If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Wondering what everyone's been watching this week? Well, spring is in the air and so is action, action, action! Every week, the popularity of movies across streaming might be determined by promotions, star power, critic raves, social media buzz, good old-fashioned word of mouth, or a new addition to a beloved franchise. While the reasons may vary, you can't argue with the numbers that streaming aggregator Reelgood collected from hundreds of streaming services in the U.S. and UK. As it has for weeks, The Batman continues to reign supreme.
In this week's real-time analytics news: HPE launched HPE Swarm Learning, a privacy-preserving, decentralized machine learning framework for the edge. Keeping pace with news and developments in the real-time analytics market can be a daunting task. We want to help by providing a summary of some of the important news items our staff came across this week. Hewlett Packard Enterprise (HPE) announced the launch of HPE Swarm Learning, an AI solution to accelerate insights at the edge, from diagnosing diseases to detecting credit card fraud, by sharing and unifying AI model learnings without compromising data privacy. HPE Swarm Learning is a privacy-preserving, decentralized machine learning framework for the edge or distributed sites.
When video games were just abstract concepts on university computers, book clubs were already popular. When Toad told Mario the princess was in another castle, introducing video game narrative to millions of living rooms, readers were already comparing notes on Jane Eyre. So, it's only natural that as video games became more narratively ambitious, they'd take this familiar page from the literary world. So, move over, Oprah--you've got competition. Delivered as multi-episode podcast seasons, these "video game book clubs" earn the moniker thanks to weekly deep dive episodes into narrative-heavy video games, and ongoing, guided discussions among their robust listener communities.
Ganesan takes the mystery out of implementing AI, showing leaders how to launch AI initiatives that get results. In The Business Case for AI, Kavita Ganesan takes the mystery out of implementing AI, showing leaders how to launch AI initiatives that get results. With real-world AI examples to spark new ideas, you'll learn how to identify high-impact AI opportunities, prepare for AI transitions, and measure your AI performance. Simple and compelling, The Business Case for AI gives leaders the information they need without the technical jargon. Whether you want to jumpstart your AI strategy, manage AI initiatives for better outcomes, or simply find inspiration for your own AI applications, The Business Case for AI is a blueprint for Business AI success.
MLOps is the machine learning operations counterpart to DevOps and DataOps. But, across the industry, definitions for MLOps can vary. Some see MLOps as focusing on ML experiment management. Others see the crux of MLOps as setting up CI/CD (continuous integration/continuous delivery) pipelines for models and data the same way DevOps does for code. Other vendors and customers believe MLOps should be focused on so-called feature engineering -- the specialized transformation process for the data used to train ML models.
I recently started a new newsletter focus on AI education and already has over 50,000 subscribers. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. If you follow this blog you know I am a fan of the Ludwig open source project. Initially incubated by Uber and now part of the Linux AI Foundation, Ludwig provides one of the best low-code machine learning(ML) stacks in the current market.
At its Digital Summit virtual event today, real-time NoSQL database player Aerospike announced a new release of its eponymous product. The v5.6 release adds a few features that together are designed to optimize the loop of real-time data processing and machine learning at the edge and "core" (cloud or corporate data center). The scenarios furthermore involve training machine learning (ML) models at the core from edge data, then pushing the models back to the edge for inferencing. ZDNet spoke with Aerospike founder and Chief Product Officer Srini Srinivasan, who briefed us on the three features that facilitate and optimize this virtuous data/ML cycle. The Aerospike connector for Spark allows real-time and historical data in the database to be used for training ML models, without requiring that data to be exported first.
This week, Oracle announced a major extension of its cloud-based Autonomous Data Warehouse service that transforms it into an end-to-end offering with a heavy dose of self-service for business users. The new version expands from being just a standalone data warehouse database service to a broader one supporting self-service capability for data ingest, loading, transformation, cataloging, and modeling. Oracle's move is very much in line with some of its rivals, such as Microsoft with Azure Synapse Analytics, and SAP with Data Warehouse Cloud, that have also broadened their cloud data warehouses to end-to-end services. But it is starkly differentiated from others like AWS, Snowflake, and Google, that are taking more portfolio-oriented or best-of-breed partner strategies. As the name states, Oracle's Autonomous Data Warehouse is built on the Autonomous Database.
Keeping up with the trend of many recent years, Deep Learning in 2020 continued to be one of the fastest-growing fields, darting straight ahead into the Future of Work. The developments were manifold and on multiple fronts. OpenAI, the AI Research organization, declared PyTorch as its new standard Deep Learning framework. PyTorch will increase its research productivity at scale on GPUs. With PyTorch backing it, OpenAI cut down its generative modeling iteration time from weeks to days. Megvii Technology, a China-based startup, said that it would make its Deep Learning framework open-source.
Thanks to many new and updated features, users can improve the efficiency of their machine vision processes. The consistent further development of all included technologies emphasizes HALCON's role as a leading standard library and software for machine vision. The new release will be available in both a Steady and a Progress edition. This means that the full range of new Progress features is now also available to HALCON Steady customers.