Category: Psychology

There’s no ignoring the recent wave of advancement in artificial intelligence or AI. From the near photo-realistic outputs of Stable Diffusion to the pseudo-coherent text produced by ChatGPT, generative machine learning algorithms have taken the world by storm. As someone with a background in mathematics and statistics, these are without doubt fascinating advancements that I’m interested in from a technical perspective. At the same time, I have numerous concerns about these algorithms from an ethical perspective that I don’t think I’m alone in holding. That cheesy line from Spiderman about power and responsibility rings truer now than ever. To assume that we can reap the rewards of AI without accepting any risk would be a fallacy of apocalyptic proportions.

If you’re looking for a review of the literature on the hidden risks of AI, there are more intelligent people than myself that you should probably be looking for in a peer-reviewed journal. There is very little I can say about ChatGPT that hasn’t already been better articulated in Stochastic Parrots. Instead, what I’d like to offer you today is a story of a stupid teenager and his chat bot. I don’t expect it to alter the course of history or anything, but maybe it’ll provide some insights into why you should care how these technologies are being used.

I started programming in my early teens and AI was always something of a holy grail. I was especially partial to the Turing Test as an indicator of consciousness. In this test, a computer program is tasked with deceiving a human into false believing that they are engaged in a text-only conversation with another human. Turing argued that if human experimenters couldn’t distinguish between programs and people then we’d have to consider the hypothesis that machines could effectively “think”. There are arguments to be made about whether or not the Turing Test measures what it intended to, but advances in Large-Language Models have made it clear that this standard is now just a matter of time.  In fact, I’d argue that the Turing Test was “effectively passed” in the early 1980’s by a program called ELIZA developed by Joseph Weizenbaum. 

ELIZA was designed to be a sort of “virtual therapist”. A human user could talk to the computer about things that were on their mind, and ELIZA would turn their statements around to form new questions. For example, if you told ELIZA “I had a rough day at work” it might acknowledge that you’re feeling upset and inquire about it: “I’m sorry to hear that. What about your day made it rough?”. ELIZA didn’t actually know very much about the world, but it could engage a human in a fluid and convincing conversation that led the user towards self-reflection. Some users walked away from ELIZA feeling like they were engaged in dialog with a real therapist. A recent preprint from UCSD researchers indicates that ELIZA’s performance on the Turing Test is between that of GPT-3.5 and GPT-4.0.  Not too bad for a program from the 80s. 

Of course, any deep interaction would reveal the lack of “intelligence” on the other side. ELIZA couldn’t answer questions about the world. All it could do is to classify different sentences into schema and then transform them into canned responses using key tokens from the input text. Simple rules like “I feel <sad>” would get matched and transformed into “Why do you feel <sad>?”, which gave ELIZA the illusion of being a good active listener. This might sound kind of sad, but ELIZA was probably better at active listening than I am – and I knew it all too well.

As a teenager in the late 90s, we didn’t have the pervasive social media outlets we have today. There was no Facebook or Twitter for you to doom-scroll. Maybe you had a public facing Geocities or MySpace page, but that was only if you were a nerd like myself. The de facto standard for internet communication was AOL Instant Messenger (or AIM) .  Even if you weren’t subscribed to AOL’s internet access, you probably still used the stand-alone AIM application for direct messages because it was literally the only service with enough members using it to be useful. You can’t have real-time communication without a shared protocol shared by people.

The application wasn’t even that great by today’s standards. It was full of what would now be considered negligent security vulnerabilities. In early versions, you could easily kick someone offline by sending them an instant message with some malformed HTML. If someone saved their password for easy login, it was literally stored in a file as plain text and could be subsequently looked up by anyone who knew where to check. It was the wild west era of the internet.

Around the same time, I discovered a project called ALICE.  Richard Wallace had taken ELIZA’s token handling foundation and generalized it into an Artificial Intelligence Mark-up Language (or AIML).  This separated out the “code” and “persona” of the chatbot into two separate data sources.  The HTML-like syntax made it easy to customize the bot’s responses into whatever you wanted.  The application would read these source templates in and use them to craft a response to any message you gave it. 

While I’m reading article after article online trying to figure out how this stuff works, I keep getting instant messages from people. These messages are not malicious in any way, but receiving them has a tendency to “pull me out” of whatever I’m doing at the time. Sometimes I’m pulled into fun stuff through these messages, but often than not they were just an annoyance. As a teenage boy in the 90s, the vast majority of these interactions went down like this:  

That’s when I got the brilliant idea to fake my own AIM account.

While exploring the flaws in the AIM application, I discovered I could hijack the message queue that distributed incoming messages to the appropriate chat window.  This allowed me to parse the incoming message text and send fake keystrokes back to that window to produce a response. All I really needed to do was to invoke the chatbot as an external process to generate something to say.

I took an open source implementation of ALICE and started modifying the AIML code.  I removed any instance where the bot acknowledges itself as an artificial intelligence and instead taught it to lie that it was me. I added custom responses for the greeting I customarily used and gave it some basic knowledge about my areas of interest.  The only difficult part of this was getting the bot to handle multiple conversations at the same time, which I managed to accomplish by running separate instances of the bot for each person to message me.

I think I let the bot run for most of the week without anyone really noticing, until this one day where I got an instant message– from a girl.  Not just any girl either, but one I totally had a crush on at the time. My heart sank as I read the messages interspersed between the AI dribble.

I felt sick. I immediately killed the process running the bot and seized control of AIM again. I don’t even remember what kind of bullshit excuse I made before abruptly going offline, but I’m pretty sure I didn’t have the courage to own up to the truth.  I had been “seen” through the eyes of another person in a way I hadn’t experienced before, and the worst part about it was that I didn’t like what I saw. I saw a person who lied to their closest friends in the name of science. 

