Research

Notes from Transform.AI - Part 2: Expectations and Starting Points

By Kathryn Hume, Sep 12, 2017

I spent the a few days in June at Transform.AI in Paris, a European conference designed for c-level executives managed and moderated by my dear friend Joanna Gordon. This type of high-quality conference approaching artificial intelligence (AI) at the executive level is sorely needed. While there’s no lack of high-quality technical discussion at research conferences like ICML and NIPS, or even part-technical, part-application, part-venture conferences like O’Reilly AI, ReWork, or the Future Labs AI Summit (which my friends at ffVC did a wonderful job producing), most c-level executives still actively seek to cut through the hype and understand AI deeply and clearly enough to invest in tools, people, and process changes with confidence. Confidence, of course, is not certainty. And with technology changing at an ever faster clip, the task of running the show while transforming the show to keep pace with the near future is not for the faint of heart.

Transform.AI brought together enterprise and startup CEOs, economists, technologists, venture capitalists, and journalists. We discussed the myths and realities of the economic impact of AI, enterprise applications of AI, the ethical questions surrounding AI, and the state of what’s possible in the field. Here are some highlights.1

Read Part 1: The Productivity Padadox

Unrealistic Expectations and Realistic Starting Points

Everyone seems acutely aware of the fact that AI is in a hype cycle. And yet everyone still trusts AI is the next big thing. They missed the internet. They were too late for digital. They’re determined not to be too late for AI.

The panacea would be like the chip Keanu Reeves uses in the Matrix, the preprogrammed super-intelligent system you just plug into the equivalent of a corporate brain and boom, black belt karate-style marketing, anomaly detection, recommender systems, knowledge management, preemptive HR policies, compliance automation, smarter legal research, optimized supply chains, etc…

If only it were that easy.

While everyone knows we are in a hype cycle, technologists still say that one of the key issues data scientists and startups face today are unrealistic expectations from executives. AI systems still work best when they solve narrow, vertical-specific problems (which also means startups have the best chance of succeeding when they adopt a vertical strategy, as Bradford Cross eloquently argued last week). And, trained on data and statistics, AI systems output probabilities, not certainties. Electronic Discovery (i.e., the use of technology to automatically classify documents as relevant or not for a particular litigation matter) adoption over the past 20 years has a lot to teach us about the psychological hurdles to adoption of machine learning for use cases like auditing, compliance, driving, or accounting. People expect certainty, even if they are deluding themselves about their own propensities for error.2 We have a lot of work to disabuse people of their own foibles and fallacies before we can enable them to trust probabilistic systems and partner with them comfortably. That’s why so many advocates of self-driving cars have to spend time educating people about the fatality rates of human drivers. We hold machines to different standards of performance and certainty because we overestimate our own powers of reasoning. Amos Tversky and Daniel Kahneman are must reads for this new generation (Michael Lewis’s Undoing Project is a good place to start). We expect machines to explain why they arrived at a given output because we fool ourselves, often by retrospective narration, that we are principled in making our own decisions, and we anthropormophize our tools into having little robot consciousnesses. It’s an exciting time for cognitive psychology, as it will be critical for any future economic growth that can arise from AI.

It doesn’t seem possible not to be in favor of responsible AI. Everyone seems to be starting to take this seriously. Conference attendees seemed to agree that there needs to be much more discourse between technologists, executives, and policy makers so that regulations like the European GPDR don’t stymy progress, innovation, and growth. The issues are enormously subtle, and for many we’re only at the point of being able to recognize that there are issues rather than provide concrete answers that can guide pragmatic action. For example, people love to ponder liability and IP, analytically teasing apart different loca of agency: Google or Amazon who offered the opensource library like Tensorflow the organization or individual upon whose data a tool was trained, the data scientist who wrote the code for the algorithm, the engineer who wrote the code to harden and scale the solution, the buyer of the tool who signed the contract to use it and promised to update the code regularly (assuming it’s not on the cloud, in which case that’s the provider again), the user of the tool, the person whose life was impacted by consuming the output. From what I’ve seen, so far we’re at the stage where we’re transposing an ML pipeline into a framework to assign liability. We can make lists and ask questions, but that’s about as far as we get. The rubber will meet the road when these pipelines hit up against existing concepts to think through tort and liability. Solon Barocas and the wonderful team at Upturn are at the vanguard of doing this kind of work well.

Finally, I moderated a panel with a few organizations who are already well underway with their AI innovation efforts. Here we are (we weren’t as miserable as we look!):

Journeys Taken; Lessons Learned Panelists at Transform.AI

The lesson I learned synthesizing the comments from the panelists is salient: customers and clients drive successful AI adoption efforts. I’ve written about the complex balance between innovation and application on this blog, having seen multiple failed efforts to apply a new technology just because it was possible. A lawyer on our panel discussed how, since the 2009 recession, clients simply won’t pay high hourly rates for services when they can get the same job done at a fraction of the cost at KPMG, PWC, or a technology vendor. Firms have no choice but to change how they work and price matters, and AI happens to be the tool that can parse text and crystallize legal know how. In the travel vertical, efforts to reach customers on traditional channels just don’t cut it in the age where the Millenials live on digital platforms like Facebook Messenger. And if a chat bot is the highest value channel, then an organization has to learn how to interface with chat bots. This fueled a top down initiative to start investing heavily in AI tools and talent.

Exactly where to put an AI or data science team to strike the right balance between promoting autonomy, minimizing disruption, and optimizing return varies per organization. Daniel Tunkelang presented his thoughts on the subject at the Fast Forward Labs Data Leadership conference this time last year.

Originally published in Quam Proxime. Learn more at http://integrate.ai

  1. Most specific names and references are omitted to respect the protocol of the Chatham House Rule.

  2. Stay tuned next week for a post devoted entirely to the lessons we can learn from the adoption of electronic discovery technologies over the past two decades.

About the author

Author photo

Kathryn Hume is VP Product & Strategy at integrate.ai, a SaaS startup applying AI to a unique combination of social, behavioral, and enterprise transaction data to help large B2C businesses optimize customer engagement. Alongside her work at integrate.ai, she is a Venture Partner at ffVC, a seed- and early-stage technology venture capital firm, where she advises early-stage artificial intelligence companies and sources deal flow. While at Fast Forward Labs, Kathryn helped Fortune 500 companies accelerate their machine learning and data science capabilities. Prior to that, she was a leader in Intapp’s Risk Practice, focused on data privacy, security, and compliance. A widely respected speaker and writer on AI, Kathryn excels at communicating how AI and machine learning technologies work in plain language. Kathryn has given lectures and taught courses on the intersections of technology, ethics, law, society at Harvard Business School, Stanford, the MIT Media Lab, and the University of Calgary Faculty of Law. She speaks seven languages, and holds a PhD in comparative literature from Stanford University and a BA in mathematics from the University of Chicago.


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