According to the tag machine learning, the following results have been found:
We at MWS have launched a language model aggregator, where you can work with multiple LLMs through a single interface. In MWS GPT, the following models are available: MTS's own models, external models like DeepSeek, or the customer's own models. These models can easily be connected to any corporate system or chatbot via API.
Hello everyone! This is Ksenia Naumova. At Positive Technologies, I research malicious network traffic and improve tools for analyzing it at the security expert center. Recently, we were tasked with creating an ML model to detect malware in the network. It had to recognize not only the malware we had previously detected, but also new threats that emerge in large numbers every day. As a first experiment, we decided to build a model to work with traffic transmitted via the HTTP protocol, as our products successfully decrypt TLS sessions, which often contain a lot of interesting data. In this article, I will describe in detail how we trained the model and share information about the mistakes we made.
Should we trust something that cannot understand the consequences of its actions? The answer seems obvious, but as the niche of linguistic models develops, we increasingly delegate routine tasks to AI.
A few years ago, at my previous job, I had an interesting discussion with a colleague from the microelectronics department. His idea was that the performance in neural network inference on NVIDIA's GPGPU surpasses our solution due to the use of a more advanced process technology, higher clock frequencies, and a larger die area. As a programmer, I couldn't agree with this, but at that time no one had the time or desire to test this hypothesis. Recently, in a conversation with my current colleagues, I remembered this discussion and decided to see it through. In this article, we will compare the performance of the NM Card module from NTC Module and the GT730 graphics card from NVIDIA.
At the end of September 2024, Netflix announced the release of a new, seventh season of the sci-fi series Black Mirror. This anthology is dedicated to dark forecasts for the near future, warning viewers about the consequences of the unpredictable development of technologies. The first episodes of Black Mirror were released in 2011, and during this time some of the series' plots have frighteningly come close to reality. Polina Sokol, Senior Data Analyst at the R&D Laboratory of the Cybersecurity Technology Center of the Solar Group, especially for Techinsider, and now for Habr, has collected several examples of the development of artificial intelligence that already exist in our lives or may soon become a reality.
The desire to understand why certain events occur is inherent in human nature. We constantly seek causal relationships to predict the future, make decisions, and improve our lives. But how does this desire manifest itself in the world of IT monitoring?
Imagine a world where artificial intelligence can communicate with aliens, and human thinking is deciphered down to the smallest details. Science fiction? Not quite. Recent advances in large language models force us to reconsider our ideas about the nature of mind and communication.
Wolfram is a cool thing. How many schoolchildren got an A because of it, and how many students passed the exam, you can't count...
Hello everyone! Today we will talk about the task of understanding video and the evolution of approaches to training multimodal large language models for this task.
We have written enough articles about optimizing your neural networks, today it is time to move on to splitting, reducing, and direct trimming, otherwise known as data quantization.
We are a team from the development department. Our department develops software for project management in the creation and design of complex engineering objects.
Finally, you can buy a camera without a lens, not set adequate ISO, shutter speed, and just observe the results.