Pinecone is a vector database for storing and searching through dense vectors. Why would you ever want to do that? Keep reading to find out. There is perhaps no greater contributor to the success of modern Natural Language Processing (NLP) technology than vector representations of language. The meteoric rise of NLP was ignited with the introduction of word2vec in 2013 [1]. Word2vec is one of the most iconic and earliest examples of dense vectors representing text.

It may be smart, but it's not that clever. Artificial intelligence is nothing without human input. The algorithms that drive AI rely on the expertise of programmers and it's still no more than a tool – albeit a powerful one – that scientists and engineers can use to solve problems. Yet this is not to say that AI isn't the fastest-growing deep technology in the world, with the potential to transform people's lives and boost nations' economies. Facilitating AI innovation has even become a priority for the UK government, as laid out in the National AI Strategy it published in September.

Lydia Winters shows off Microsoft's "Minecraft" ahead of the Electronic Entertainment Expo, in 2015. Cybersecurity experts say Minecraft players were quick to exploit a critical flaw in widely used software that intelligence firms raced to patch Friday. Lydia Winters shows off Microsoft's "Minecraft" ahead of the Electronic Entertainment Expo, in 2015. Cybersecurity experts say Minecraft players were quick to exploit a critical flaw in widely used software that intelligence firms raced to patch Friday. BOSTON -- A critical vulnerability in a widely used software tool -- one quickly exploited in the online game Minecraft -- is rapidly emerging as a major threat to organizations around the world.

Tiktoker Hannah Thelen and cyber journalist Kurt Knutsson join'Fox & Friends' to discuss what steps people can take to protect their data Security experts around the world raced Friday to patch one of the worst computer vulnerabilities discovered in years, a critical flaw in open-source code widely used across industry and government in cloud services and enterprise software. "I'd be hard-pressed to think of a company that's not at risk," said Joe Sullivan, chief security officer for Cloudflare, whose online infrastructure protects websites from malicious actors. Untold millions of servers have it installed, and experts said the fallout would not be known for several days. New Zealand's Computer emergency response team was among the first to report that the flaw in a Java-language utility for Apache servers used to log user activity was being "actively exploited in the wild" just hours after it was publicly reported Thursday and a patch released. The vulnerability, dubbed'Log4Shell,' was rated 10 on a scale of one to 10, the worst possible.

In recent years, there have been great advances in the field of decentralized learning with private data. Federated learning (FL) and split learning (SL) are two spearheads possessing their pros and cons, and are suited for many user clients and large models, respectively. To enjoy both benefits, hybrid approaches such as SplitFed have emerged of late, yet their fundamentals have still been illusive. In this work, we first identify the fundamental bottlenecks of SL, and thereby propose a scalable SL framework, coined SGLR. The server under SGLR broadcasts a common gradient averaged at the split-layer, emulating FL without any additional communication across clients as opposed to SplitFed. Meanwhile, SGLR splits the learning rate into its server-side and client-side rates, and separately adjusts them to support many clients in parallel. Simulation results corroborate that SGLR achieves higher accuracy than other baseline SL methods including SplitFed, which is even on par with FL consuming higher energy and communication costs. As a secondary result, we observe greater reduction in leakage of sensitive information via mutual information using SLGR over the baselines.

Safety is the major consideration in controlling complex dynamical systems using reinforcement learning (RL), where the safety certificate can provide provable safety guarantee. A valid safety certificate is an energy function indicating that safe states are with low energy, and there exists a corresponding safe control policy that allows the energy function to always dissipate. The safety certificate and the safe control policy are closely related to each other and both challenging to synthesize. Therefore, existing learning-based studies treat either of them as prior knowledge to learn the other, which limits their applicability with general unknown dynamics. This paper proposes a novel approach that simultaneously synthesizes the energy-function-based safety certificate and learns the safe control policy with CRL. We do not rely on prior knowledge about either an available model-based controller or a perfect safety certificate. In particular, we formulate a loss function to optimize the safety certificate parameters by minimizing the occurrence of energy increases. By adding this optimization procedure as an outer loop to the Lagrangian-based constrained reinforcement learning (CRL), we jointly update the policy and safety certificate parameters and prove that they will converge to their respective local optima, the optimal safe policy and a valid safety certificate. We evaluate our algorithms on multiple safety-critical benchmark environments. The results show that the proposed algorithm learns provably safe policies with no constraint violation. The validity or feasibility of synthesized safety certificate is also verified numerically.

Reinforcement learning has achieved great success in many applications. However, sample efficiency remains a key challenge, with prominent methods requiring millions (or even billions) of environment steps to train. Recently, there has been significant progress in sample efficient image-based RL algorithms; however, consistent human-level performance on the Atari game benchmark remains an elusive goal. We propose a sample efficient model-based visual RL algorithm built on MuZero, which we name EfficientZero. Our method achieves 194.3% mean human performance and 109.0% median performance on the Atari 100k benchmark with only two hours of real-time game experience and outperforms the state SAC in some tasks on the DMControl 100k benchmark. This is the first time an algorithm achieves super-human performance on Atari games with such little data. EfficientZero's performance is also close to DQN's performance at 200 million frames while we consume 500 times less data. EfficientZero's low sample complexity and high performance can bring RL closer to real-world applicability. We implement our algorithm in an easy-to-understand manner and it is available at https://github.com/YeWR/EfficientZero. We hope it will accelerate the research of MCTS-based RL algorithms in the wider community.

Led by Caitlin Doogan, this project seeks to address the need for insights about adherence to Public Health Measures (PHMs) during the COVID-19 global pandemic. Without a vaccine, the spread of COVID-19 can only be stopped with PHMs such as lockdowns, social distancing and face masks. But as fatigue sets in and compliance decreases, governments need new ways of encouraging people to adhere to PHMs. To do this, they need to know the public's attitudes and understandings of these measures are and why. In this research, we used machine learning methods to analyse millions of tweets from six different countries.

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. Affirm, Inc. proudly includes Affirm, PayBright, and Returnly. Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. The ML Platform team's mission is to develop a self-service foundation for applications of machine learning that will provide scalable business impact. This team works in close partnership with ML engineers to deliver systems that enable fast paced ML development.

Decision sets and decision lists are two of the most easily explainable machine learning models. Given the renewed emphasis on explainable machine learning decisions, both of these machine learning models are becoming increasingly attractive, as they combine small size and clear explainability. In this paper, we define size as the total number of literals in the SAT encoding of these rule-based models as opposed to earlier work that concentrates on the number of rules. In this paper, we develop approaches to computing minimum-size "perfect" decision sets and decision lists, which are perfectly accurate on the training data, and minimal in size, making use of modern SAT solving technology. We also provide a new method for determining optimal sparse alternatives, which trade off size and accuracy. The experiments in this paper demonstrate that the optimal decision sets computed by the SAT-based approach are comparable with the best heuristic methods, but much more succinct, and thus, more explainable. We contrast the size and test accuracy of optimal decisions lists versus optimal decision sets, as well as other state-of-the-art methods for determining optimal decision lists. Finally, we examine the size of average explanations generated by decision sets and decision lists.