The growth in the AI industry has led to the increasing size of the AI models. OpenAI's ChatGPT and Google's Bard, for instance, are composed of more than 100 billion parameters. GPT-4 is estimated to be built out of over 1 trillion parameters. However, these large parameters require substantial computing power, have high operating costs, and can perpetuate harmful biases if not carefully monitored. The high resource requirement makes these large AI models inaccessible to smaller players. Mountain View-based H2O.ai is helping democratize AI adoption by working on smaller AI models, some that weigh as little as 1.8 billion parameters. H2O.ai's Offerings Founded in 2012 by open-source focused Cliff Click and Satish Ambati, H2O.ai began with the idea that there should be freedom around the creation and use of AI. H2O.ai's open source framework known as H2O allows data scientists and developers access to a fast machine learning engine for their applications. It works bo
According to a recent report, the global artificial intelligence (AI) market is expected to grow at 37% CAGR from $136.55 billion in 2022 through 2030. The growth in the industry is expected to be driven by the continued research and innovation by tech giants who are helping increase the adoption of this technology in industry verticals, such as automotive, healthcare, retail, finance, and manufacturing. H2O.ai is a leading player in the run to get enterprises comfortable with the AI revolution. H2O.ai's Offerings Founded in 2012 by Cliff Click and Satish Ambati, Mountain View-based H2O.ai is an AI cloud company working to democratize AI for everyone, allowing customers to use its H2O AI Hybrid Cloud platform to rapidly solve complex business problems. H2O.ai began as a group of open source focused individuals who were driven by the idea that there should be freedom around the creation and use of AI. Today, its customers use the H2O AI Hybrid Cloud platform to build, operate and
Cliff Click discusses the Von Neumann architecture, CISC vs RISC, Instruction-Level Parallelism, pipelining, out-of-order dispatch, cache misses, memory performance, and tips to improve performance.
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Like so many others in the Apache Cassandra community, I’m extremely excited to see that the 4.0 release is finally here. There are many, many improvements to Cassandra 4.0. One enhancement that is more important than it might look is the addition of support for Java versions 9 and up. This was not trivial, because Java 9 made changes to some internal APIs that the most performance-oriented Java projects like Cassandra relied on (you can read more about this here).
This is a big deal because with Cassandra 4.0, you not only get the direct improvements to performance added by the Apache Cassandra committers, you also unlock the ability to take advantage of seven years of improvements in the JVM (Java Virtual Machine) itself.