Neural Information Processing Systems http://nips.cc/

But assigning responsibility is complicated. These three visualizations help explain why. Take advantage of epic savings on award-winning reporting, razor-sharp analysis, and expert insights on your favorite technology topics. Subscribe today to save 50% on an annual subscription, plus receive a free digital copy of our "Generative AI and the future of work" report. This could be the cultivated meat industry's future: as a luxury product for the few.

Google is releasing its latest round of Pixel updates today, including the free VPN the company teased at its October event. Clear Calling also launches to the public alongside updates for its voice memo app and new sleep features for the Pixel Watch. Pixel 7 and Pixel 7 Pro users can now use the Google One VPN on their phones for free (rather than buying it bundled with a $10-per-month storage plan). Clear Calling, Google's voice isolation for calls which had been in beta since October, is now available for all Pixel 7 series owners. The AI-powered feature makes calls in noisy environments sound better by bringing your voice to the forefront while drowning out background noise. Recorder, Google's AI-based voice memo app, now transcribes and organizes recordings for multiple people.

Founded in 2010, CLEAR's mission is to create frictionless experiences. With more than 12 million members and hundreds of partners across the world, CLEAR's identity platform is transforming the way people live, work, and travel. Whether it's at the airport, stadium, or right on your phone, CLEAR connects you to the things that make you, you - making everyday experiences easier, more secure, and more seamless. Since day one, CLEAR has been committed to privacy done right. Members are always in control of their own information, and we never sell member data.

With CLEAR, you are always you. CLEAR's mission is to enable frictionless and safe journeys using your identity. With more than 8 million members and 100 partners across North America, CLEAR's identity platform connects you to the cards in your wallet - transforming the way you live, work and travel. Trust and privacy are the foundation of CLEAR. We have a commitment to members being in control of their own information and never sell member data.

Continual learning (CL) is widely regarded as crucial challenge for lifelong AI. However, existing CL benchmarks, e.g. Permuted-MNIST and Split-CIFAR, make use of artificial temporal variation and do not align with or generalize to the real-world. In this paper, we introduce CLEAR, the first continual image classification benchmark dataset with a natural temporal evolution of visual concepts in the real world that spans a decade (2004-2014). We build CLEAR from existing large-scale image collections (YFCC100M) through a novel and scalable low-cost approach to visio-linguistic dataset curation. Our pipeline makes use of pretrained vision-language models (e.g. CLIP) to interactively build labeled datasets, which are further validated with crowd-sourcing to remove errors and even inappropriate images (hidden in original YFCC100M). The major strength of CLEAR over prior CL benchmarks is the smooth temporal evolution of visual concepts with real-world imagery, including both high-quality labeled data along with abundant unlabeled samples per time period for continual semi-supervised learning. We find that a simple unsupervised pre-training step can already boost state-of-the-art CL algorithms that only utilize fully-supervised data. Our analysis also reveals that mainstream CL evaluation protocols that train and test on iid data artificially inflate performance of CL system. To address this, we propose novel "streaming" protocols for CL that always test on the (near) future. Interestingly, streaming protocols (a) can simplify dataset curation since today's testset can be repurposed for tomorrow's trainset and (b) can produce more generalizable models with more accurate estimates of performance since all labeled data from each time-period is used for both training and testing (unlike classic iid train-test splits).

GRT planner works in two phases. Although it did not gain any prize, it gave us good prospects for the future. STRIPS planners did not take part. The competition results have shown that the performance of the domain-independent heuristic planners is strongly affected by the representation of the domains. All GRT-related stuff is available at www.csd.auth.