As language models become increasingly common, it becomes crucial to employ a broad set of strategies and tools in order to fully unlock their potential. Foremost among these strategies is prompt engineering, which involves the careful selection and arrangement of words within a prompt or query in order to guide the model towards producing theContinue reading “The LLM Triad: Tune, Prompt, Reward”
Author Archives: Ben Lorica
Maximizing the Potential of Large Language Models
Subscribe • Previous Issues The LLM Three-Pronged Strategy: Tune, Prompt, Reward As language models become increasingly common, it becomes crucial to employ a broad set of strategies and tools in order to fully unlock their potential. Foremost among these strategies is prompt engineering, which involves the careful selection and arrangement of words within a prompt or queryContinue reading “Maximizing the Potential of Large Language Models”
Vector Database Primer
From the Data Exchange podcast, we present recent conversations with creators of popular vector databases. The popularity of vector databases and vector search has been increasing among technical teams. This is mainly due to the widespread use of dense vector representations of data, made possible by advances in neural networks. Furthermore, the decision of technologyContinue reading “Vector Database Primer”
Navigating the Future of Search
ChatGPT has revived interest in search. It revealed that a blend of artificial intelligence and a prompt-driven interface is exceptionally well-matched for search applications. As a result of ChatGPT, some well-funded search startups are emphasizing their use of large language models (LLM) and launching chatbot-like interfaces. ChatGPT has been receiving a great deal of attentionContinue reading “Navigating the Future of Search”
Insights from New Data and AI Pegacorns
Meet the Private Companies That Have Reached the $100m Revenue Milestone in the Data Engineering and AI Space. By Ben Lorica and Kenn So. In 2022, we published our first annual list of data and AI pegacorns – private companies that have reached the $100m revenue milestone. The selection criteria for data pegacorns focused onContinue reading “Insights from New Data and AI Pegacorns”
The Future of Search and How You Can Shape It
Subscribe • Previous Issues Navigating the Future of Search ChatGPT has revived interest in search. It revealed that a blend of artificial intelligence and a prompt-driven interface is exceptionally well-matched for search applications. As a result of ChatGPT, some well-funded search startups are emphasizing their use of large language models (LLM) and launching chatbot-like interfaces. ChatGPT hasContinue reading “The Future of Search and How You Can Shape It”
Knowledge Graphs and Foundation Models
Knowledge graphs are not new. I expect usage of knowledge graphs to grow in the coming years as language models and AI applications gain traction. An AI application can benefit from knowledge graphs because they provide a structured, interconnected representation of data. This allows AI algorithms to better understand and make use of the informationContinue reading “Knowledge Graphs and Foundation Models”
Call Center Survey Results: Exploring Solutions for Call Center Agent Challenges
A Survey of 270 Call Center Workers in India Examines the Impact of Experience and Assistive Technologies on Job Performance and Communication. For many brands, call centers are the lifeblood of customer service. While tools like chatbots and email/text support have become more common, they are not a replacement for speaking live to a humanContinue reading “Call Center Survey Results: Exploring Solutions for Call Center Agent Challenges”
Unveiling the Future of AI: Insights on AI Chips, Knowledge Graphs, and AI Regulations
Subscribe • Previous Issues AI Chips: Past, Present, Future In a recent collaboration with Assaf Araki of Intel Capital, we delve into specialized hardware for Artificial Intelligence. The training of massive AI models has historically been dominated by Nvidia GPUs and Google TPUs. There is a significant challenge facing new entrants to the market: the lack ofContinue reading “Unveiling the Future of AI: Insights on AI Chips, Knowledge Graphs, and AI Regulations”
Experimentation and Optimization Tools for Data Science Teams
In the words of Norvig, Spector et.al. – “Data science is the study of extracting value from data – value in the form of insights and questions.” In practice, industrial data science teams wear multiple hats and, depending on the company, are often responsible for reports (analytics and BI), models (including machine learning and AI), andContinue reading “Experimentation and Optimization Tools for Data Science Teams”