Humans of SymSys: Haroun Ahmed

Tell us about yourself!

Hello! I am a Master's Student studying Symbolic Systems, with coursework primarily in Computer Science and Neuroscience. I took a few quarters off of school to work at Slack as a technical architect during COVID.

What is your concentration and why did you choose it?

Master's degrees don't have concentrations, but my undergraduate degree was in cognitive neuroscience and I self-taught a lot of the technical background to become more well-versed in artificial intelligence topics.

What's your favorite class that you’ve taken so far? (SymSys-related, or not)

My non-SymSys related class is ENGR 248 (Principled Entrepreneurial Decisions). I still talk to the professors and it's a wonderful class for learning how to apply ideas to real life. My SymSys-related recommendation is PHIL 167D (Philosophy of Neuroscience). It's a seminar style course and I got the worst grade of my life from that class on my very first paper in that class. The professor knew I wrote the paper at the last minute and was willing to challenge me. She wouldn't just let anyone BE there–participation was key. In the end, I got a lot out of the class. I really respected the journey.

Are you involved in research? If so, tell us about a project you are working on:

I work with the Stanford Center for Deliberative Democracy on using natural language processing techniques to analyze deliberative polling. Essentially, we get transcripts on polling seminars from across the country, where we poll focus groups on an issue such as gun control or abortion. Then, the participants take seminars with unbiased experts and are polled again. We use natural language processing for key words and other nuances.

What is your experience with applying SymSys knowledge in industry?

I would say so. I've worked in enterprise AI and various industries, and I've found that SymSys gives you what is required from a data and computational background. You can understand how people think and an element of cooperation.

What is one piece of advice you'd like to offer to younger students?

There is a lot of emphasis for undergrads to do well in classes, but so much more to college experience than just classes. Don't be afraid to make connections and reach out whether that be people in your dorm or professors. Professors love nurturing students and are passionate about it. As I like to say, closed mouths don't get fed. Take advantage of Stanford and all its resources.

What's the most memorable moment for you during your time at Stanford?

When I first declared Cog Sci as an undergrad, I read Incognito: The Secret Lives of the Brain by David Eagleman. I liked the style of writing and breaking down foundational science principles for the average person. Much later, I watched the first season of Westworld and realized that the chief science advisor for the show was David Eagleman, the author. Finally, he recently moved to Stanford, and through a series of connections, I was able to meet him and he signed my book! That was amazing. 

What do you like to do for fun during your free time? What activities were you involved in at Stanford?

I love playing basketball, listening dance music, and going to concerts and festivals. I've also solo backpacked across 32 countries. There is this one beach town in Nicaragua where everyone comes in on Sunday and throws a massive party and then leaves the next day. At Stanford, I'm involved in CS+Social Good and was the former president of SymSys Society.

What’s the coolest (loosely) SymSys-related topic that you’re excited about right now?

GPT-3 and open AI are fascinating advancements we are making, the idea of making symbolic constructs ideas into computational abstractions.

What are your future plans? 

"What are you, my parents?"

Just kidding. I'm currently working on building a start-up and raising funding. The start-up focuses on building up the music space in Web3, allowing independent, emerging musicians to create digital collectibles and currencies for fans, etc. Of course, I'm also looking forward to traveling–I'm thinking South Africa next.

Thanks so much, Haroun!

Check out http://harounahmed.com/ for more from him.

Humans of SymSys: Ari Qayumi

Tell us about yourself!

Hi! My name is Ari Qayumi, and I am Managing Partner of Mindful Venture Capital, a Menlo Park based early-stage venture firm that leverages Applied AI and a proprietary actionable dataset of early-stage founders’ digital habits to find stage-specific, outperforming assets with high-growth potential. I have about a decade of experience investing in early-stage technology companies that leverage intelligent automation across infrastructure, healthcare, and financial services. I am a recent graduate of the Symbolic Systems program at Stanford University, and while at Stanford, I was Lead Researcher of the Behavior Design Lab where I studied with BJ Fogg. Outside of investing and researching, I love to hike The Dish and practice yoga!

What is your concentration and why did you choose it?

I studied Human-Computer Interaction (HCI) with a focus on Applied Behavior Design (ABD). I care about creating environments that help people help themselves and help them be successful. Studying Human-Computer Interaction directly influenced my ability to build tools that analyze, augment, and amplify human ability, use applied behavior design to help companies realize their full market potential, and facilitate founders’ ability to align behavior design to business models from the early-stage and beyond.

