by 76% using a hierarchical mixture-of-experts framework with selective multi-head attention.

Visual-RFT: Visual Reinforcement Fine-Tuning – Improves Large Vision-Language Models with reward-driven fine-tuning, significantly enhancing classification and object detection accuracy.

STORM: Token-Efficient Long Video Understanding – Optimizes long-video processing for multimodal models, reducing token requirements while maintaining high accuracy.

EgoLife: Towards Egocentric Life Assistant – Advances AI-powered personal assistants using egocentric video datasets for long-term recall and event tracking.

Cognitive Behaviors that Enable Self-Improving Reasoners – Identifies cognitive patterns that help LLMs improve through reinforcement learning, even when correctness is not guaranteed.

LADDER: Self-improving LLMs through Recursive Problem Decomposition – Boosts mathematical problem-solving by breaking down complex problems into simpler subproblems.

START: Self-taught Reasoner with Tools – Enhances LLM reasoning with external tool use, refining performance through guided prompt-based fine-tuning.

Process-based Self-Rewarding Language Models – Enables LLMs to iteratively refine their reasoning by incorporating self-evaluated feedback loops.

When an LLM is Apprehensive About Its Answers – Assesses LLM uncertainty by comparing entropy-based predictions against domain-specific correctness.

Mask-DPO: Generalizable Fine-grained Factuality Alignment – Aligns LLMs with factual accuracy by selectively training on verifiable statements, improving knowledge representation.

Large-Scale Data Selection for Instruction Tuning – Improves instruction-tuning by evaluating different dataset selection techniques, highlighting a more effective approach.

Lingoly-Too: Disentangling Memorization from Reasoning – Tests whether LLMs truly reason or just memorize by applying obfuscation techniques to language datasets.

MPO: Boosting LLM Agents With Meta Plan Optimization – Improves LLM-based agents by introducing high-level meta-plans, refining their decision-making process.

ATLAS: Agent Tuning via Learning Critical Steps – Enhances LLM-driven agents by selectively fine-tuning them on critical decision-making steps.

Reliable and Efficient Multi-Agent Coordination via GNN-VAEs – Optimizes multi-agent planning using graph neural networks, ensuring scalability for real-world applications.

PokéChamp: an Expert-level Minimax Language Agent – Develops an LLM-based Pokémon battle agent that outperforms rule-based and LLM-assisted competitors.

Kodcode: A Diverse, Challenging, and Verifiable Coding Dataset – Improves code generation benchmarks with a 447K-problem dataset containing verified solutions and test cases.

Fine-Tuning Small Language Models for Domain-Specific AI – Optimizes small-scale models for edge AI applications, balancing efficiency and task-specific accuracy.

A Multimodal Symphony: Integrating Taste and Sound Through Generative AI – Explores the intersection of taste perception and music generation using generative AI models.

Language Models can Self-Improve at State-Value Estimation – Enhances search efficiency in interactive planning tasks by refining LLM state-value estimation.

HoT: Highlighted Chain of Thought for Referencing Supporting Facts – Improves fact-based reasoning in LLMs by introducing highlighted references to key information.

UFO: A Unified Approach to Fine-grained Visual Perception – Integrate object detection, segmentation, and vision-language tasks under a single, open-ended framework.

L^ 2M: Mutual Information Scaling Law for Long-Context Language Modeling – Establishes theoretical foundations for improving long-range dependencies in language models.