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Amr Nour-Eldin, Vice President of Technology at LXT – Interview Series

Unite.AI

I got the chance to apply those techniques to Conversational AI products across multiple domains. Artificial intelligence solutions are transforming businesses across all industries, and we at LXT are honored to provide the high-quality data to train the machine learning algorithms that power them.

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The risks and limitations of AI in insurance

IBM Journey to AI blog

It requires careful curation of knowledge representations in database, decomposition of data matrices to reduce dimensionality, and pre-processing of datasets to mitigate the confounding effects of missing, redundant and outlier data. But insurers should contribute their insurance domain expertise to AI technologies development.

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How AI Can Boost Sales Efficiency and Drive Business Success

Unite.AI

For example, AI can analyse past purchases and browsing history to recommend products or services which are most likely to interest the customer. This tailored approach increases the likelihood of engagement and conversion. AI-driven personalisation can be particularly powerful in B2B sales.

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Deploying Conversational AI Products to Production With Jason Flaks

The MLOps Blog

Every episode is focused on one specific ML topic, and during this one, we talked to Jason Falks about deploying conversational AI products to production. Today, we have Jason Flaks with us, and we’ll be talking about deploying conversational AI products to production. What is conversational AI?

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How RLHF Preference Model Tuning Works (And How Things May Go Wrong)

AssemblyAI

The exploding popularity of conversational AI tools has also raised serious concerns about AI safety. The advent of RLHF fine-tuning has arguably revolutionized conversational AI. Unraveling the exact scaling laws that govern the balance between demonstration data and RLHF or similar techniques (e.g.

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BARE: A Synthetic Data Generation AI Method that Combines the Diversity of Base Models with the Quality of Instruct-Tuned Models

Marktechpost

The evaluation of BARE focuses on diversity, data quality, and downstream performance across the same domains and baselines discussed earlier. 70B-Instruct for refinement, BARE maintains data diversity while improving generation quality. Implementing Llama-3.1-70B-Base 70B-Base for initial generation and Llama-3.1-70B-Instruct

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ACECODER: Enhancing Code Generation Models Through Automated Test Case Synthesis and Reinforcement Learning

Marktechpost

Code generation models have made remarkable progress through increased computational power and improved training data quality. These models undergo pre-training and supervised fine-tuning (SFT) using extensive coding data from web sources. State-of-the-art models like Code-Llama, Qwen2.5-Coder,