What's your favorite class that you’ve taken so far? (SymSys-related, or not)

Understanding Users (CS 377U). Frank Bentley and the TA team provided an excellent roadmap of skills that need to be expected of someone graduating from the Symbolic Systems HCI track at Stanford, especially for those who wish to succeed in founding their own ventures. This course empowers students to not only learn the fundamentals of applying scientific analysis to understanding users’ needs and how they interact with technologies, but also practice evaluating and architecting metrics and mechanisms that can be leveraged to facilitate positive behavior change.

Are you involved in research? If so, tell us about a project you are working on:

Yes, I currently serve as an Advisor to the Stanford Behavior Design Lab, led by BJ Fogg. One current research project that I’m keen about is the Rosetta Stone project, which is focused on accelerating the transfer of a technology concept from lab to usable product in industry.

What is your experience with applying SymSys knowledge in industry?

Outside of the Stanford bubble in the present day, a minority of people know what Symbolic Systems is. But the minority that do know, happen to be some of the most influential and impactful people across Academia, Industry, and Tech. Leveraging an applied Symbolic Systems approach to analyzing the relationships between individual/group behavior, Enterprise Technology, and applied AI in early-stage companies is central to my edge as an early-stage venture capitalist.

What is one piece of advice you'd like to offer to younger students?

Have a vision of the kind of world you want to live in one day, surround yourself with people who want to build this world with you, and form strong bonds with them during your time at Stanford.

What's the most memorable moment for you during your time at Stanford?

I was invited to represent Stanford at the HealthByTech Conference in Amsterdam and share my work on screen time and behavior change.

What do you like to do for fun during your free time? What activities were you involved in at Stanford?

I like comedy, whether it be movies, shows, and/or my friends – I love laughing! While at Stanford, I was involved with the Varsity Rowing team, Behavior Design Lab, Symbolic Systems Program, University Singers, and Memorial Church Choir.

What’s the coolest (loosely) SymSys-related topic that you’re excited about right now?

Taking an applied behavior design approach to venture capital by leveraging symbolic systems analysis has never been done to date. I’m excited for the early success we’ve already seen in applying this to venture capital, and I am eager to share/pay it forward to the SymSys community as we continue to break ground.

What are your future plans? 

My belief is that with the power of engineering and the resilience of economics, real world catastrophes can be hedged by not only further understanding human behavior, but also applying this knowledge in a variety of segments: policy proposals, business infrastructure, education systems, and so on. I intend to continue practicing as an early-stage venture capitalist and symbolic systems scientist.

Thanks so much, Ari! 

Check out mindfulventurecapital.com and Ari’s LinkedIn for more from her, and feel free to reach out at ari@mindfulventurecapital.com.

Coffee Chat with Prof. Jay McClelland (Psychology)

Professor Jay McClelland joined SymSys Society for a coffee chat! We discussed the capacity for computational and mathematical models to account for consciousness, mused upon emergent questions in cognition at the intersection of psychology, philosophy, and mathematics, and heard about Professor McClelland’s thoughts on the debates between researchers about computational modeling of cognition.

For me, a highlight of the chat was our discussion on how the study of neural networks has evolved over time. Fun fact: Professor McClelland first got interested in neural networks way back in the 70s (!!), and he’s been excited to see research of neural networks pick up again in recent years following a period of unpopularity. He’s been consistently interested in neural networks for decades, so it’s been a gratifying I-told-you-so experience for him!

We also discussed some of his latest research in Bayesian reasoning and how to build models that can account for the complex cognitive processes that humans engage in when making decisions.

Big thanks to all who joined us!

SymSys Society Meets ProFros!

wish we took a pic of all our cute SymSys Zoom backgrounds together T_T  but here’s a masterful photoshop substitute

During Admit Week, SymSys Society had a blast talking to prospective freshmen. Our Activities Fair booth was a casual, interactive Ask Us Anything Q&A. 

Below are the most popular questions that ProFro’s asked us, so keep reading if you want to get the tea on SymSys at Stanford! 🍵

What is SymSys?

