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.

GPT-3, or Generative Pre-trained Transformer 3, is a piece of AI from the OpenAI group that takes text from the user, and writes a lot more for them.

And, freaking heck am I am impressed at what folks have managed to build around the GPT-3 technology.

The modern project of creating human-like artificial intelligence (AI) started after World War II, when it was discovered that electronic computers are not just number-crunching machines, but can also manipulate symbols. It is possible to pursue this goal without assuming that machine intelligence is identical to human intelligence. This is known as weak AI. However, many AI researcher have pursued the aim of developing artificial intelligence that is in principle identical to human intelligence, called strong AI. Weak AI is less ambitious than strong AI, and therefore less controversial. However, there are important controversies related to weak AI as well. This paper focuses on the distinction between artificial general intelligence (AGI) and artificial narrow intelligence (ANI). Although AGI may be classified as weak AI, it is close to strong AI because one chief characteristics of human intelligence is its generality. Although AGI is less ambitious than strong AI, there were critics almost from the very beginning. One of the leading critics was the philosopher Hubert Dreyfus, who argued that computers, who have no body, no childhood and no cultural practice, could not acquire intelligence at all. One of Dreyfus’ main arguments was that human knowledge is partly tacit, and therefore cannot be articulated and incorporated in a computer program. However, today one might argue that new approaches to artificial intelligence research have made his arguments obsolete. Deep learning and Big Data are among the latest approaches, and advocates argue that they will be able to realize AGI. A closer look reveals that although development of artificial intelligence for specific purposes (ANI) has been impressive, we have not come much closer to developing artificial general intelligence (AGI). The article further argues that this is in principle impossible, and it revives Hubert Dreyfus’ argument that computers are not in the world.

Google's DeepMind has just released a new academic paper on AlphaZero -- the general purpose artificial intelligence system that mastered chess through self-play and went on to defeat the world champion of chess engines, Stockfish. In this video chess International Master Anna Rudolf takes a look at a never-before-seen game from a match played in January 2018, and discusses how the playing style and attacking chess of AlphaZero compare to computers and humans.

All the essential Deep Learning Algorithms you need to know including models used in Computer Vision and Natural Language Processing.

We are releasing HiPlot, a lightweight interactive visualization tool to help AI researchers discover correlations and patterns in high-dimensional data.

In this task we will try our first approach at training a conversational model.

Clearview AI devised a groundbreaking facial recognition app. You take a picture of a person, upload it and get to see public photos of that person, along with links to where those photos appeared. The system — whose backbone is a database of more than three billion images that Clearview claims to have scraped from Facebook, YouTube, Venmo and millions of other websites — goes far beyond anything ever constructed by the United States government or Silicon Valley giants.

Facebook AI has developed the first neural network that uses symbolic reasoning to solve advanced mathematics problems.

Federal public comment websites currently are unable to detect Deepfake Text once submitted. I created a computer program (a bot) that generated and submitted 1,001 deepfake comments regarding a Medicaid reform waiver to a federal public comment website, stopping submission when these comments comprised more than half of all submitted comments. I then formally withdrew the bot comments.