Since the release of ChatGPT in late 2022, AI has received large and increasing amounts of attention and investment. We believe this is entirely warranted – AI in various forms is poised to change the way that businesses work. But one consequence of the ChatGPT release being the catalyst for this wave of attention is that people equate AI with large language models (LLMs), and they equate LLMs with chatbots.
We love chatbots – ChatGPT and others in its class are amazing tools – but, as an AI consultancy with a long history of projects in the space before the current mania, we’re sensitive to the conflation of LLMs and chatbots. Many of the most exciting potential uses for LLMs have little to do with the chatbot interface, and we think those should get more attention.
Read moreThink back to your last telehealth visit with a doctor. Perhaps your kid had a persistently high fever, or you had worrying chest pain. Are you sure you were interacting with a human? What makes you sure? Perhaps the doctor listened attentively to your symptoms, asked pertinent questions, and even picked up on subtle cues in your language that hinted at the severity of your condition.
Read moreLLMs aren’t yet widely used as an architectural component in production — the core issue is reliability. Not knowing how to engage with the reliability challenge – in a structured and productive manner – is what I think limits the success of most teams building LLM-powered applications. In our projects at Hop, we’ve developed a relatively uncommon perspective on how to effectively engage with this reliability challenge.
Read moreI view research (and especially applied research of the type that Hop does) as a type of multi-armed bandit problem — one that tries to balance new approaches (exploration) with successful approaches (exploitation). The code quality/technical debt conversation is usually a bit muddled these days, but it becomes a bit easier to think about if you articulate where on the exploration/exploitation spectrum you currently are.
Read moreIf you're an executive with deep domain expertise but no ML background, and you're trying to understand if ML will be useful in your space, it can be useful to think of an ML system as a type of factory. In particular, it's a "decision factory".
Read moreA question we often get from executives exploring Machine Learning for their organizations is: "What is the minimum viable machine learning team?". There are likely many right answers, and some industries have unique constraints. However, in our experience, a minimal-but-effective ML team requires a few specific roles to be filled. In prioritized order, we believe these to be…
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