The $184 billion market for artificial intelligence shows no signs of slowing. A big slice of that market is organizations, from businesses to government agencies, that rely on AI to help make decisions. A 2023 study by IBM found 43% of CEOs are using AI to make strategic decisions.
Many beginners will initially rely on the train-test method to evaluate their models. This method is straightforward and seems to give a clear indication of how well a model performs on unseen data. However, this approach can often lead to an incomplete understanding of a model’s capabilities. In this blog, we’ll discuss why it’s important …
Read more “From Train-Test to Cross-Validation: Advancing Your Model’s Evaluation”
AI chatbots and virtual assistants have become increasingly popular in recent years thanks the breakthroughs of large language models (LLMs). Trained on a large volume of datasets, these models incorporate memory components in their architectural design, allowing them to understand and comprehend textual context. Most common use cases for chatbot assistants focus on a few …
OpenAI quietly releases a cheaper, more powerful GPT-4o update as the company faces leadership exodus and fierce competition in the rapidly evolving AI landscape.Read More
From tricking companies into handing over victims’ personal data to offering violence as a service, the online doxing ecosystem is not just still a problem—it’s getting more extreme.
A new cross-disciplinary study by Washington University in St. Louis researchers has uncovered an unexpected psychological phenomenon at the intersection of human behavior and artificial intelligence: When told they were training AI to play a bargaining game, participants actively adjusted their own behavior to appear more fair and just, an impulse with potentially important implications …
Read more “Humans change their own behavior when training AI”
If you’re familiar with machine learning, you know that the training process allows the model to learn the optimal values for the parameters—or model coefficients—that characterize it. But machine learning models also have a set of hyperparameters whose values you should specify when training the model. So how do you find the optimal values for …
Read more “Tips for Tuning Hyperparameters in Machine Learning Models”
Large language models are trained on massive scrapes of the web, which are often unstructured, noisy, and poorly phrased. Current scaling laws show that learning from such data requires an abundance of both compute and data, which grows with the size of the model being trained. This is infeasible both because of the large compute …
Read more “Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling”
With last month’s blog, I started a series of posts that highlight the key factors that are driving customers to choose Amazon Bedrock. I explored how Bedrock enables customers to build a secure, compliant foundation for generative AI applications. Now I’d like to turn to a slightly more technical, but equally important differentiator for Bedrock—the …
Read more “Build custom generative AI applications powered by Amazon Bedrock”
Among application developers, LangChain is one of the most popular open-source LLM orchestration frameworks. To help developers use LangChain to create context-aware gen AI applications with Google Cloud databases, in March we open-sourced LangChain integrations for all of our Google Cloud databases including Vector stores, Document loaders, and Chat message history. And now, we’re excited …
Read more “Announcing LangChain on Vertex AI for AlloyDB and Cloud SQL for PostgreSQL”