Mauricio Torres Echenagucía

Fíjate que uso la palabra algo varias veces, porque eso que estoy haciendo es simplemente en la búsqueda de algo lindo, algo medio poético que diga que estoy usando la inteligencia artificial o realmente estoy teniendo resultados con eso. Todavía los resultados requieren un poquito más de trabajo.

Fíjate que uso la palabra algo varias veces, porque eso que estoy haciendo es simplemente en la búsqueda de algo lindo, algo medio poético que diga que estoy usando la inteligencia artificial o realmente estoy teniendo resultados con eso. Todavía los resultados requieren un poquito más de trabajo.

¿Cómo es esto? Tenemos una mezcla. Nosotros tenemos algunos clientes en los cuales la relación es tan grande y de tantos años, a los cuales le proponemos lo que nos llamamos cliente cero. Le decimos, mira, ya IBM ha hecho en el mundo todas estas cosas con licencia artificial. Hemos logrado todos estos retornos. Ya lo hemos implementado y funciona. Exacto.

¿Cómo es esto? Tenemos una mezcla. Nosotros tenemos algunos clientes en los cuales la relación es tan grande y de tantos años, a los cuales le proponemos lo que nos llamamos cliente cero. Le decimos, mira, ya IBM ha hecho en el mundo todas estas cosas con licencia artificial. Hemos logrado todos estos retornos. Ya lo hemos implementado y funciona. Exacto.

We also test them depending on the industry. For example, a Telco customer here in Mexico, we tell him, we are doing this in the United States, we are doing it in Europe. So we can show them how that did work in other clients who have gone that way.

We also test them depending on the industry. For example, a Telco customer here in Mexico, we tell him, we are doing this in the United States, we are doing it in Europe. So we can show them how that did work in other clients who have gone that way.

Because it is very important, Javier, the issue of not getting into things in which you are going to vote money and you are going to realize three or four years later that it did not work. It is too expensive. Exactly. From the point of view of reputation, from the point of view of costs, you have been able to use that money in something else.

Because it is very important, Javier, the issue of not getting into things in which you are going to vote money and you are going to realize three or four years later that it did not work. It is too expensive. Exactly. From the point of view of reputation, from the point of view of costs, you have been able to use that money in something else.

So, the truth is that customers today are questioning, hey, how do I get into projects of artificial intelligence that are tested? At the same time, in your own industry, you also want to have some innovation. If you want to take a little risk, but not as much as to be able to risk your reputation or to be able to risk a lot of money.

So, the truth is that customers today are questioning, hey, how do I get into projects of artificial intelligence that are tested? At the same time, in your own industry, you also want to have some innovation. If you want to take a little risk, but not as much as to be able to risk your reputation or to be able to risk a lot of money.

Well, the origin of all this was simply to use artificial intelligence, to use everything that was machine learning, and the first concepts were expensive, it was difficult, and only a few could afford to do that.

Well, the origin of all this was simply to use artificial intelligence, to use everything that was machine learning, and the first concepts were expensive, it was difficult, and only a few could afford to do that.

What has happened now is not only because you have access to technology in the cloud, in any of the providers, including, of course, IBM, which makes it really very cheap to make use of that technology.

What has happened now is not only because you have access to technology in the cloud, in any of the providers, including, of course, IBM, which makes it really very cheap to make use of that technology.

What this whole issue of the generative artificial intelligence has again is precisely the use of models, the use of foundational models that allow you to build with a base that is relevant for your company, the whole world of information you need. And as information, today we have plenty of it, plenty outside and plenty inside the same company, how to put it into practice.

What this whole issue of the generative artificial intelligence has again is precisely the use of models, the use of foundational models that allow you to build with a base that is relevant for your company, the whole world of information you need. And as information, today we have plenty of it, plenty outside and plenty inside the same company, how to put it into practice.

That's what we did with WatsonX. What we did was create a platform that has three parts. One part that is like a laboratory in which you can put your models to work, you can test them, everything that is machine learning, you can basically play with modeling all this. That's called WatsonX AI.

That's what we did with WatsonX. What we did was create a platform that has three parts. One part that is like a laboratory in which you can put your models to work, you can test them, everything that is machine learning, you can basically play with modeling all this. That's called WatsonX AI.

Then you have a part called WatsonX.data in which you build a data lake with all the data you have in your company and that you have outside and you give access to that data in an organized way, in a structured way, so that the model you created in AI can really take all that data and manage it properly.

Then you have a part called WatsonX.data in which you build a data lake with all the data you have in your company and that you have outside and you give access to that data in an organized way, in a structured way, so that the model you created in AI can really take all that data and manage it properly.