James Hope
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    • Graph-Oriented Reinforcement Learning (GORL) for Enterprise AI
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    • AI Generated Metadata Enrichments for Unstructured Data with IBM Spectrum Discover & watsonx.ai
    • Operating AI at Scale with OpenShiftAI, KubeFlow Pipelines and watsonx
    • Tool-Agents with the watsonx LangChain BaseChatModel
    • Improving Language Models Inductive Bias with Q*
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    • Algorithmically optimising LM prompts with IBM watsonx models and DSPy
    • Programmable, semantically-matched guardrails with NVIDIA/NeMo-Guardrails and watsonx.ai
    • Approaches that mitigate against language models misalignment including when semantic search alone is just good enough
    • Reconstructing user context to reduce the risk of policy misaligned generated content in LLM enabled conversational assistants
    • Governance of AI enabled services and applications with AI Guardrails and watsonx
    • Beyond declarative flows in virtual assistants with language models for single-turn and multi-turn reasoning
    • Supervised fine tuning of a large language model using quantized low rank adapters
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    • Implementation of the Stable Marriage Algorithm.
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Build a Machine Learning Recommender

Jul 29, 2019 · 1 min read

This article was published in the journal Towards Data Science. Please click here to access the article.

Last updated on Jul 29, 2019
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