Exploring the interactions between reinforcement learning (RL) and large language models (LLMs) sheds light on an exciting area of computational linguistics. These models, largely enhanced by human feedback, show remarkable prowess in understanding and generating text that mirrors human conversation. Yet, they are always evolving to capture more subtle human preferences. The main challenge lies…
Researchers at UT Austin have developed an effective and efficient method for training smaller language models (LM). Called "Inheritune," the method borrows transformer blocks from larger language models and trains the smaller model on a minuscule fraction of the original training data, resulting in a language model with 1.5 billion parameters using just 1 billion…
Scaling up language learning models (LLMs) involves substantial computational power and the need for high-density datasets. Language models typically make use of billions of parameters and are trained using datasets that contain trillions of tokens, making the process resource-intensive.
A group of researchers from the University of Texas at Austin have found a solution. They’ve…
Six free artificial intelligence (AI) courses offered by Google provide a beginner's guide to exploring the realm of AI. These courses are designed to deliver fundamental concepts and practical applications in a comprehensive and manageable format, each estimated to take approximately 45 minutes for completion. On successful completion of each course, learners are rewarded with…
These six free artificial intelligence (AI) courses from Google provide a comprehensive pathway for beginners starting their journey into the AI world. They introduce key concepts and practical tools in a format that is easy to digest and understand.
The first course, Introduction to Generative AI, gives an introductory overview of Generative AI. The course highlights…
Google's AI research team has unveiled the ScaNN (Scalable Nearest Neighbors) vector search library, intended to address the growing need for efficient vector similarity search, a fundamental component of many machine learning algorithms. Current methods for calculating vector similarity are adequate for small datasets but as these datasets grow and new applications emerge, the requirement…
Code generation is a critical domain for assessing and employing Large Language Models (LLMs). However, numerous existing coding benchmarks, such as HumanEval and MBPP, have reached solution rates over 90%, indicating the requirement for more challenging benchmarks. These would underline the limitations of current models and suggest ways to improve their algorithmic reasoning capabilities.
Competitive programming…
The proliferation of deep learning technology has led to significant transformations across various industries, including healthcare and autonomous driving. These breakthroughs have been reliant on parallel advancements in hardware technology, particularly in GPUs (Graphic Processing Units) and TPUs (Tensor Processing Units).
GPUs have been instrumental in the deep learning revolution. Although originally designed to handle computer…
Tech conglomerate Meta has announced the introduction of its artificial intelligence (AI) chatbot, Meta AI. Powered by Meta's latest and most powerful large language model (LLM) known as Meta Llama 3, the AI chatbot is set to offer stiff competition to similar technologies such as Google's Gemini and OpenAI's ChatGPT.
Meta Llama 3 serves as…
The human face serves an integral role in communication, a feature that is not lost on the field of Artificial Intelligence (AI). As AI technology advances, it is now creating talking faces that mimic human emotions and expressions. Particularly useful in the area of communication, the technology offers numerous benefits, including enhanced digital communication, higher…
Artificial intelligence (AI), machine learning, and statistics are constantly advancing, pushing the limits of machine capabilities in learning and predicting. However, validation of emerging AI methods relies heavily on the availability of high-quality, real-world data. This is problematic as many researchers utilize simulated datasets, which often fail to completely represent the intricacies of natural situations.…
Creating AI agents capable of executing tasks autonomously in digital surroundings is a complicated technical challenge. Conventional methods of building these systems are complex and code-heavy, often restricting flexibility and potentially hindering innovation.
Recent developments have seen the integration of Large Language Models (LLMs) such as GPT-4 and the Chain-of-Thought prompting system to make these agents…