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Can ChatGPT Compete with Domain-Specific Sentiment Analysis Machine Learning Models?

Topbots

ChatGPT is a GPT ( G enerative P re-trained T ransformer) machine learning (ML) tool that has surprised the world. This is the case because it was uncommon for most domains to find an out-of-the-box solution that could do well enough without some fine-tuning. GloVe) for usage in domain-specific tasks.

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Knowledge Bases for Amazon Bedrock now supports hybrid search

AWS Machine Learning Blog

For RAG-based applications, the accuracy of the generated response from large language models (LLMs) is dependent on the context provided to the model. Retrieve API The Retrieve API is designed to fetch relevant search results by providing the user query, knowledge base ID, and number of results that you want the API to return.

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Improve multi-hop reasoning in LLMs by learning from rich human feedback

AWS Machine Learning Blog

We collect two such datasets of human feedback in the form of (correction, explanation, error type) for StrategyQA and Sports Understanding datasets, and evaluate several common algorithms to learn from such feedback. More concretely, we fine-tune Flan-T5 (text to text) with the objective maximize p(c|q) + p(t|q, m) + p(d|q, m).

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Top Real-Life Examples of GPT Integration: Learn How to Enhance Your Products with AI

Dlabs.ai

The surge in GPT’s popularity, and by extension AI’s, is reshaping the digital landscape swiftly. This cutting-edge solution is fittingly named ‘ Nabla Copilot.’ This innovative feature brings a touch of human-like, AI-generated interactions right to your wristwatch.

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The AI Process

Towards AI

Last Updated on August 17, 2023 by Editorial Team Author(s): Jeff Holmes MS MSCS Originally published on Towards AI. Jason Leung on Unsplash AI is still considered a relatively new field, so there are really no guides or standards such as SWEBOK. 85% or more of AI projects fail [1][2]. 85% or more of AI projects fail [1][2].

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Modular functions design for Advanced Driver Assistance Systems (ADAS) on AWS

AWS Machine Learning Blog

This post covers build approaches, different functional units of ADAS, design approaches to building a modular pipeline, and the challenges of building an ADAS system. DNN training methods and design AV systems are built with deep neural networks. When it comes to the design of an AV system, there are two main approaches.

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Generative AI and multi-modal agents in AWS: The key to unlocking new value in financial markets

AWS Machine Learning Blog

Multi-modal agents are AI systems that can understand and analyze data in multiple modalities using the right tools in their toolkit. They are able to connect insights across these diverse data types to gain a more comprehensive understanding and generate appropriate responses. The following screenshot shows an example of the UI.