That’s a question we often ask, too. Everyone seems to have their own way of defining the major. We generally think about it as the study of artificial and natural intelligence – how do we define, understand, simulate or replicate the mind? These sorts of questions lead SymSys students to study fields like philosophy, psychological, computer science, and linguistics. We can then apply this knowledge to more fields, like human computer interaction, natural language processing, or any of the SymSys concentrations. With all the diverse subjects I just threw out in just this explanation, you can imagine how interdisciplinary and multifaceted the SymSys community is!

How can I get involved with SymSys Society--on Board or as a member? 

Yay! It’s great that you’re interested in joining us. You can apply for Board at the beginning of each school year. Applications are usually sent out in late September and are due in October. To participate as a member, just stop by our events and hop on the mailing list! 

Events calendar: link 

Mailing list: link

What roles are there on SymSys Society board?

We have co-presidents, a financial officer, social coordinators, events coordinators, alumni outreach coordinators, and more. If you’re curious, check out our Who Are We page!

Do you have to be a SymSys major to be involved?

Not at all! Students on Board (and the club) and within the community come from a wide range of majors. Whether you study hardcore STEM or humanities--or anywhere in between, there’s a place for you in SymSys Society. 

What’s the difference between CS and SymSys? 

CS and SymSys share concentrations, such as AI and HCI, so there are opportunities to develop the same technical skills from CS in the SymSys major. The difference is in the requirements! 

With CS, you’ll complete a slew of physics and engineering courses, whereas with SymSys, you’ll supplement your schedule with your choice of interdisciplinary courses, like psychology, philosophy, or linguistics. 

What’s your favorite event that SymSys Society has put on?

In in-person times, our annual Thanksgiving dinners are a splendid vibe, with professors, students, and mashed potatoes milling around and chatting (not the mashed potatoes though)!

In virtual times, we love our biweekly Sunday Socials (shoutout to Miles!) - they’re a casual hangout space for SymSys folks to drop by, listen to music, chat in breakout rooms, and study together.  

What are some cool opportunities that SymSys Society has provided?

This harks back to the days of yore, but Mark Tessier-Lavigne (Stanford’s president!) once sat down with a group of SymSys students to talk about his adjacent background in neuroscience and cognitive science. Tldr; join SymSys Society for coffee chats with Daddy MTL. 

We also regularly host coffee chats with faculty from a wide range of fields, from linguistics to design to CS and beyond. These are great opportunities to get to know professors in a small group environment! 

And of course, one of the coolest things about SymSys Society is our socials, where you can get to know the broader SymSys community at Stanford. You’ll make surprising connections and meet incredible peers! 

What do SymSys alumni do?

SymSys alumni have gone on to work in tech, healthcare, research, consulting, media, communications, and beyond. Many also move to grad school (law, med, business, PhD). The possibilities are endless! Read more here. 

What water do Stanford students drink?

This question created our most dramatic moment during Activities Fair. It revealed differing philosophies in the question: To tap or not to tap (water)? In virtual times, we’re getting (bougie and beautiful!) boxed waters, and in normal times, we turn to filtered tap water! There’s always a bottle refilling station available, and there’s also fruit-infused water in the dining halls. 😊

Coffee Chat with Chris Potts (Linguistics, CS)

All smiles when computational linguistics is the topic on the table!

Thoughts from Regina T.H. Ta ‘23

Professor Chris Potts joined SymSys Society for a virtual coffee chat! We discussed the current limits of computational linguistics, how we encode emotional expression into language, and what’s missing from deep learning data sets. We also debated whether machines could become “superstitious”: observing correlations and applying unknown causes. 

For me, a highlight of the chat was our discussion on online translation--and how we can make it more robust! Fun fact from Prof. Potts: if you put “soy milk” into Google Translate, the Spanish translation ends up being: “I am milk!” The challenge is building translations that tune into the pragmatics, as well--not just the semantics. 

We also gathered course recommendations for those interested in the intersection between linguistics and CS: take Linguist 130A for a theoretical foundation, then try CS 224U for a computational implementation! 

Big thanks to all who joined us!

If you’re curious for more, here are Prof. Potts’ responses to questions that students submitted: 

------------------------------

Since our brains are models of computation and even babies unconsciously use Bayesian probabilities to process language, what stops humans from being computers themselves?

I suppose my view on this is that the human brain just simulates or approximates the computational operations that we can define crisply in abstract terms. This is the best that can be done with a bunch of nerve fibers and other gunk! It's mysterious that we can have even partial knowledge of these computational operations.

