I do not think it will shock anyone to learn that big tech is aggressively pushing AI products. But the extent to which they have done so might. The sheer ubiquity of AI means that we take for ground the countless ways, many invisible, that these products and features are foisted on us—and how Silicon Valley companies have systematically designed and deployed AI products onto their existing platforms in an effort to accelerate adoption.

The role of the IC (Individual Contributor) is evolving fast—and AI is accelerating the shift. As AI tools become deeply integrated into development workflows, many engineers find themselves stepping into responsibilities once reserved for engineering managers. This isn’t a hypothetical trend—it’s already happening in high-performing teams.

Napkin turns your text into visuals so sharing your ideas is quick and effective.

Our new AI system accurately identifies errors inside quantum computers, helping to make this new technology more reliable.

With the advent of Llama 2, running strong LLMs locally has become more and more a reality. Its accuracy approaches OpenAI's GPT-3.5, which serves well for many use cases.

In this article, we will explore how we can use Llama2 for Topic Modeling without the need to pass every single document to the model. Instead, we are going to leverage BERTopic, a modular topic modeling technique that can use any LLM for fine-tuning topic representations.

An LLM is no black box but an ML model (based on Neural Networks) that predicts the ‘next’ token given a sequence of previously predicted tokens and input prompt.
How is it able to get the context of the input? Using multi-head attention helps in focusing on important words compared to other tokens in the input sentence. If you’re interested in mathematics, you can read the below blog.

nGPT: A hypersphere-based Transformer achieving 4-20x faster training and improved stability for LLMs.

Artificial intelligence has made tremendous advances since its inception about seventy years ago. Self-driving cars, programs beating experts at complex games, and smart robots capable of assisting people that need care are just some among the successful examples of machine intelligence. This kind of progress might entice us to envision a society populated by autonomous robots capable of performing the same tasks humans do in the near future. This prospect seems limited only by the power and complexity of current computational devices, which is improving fast. However, there are several significant obstacles on this path. General intelligence involves situational reasoning, taking perspectives, choosing goals, and an ability to deal with ambiguous information. We observe that all of these characteristics are connected to the ability of identifying and exploiting new affordances—opportunities (or impediments) on the path of an agent to achieve its goals. A general example of an affordance is the use of an object in the hands of an agent. We show that it is impossible to predefine a list of such uses. Therefore, they cannot be treated algorithmically. This means that “AI agents” and organisms differ in their ability to leverage new affordances. Only organisms can do this. This implies that true AGI is not achievable in the current algorithmic frame of AI research. It also has important consequences for the theory of evolution. We argue that organismic agency is strictly required for truly open-ended evolution through radical emergence. We discuss the diverse ramifications of this argument, not only in AI research and evolution, but also for the philosophy of science.

Hi, I am Noctie, a human-like digital chess AI! Play against me and I'll try to match your skill level and estimate your rating.

In recent years, large-scale transformer-based language models have become the pinnacle of neural networks used in NLP tasks. They grow in scale and complexity every month, but training such models requires millions of dollars, the best experts, and years of development. That’s why only major IT companies have access to this state-of-the-art technology. However, researchers and developers all over the world need access to these solutions. Without new research, their growth could wane. The only way to avoid this is by sharing best practices with the developer community.

We’ve been using YaLM family of language models in our Alice voice assistant and Yandex Search for more than a year now.