Hello friends! I was lucky enough last week to attend PHLAI, a Comcast-sponsored conference on machine learning and artificial intelligence. The dreary weather did not dampen our spirits as practitioners and business stakeholders met to discuss one of the most important trends in our lifetime.
The talks ranged from high-level, entertaining overviews to deep-dive technical lectures. The discussions were very focused and targeted on pragmatic approaches to solving business problems using machine learning and AI, and it’s amazing to see how much progress is being made in a seemingly short amount of time.
Here are a few takeaways.
This topic sprung up everywhere. The ability to understand why a model predicts something has a great bearing on regulatory concerns, racial profiling, and security. We can’t make meaningful progress in AI without taking steps to make these models as explainable as possible. And it doesn’t even have to be something as explicit as opening the black box and producing a deterministic formula, we just need some insight as to why models predict the way they do.
I enjoyed the constant focus on simplicity and picking the right tool for the job. Why don’t you put down those neural nets and try a simple regression? Or maybe use specific models for specific tasks and (gasp) use imperative or brute force techniques for other tasks. I must have heard the old hammer and nail adage in at least three separate talks, which is great. I think most experienced software engineers have sat down their junior teammates and said the same quote. It’s important to be mindful of our own biases and think about what delivers value to your client/business stakeholder by using the simplest tool for the job.
The final trend I noticed was the focus on distributing ML/AI thinking among several teams rather than having it centralized in one silo. This idea was backed up by studies that showed companies who took a distributed approach showed better sales/ROI numbers that companies who silo-ed their innovation efforts on isolated teams.
From an investment perspective, I also appreciated Kartik Hosanagar’s’s thoughts on a balanced AI portfolio. His studies showed that focusing mostly on quick, iterative wins with a few longer-term projects led to positive ROI. I love how practical this idea is. Speaking in terms of dollars and cents resonates much more strongly with the business stakeholders and aligns these projects with the goals of the entire organization.
I’ve been with Chariot Solutions for a few years now, and as such have had the opportunity to attend several conferences like this. Taking this time to think and reflect is essential in ALL fields, especially a field as fast-moving and relevant as artificial intelligence.Bill Gates famously takes an annual “think week” to explore and reflect on big ideas. Conferences are even better, they give you a chance to talk to other people in the field (talking being still one of the most effective forms of information gathering).
But what’s the point of these conferences if we just go back to our day jobs and carry on with business as usual? We need to find a way to actively engage with these ideas. That engagement could be different for everyone. For some it could mean creating a small project using a new AI framework. Or reading a book about a specific trend or application. Or writing a blog post to organize your thoughts and make an argument. Either way, I’d argue that what you do after the conference is just as important as what you do during the conference.