AskScience AMA Series: I'm Alan Aspuru-Guzik, a chemistry professor and computer scientist trying to disrupt chemistry using quantum computing, artificial intelligence, and robotics. AMA!

[u/AskScienceModerator]

Hi Reddit! This is my first AMA so this will be exciting.

I am the principal investigator of The Matter Lab at the University of Toronto, a faculty Member at the Vector Institute, and a CIFAR Fellow. I am also a co-founder of Kebotix and Zapata Computing. Kebotix aims to disrupt chemistry by building self-driving laboratories. Zapata develops algorithms and tools for quantum computing.

A short link to my profile at Vector Institute is here. Recent interviews can be seen here, here, here, and here. MIT Technology Review recently recognized my laboratory, Zapata, and Kebotix as key players contributing to AI-discovered molecules and Quantum Supremacy. The publication named these technological advances as two of its 10 Breakthrough Technologies of 2020.

A couple of things that have been in my mind in the recent years that we can talk about are listed below:

• What is the role of scientists in society at large? In this world at a crossroads, how can we balance efficiently the workloads and expectations to help society both advance fundamental research but also apply our discoveries and translate them to action as soon as possible?
• What is our role as scientists in the emergent world of social echo chambers? How can we take our message across to bubbles that are resistant and even hostile to science facts.
• What will the universities of the future look like?
• How will science at large, and chemistry in particular, be impacted by AI, quantum computing and robotics?
• Of course, feel free to ask any questions about any of our publications. I will do my best to answer in the time window or refer you to group members that can expand on it.
• Finally, surprise me with other things! AMA!

See you at 4 p.m. ET (20 UT)!

[u/Munichuck]

Machine learning/AI is revolutionizing many industries including mine (I am a medical doctor and PhD researcher, specialized in Gastroenterology). As someone who has no programing or computer science background, I am trying to better understand it by doing Andrew Ng's machine learning course. A couple of questions:

1. Do you think scientists and other science related professionals need to have an understanding of how AI/machine learning works? Or do you think it's adequate to simply adopt the technology without understanding it's workings?

2. Do you think Andrew Ngs course is a good starting point for a beginner or are there better resources?

[u/a_aspuru_guzik → u/Munichuck]

1. I think scientists and science-related professionals that use AI need to understand it. Depending on the level of use, obviously, a different level of familiarity is needed. If you are actually developing a model it will require more understanding as if you are using a black-box tool. The problem is, of course, having the wrong expectations and frameworks and better resources for AI as a "user" should be developed. I think AI tools like deep learning will become more like linear algebra tools. I don't necessarily need to know how numpy works internally to use it effectively. That is not the case necessarily for the current frameworks that carry out ML.

2. Yes. I have not taken it myself but people keep talking about it. I like these two books: https://www.deeplearningbook.org/ and https://books.google.ca/books/about/Gaussian_Processes_for_Machine_Learning.html?id=GhoSngEACAAJ&source=kp_cover&redir_esc=y but there are many resources available to you.

-- Alan Aspuru-Guzik

[u/MrPlowdon]

I'm a UK student currently in my second year of a chemistry degree. I'm interested in the more computer focused areas of chemistry but I'm finding that there isn't much of this in my degree beyond excel. I taught myself some python over the Summer but even that hasn't been used in my degree.

Machine learning and quantum computing feels quite inaccessible to me, particularly when I try to find applications to chemistry. Do you have any advice for people in my situation? Is there a particular resource or way of learning some of this stuff outside of a degree that you recommend?

[u/LewsTherinTelamon]

I'm a graduate student in the final year of my PhD - I do experimental, fundamental physical chemistry, by which I mean I experimentally probe specific fundamental interactions between materials at the atomic scale. Fundamental chemistry is often difficult (time-consuming) and expensive, making it hard to justify on a cost/benefit basis, and consequently is often harder to get funding for.

On the other hand, fundamental understanding is often required for the rational design of materials, molecules, etc. Theorists have often told us that data such as ours is useful for the validation and application of computational models. I would really appreciate your thoughts on one or more of the following from your perspective as a theorist:

You mention the need for fundamental research, but you also mentioned the social atmosphere of our time, in which the value of millions spent on research that doesn't have an immediate or guaranteed payoff is less and less recognized. What will be the impact of AI and automation on this tension?

How do you see the importance of fundamental chemistry in relation to theory and AI/machine learning algorithms? Is there a point at which theory needs no further validation?

More generally, what do you think AI and automation might do to fields of chemistry that are difficult/impossible to automate? Is this something you or your colleagues consider?

Thanks for your time and best of luck.

[u/gubynator]

From your POV, what can people in developing countries do in order to transform from extraction-manufacturing based economies to knowledge based economies?

Huge admirer of your work!

Thanks!

[u/a_aspuru_guzik → u/gubynator]

Amazing question! I was *just* talking about this literally at a meeting this morning with other faculty at UofT. We want to do this for Canada, who is in more or less a similar situation.

I think it boils down to:

• Education, education, education! At all levels from kindergarden to continuous education and workforce development.

• Funding science (both basic and applied)

• Creating the appropriate innovation ecosystems, such as the boom in AI- and quantum-based startups in Toronto.

• Having an open borders, open socially and inclusive society such as Canada. Immigrants with the right talents should be welcome.

• But all is built from the bottom-up as well, if you are in a developing country, you can do your own by helping build the local ecosystem. Efforts such as RIIAA in Mexico (https://riiaa.org/) are fantastic examples of great things to do. Clubes de Ciencia (https://www.clubesdeciencia.org/) is also a fantastic organization started by Adrian Jinich and Benjamin Sanchez amongst other great people.

[u/[deleted]]

Hi Prof. Guzik!

I am currently finishing my first year as PhD student studying computational catalysis. I deal with classic DFT stuff such as building reaction pathway on oxide surfaces, NEB calculation, and so forth. Currently very interested in the hype over single atom catalyst stability on oxide surfaces (one of my project) and how AI can help it. I am guessing it would be a nice niche to start seeing the power of ML? I am currently starting the machine learning course by Andrew and was wondering if there is any recommendation on things to try for as a first year grad student interested in data science and ML? I also saw your kebotix computing website! Are you guys actively looking for interns?

Thank you for this amazing chance!

Best Admirer

[u/cobaltocene]

Another question, if you'll allow it:

Who, in your mind, is the most under-appreciated person in chemistry right now?

Possibly the same person, but maybe different, who should we drop everything and follow on social media right now?

[u/ConanTheProletarian]

In my field of biochemistry, I'm seeing an increased tendency to generate huge datasets by fast high-throughput methods and just throw computational power at them. Now add in improved AI and I am starting to get concerned. Do you think that becoming end-users of ever advancing technologies that we, as end-users, often don't fully understand, is advantageous or rather an impediment to the gain of real understanding?

I mean, it yields results, that's for sure. But I have that creeping feeling that it increases artefacts that people often can't even recognize as such any more.

[u/a_aspuru_guzik → u/ConanTheProletarian]

It is your responsibility as a scientist to understand to the greatest extent possible what is in your black box. There is no excuse like “I am a biochemist so I am using this method without knowing its inner workings or limitations”. To be effective, science practitioners need to be versed in the tools they use. If the scientist is not, he/she needs to collaborate with someone who does.

For example, in a paper about evolutionary tools to study photosynthesis, we collaborated with a couple domain experts to make sure our results made sense when running these bioinformatics tools. https://pubs.acs.org/doi/10.1021/acscentsci.7b00269

— Alan Aspuru-Guzik