Sales Optimization Leader
Posted: Wed Dec 18, 2024 7:23 am
I want to ask you a question about data. Many people think that the success of I is closely related to data. We understand that your company gets a lot of data from the Internet and other sources. So what do you think is the relationship between data and models? Is it simply that the more data you input, the more powerful the model is? Or does it take a lot of time to sort out different types of data to make the model effective? Finally, how do you resolve the need for large amounts of human-generated data and the ownership and rights of this data? ir ri: Regarding the relationship between data and models, this is actually a misunderstanding that many people have about I models, especially large language models.
The people who develop these models don't pre-program indonesia phone number details them to do specific things, but input large amounts of data. These models become excellent pattern matching systems through this process, and intelligence is generated in this way. So they learn to write, code, do basic math, summarize information, and so on. We don't know exactly how it works, but we know it works. Deep learning is very powerful. This is important because people keep asking how it works, and that gets into transparency. We can describe the tools that we use to provide transparency to the public.
This is very important to understand because it shows how large language models work, combined with neural network architectures, lots of data, and computing power, produce amazing intelligence, and this capability will continue to improve as data and computing power increase. Of course, we need to do a lot of work to make this data available to the models. But this is the basic structure of it. We are thinking about how to provide transparency to understand how the model behavior works. We have tools that allow people to feel confident using these models and feel that they are involved.
The people who develop these models don't pre-program indonesia phone number details them to do specific things, but input large amounts of data. These models become excellent pattern matching systems through this process, and intelligence is generated in this way. So they learn to write, code, do basic math, summarize information, and so on. We don't know exactly how it works, but we know it works. Deep learning is very powerful. This is important because people keep asking how it works, and that gets into transparency. We can describe the tools that we use to provide transparency to the public.
This is very important to understand because it shows how large language models work, combined with neural network architectures, lots of data, and computing power, produce amazing intelligence, and this capability will continue to improve as data and computing power increase. Of course, we need to do a lot of work to make this data available to the models. But this is the basic structure of it. We are thinking about how to provide transparency to understand how the model behavior works. We have tools that allow people to feel confident using these models and feel that they are involved.