InfoQ Homepage Artificial Intelligence Content on InfoQ
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Modern Compute Stack for Scaling Large AI/ML/LLM Workloads
Jules Damji discusses which infrastructure should be used for distributed fine-tuning and training, how to scale ML workloads, how to accommodate large models, and how CPUs and GPUs can be utilized.
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Building Guardrails for Enterprise AI Applications W/ LLMs
Shreya Rajpal introduces Guardrails AI, an open-source platform designed to mitigate risks and enhance the safety and efficiency of LLMs.
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Combating AI-Generated Fake Images with JavaScript Libraries
Kate Sills discusses JavaScript libraries to use for cryptographic hashes, digital signatures and timestamping, the traditional archival process, and how cryptographic hashes can prevent tampering.
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Generative AI: Shaping a New Future for Fraud Prevention
Neha Narkhede discusses a vision for fraud and risk management that leverages the advancements in generative AI.
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Responsible AI: from Principle to Practice!
Mehrnoosh Sameki discusses Responsible AI best practices to apply in a machine learning lifecycle and shares open source tools to incorporate to implement Responsible AI in practice.
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Needle in a 930M Member Haystack: People Search AI @LinkedIn
Mathew Teoh explores how LinkedIn's People Search system uses ML to surface the right person that you're looking for.
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ChatGPT and AI: What's Next in Large Language Model (LLM) Architectures
The panelists discuss what's next in Large Language Model (LLM) architectures used in tools like ChatGPT and how these tools will further disrupt the AI/ML space.
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AI Bias and Sustainability
Leslie Miley discusses how the road to ubiquitous AI is clouded by the dangers of the inherent bias in Large Language Models and the increased CO2 emissions that come with deployment at scale.
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A Bicycle for the (AI) Mind: GPT-4 + Tools
Sherwin Wu and Atty Eleti discuss how to use the OpenAI API to integrate large language models into your application, and extend GPT’s capabilities by connecting it to the external world via APIs.
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Operationalizing Responsible AI in Practice
Mehrnoosh Sameki discusses approaches to responsible AI and demonstrates how open source and cloud integrated ML help data scientists and developers to understand and improve ML models better.
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Solving Data Quality Issues to Diagnose Health Symptoms with AI
Lola Priego and Jose del Pozo discuss how they improved the user input accuracy, normalized lab data using a scoring algorithm, and how this work finishes with an AI to diagnose health.
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The Unreasonable Effectiveness of Zero Shot Learning
Roland Meertens shows how one can get started deploying models without requiring any data, discussing foundational models, and examples of them, such as GPT-3 and OpenAI CLIP.
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