Remove Auto-complete Remove BERT Remove Information
article thumbnail

Beyond ChatGPT; AI Agent: A New World of Workers

Unite.AI

Systems like ChatGPT by OpenAI, BERT, and T5 have enabled breakthroughs in human-AI communication. Current Landscape of AI Agents AI agents, including Auto-GPT, AgentGPT, and BabyAGI, are heralding a new era in the expansive AI universe. Their primary focus is to minimize the need for human intervention in AI task completion.

article thumbnail

Making Sense of the Mess: LLMs Role in Unstructured Data Extraction

Unite.AI

This method involves hand-keying information directly into the target system. But these solutions cannot guarantee 100% accurate results. Text Pattern Matching Text pattern matching is a method for identifying and extracting specific information from text using predefined rules or patterns.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

FastGen: Cutting GPU Memory Costs Without Compromising on LLM Quality

Marktechpost

Recently, a technique that adds a token selection task to the original BERT model learns to select performance-crucial tokens and detect unimportant tokens to prune using a designed learnable threshold. It is important to consider pruning tokens’ potential within the KV cache of auto-regressive LLMs to fill this gap.

LLM 117
article thumbnail

Best Large Language Models & Frameworks of 2023

AssemblyAI

Engineers train these models on vast amounts of information. It offers a simple API for applying LLMs to up to 100 hours of audio data, even exposing endpoints for common use tasks It's smart enough to auto-generate subtitles, identify speakers, and transcribe audio in real time.

article thumbnail

ChatGPT & Advanced Prompt Engineering: Driving the AI Evolution

Unite.AI

By enhancing their efficiency and safety, we pave the way for innovative applications such as information extraction. In zero-shot learning, no examples of task completion are provided in the model. In this demonstration, we will use the model's ability to understand and summarize complex information from academic texts.

article thumbnail

UC Berkeley Researchers Propose CRATE: A Novel White-Box Transformer for Efficient Data Compression and Sparsification in Deep Learning

Marktechpost

The goodness of such a representation can be evaluated using a principled measure called sparse rate reduction that simultaneously optimizes the intrinsic information gain and extrinsic sparsity of the learned representation. Therefore, encoders, decoders, and auto-encoders can all be implemented using a roughly identical crate design.

article thumbnail

TimesFM — Google’s Foundational Model for Time Series Forecasting

Towards AI

Their decoder-only model, inspired by NLP giants like BERT, uses a patch-based approach to handle data efficiently. Generating Longer Forecast Output Patches In Large Language Models (LLMs), output is generally produced in an auto-regressive manner, generating one token at a time. However, there is a trade-off.