…where Bruce Wilson blogs about business and technology
Author: Bruce Wilson
All of my posts, new and old, are now published under my personal (rather than professional) persona: "bruce2b" (get it?). Lot's of topics interest me! Drop a comment on one of these posts to start a conversation about something you're passionate about.
Be it the core of their product, or just a component of the apps they use, every organization is adopting machine learning and AI at some level. Most organizations are adopting it an ad hoc fashion, but there are a number of considerations—with significant potential consequences for cost, timing, risk, and reward—that they really should consider together.
That’s why I developed the following framework for organizations planning to adopt machine learning or wanting to take their existing machine learning commitment to the next level.
What’s so important about interpretability in machine learning?
It’s a poorly kept secret that we lack insight into how complex machine learning models like neural networks make decisions. We see the data that goes in, the math that goes in, and the results that come out. But in the middle, where we want to see a chain of reasoning like a human could give us to explain decisions, there’s only a black box. Neither data scientists nor these complex machine learning models can provide insight into “why” a model chose output A rather than output B.
What does it matter whether we have an understandable explanation for why a machine learning model delivers a specific result? For example, when diagnosing whether or not a patient has cancer, isn’t it enough that the model is accurate, according to rigorous testing? I’ll look deeper into the implications of interpretability in future blog posts. But Continue reading “Understanding Decisions Made By Machine Learning”
“Social selling” is never a one-size-fits-all, turnkey proposition. Here’s a list of questions I put together for organizations who are thinking about creating or expanding a social selling program. By answering these questions—at least provisionally—an organization can create an action plan, line up people and tools, and start social selling at the scale that makes the most sense for them.
I. What’s Our Starting Point?
A. What results do we want to get?
1. Lead generation – new customers
2. Customer loyalty – current customer renewals, cross-selling
3. New/deeper relationships with Influencers – analysts, journalists, experts
I decided to share some links to a few of my favorite (mostly recent) articles and videos about #AI, aka artificial intelligence, and #ML, aka machine learning, in a post here. If anyone wants to submit additions, feel free to contribute in the comments below.
Recent overview articles about AI / Machine Learning
In an ideal world, every company’s executive leadership would project an authentic thought-leadership presence in Twitter, LinkedIn, and other digital social channels. In reality this is too time-consuming for many executives, both because of the learning curve and because of the the daily effort required to curate and personalize high quality social content. The solution I recommend—based on a number of years coaching executives in social media, and my former role at a leading social selling solutions provider — is to minimize the time required for the executive without eliminating the authenticity of the executive’s social presence. This can be accomplished by outsourcing just the right amount of executives’ workload to the combination of trusted assistants and technology, as described in this post.