Insights & Perspectives
Ideas for real-world impact.
Sweet and Simple: AI Techniques Explained
Just like success in business often comes down to fundamentals, there are a handful of AI techniques that power most modern business applications.
Crafting a Winning AI Strategy: Critical Questions for Executives
As an executive, you're likely facing the challenge of creating an AI strategy that drives real value for your organization. We've identified two levels of questions that can serve as the pillars of a robust AI strategy.
Four Truths About AI Strategies
Before building your AI strategy, it's helpful to consider some foundational truths. These are some things we believe about AI strategies that are not necessarily widely understood.
Unproductive Claims about AI in 2024
I'm all for reasonable disagreements, but I find a lot of the current conversation around generative AI relatively unproductive. Here are some of the phrases that people use almost axiomatically that I don't think are actually true.
How Does the Agile Manifesto Apply to Research Engineering?
Agile methodology is widely used and well understood in software engineering. However, there are some unique considerations when it comes to employing Agile within ML engineering specifically.
Online Connections Are a Remote Substitute for Real Life
Technology offers many benefits in our day-to-day lives, including social opportunities not possible without it. However, awareness of its limitations, as well as our fundamental need for real-life connection, is key to staying healthy.
Beyond Prompt Engineering: The Toolkit for Getting LLMs to Do What You Want, Part 2
Prompt engineering, fine tuning, and LLM chaining are most commonly considered in guiding LLM behavior. In this post, we cover three more tools, from de rigueur for complex LLM applications to speculative techniques that may not be production-ready for some time.
Beyond Prompt Engineering: The Toolkit for Getting LLMs to Do What You Want, Part 1
When creating LLM applications, a lot of emphasis is on the foundation model underpinning the app. Most likely you’ll want to modify the behavior of that model; here are some techniques for achieving that behavior modification, from well-trod to exploratory.
Hear Me Out: The Potential of Low-Latency Voice AI
Picture this: two users need advice on a health issue. One employs an AI text interface, resulting in a plan that leaves them feeling informed and empowered. A voice interface leads the other through a back-and-forth conversation with the AI; they feel cared for, supported. Same need, two very different experiences. All because of the interface.
The Most Important Uses for LLMs Aren’t 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.
Could You Be Talking to an AI Doctor?
Think 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?
Why Most LLM App POCs Fail
LLMs 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 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.
Code Quality for Research
Research (and especially applied research of the type that Hop does) is 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 becomes a bit easier to think about if you articulate where on the exploration/exploitation spectrum you currently are.
ML Systems as Decision Factories
If 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".
Hiring Your Minimum Viable Machine Learning Team
A question we often get from executives exploring machine learning and AI for their organizations is: "What is the minimum viable ML/AI team?" There are likely many right answers, and some industries have unique constraints. However, in our experience, a minimal-but-effective ML/AI team requires a few specific roles to be filled.