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”
Judge Amit Mehta’s ruling has triggered a potentially yearslong process to decide how to punish the company. For users, it could mean a future in which Google isn’t front and center everywhere.
A growing body of AI tools screen how people talk, searching for subtle changes that could indicate mental health concerns like depression or anxiety. A study finds that these tools don’t perform consistently across people from different genders and races.
Statistics and Machine Learning both aim to extract insights from data, though their approaches differ significantly. Traditional statistics primarily concerns itself with inference, using the entire dataset to test hypotheses and estimate probabilities about a larger population. In contrast, machine learning emphasizes prediction and decision-making, typically employing a train-test split methodology where models learn from …
Read more “Integrating Scikit-Learn and Statsmodels for Regression”
The clock strikes 3 AM. A potential buyer is browsing your website, eager to make a purchase, but they have a question. Your support team is fast asleep, and the sale slips away. Sound familiar? Today’s consumers expect instant gratification, and companies are feeling the pressure — both on their resources and their bottom line. Meeting these …
Read more “Can Chatbots Reduce Business Costs Dramatically? Expert Opinion and Insights”
Food security, healthcare and exploring the cosmos are among the ways students of a new pan-African Master’s program aspire to apply AI
This paper was accepted at IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) 2024 Programmers frequently engage with machine learning tutorials in computational notebooks and have been adopting code generation technologies based on large language models (LLMs). However, they encounter difficulties in understanding and working with code produced by LLMs. To mitigate these challenges, …
Read more “BISCUIT: Scaffolding LLM-Generated Code with Ephemeral UIs in Computational Notebooks”