Analytics DeepLearning.AI - The Batch | DeepLearning.AI (Page 11)

Courtroom scene with teams analyzing neural networks, symbolizing AI ethics and legal implications.

Data Points

Open-source DeepCoder matches top models: ML researchers accept an AI-written workshop paper

Google’s A2A protocol helps agents work together. Amazon debuts unified speech-to-speech model. Claude’s new subscription plan for power users. Elon Musk’s battle with OpenAI takes a new turn.

Black toddler sneakers with white soles on wooden floor, featuring Velcro strap and soft inner lining for comfort.

Letters

The Impact of U.S. Tariffs on AI: Broad tariffs will create challenges for AI and beyond, but I see a few silver linings. Here’s what’s in store.

I am so sorry that the U.S. is letting down our friends and allies.

The Batch Newsletter

Inside the Mind of Claude, Llama 4’s Mixture of Vision-Language Experts, More Open Multimodal Models, Neural Net for Tabular Data

The Batch AI News and Insights: I am so sorry that the U.S. is letting down our friends and allies.

TabPFN neural network diagram showing synthetic training, prediction on real-world tabular data, and attention layers.

Machine Learning Research

Better Than Trees for Tabular Data: Transformers can outperform decision trees at predicting unlabeled spreadsheet cells

If you have a collection of variables that represent, say, a cancer patient and you want to classify the patient’s illness as likely cancer or not, algorithms based on decision trees, such as gradient-boosted trees, typically perform better than neural networks.

Architecture of Qwen2.5-Omni showing multimodal processing with vision and audio encoders, thinker, talker, and decoder.

Machine Learning Research

Better Multimodal Performance With Open Weights: Qwen2.5-Omni 7B raises the bar for small multimodal models

Alibaba’s latest open-weights system raises the bar for multimodal tasks in a relatively small model.

Llama 4 Behemoth benchmark chart comparing coding, reasoning, and multilingual scores with Claude, Gemini, and GPT-4.5.

Machine Learning Research

Llama’s Mixture of Vision-Language Experts: Meta releases Llama 4 models, claims edge over AI competitors

Meta updated its popular open-weights models, claiming performance superior to closed competitors in three size classes.

Diagram comparing original transformer model with a replacement model using token-level attention and neuron-level outputs.

Machine Learning Research

Ordinary LLMs Implicitly Take Reasoning Steps: Anthropic experiment finds Claude shows signs of unprompted reasoning

Even without explicit training in reasoning, large language models “think” in ways that may be more deliberate than previously understood.

Leader alpaca standing on a rock surrounded by herd in green hills under dramatic sunset sky.

Data Points

Meta’s got a brand new herd: Gemini 2.5 Pro gets a price tag

Microsoft personalizes its multitasking Copilot. Midjourney overhauls its leading image model. An early vibe coding entrant reenters a crowded field. A cybersecurity-optimized version of Gemini.

Classroom with students using laptops and a large server rack in the front of the room.

Data Points

OpenAI promises a more open model: Runway seeks to stabilize scenes in AI video

Amazon gets into browser use with Nova Act. Claude goes back to school with program for higher ed. Google becomes a harder place for AI researchers to publish. Evaluating models’ ability to replicate cutting-edge AI research.

Cartoon of a relaxed man saying “Relax! I’m lazy prompting!” while lounging under a beach umbrella near a stressed coworker at a desk.

Letters

The Benefits of Lazy Prompting: You don’t always need to provide context when prompting a large language model. A quick prompt can be enough.

Contrary to standard prompting advice that you should give LLMs the context they need to succeed, I find it’s sometimes faster to be lazy and dash off a quick, imprecise prompt and see what happens.

The Batch Newsletter

Open Voice-to-Voice With Vision, ChatGPT Creates Emotional Bonds, Human Action in 3D, Web Scrapers Caught in Maze

The Batch AI News and Insights: Contrary to standard prompting advice that you should give LLMs the context they need to succeed, I find it’s sometimes faster to be lazy and dash off a quick, imprecise prompt and see what happens.

3D scene comparison of human-object interaction for ZeroHSI, LINGO, and CHOIS models in a synthetic indoor environment.

Machine Learning Research

Human Action in 3D: Stanford researchers use generated video to animate 3D interactions without motion capture

AI systems designed to generate animated 3D scenes that include active human characters have been limited by a shortage of training data, such as matched 3D scenes and human motion-capture examples. Generated video clips can get the job done without motion capture.