In our new blog series, “Meet our Data Champions,” we showcase the exciting work Google Cloud customers are doing with data and AI/ML. In this edition, we talk to Bob Summers, CEO and founder of Goodcall, a company whose phone AI service leverages Google Cloud Speech AI technologies to bring the power of AI to …
Denoising Diffusion models have demonstrated their proficiency for generative sampling. However, generating good samples often requires many iterations. Consequently, techniques such as binary time-distillation (BTD) have been proposed to reduce the number of network calls for a fixed architecture. In this paper, we introduce TRAnsitive Closure Time-distillation (TRACT), a new method that extends BTD. For …
Read more “TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation”
In his book The Book of Why, Judea Pearl advocates for teaching cause and effect principles to machines in order to enhance their intelligence. The accomplishments of deep learning are essentially just a type of curve fitting, whereas causality could be used to uncover interactions between the systems of the world under various constraints without …
Practical machine learning (ML) is a trial and error process. ML practitioners compare different performance metrics by running ML experiments till you find the best model with a given set of parameters. Because of the experimental nature of ML, there are many reasons for tracking ML experiments and making them reproducible including debugging and compliance. …
Read more “How you can automate ML experiment tracking with Vertex AI Experiments autologging”
Posted by Piotr Padlewski and Josip Djolonga, Software Engineers, Google Research Large Language Models (LLMs) like PaLM or GPT-3 showed that scaling transformers to hundreds of billions of parameters improves performance and unlocks emergent abilities. The biggest dense models for image understanding, however, have reached only 4 billion parameters, despite research indicating that promising multimodal …
Read more “Scaling vision transformers to 22 billion parameters”
This post was co-written with Tony Momenpour and Drew Clark from KYTC. Government departments and businesses operate contact centers to connect with their communities, enabling citizens and customers to call to make appointments, request services, and sometimes just ask a question. When there are more calls than agents can answer, callers get placed on hold …
AI has never been more front and center for both consumers and development teams. I have hosted AI conversations, and one of the top comments I get is how to get started in AI without a ton of resources or ML developers on hand. The first place I send people is the treasure trove of …
Read more “Got your eye on AI? Try these 6 interactive tutorials”
There is such a thing as too much data — when it becomes too overwhelming to properly figure out how to use it. Keeping track and making sense of what data your organization has, where it came from, who owns it, and how they can use it, alongside a myriad of other questions, can be a daunting …
Posted by Peter Mattson, Senior Staff Engineer, ML Performance, and Praveen Paritosh, Senior Research Scientist, Google Research, Brain Team Machine learning (ML) offers tremendous potential, from diagnosing cancer to engineering safe self-driving cars to amplifying human productivity. To realize this potential, however, organizations need ML solutions to be reliable with ML solution development that is …
Read more “Data-centric ML benchmarking: Announcing DataPerf’s 2023 challenges”
Bounding box annotation is a time-consuming and tedious task that requires annotators to create annotations that tightly fit an object’s boundaries. Bounding box annotation tasks, for example, require annotators to ensure that all edges of an annotated object are enclosed in the annotation. In practice, creating annotations that are precise and well-aligned to object edges …