As someone who studies AI and language, do you have special insights about the phenomenon wherein AI speakers like Alexa can’t understand people with thick accents in English, and how CS might be able to solve that problem?

It seems like data is the primary problem here. These systems just aren't trained on enough data of the right type. It's not that some accents are intrinsically easier or harder -- even if there are differences of that type, they aren't large enough to account for the observed differences in performance. That said, as we figure out how to get more data of the right kind, we might discover that there are intrinsic biases in the systems themselves. This was more obviously a factor in the days of lots of hand-built features than it is now in the era of deep learning, but it would be too hasty to assume that such biases aren't latent in today's models as well.

What are some findings of your research so far?

That's a big one! In recent work: 

* I am proud that we settled (I hope!) a long-standing question about whether neural networks can truly learn to do reasoning about equality: https://arxiv.org/abs/2006.07968

* I'm also really excited by the mix of formal results and practical insights emerging from work in this mode: https://www.aclweb.org/anthology/2020.blackboxnlp-1.16/

Atticus Geiger is one of the authors with me on both these papers. He was a SymSys undergrad, and I feel like you can trace some of the above all the way back to his (award-winning) SymSys honors thesis.

Can your research be applicable globally even though every culture expresses emotion differently? 

I hope so! On the linguistic side, I want to be offering insights about language in a very general sense. On the NLP side, I am striving for modeling approaches that are robust to variation as well!

How would you describe computational research methods, specifically in relation to your work and SymSys? Would you say it is especially applicable in what you are studying?

I would say that, at this point, knowing about computational methods is part of scientific literary. Not everyone needs to be a programmer or anything, but awareness of what is involved in programming and writing programs is important. Stanford is great for this because there are many, many contexts in which one can learn these skills.

For me, computational methods are vital because I can process lots and lots of data in interesting ways to learn things about how people use language. 

What's your thoughts on the current big-Transformer stack approach to language? 

I think it's great for lots of reasons. It has lifted all boats. It has led to sharing of model parameters that is very productive. It means you can do meaningful things with small datasets, via fine-tuning of large models. And contextual word representations are much closer to how linguists think about words and phrases that static vectors are.

Do you think that models in the future should incorporate more linguistic priors? If so, which approaches do you think hold the most promise? 

Tough to say! The Transformer is a triumph of low-bias model structure with hardly any priors. 

Does language cause intelligence or vice versa?

I am sure it's a back and forth. Language is clearly a cognitive tool. Maybe a more controversial question would be whether *communication* with language is a necessary piece.

What's a current limit of computational linguistics that you're most excited about pushing past? 

I think we need models that are more grounded in aspects of the human experience that go beyond mere streams of text. For more nuance on this, perhaps check out his blog post I did:

“Is it possible for language models to achieve language understanding?” https://chrisgpotts.medium.com/is-it-possible-for-language-models-to-achieve-language-understanding-81df45082ee2

When you first started working computational methods into linguistics, what was your long-term project or goal, if you had one?

I started using computational methods because I wanted to learn more about what people mean when they swear! To do this, I needed to see a lot of instances of swears in the real world, and computational linguistics is the tool-kit for that. I later realized that I was doing what had recently (at the time) been established as "sentiment analysis" within NLP.

Are you studying and finding emotions based on the connotations of words, or also the tone of voice? Is it possible to use computational methods/CS to analyze tone of voice yet?

I think there is progress on this in speech research. Some if it is covered in CS224s, I believe: http://web.stanford.edu/class/cs224s/syllabus/

How supportive would you say is the Stanford Symsys community to your research, and are there others studying similar things?

I feel like SymSys is my most natural intellectual home. It's the only place in the world that brings together a real mix of people doing work that's computational, linguistic, social, psychological, philosophical, and on and on, and I love how many creative ways people in the SymSys community are finding to connect this work to things with real social significance and impact.

Do you think traditional linguistic theory on semantics/pragmatics has/will have a role to play in the development or analysis of natural language understanding technologies, and if so, what does/will it look like?

I think so. NLPers tend to overlook, or forget about, all the things they do that are shaped by linguistic theory. I expect that to continue, with pragmatics being the latest place where we see these rich influences.

I should add that I don't think this is a requirement, for linguistics or any science. We shouldn't measure the worth of a science based on how much it contributes to engineering efforts. I myself am always seeking such connections, but I am glad that there are many linguists who are not doing that, but rather trying to understand and document languages for their own sake.