How extensive language models will impact businesses

How extensive language models will impact businesses
How extensive language models will impact businesses

Tech4Impact 2024 of OrangeX was an event that was held this Thursday in which more than 30 talks, workshops and activities were held on artificial intelligenceproduct development, data analysis, programming and security. Ambit participated in the day took place in Buenos Airesalthough it also occurred simultaneously at the headquarters in Cordova.

One of the talks was given by Natalia Perezsolutions specialist Microsoftwho provided details of how they will impact companies extensive language models.

Basically what we do is help clients develop data and artificial intelligence products. In reality, we don’t focus so much on what the products are, but rather on meeting their needs and helping them in their daily lives to generate impact. That’s what matters most to us. And we are going to focus on explaining the life cycle of the LLM“, Pérez said at the beginning of the presentation.

The LLM They are based on machine learning: it is a type of neural network. In simpler terms, An LLM is a computer program that has been given enough examples to be able to recognize and interpret human language or other complex data types. “We are living in a moment in history like never before. This technology has only been with us for a year. We are truly living a revolution.“he said of AI.

Unlike traditional machine learning models that are built or trained from scratch, LLMs are generated from a base model and refined with new data to improve its performance in a particular area. In turn, language model operations big size are known as LLMOps.

These LLMOps are a set of practices, techniques and tools that are used to the operational management of these extensive language models in production environments. They differ from the operations of machine learning (MLOps) who are in charge of optimize the implementation and maintenance process of other models such as machine learning (ML).

Generative Intelligence, LLM and the life cycle

“Today we have generative artificial intelligence in all the products we use”the Microsoft expert told the audience and asserted that “we are living a little a paradigm shift“In traditional applications, we have a series of interactions that are restricted but In intelligent applications what we want is to be able to interact with natural language“he described.

He later mentioned that in the intelligent “The idea is that you can learn from the experience and that when it comes to having to make changes it is not so painful, but rather it is much more dynamic and we can deliver value as quickly as possible.” One of the barriers you mentioned is that When we talk about exploration, every week there is a new model.

“Sometimes it is difficult to find documentation nor are there many people with experience in the market”, said. The second pitfalls they may encounter is that, when a company adopts a model, it is already trained. All we do is use it, so it doesn’t know about our specific data.I have to give him that context so that he can answer me what I want,” he explained.

After this decision comes the stage of assessment. “Many times this type of technology can reach hallucinate or to give me answers that are not entirely correct then,” he specified. In the field of artificial intelligence, an artificial hallucination or hallucination is a response from an AI that does not seem to be justified by its training data.

Finally, we have the stage of operationalization. “It’s about how this process we make it sustainable over timeas we do it automated,” said the specialist. “We have the operationalization for the business, what you will want is for us to give quick resultsthat we add value, and the other is for IT that has to do with make it replicable, scale it, be able to repeat it and not have manual components in the middle“, hill.

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