According to the tag learning, the following results have been found:
A system developer at Kryptonite IT company wrote an article about a new Rust tool that makes it easier to run machine learning models and integrate them into applications. Next, we publish the text in the first person.
Interesting post I saw on Reddit about how the younger generation uses ChatGPT for mindlessly solving homework assignments. Below the cut is my opinion, a brief historical excursion, and suggestions for improving the education system.
In this post, we will talk in detail about RAG at each of its stages, its modifications, and its promising directions of development at the time of writing.
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.
I asked the following question to the most popular LLMs.
At every technical conference lately, the word "agents" is sure to be mentioned. They are presented in different ways: as the next step after RAG, as a silver bullet for solving all problems, and as an absolute replacement for all classic pipelines. And those who do not use agents yet are hopelessly behind progress.
Any CV project starts with annotating large volumes of images and videos. Only successful results and high-quality data ensure that the model can be trained correctly.
Greetings to all readers!
YOLO stands for You Only Look Once. It is a widely known computer vision architecture, famous for its numerous versions: the first one was released in 2016 and only solved the task of object detection in images, while the latest one – the eleventh – appeared in September this year and is already a fundamental model that can be used for classification, object tracking in videos, pose estimation tasks, etc. All of this is in real-time.
Hello, tekkix! We at the Security Analysis Department of the "Astra Group" recently had our first experience participating in the Big Mathematical Workshop, and we would like to share it in this article. We will tell you how this participation helped the team test a new methodology for solving internal problems. It feels like our case may be useful to colleagues in the field.
The rapid development of artificial intelligence has led to a surge in job openings in this field, and today many people are building exciting careers in it. For many years, American scientist Andrew Ng has observed how this happens with thousands of students, as well as engineers in large and small companies. Now he offers a practical scheme by which you can pave your own career track.
Let's imagine that you are a novice or experienced bioinformatician, or a mere mortal who wants to delve into the analysis of biological data. Spoiler: bioinformaticians are also mortal! Often, not everyone has enough valuable time to check huge sequences of genomic data, whether it is searching for various mutations or predicting protein structures based on amino acid sequences.
OpenAI has released Sora, a neural network for generating short videos. The service can be used by owners of paid ChatGPT Plus and Pro subscriptions, but even after the release, they have to wait in a multi-hour queue. In this article, we look at the Sora interface, video examples, understand the limitations, and try to generate our own video.
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.
In this post, we will discuss the differences between two modeling approaches, namely, how physics-based models differ from data-driven models. In fact, there is something in between these two concepts that is becoming more relevant in solving scientific problems. But more on that later.
Hello, tekkix! This is Misha Stepnov, head of the R&D Big Data center at MTS Digital. If you work with language models or dream of creating your own AI assistant, it is important to choose the right tools: they will simplify the development process and make interaction with the model as convenient as possible. Today I will share a small selection of useful interfaces, platforms, and templates that we have tested at MTS - use them to your health. And if you have your own "favorites", write about them in the comments.