Deep reinforcement learning could be used to learn dexterous robotic policies but it is challenging to transfer them to new robots with vastly different hardware properties. It is also prohibitively expensive to learn a new policy from scratch for each robot hardware due to the high sample complexity of modern state-of-the-art algorithms. We propose a novel approach called Hardware Conditioned Policies where we train a universal policy conditioned on a vector representation of robot hardware. We considered robots in simulation with varied dynamics, kinematic structure, kinematic lengths and degrees-of-freedom. First, we use the kinematic structure directly as the hardware encoding and show great zero-shot transfer to completely novel robots not seen during training. For robots with lower zero-shot success rate, we also demonstrate that fine-tuning the policy network is significantly more sample-efficient than training a model from scratch. In tasks where knowing the agent dynamics is important for success, we learn an embedding for robot hardware and show that policies conditioned on the encoding of hardware tend to generalize and transfer well. Videos of experiments are available at: https://sites.google.com/view/robot-transfer-hcp.

Zero-Shot Learning (ZSL) is generally achieved via aligning the semantic relationships between the visual features and the corresponding class semantic descriptions. However, using the global features to represent fine-grained images may lead to sub-optimal results since they neglect the discriminative differences of local regions. Besides, different regions contain distinct discriminative information. The important regions should contribute more to the prediction. To this end, we propose a novel stacked semantics-guided attention (S2GA) model to obtain semantic relevant features by using individual class semantic features to progressively guide the visual features to generate an attention map for weighting the importance of different local regions. Feeding both the integrated visual features and the class semantic features into a multi-class classification architecture, the proposed framework can be trained end-to-end. Extensive experimental results on CUB and NABird datasets show that the proposed approach has a consistent improvement on both fine-grained zero-shot classification and retrieval tasks.

Your Out-of-Distribution Detection Method is Not Robust!

A technical challenge of deep learning is recognizing target classes without seen data. Zero-shot learning leverages semantic representations such as attributes or class prototypes to bridge source and target classes. Existing standard zero-shot learning methods may be prone to overfitting the seen data of source classes as they are blind to the semantic representations of target classes. In this paper, we study generalized zero-shot learning that assumes accessible to target classes for unseen data during training, and prediction on unseen data is made by searching on both source and target classes. We propose a novel Deep Calibration Network (DCN) approach towards this generalized zero-shot learning paradigm, which enables simultaneous calibration of deep networks on the confidence of source classes and uncertainty of target classes. Our approach maps visual features of images and semantic representations of class prototypes to a common embedding space such that the compatibility of seen data to both source and target classes are maximized. We show superior accuracy of our approach over the state of the art on benchmark datasets for generalized zero-shot learning, including AwA, CUB, SUN, and aPY.

Where OpenAI's technology could show up in Iran Three places to watch, from the margins of war to the center of combat. It's been just over two weeks since OpenAI reached a controversial agreement to allow the Pentagon to use its AI in classified environments. There are still pressing questions about what exactly OpenAI's agreement allows for; Sam Altman said the military can't use his company's technology to build autonomous weapons, but the agreement really just demands that the military follow its own (quite permissive) guidelines about such weapons. OpenAI's other main claim, that the agreement will prevent use of its technology for domestic surveillance, appears equally dubious . It's not the first tech giant to embrace military contracts it had once vowed never to enter into, but the speed of the pivot was notable. Perhaps it's just about money; OpenAI is spending lots on AI training and is on the hunt for more revenue (from sources including ads).

The encyclopedia company's lawsuit also said ChatGPT cannibalizes traffic to the Britannica and Merriam-Webster websites. OpenAI has been hit with another lawsuit. According to the lawsuit, ChatGPT generates made-up content or ' hallucinations ' and falsely attributes them to Encyclopedia Britannica. The lawsuit doesn't specify an amount for monetary damages, but Britannica is also seeking an injunction to prevent OpenAI from repeating these accusations. When reached out for comment, a spokesperson for OpenAI told Engadget that, ChatGPT helps enhance human creativity, advance scientific discovery and medical research, and enable hundreds of millions of people to improve their daily lives.

OpenAI's adult mode reportedly won't generate pornographic audio, images or video The company's own council on wellbeing and AI appears to be against the feature. OpenAI's forthcoming adult mode will allow users to engage in lewd conversations with ChatGPT, but not use the chatbot to generate explicit images, audio or video. In response to reporting from an OpenAI spokesperson characterized the upcoming release as capable of producing smut rather than pornography. OpenAI CEO Sam Altman first floated the idea of allowing people to use ChatGPT for erotica, saying the company wanted to treat adult users like adults. OpenAI originally planned to release adult mode at the start of 2026.

If you haven’t tried Claude yet or want to tinker with a “frontier” AI model, now’s the time.

The Online Services Accord Against Scams was signed by major tech companies including Google, Microsoft and OpenAI. A coalition of Big Tech companies is working on a more comprehensive solution to combat online scams . As first reported by, Google, Microsoft, LinkedIn, Meta, Amazon, OpenAI, Adobe and Match Group announced the signing of the Online Services Accord Against Scams. The new agreement is meant to put up a united industry-wide front against online fraud and scams, particularly those from sophisticated criminal networks that use multiple platforms. According to the report, the measures will include adding fraud detection tools, introducing new user security features, and requiring more robust verification for financial transactions.

PCWorld explores the reality of "vibe coding" with AI tools, where the author attempted to build four apps using Claude Code and Google's Antigravity. Only one Docker Swarm dashboard succeeded after a week of effort, while three OpenClaw replications failed due to vague prompts and poor planning. The experience reveals that AI-assisted development still requires significant human creativity, detailed blueprints, and specific instructions to avoid "garbage in, garbage out" results. Like so many others, I jumped onto the vibe coding bandwagon, entranced by the idea of building my own incredibly useful apps with nothing but an AI prompt. Over the course of about six weeks, I did manage to build my own apps-four of them, to be precise.