Sparse embeddings of data form an attractive class due to their inherent interpretability: Every dimension is tied to a term in some vocabulary, making it easy to visually decipher the latent space. Sparsity, however, poses unique challenges for Approximate Nearest Neighbor Search (ANNS) which finds, from a collection of vectors, the k vectors closest to a query. To encourage research on this underexplored topic, sparse ANNS featured prominently in a BigANN Challenge at NeurIPS 2023, where approximate algorithms were evaluated on large benchmark datasets by throughput and accuracy. In this work, we introduce a set of novel data structures and algorithmic methods, a combination of which leads to an elegant, effective, and highly efficient solution to sparse ANNS. Our contributions range from a theoretically-grounded sketching algorithm for sparse vectors to reduce their effective dimensionality while preserving inner product-induced ranks; a geometric organization of the inverted index; and the blending of local and global information to improve the efficiency and efficacy of ANNS. Empirically, our final algorithm, dubbed Seismic, reaches sub-millisecond per-query latency with high accuracy on a large-scale benchmark dataset using a single CPU.

We contribute a theoretical and operational framework for neurosymbolic AI called DeepLog. DeepLog introduces building blocks and primitives for neurosymbolic AI that make abstraction of commonly used representations and computational mechanisms used in neurosymbolic AI. DeepLog can represent and emulate a wide range of neurosymbolic systems. It consists of two key components. The first is the DeepLog language for specifying neurosymbolic models and inference tasks. This language consists of an annotated neural extension of grounded first-order logic, and makes abstraction of the type of logic, e.g. boolean, fuzzy or probabilistic, and whether logic is used in the architecture or in the loss function. The second DeepLog component is situated at the computational level and uses extended algebraic circuits as computational graphs. Together these two components are to be considered as a neurosymbolic abstract machine, with the DeepLog language as the intermediate level of abstraction and the circuits level as the computational one. DeepLog is implemented in software, relies on the latest insights in implementing algebraic circuits on GPUs, and is declarative in that it is easy to obtain different neurosymbolic models by making different choices for the underlying algebraic structures and logics. The generality and efficiency of the DeepLog neurosymbolic machine is demonstrated through an experimental comparison between 1) different fuzzy and probabilistic logics, 2) between using logic in the architecture or in the loss function, and 3) between a standalone CPU-based implementation of a neurosymbolic AI system and a DeepLog GPU-based one.

Seismic, the global leader in enablement, announced a new partnership with Microsoft for its seller experience application, Viva Sales. Together, Microsoft and Seismic will transform the future of sales and streamline daily workflows for the modern salesperson. Together, Microsoft and Seismic will transform the future of sales and streamline daily workflows for the modern salesperson. Breaking down silos of data, Viva Sales empowers sellers in their flow of work within Microsoft 365 and Teams, reducing busy work and maximizing sellers' time for the most valuable area of their work – engaging with customers and closing deals. Embedded within the Viva Sales workflow, Seismic will provide content production, collaboration, task automation, and engagement intelligence for Viva Sales users across the meeting experience to help drive deals and relationships forward.

AI is integrating ever deeper into even small companies. So much so that there are new Artificial Intelligence Tools coming to market seemingly daily. You can use it to streamline your B2B sales processes in real-time. Although big corporations first embraced the concept of AI, it's becoming a trend used by many small businesses to prospect, get leads, understand user behaviors and customer needs, and of course, grow revenue. Research by Marketing Interactive shows that by the end of 2020, 30% of all B2B companies will leverage Artificial Intelligence systems to augment one or many sales processes that can result in more conversions.

NEW YORK – U.S. startups focusing on care products and services for the elderly are tapping into the graying Japanese market, where more than 35 million people are over the age of 65. Seismic, a California-based apparel company, hopes to expand in Japan with its Powered Clothing, a body suit using robotics and sensor technology inside the garment to mimic human movements and increase strength. The body suit is meant for all ages, but Seismic has found particular success with elderly people who enjoy sports and travel in the United States, where the population is also graying. The number of people age 65 and older in the United States is projected to grow from 52 million in 2018 to 95 million by 2060, according to the Population Reference Bureau. In November, Seismic partnered with Obayashi Corp. to provide its construction workers with the suits.

Often, the phrase "digital health" conjures images of smart watches or apps that process your health data to give a readout of a parameter like heart rate. That is part of digital health, to be sure, but at the Big Data in Precision Health conference last week, four speakers during the last session of the conference offered a much more expansive vision for digital health technologies. They discussed robotics, precision mental health and personal behaviors in health practices -- the ever-elusive key to actually making changes in your own health. "Despite the mass amounts of data that we have today, we've still yet to understand how to change [health] behavior," said Jennifer Schneider, MD, the chief medical officer at Livongo, a company that develops products tailored to individuals with chronic diseases, such as devices that monitor blood sugar and provide personalized reminders to people with diabetes. But in areas such as mental health, it's not always easy to know what actions to take, even if you are motivated.