Autonomous task completion: Genie's ability to perform a wide range of programming tasks autonomously suggests it can explore solution spaces without constant human guidance.

Human-like reasoning: By training on datasets that capture the decision-making processes of real software engineers, Genie may be able to approach problems with a more open-ended, creative mindset.

Collaborative potential: The model's ability to work alongside human developers opens up possibilities for human-AI collaborative open-ended discovery in software development.

At its "Made by Google" event, Google shifted gears from AI hype to practical applications, showcasing its Gemini model integrated deeply into Android. Senior VP Rick Osterloh made it clear: no more empty promises—it's time for AI to deliver. Yet, despite this, Pixel's market impact remains minimal. Meanwhile, the DOJ's antitrust scrutiny looms, potentially threatening Google's integrated AI strategy.
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Snowflake and Databricks are locked in a fierce battle for AI supremacy, with Databricks outbidding Snowflake for Tabular. This rivalry has heated up with aggressive moves like Databricks' "SnowMelt" campaign. But with tech giants like Microsoft entering the fray, both companies might face an even tougher fight ahead.
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Anthropic's latest innovation, prompt caching for its Claude models, slashes costs by up to 90% and cuts latency by 85%. Available in public beta, this feature is a game-changer for extended AI conversations and complex tasks, with Notion already onboard to optimize its AI assistant.
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MIT has launched an AI Risk Repository, a detailed catalog of over 700 risks associated with AI. With categories ranging from causal to domain-specific risks, this tool is invaluable for developers, researchers, and policymakers navigating the increasingly complex AI landscape.
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Midjourney has rolled out a unified AI image editor, bringing together inpainting, outpainting, and more under one roof. Despite facing a class-action lawsuit, Midjourney pushes forward with innovations like a virtual "brush" tool and seamless message mirroring between web and Discord platforms.

New LLM Pre-training and Post-training Paradigms by Sebastian Raschka

On Nous Hermes 3 and classifying a "frontier model" by Nathan Lambert

California's controversial AI bill is on the verge of becoming law by The Platformer

Falcon Mamba 7B – an open-source State Space Language Model (SSLM) that outperforms traditional transformer models, offering efficient processing for long text generation →read TII blog

Hermes 3 – a versatile open-source model that excels in multi-turn conversations and roleplaying, available in multiple sizes, and sets a new benchmark in its class →read the paper

Grok-2, which excels in code, math, and reasoning tasks, outperforming major competitors and improving instruction following and factuality →read the paper

Imagen 3 – a text-to-image model that surpasses competitors in quality and accuracy, with robust safety measures to prevent misuse →read the paper

xGen-MM (BLIP-3) – an advanced multimodal model framework that excels in visual-language tasks and supports both single and multi-image inputs →read the paper

JPEG-LM – an LLM that generates images as compressed JPEG files, simplifying visual generation and improving image quality, especially for complex elements →read the paper

AQUILA2 TECHNICAL REPORT introduces the Aquila2 series, bilingual models that outperform competitors with efficient training and strong performance, even after quantization →read the paper

Towards Flexible Perception with Visual Memory

Researchers from Google DeepMind propose a new visual memory model combining deep neural networks with a flexible database to enhance image classification. This model allows for easy addition and removal of data, enabling scalability from individual samples to billion-scale datasets without retraining. It introduces RankVoting, which outperforms previous aggregation methods, achieving 88.5% top-1 accuracy on ImageNet. The system demonstrates capabilities in lifelong learning, machine unlearning, and interpretable decision-making, showcasing the benefits of an explicit visual memory in deep learning →read the paper

Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability

Researchers from Google DeepMind studied hallucinations in LLMs by training models on knowledge graphs, where factual content is fully controlled. They found that larger and longer-trained models hallucinate less on seen data but still struggle with unseen data, requiring significantly more compute than previously thought optimal. Despite this, detecting hallucinations becomes harder as models scale up, showing a trade-off between model size, training duration, and hallucination detectability →read the paper

Automated Design of Agentic Systems

Researchers from the University of British Columbia and the Vector Institute propose the Automated Design of Agentic Systems (ADAS) to autonomously create and improve agentic systems using Foundation Models (FMs). Their method, Meta Agent Search, allows a "meta" agent to program new agents iteratively in code. Experiments show these automatically discovered agents outperform state-of-the-art, manually designed systems in diverse domains, including math and reading comprehension, and demonstrate strong cross-domain generalization and robustness →read the paper

Layerwise Recurrent Router for Mixture-of-Experts introduces a new approach to enhance routing in large models by sharing routing information across layers, improving both efficiency and performance →read the paper

Solving a Rubik’s Cube Using Its Local Graph Structure proposes a novel method for solving the Rubik’s Cube by modeling it as a graph, enhancing search efficiency while reducing the solution length →read the paper

How to Prune and Distill Llama-3.1 8B to an NVIDIA Llama-3.1-Minitron 4B Model demonstrates a method to effectively reduce LLM sizes through pruning and distillation, improving performance while cutting down compute costs →read the paper

I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm introduces a novel iterative self-enhancement approach for LLMs, enabling continuous self-alignment and significant performance improvements using minimal external signals →read the paper

Amuro & Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models investigates the interaction between pre-training and fine-tuning in LLMs, revealing insights into how these processes impact performance and task retention →read the paper

Can Large Language Models Understand Symbolic Graphics Programs? evaluates LLMs' ability to understand symbolic graphics programs, introducing a new benchmark and technique to improve comprehension of these programs →read the paper

Diversity Empowers Intelligence: Integrating Expertise of Software Engineering Agents proposes a framework that integrates the diverse strengths of software engineering agents, significantly improving problem-solving capabilities →read the paper