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Editor’s note: Kumo AI was one of the finalists Turn a vb In our time during our time Annual Innovation Showcase and submitted RFM from pirag Turn a vb Wednesday.
This generative ai Boom, writing, summarizes and gave strong language models that can lead to large amounts of text and other types of information. When it comes to high-valuable predictive tasks such as predicting customer bar or discovering fraud from structured data, relational data stuck in the world of traditional machinery learning.
Stanford professor and Moderate A Kooritor Jury Leskovec claims to have a critical missing work. The company’s vehicle, relative foundation model (RFM), large language models (LLS) are a newly designed EU that brings the “zero-stroke” capabilities into a structured database.
“To predict something you don’t know, it is to make a prediction about something that doesn’t happen yet,” said Leskovec Ventureat. “And this is a major opportunity, I would argue, missing the current pirview of what we think like a Gen EU.”
Why Predictor ML “30-year-old technology”
LLMS and Returned generation (Dwarf) systems can answer questions about existing knowledge, they are radically retrospective. They already inform the information there. Companies for predictive work assignments still trust learning a classic machine.
For example, a business must hire a group of data scientists who have a business, to make a working process, to build a model of predicting a business, to create prediction signals with manual forecast signals. It covers the expansion of complex data for customer’s purchase history and web-clicks to create a customer’s purchase history and web-clicking, such as a customer’s purchase history and website.
“If you want to learn the car (ML), Sorry, you are stuck in the past,” Leskovec said. Expensive and time-consuming distress prevents the data from most organizations to be really flexible.
How to summarize transformers for Kumo database
Kumo’s approach, “Related Deep Learning”, burns this hand process with two main understanding. First, it automatically represents any associated database alone, as a graphic connected to each other. For example, if the database has a “users” schedule to record the “Orders” schedule to record customer information and customer purchases, each line is node in the schedule, each line in the order schedule is node and so on. These nodes create a rich map of all databases that do not have hand efforts, using existing connections, such as external keys, such as external keys.
Second, Kumo summarized Transformer architectureThe engine behind the LLMs to learn directly from this graphic representation. Transformers are superior to understanding the significance using the “focus mechanism” to the weight of different verses related to each other.
Kumo’s rfm At the same time, this focus mechanism that allows you to learn complex samples and connections in more than one table. Leskovec compares this leap compared to computer vision. In the early 2000s, ML engineers had to manually design features such as edges and forms to detect an object. However, new architecture such as Congutball Neuron Networks (CNN) can take raw pixels and automatically learn the relevant features.
Similarly, the RFM accepts raw database tables and allows the network to discover unique signals without effort.
The result is a predetermined foundation model that can perform the predictor tasks in a new database immediately, “zero-stroke”. During a demo, Leskovec showed that a user could write a simple request to predict that a customer will order a certain customer in the next 30 days. Within seconds, the system returned an explanation of the information points that caused a probability account and the user as the last activity or lack of the user. The model was not prepared in the database and adapted to this in real time Learning in context.
“I just have a pre-made model you point to your data, and it will then give a clear forecast,” said Leskovec. Added that, “as he says, it may be accurate to a few weeks.”
Interface data analysts are designed to get acquainted with the ability to democratize access to predictive analysts, not only machine learning professionals.
Strengthening the agent
This has a significant effect for the development of technology AI agents. It is necessary to do more than the process language for an agent to perform meaningful tasks within a company; Must make intelligent decisions based on the company’s personal information. RFM can serve as a predictor engine for these agents. For example, a customer service agent can ask RFM to determine the value of a client’s whip or potential future or their potential future, then use an LLM to adapt his conversation and suggestions accordingly.
“If an agent is in the future, agents must make decisions aimed at private data. This is a way to make a decision,” said Leskovec.
Kumo’s case points to a future with the EU’s EU addition to two extra domains: RFMS for retrospective knowledge and structured data in the unstructive text. Eliminating the feature engineering Battleneck, RFM promises to reduce the time and costs to put strong ML tools into the hands of more enterprises and to make information from information.
The company released a public demonstration of RFC and start a version that allows users to close their data in the coming weeks. For organizations that require maximum accuracy, Kumo will also offer a delicate system for further increase in private data locations.
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