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Meta AI’s Scalable Memory Layers: The Future of AI Efficiency and Performance

Unite.AI

However, as AI becomes more powerful, a major problem of scaling these models efficiently without hitting performance and memory bottlenecks has emerged. For years, deep learning has relied on traditional dense layers, where every neuron in one layer is connected to every neuron in the next.

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Sarah Assous, Vice President of Product Marketing, Akeneo – Interview Series

Unite.AI

AI-powered algorithms can detect and correct inconsistencies, fill in missing attributes, and classify products based on predefined rules or learned patterns, reducing manual errors and ensuring uniformity across marketplaces, eCommerce platforms, print catalogs, and anywhere else you sell.

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Alix Melchy, VP of AI at Jumio – Interview Series

Unite.AI

Alix Melchy is the VP of AI at Jumio, where he leads teams of machine learning engineers across the globe with a focus on computer vision, natural language processing and statistical modeling. At Jumio, we invest a significant amount of resources on our people, processes, and technology.

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AI Learns from AI: The Emergence of Social Learning Among Large Language Models

Unite.AI

Since OpenAI unveiled ChatGPT in late 2022, the role of foundational large language models (LLMs) has become increasingly prominent in artificial intelligence (AI), particularly in natural language processing (NLP). This suggests a future where AI can adapt to new challenges more autonomously.

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AI Lie Detectors: Breaking Down Trust or Building Better Bonds?

Unite.AI

The pursuit of truth now benefits from Artificial Intelligence (AI). AI-powered lie detection systems analyze data using machine learning , Natural Language Processing (NLP) , facial recognition , and voice stress analysis. They can identify deception patterns more accurately than traditional methods.

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AI and the Future of Work: Reskilling the Workforce in an Age of AI

Unite.AI

With this new wave of AI, there is a new category of machine learning engineers who are focused only on “prompt engineering.” ” This role is different from traditional software development, but it has arisen from the need for new ways to work with AI models.

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Breaking down the advantages and disadvantages of artificial intelligence

IBM Journey to AI blog

Data is often divided into three categories: training data (helps the model learn), validation data (tunes the model) and test data (assesses the model’s performance). For optimal performance, AI models should receive data from a diverse datasets (e.g.,