Machine Learning & AI for Non-Technical Businesspeople (Part I)

Part I: What is Machine Learning? Combining Measurements and Math to Make Predictions

The labels “machine learning” and “artificial intelligence” can be used interchangeably to describe computer systems that make decisions so complicated that until recently only humans could make them. With the right information, machine learning can do things like…

• look at a loan application, and recommend whether a bank should lend the money
• look at movies you’ve watched, and recommend new movies you might enjoy
• look at photos of human cells, and recommend a cancer diagnosis

Machine learning can be applied to just about anything that can be counted/measured, including numbers, words, and pixels in digital photos.

pattern
What is it we see in this photo? How would you describe the details that let us recognize this? (Photo by Mike Tinnion on Unsplash)

What makes it different is that, with machine learning computers don’t need humans to write out incredibly detailed instructions about how to identify bad loans, good movies, cancer cells, etc. Instead, computers are given examples (or goals) and math, and Continue reading “Machine Learning & AI for Non-Technical Businesspeople (Part I)”

Lessons in Agile Machine Learning from Walmart

Takeaways from Sam Charrington’s May, 2017 interview with Jennifer Prendki, senior data science manager and principal data scientist for Walmart.com


I am very grateful to Sam Charrington for his TWiML&AI podcast series. So far I have consumed about 70 episodes (~50 hours). Every podcast is reliably fascinating: so many amazing people accomplishing incredible things. It’s energizing! The September 5, 2017 podcast, recorded in May, 2017 at Sam’s Future of Data Summit event, featured his interview with Jennifer Prendki, who at the time was senior data science manager and principal data scientist for Walmart’s online business (she’s since become head of data science at Atlassian). Jennifer provides an instructive window into agile methodology in machine learning, a topic that will become more and more important as machine learning becomes mainstream and production-centric (or “industrialized”, as Sam dubs it). I’ve taken the liberty of capturing key takeaways from her interview in this blog post. (To be clear, I had no part in creating the podcast itself.) If this topic matters to you, please listen to the original podcast – available via iTunes, Google Play, Soundcloud, Stitcher, and YouTube – it’s worth a listen.


Overview

Jennifer Prendki was a member of an internal Walmart data science team supporting two other internal teams, the Perceive team and the Guide team, delivering essential components of Walmart.com’s search experience. The Perceive team is responsible for providing autocomplete and spell check to help improve customers’ search queries. The Guide team is responsible for ranking the search results, helping customers find what they are looking for as easily as possible. Continue reading “Lessons in Agile Machine Learning from Walmart”

EU Guidelines on Using Machine Learning to Process Customer Data

Summary: Every organization that processes data about any person in the EU must comply with the GDPR. Newly published GDPR Guidelines clarify that whenever an organization makes a decision using machine learning and personal data that has any kind of impact, a human must be able to independently review, explain, and possibly replace that decision using their own independent judgment. Organizations relying on machine learning models in the EU should immediately start planning how they are going to deliver a level of machine model interpretability sufficient for GDPR compliance. They should also examine how to identify whether any groups of people could be unfairly impacted by their machine models, and consider how to proactively avoid such impacts.


In October 2017, new Guidelines were published to clarify the EU’s GDPR (General Data Protection Regulation) with respect to “automated individual decision making.” These Guidelines apply to many machine learning models making decisions affecting EU citizens and member states. (A version of these Guidelines can be downloaded here—for reference, I provide page numbers from that document in this post.)

The purpose of this post is to call attention to how the GDPR, and these Guidelines in particular, may change how organizations choose to develop and deploy machine learning solutions that impact their customers.

Continue reading “EU Guidelines on Using Machine Learning to Process Customer Data”

Machine Learning Enterprise Adoption Roadmap

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.

machine learning adoption roadmap preview
click to enlarge

 

Define: Identify opportunities to adopt machine learning solutions in every part of your organization.

Does your organization have well-defined problems that can be solved using machine learning? Continue reading “Machine Learning Enterprise Adoption Roadmap”

Understanding Decisions Made By Machine Learning

blackboard
Photo by Roman Mager on Unsplash

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”

Machine Learning / AI roundup for last week

optometrist
Photo by Markus Spiske on Unsplash

There’s  much new goodness on the interwebs since my last machine learning and artificial intelligence roundup post (only 2 weeks ago). This isn’t by any means comprehensive, but take a quick skim and see if there’s anything you missed that’s relevant to you.

Business Uses

Recruiting / Hiring / HR: Video: Predictive Analytics for Hiring in 5 Minutes (Koru co-founder Josh Jarrett presenting at New Tech Seattle, August 8, 2017) – I saw this one live. Josh is a good presenter and the promise of his product is intriguing.

Sales: 3 Ways AI Is Upending the B2B Sales Experience (Seismic CEO Doug Winter, Entrepreneur.com, August 14 2017) – Useful overview from an industry insider.

Healthcare: Google’s Machine Learning Looks to Improve Predictions in Health Care (Dan Ochwat, H&HN.com [Hospitals & Health Networks], August 8 2017)

Energy: Google enters race for nuclear fusion technology (Damian Carrington, The Guardian, July 25 2017) – This is the first time I heard the phrase “Optometrist Algorithm”—the machine learning makes suggestions to the humans, who choose Continue reading “Machine Learning / AI roundup for last week”

Scoping Questions for Starting or Expanding a Social Selling Program

apples
Photo by Raquel Martínez on Unsplash

 

“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

4. Sales enablement – speeding up & amplifying sales rep effectiveness Continue reading “Scoping Questions for Starting or Expanding a Social Selling Program”