Category: ai

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What you—yes you—need to do about Data and AI Ethics

What do you need to know about Data Ethics?

If you work for an organization that uses data and artificial intelligence (AI), or if you are a consumer using data and AI-powered services, what do you need to know about data ethics?

Quite a bit, it turns out. The way things are going, it seems like every few days new ethics controversies, followed by new commitments to privacy and fairness, arise from the ways that businesses and government use data. A few examples:

• Voice assistants like Amazon’s Alexa, Siri, and “Hey Google” are everywhere, on smart phones, computers, and smart speakers. Voice commands satisfy more and more of our needs without resorting to keyboards, touch screens, or call centers. But recently one such assistant, while listening in on a family’s private conversations, recorded a conversation without the family’s knowledge and emailed that recording to a family member’s employee.

doing data ethics
Photo by rawpixel on Unsplash

Continue reading “What you—yes you—need to do about Data and AI Ethics”

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Who Needs Reasons for AI-Based Decisions?

Deep learning systems, which are the most headline-grabbing examples of the AI revolution—beating the best human chess and poker players, self-driving cars, etc.—impress us so very much in part because they are inscrutable. Not even the designers of these systems know exactly why they make the decisions they make. We only know that they are capable of being highly accurate…on average.

Meanwhile, software companies are developing complex systems for business and government that rely on “secret sauce” proprietary data and AI models. In order to protect their intellectual property rights, and profitability, the developers of these systems typically decline to reveal how exactly their systems work. This gives rise to a tradeoff between profit motive, which enables rapid innovation (something government in particular isn’t known for), and transparency, which enables detection and correction of mistakes and biases. And mistakes do occur…on average.

pay no attention to the man behind the curtain
Photo by Andrew Worley on Unsplash

On the one hand, a lack of transparency in deep learning and proprietary AI models has led to criticism from a number of sources. Organizations like AI Now  and ProPublica are surfacing circumstances where a lack of transparency leads to abuses such as discriminatory bias. The EU has instituted regulations (namely GDPR) that guarantee its citizens the right to an appeal to a human being when AI-based decisions are being made. And, last but not least, there is growing awareness that AI systems—including autonomous driving and health care systems—can be invisibly manipulated by those with a motive like fraud or simple mischief. Continue reading “Who Needs Reasons for AI-Based Decisions?”

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What’s the difference between machine learning and artificial intelligence?

Part II of The Completely Non-Technical Guide to Machine Learning and AI

My previous post raised the question “what is machine learning and artificial intelligence (AI)?” and answered with a functional definition: computer systems that combine measurements and math to make decisions so complicated that until recently only humans could make them.

Now a new question: “What’s the difference between machine learning and AI?” Continue reading “What’s the difference between machine learning and artificial intelligence?”

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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)”

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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”

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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”

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It’s An AI (and Machine Learning) Thing

tabulator
Photo by Andrew Branch on Unsplash

AI and Machine Learning in Business and Education

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

The Business of Artificial Intelligence / What it can — and cannot — do for your organization (Erik Brynjolfsson & Andrew McAfee, Harvard Business Review, July 2017)

Building machines that learn and think like people (Josh Tenenbaum, O’Reilly Artificial Intelligence, June 28 2017)

Video: Three Ways Businesses Use Artificial Intelligence (Tom Davenport with Allison Ryder, MIT Sloan Management Review, July 24 2017)

Continue reading “It’s An AI (and Machine Learning) Thing”

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