Big Ideas: “AI can help us stop terrorism and human trafficking” with Dr. Eric Daimler

We are now stuck with reacting to developments in AI. We need to be proactive. Those doing research and development in this field are human. They will make mistakes. We should wait until conclusions are reached and products fully formed before we co-develop new products. Asa part of my series about “Big Ideas That Might […]

We are now stuck with reacting to developments in AI. We need to be proactive. Those doing research and development in this field are human. They will make mistakes. We should wait until conclusions are reached and products fully formed before we co-develop new products.

Asa part of my series about “Big Ideas That Might Change The World In The Next Few Years” I had the pleasure of interviewing Dr. Eric Daimler. Dr. Daimler is an authority in artificial Intelligence and robotics with over 20 years of experience in the field as an entrepreneur, investor, academic researcher, and policymaker. Daimler has co-founded six technology companies that have done pioneering work in fields ranging from storage software systems to statistical arbitrage. Daimler is the author of the forthcoming book Every Business is an AI Business, a guidebook for entrepreneurs, engineers, policymakers, and citizens on how to understand — and benefit from — the unfolding revolution in AI and robotics. A frequent speaker, lecturer, and commentator, he works to empower communities and citizens to leverage AI and robotics for a more sustainable, secure, and prosperous future. As a Presidential Innovation Fellow in the White House Office of Science and Technology Policy during the Obama Administration, Daimler helped drive the agenda for U.S. leadership in research, commercialization, and public adoption of AI and robotics. He has also served as Assistant Dean and Assistant Professor of Software Engineering in Carnegie Mellon’s School of Computer Science. His academic research focuses on the intersection of machine learning, computational linguistics, and network science (Graph Theory). He has a specialization in public policy and economics, helped launch Carnegie Mellon’s Silicon Valley campus, and founded its entrepreneurial management program. Daimler studied at Stanford University, the University of Washington-Seattle, and Carnegie Mellon University, where he earned his PhD in computer science.

Thank you so much for joining us! Can you tell us a story about what brought you to this specific career path?

Ihad the good fortune of being exposed to computers very early and having role models fully engaged in industry. For whatever reason, at nine years of age I saw very clearly the potential for computers to give power to individuals. With neither prompting nor assistance, I wrote down what I wanted to help catalyze a new phase of humanity through the use of these tools.

Alongside climate change, I see AI as the most important issue of our lifetime. It has the potential to usher in a great new world. Many of the choices we make now as a society about our engagement will determine where in the balance we experience the upsides and the downsides. Property taxes shape our cities. Cars shape our cities. The impacts last many decades.

Can you share the most interesting story that happened to you since you began your career?

I have many, but I can give you a couple. The first is one about the power our new machines and the systems-thinking required to flourish. After 9/11, I benefited greatly from the increase in research funding directed toward technologies that could disrupt terrorist networks. We used these technologies to look at complex relationships such as those in language — among the innovations in storage, networking, and processing power, what isn’t often covered is innovations in mathematics. After 9/11 there was a huge expansion of interest in Graph Theory. This is the study of relationships. You have seen the visual representation of the outcomes in data about the news media or the internet itself. They can often look like a mix of an exploding star and a spider web. Palantir was started in no small part from the beauty of their visualizations based upon these innovations in math that we were all using.

The Pentagon had a problem of interpreting Arabic. That we all know. They made the recruiting and retention difficult for those with the most natural profile to be effective translators: native speakers often born outside the US. This was compounded by a huge gap between the latent capability and the abrupt new interest. Further, it wasn’t clear that human translators could even solve this problem. Try this thought experiment: In pursuing knowledge about current events in the US, you want to scour the newspapers of the ten largest US cities and then summarize them for the chief military commander. This would go through layers of summarization that meaning would be easily lost and the process would be slow.

Enter our machines. We could translate and connect words in ways that could identify relationships between articles and between newspapers. We didn’t attempt to summarize articles. Instead what we did was work to connect facts and names between articles. This was valuable on its own, but also it helped to reveal articles worthy of a more careful reading. This whole process then linked names in a way that helped us to visualize what a terrorist network looked like. In many ways, they were communicating in the open. Based on some characteristics we identified, this analysis could help field commanders in their decisions on where to direct their on-the-ground military engagements. This is an example of new technology being put to real use in a way that still doesn’t get covered in the media for the role it played in defeating terrorist adversaries.

