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Google Cloud enables organizations to digitally transform into smarter companies. It offers cloud processing, data analysis as well as the latest tools of artificial intelligence (AI) and machine learning.
By using our AI research, we improve these solutions for Google Cloud customers around the world.
Our research involves deciphering written documents, increasing the value of wind energy and facilitating the use of Alfafold – our groundbreaking AI system designed to better predict protein structures.
Extending the innovation of products in individual artificial documents
From wedge tablets to the printing press, countless ways to share written knowledge have been developed throughout the history. Modern documents differ depending on countries, languages and industries – making it difficult to separate and use this information, especially on a scale.
Google Cloud AI document It enables users to create digital, printed or handwritten information contained in the document – such as an invoice or tax form – possible to extract and inquiries.
Before the AI document, industries that want to use AI tools to understand documents need huge amounts of training data to achieve good. But these data are often inaccessible, incomplete or do not have adequate annotation, preventing the widespread acceptance of AI.
Working with the AI Document Google Cloud team, we have developed innovative machine learning models that need 50-70% less training data than others to analyze documents such as media bills and purchasing orders.
We are also working on improving the efficiency of AI documents in languages with smaller data sets. In this way, we can help more clients in various industries and geography use the benefits of AI documents.
Increasing the value of wind energy
As part of our efforts to use artificial intelligence Achieving net emissions by 2030.We have established cooperation with Google Cloud Professional Services to develop the wind energy sector-and help build the future free of emission for everyone.
Wind farms are an important source of electricity without coal, but their power can change depending on the weather. To balance supply and demand in the electricity network, operators rely on energy production forecasts. If operators may commit to selling a certain amount of electricity based on the forecast the next day, they can get a better price.
In cooperation with Google Cloud, We helped develop a non -standard AI tool To better predict the starting power of the wind. This tool has been trained in the field of weather forecasts and historical data of the customer's wind turbine. An additional model recommends how much energy the operator may undertake to deliver to the electricity network, day by day.
Global energy supplier and renewable energy sources ENGIE pilotes this technology in Germany. If the pilot succeeds, Enggie can use technology throughout Europe. Making wind energy more economically attractive – and improving its reliability – will encourage you to collect renewable energy sources. This is a victory for everyone.
Facilitating Alfafold in use with Vertex AI
The development of a new machine learning model includes many stages – from design to implementation. It also needs good data infrastructure. To support scientists and companies, Google Cloud has built Vertex AI, one platform for access to machine learning tools for each stage of development.
After release Our groundbreaking alpofold systemwhich exactly predicts the 3D protein structure, we did it Available on Vertex AI. Now scientists working in areas as diverse as discovering drugs or the fight against plastic pollution can easier to start the flow of the work of Alfafold forecasting, following experiments, optimizing the choice of equipment – and managing it on a large scale.
In 2022, we also expanded the database of Alfafold protein structure with almost all cataloged proteins known to science. We worked with Google Cloud to host this huge database, offering more than 200 million proteins to download loose. Billions of structures have already been collected, and the database quickly became an indispensable tool for the scientific community, catalyzing a new wave of progress in biology.
