Think AI Is Too Scary? This Expert Wants to Calm Your Fears

<pre>Think AI Is Too Scary? This Expert Wants to Calm Your Fears

How do you feel about artificial intelligence? Excited? Apprehensive?

Perhaps you wish there was “grown-up supervision” on hand. Relax, there is: Founded in 1979, the Association for the Advancement of Artificial Intelligence (AAAI) has kept a fairly low profile in the background of academic arenas, emerging in Washington.

But the organization now has 4,000 members worldwide and its goal is to: promote research in and guide the responsible use of AI; enhance public understanding of the field; raise standards in training AI innovators; and provide guidance to those funds major AI initiatives.

To find out more, we spoke with Dr. Yolanda Gil, who just took over the AAAI's 24th president, via phone, at her office in the USC's Information Sciences Institute (ISI). Dr. Gil joined ISI in 1992 and is currently its Director of Knowledge Technologies and Associate Division Director; she's also a Research in Computer Science and in Spatial Sciences with a focus on intelligent interfaces for knowledge capture and discovery. Here are edited and condensed excerpts from our conversation.

Dr. Gil, could you tell us why did you take the AAAI president?
These are the exciting times as AI increasingly permeates our lives. We see it in systems from chatbots to self-driving cars to scientific discovery and many other applications. I believe AAAI is the leading forum to coordinate many areas of AI, and that we also have a strong responsibility to design AI systems that have-and encourage-ethical and responsible behaviors. My career has always included a focus on service to the AI ​​and computer science communities. I'm really excited about it.

Can you talk about the three major objectives you have for AAAI moving forward?
The first thing to tell you is that I can be responsive to what the community is looking for. Having said that, one big area is to enhance and strengthen AAAI links with industry. Our annual conference has a lot of participants from the industry. Traditionally it's been a very academic conference but today, many professors spend time in industry. We need to give a lot of presence. That's a major focus. I am also looking to include underserved communities in our membership to diversify it strongly; launch K-12 initiatives to grow the pipeline; and to ensure we include professionals in other areas.

As young students learn about AI but go into other fields, it'll spread understanding, rather than fear?
Exactly. Many K-12 students will go on to be doctors, entrepreneurs, engineers, or whatever they choose. But through exposure to AAAI, they'll know more about the potential for AI in their chosen field.

Your predecessor has started on a focus on AI and ethics.
Yes, and we'll be continuing that through 2019 … especially within the second conference on “AI and Ethics in Society”. We need to look at employing ethics within AI at every level: how systems need to be designed with different mechanisms to respond ethically to events; understand when an AI system could do harm; and so on. I'm very excited about our initiative-it's in partnership with ACM-and, as a community, we need to take a leadership role and do more research in this area.

Pivoting to your own research, for a moment, we first encountered your work back in 2015, at DARPA. How was that project progressed since then?
At the time [at DARPA]we were just starting that project on, using intelligent systems for scientific discovery, assisting scientists with intelligent systems that analyze data, test hypotheses, and make new discoveries. At the beginning, we were focused on capturing scientific processes as semantic workflows. We have been working on several science domains.

Yes, for example, in
Yes, for example, in the scientific field of proteomics, the biochemical study of proteins within an organism, we have now taken a lot of workflows about this kind of analysis. And, interestingly, what we realized is that, when the studies are published, they just used one model [to identify the proteins] but they did not explore others, which means many are missed.

And your AI system is smart enough to comprehend this omission and correct it?
Precisely. Our systems are now intelligent enough to be diligent and keep trying other methods, other algorithms, to detect hundreds of proteins that have been left out otherwise. The system itself is working on making new discoveries.

That's amazing. Your AI systems will be making new scientific breakthroughs. A popular lab partner, one assumes.
[Laughs] We hope so. We are now working on a mechanism to measure, “interestingness,” so that when it is a significant breakthrough. This is a very challenging; it involves that the system has knowledge on what is the state of the art in the field, the latest thinking on those proteins.

Otherwise it might just get excited, but the proteomics researchers might say: “yeah, but that's pros a a prosaic protein. “
That is right. And a good lab partner would not bother a scientist with minutia or unimportant findings. So we have to design an “interesting lab partner.”

Can your AI system ingest and analyze multiforms of data input?
Yes. We're now automatically generating machine learning workflows. We give it data and a metric-i.e. the desired goal. If it's audio, it will look for a way to process it. If it's visual data, it will find a means to understand and catalog that. Then it will apply algorithms to maximize the metric (eg the accuracy of the solution). It's very systematic.

The DARPA award?
Yes, that award is for a 4-year project called MINT for Model INTegration, part of DARPA's World Modelers program. We are building workflows to integrate complex models of the world that cover hydrology, food production, climate, social and economics. We are asking our intelligent systems to help us understand, in particular, prospective food shortages, and food insecurity for at-risk communities around the world.

Right now, we're collaborating with Kimetrica, the data company large-scale investment appraisals and government statistics reports, to use our automated machine-learning workflows, and it turns out that they are better than the ones they did by hand. This is still in the initial stages, but we're getting good results. It's exciting to have their domain expertise, combined with our AI research finding solutions to important world problems.

In the current issue of AI Magazine, Dr.Sc. Lynne Parker, co-leader of the National Artificial Intelligence Research and Development Strategic Plan, writes: “It is incumbent upon us as technologists to focus on the positive, ethical development and use of AI, ensuring that everyone can benefit from the practical application of AI across society, regardless of which nation leads in the strategic development of the technology. ” What was the significance of this document?
AI has always been very international-and distributed-with cultural richness embedded in all regions, many of which take different approaches to AI. For example, in Europe there's an incredible tradition of logic-based approaches, in Asia it comes from advanced computational mathematics, whereas in Africa and Australia there's a strong focus on applied research. The United States recognizes the importance of having a national plan and making strategic investments.

An increased focus to compete with China and elsewhere?
To be clear, the US government has always made an an important area for investment, but it has not been traditionally a large investment, whereas the EU and China have been prioritizing government-led research investments in AI for a while. Needs to continue to play a leadership role. It was a great document, very thorough and its recommendations are very right.

One thing it is pointed out is that machine learning is important but human-computer collaboration is something that we can not neglect. It is also crucial in the study of AI, when you invest for 10, 20, or 30 years into the hard problems we have in AI-like speech recognition-you can see amazing results, but only when investments are sustained over a significant period of time. time.

It's very frustrating for a major speech-recognition. “
Right! [Laughs] Speech-recognition research took decades, and some did not work, and had to be abandoned. But because there was a significant investment in people, the stuck with the problem and was being creative and pushing forwards.

What about the current administration?
Actually, the current administration is just the right thing to do with a roadmap for AI research. I will be co-chairing that effort.

Well that's a cheering thought. When, as the co-chair of this new AI, the strategic plan for the US, are you delivering the first paper?
We are going to take the National Artificial Intelligence Research and Development Strategic Plan and thinking very hard about basic research, human-computer collaboration, understanding the benefits of society as we invest-particularly around health and scientific discoveries and so on-to create an extensive roadmap by spring 2019. We also want to emphasize how innovation can and government sectors.

To learn more, the AAAI Fall Symposium is scheduled for October. 18-20 in Arlington, Virginia. The registration deadline is Sept. 21.

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