Whoop's subscription plans are discounted, and accessories are up to 70 percent off. In the world of fitness trackers, Whoop has a unique business model. Rather than upgrading your wearable every few years, Whoop offers its screenless wristband for free with a monthly subscription that allows you to get the latest software updates as they arrive. This model did not work so well for a few years when Whoop released very few updates of any kind, software-or hardware-related. But this year, Whoop debuted two new models, the Whoop 5.0 and the Whoop MG (8/10, WIRED Recommends) .

In a sea of nearly identical fitness trackers, Whoop stands apart. Since it started in 2012, the company has understood that the hardware was secondary to software. For a pricey monthly membership, you get access to a (theoretically) never-ending series of new features in the Whoop app, and the company throws in the small, screenless sensor for free. This was once a pretty good bargain, but for the past several years, Whoop hasn't done much. In 2023, the company released its OpenAI-powered personalized fitness service, Whoop Coach.

Measuring how real images look is a complex task in artificial intelligence research. For example, an image of a boy with a vacuum cleaner in a desert violates common sense. We introduce a novel method, which we call Through the Looking Glass (TLG), to assess image common sense consistency using Large Vision-Language Models (LVLMs) and Transformer-based encoder. By leveraging LVLMs to extract atomic facts from these images, we obtain a mix of accurate facts. We proceed by fine-tuning a compact attention-pooling classifier over encoded atomic facts. Our TLG has achieved a new state-of-the-art performance on the WHOOPS! and WEIRD datasets while leveraging a compact fine-tuning component.

Recent work shows that reinforcement learning(RL) can markedly sharpen the reasoning ability of large language models (LLMs) by prompting them to "think before answering." Yet whether and how these gains transfer to audio-language reasoning remains largely unexplored. We extend the Group-Relative Policy Optimization (GRPO) framework from DeepSeek-R1 to a Large Audio-Language Model (LALM), and construct a 32k sample multiple-choice corpus. Using a two-stage regimen supervised fine-tuning on structured and unstructured chains-of-thought, followed by curriculum-guided GRPO, we systematically compare implicit vs. explicit, and structured vs. free form reasoning under identical architectures. Our structured audio reasoning model, SARI (Structured Audio Reasoning via Curriculum-Guided Reinforcement Learning), achieves a 16.35% improvement in average accuracy over the base model Qwen2-Audio-7B-Instruct. Furthermore, the variant built upon Qwen2.5-Omni reaches state-of-the-art performance of 67.08% on the MMAU test-mini benchmark. Ablation experiments show that on the base model we use: (i) SFT warm-up is important for stable RL training, (ii) structured chains yield more robust generalization than unstructured ones, and (iii) easy-to-hard curricula accelerate convergence and improve final performance. These findings demonstrate that explicit, structured reasoning and curriculum learning substantially enhances audio-language understanding.

Quantifying the realism of images remains a challenging problem in the field of artificial intelligence. For example, an image of Albert Einstein holding a smartphone violates common-sense because modern smartphone were invented after Einstein's death. We introduce a novel method for assessing image realism using Large Vision-Language Models (LVLMs) and Natural Language Inference (NLI). Our approach is based on the premise that LVLMs may generate hallucinations when confronted with images that defy common sense. Using LVLM to extract atomic facts from these images, we obtain a mix of accurate facts and erroneous hallucinations. We proceed by calculating pairwise entailment scores among these facts, subsequently aggregating these values to yield a singular reality score. This process serves to identify contradictions between genuine facts and hallucinatory elements, signaling the presence of images that violate common sense. Our approach has achieved a new state-of-the-art performance in zero-shot mode on the WHOOPS!

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Venture capitalists are in the business of predicting the next big thing, even if they get burned in the process. While everyone piled onto crypto in 2021 -- and many remain bullish about its future despite multiple failures this year -- 2022 saw the rise of generative AI. But as is the case with any transformative new tech, hype is sure to accompany growing adoption, and generative AI has garnered so much attention and money that many VCs already feel the budding sector will be the next bubble. TechCrunch recently surveyed more than 35 investors working in different geographies, investment stages and sectors about how they were feeling about next year. One of our questions sought their prediction of where the next bubble would be, and almost half of those surveyed mentioned generative AI.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

From mobile work to security and maintenance, perhaps no company has done more for the advancement of technology in today's society than SkyNet, a promising start up out of Austin, Texas that has made great strides during the COVID-19 pandemic. Last year, their T-400 model of home assistant swept the country, combining at home personal assistants with a walking talking android that actually helped with chores and tasks around the house. We had the opportunity to sit down with Barry Snow, the CEO of the skyrocketing company, about SkyNet's future and some of the backlash to what some have called "unnecessarily violent home assistants." Can I have one of those waters? So, your company was already gaining steam a few years ago, but it really seems that during the pandemic you pulled ahead of a lot of your peers with your home androids.