I have one about the power of unexpected benefits of this technology. Ships at sea are required to have unique identifiers that allow for their easy tracking on the open ocean relative to other ships. The system is called AIS. All container ships have them and are required to have them on at all times. The map of ships around the world with AIS looks similar to the map of airplanes in the air. It is just a mess for any human to make sense of the whole. You or I even have a hard time identifying the traffic in a single port. A colleague thought about tracking all of these using our new technology. He wanted to look at unusual behavior in these ships, such as rarely used ports or longer stays. He thought that he might identify the trafficking of weapons to terrorist networks. He did first-rate work. Unfortunately, he failed to identify anything suggestive of weapons trading. However, what he did find brought tears to our eyes when we realized what we saw in the data: We saw the trading networks of human beings.

This happened many years ago, so the technology is now applied widely to disrupt all manner of illicit activity. This story is one of the reasons that I can be optimistic about our future: bad behavior can be harder to hide. Like a DNA sample, we are often unaware of the trails we leave.

Can you tell us about your “Big Idea That Might Change The World”?

We would all benefit from expanding our notion of what AI is. Currently we think of it in the terms defined by researchers. We need to bring this into the inputs and outputs.

How do you think this will change the world?

We are now stuck with reacting to developments in AI. We need to be proactive. Those doing research and development in this field are human. They will make mistakes. We should wait until conclusions are reached and products fully formed before we co-develop new products.

Keeping “Black Mirror” and the “Law of Unintended Consequences” in mind, can you see any potential drawbacks about this idea that people should think more deeply about?

A set of principles won’t do it. Thinking we will be OK as long as we have committees and ethics documents and talking shops will take us down the wrong path. We need to expand our notions of what “AI expert” means. It needs to include a lot more than just those who code. That thinking is overly reductionist.

Was there a “tipping point” that led you to this idea? Can you tell us that story?

You can likely, and sadly, do this experiment yourself: ask an AI researcher how any weakness in AI, such as bias, will be solved. The answer will almost always be some version of “more tech.” That is, we will fix tech problems with still more tech. That thinking I find to be too narrow.

What do you need to lead this idea to widespread adoption?

Talk about it. Have it be OK. We need to claim AI for ourselves. It is not just for the high priesthood of AI PhDs.

The future of work is a common theme. What can one do to “future proof” their career?

Easy answer: do more human things. Practice empathy. Practice love. Practice listening. 
The one word to have: Curiosity. This is a much more helpful word than “agility.” Be curious about how new tools be help you to become more helpful.

Based on the future trends in your industry, if you had a million dollars, what would you invest in?

I feel very strongly about this: The next wave of AI will be in specialized applications to specific problems and industries. I have particular interests here and they are all expressions of this thesis.

Which principles or philosophies have guided your life? Your career?

There are so many great thinkers. I find intellectual sympathy with Spinoza. He articulates a holistic view of world and the wonder around us. But there is a lot of goodness to be found in many wisdom traditions.

Goethe said that “Everything that is worth thinking has already been thought. One must only try to think it again.” There was a saying credited to Socrates that said something to the effect “Let him that would move the world, first move himself.” These two speak to me about AI. We need to get active and be engaged, but we don’t need to overthink the solutions.

Can you share with our readers what you think are the most important “success habits” or “success mindsets?”

Kaizen. A continual, relentless drive for improvement. Continual curiosity is what drives progress of all kinds.

Some very well known VCs read this column. If you had 60 seconds to make a pitch to a VC, what would you say? He or she might just see this if we tag them 🙂

In reality, I probably would not say anything. I am very selective in the investors with whom I want to work. I think that this is taken far too casually.

The generous answer is that their calculus has mostly run its course.

How can our readers follow you on social media?

IG, FB, LI @ericdaimler

Twitter: @ead

Thank you so much for joining us. This was very inspirational.

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