I know this won’t make up for what I did, but I’m sorry.

I’ve since then learned a lot about research ethics in psychological studies.  Sometimes having deception is a necessary component to studying phenomena you’re interested in, but that is not sufficient reason to forgo the acquisition of “informed consent” from the people involved in the study.  

I think this is the reason why I’m frustrated with the current zeitgeist regarding AI. It seems like we’re rapidly falling into the trap outlined by Ian Malcom in Jurassic Park.  Some people are so preoccupied with what they could do with AI that they don’t stop to think about whether or not they should.  As a result, we’ve all become unknowing participants in an unethical research study.  While this behavior might be excusable coming from a punk teen who doesn’t know any better, this should be considered completely unacceptable coming from multi-billion dollar companies claiming to be advancing the forefront of intelligence. It’s not that “I’m scared of AI”, it’s that “I’m scared of what people will do with AI” when they acquire that power without putting in the effort to truly understand it. 

The wave of AI images coming from DALL-E and Midjourney on all my social media don’t self-identify themselves as being artificially produced. The burden of identifying them has been left to unwitting viewers and it will only become more difficult over time. While this makes for entertaining stories on Last Week Tonight, there’s a big difference between using AI to make funny pictures to share with your friends and using it to develop sophisticated social engineering methods to separate users from their passwords.  

The reality of our time is that many AI offerings are being falsely advertised as a solution for intractable problems. No image generator could possibly produce pictures of John Oliver marrying a cabbage without first being trained on a set of labeled images including the likeness of John Oliver.  Any AI image generator trained solely on ethically produced data sets, like the one from Getty, will inherently lack the capacity to reproduce the likeness of pop-culture celebrities. Either the generative AI will fail to produce the likeness of John Oliver or it was trained on a data set including his likeness without seeking prior permission.  You can’t have it both ways.  

In much the same vein, it would be impossible to ask ChatGPT to produce writing “in the style of Ryan Ruff” without it first being trained on a data set that includes extensive samples of my writing. Obviously, such samples exist because you’re reading one right now. However, the act of you reading it doesn’t change my rights as the “copyright holder”. The “Creative Commons” licenses I typically release my work under (either CC-BY or CC-BY-NC-SA depending on context) require that derivative works provide “attribution”.  Either AI will fail to reproduce my writing style or it illegally scraped my work without adhering to the preconditions.  In the event my work is stolen, I’m in no financial position to take a megacorp to court for compensation.

In discussions about AI we often neglect the fact that deception is an inherent component of how they are constructed. How effective we measure AI to be is directly linked to how effectively it deceives us. As poor of an intelligence measure as the Turing Test is, it’s still the best metric we have to evaluate these programs. When the measure of “intelligence quotient” (IQ) in humans is a well-established “pseudoscientific swindle”, how could we possibly measure intelligence in machines? If you want a computer program that separates true statements from false ones, you don’t want an “artificial intelligence” but rather an “automated theorem prover” like Lean 4. The math doesn’t lie.

I think one of the big lessons here is that the Turing Test wasn’t originally designed with machines in mind.  I still remember discovering this when I looked up the original paper in Doheny Library. The “imitation game” as originally described by Turing was primarily about gender.  It wasn’t about a machine pretending to be a human, but rather a man pretending to be a woman.  

Personally, my present hypothesis is that Turing was actively trying to appeal to “the straights” with how he described the imitation game. My incident with my AIM chatbot had taught me that there were large differences between how I interacted with “boy” and “girls”.  Conversations with “boys” were shallow and unfeeling – easily replicated by my script. Conversations with “girls”, however, were more about getting to know the other person’s perspective to determine if we’re a potentially compatible couple.  Casual conversation and romantic courtship require two entirely different strategies. Maybe the Turing Test was less about determining if machines could think and more about determining if machines could love.

Every now and then I feel overwhelmed by the flood of interaction that is constantly and perpetually produced by social media. Sometimes I wonder if my presence could be automated so that I never miss a “Happy Birthday!” or “Congratulations!” message ever again, but then I remember this story and remember that’s not really what I want.  I don’t care about “likes”. I care about building “friendships” and there’s no possible way a bot can do that on my behalf.  

Maybe I could be a better friend if I collected data on my social interactions.  At the same time, I don’t need any sophisticated statistics to tell me that I’m kind of an asshole. I’d like to think that the people I call my friends are the same people that would call bullshit on me when necessary, so I trust in them to do so. This is the difference between real human relationships and fleeting conversations with a chatbot. 

There’s nothing inherently wrong with the class of algorithms that have fallen under the “AI” umbrella, but how we use these tools matters.  Presently these bots are being marketed as a substitute for human labor but the reality of our legal system dictates that there still needs to be a human accountable for their actions.  The only viable path to survival for AI focused companies is to become “too big to fail” before they get caught for using pirated data.

I’m not going to sit here and pretend that AI won’t have its uses. Maybe AI will come up with solutions to important problems that humans would never have thought up. If every other technique for solving a problem is unreliable, there’s less harm to be caused by attempting to solve that problem through massive amounts of data. It makes sense that such statistical tools might come in handy in fields like marketing or cybersecurity where the rules of the system are ambiguously defined.  

What is clear to me is that there exist problems for which AI will never be a viable solution. GitHub’s Copilot won’t ever magically solve Turing’s Halting Problem. It’s called “undecidable” for a reason. Using ChatGPT won’t make me a better writer, nor will using DALL-E make me a better artist. There is no substitute for the process of turning my thoughts into a concrete form, and the only way to get better at those skills is to engage in them myself. Learning the internals of how AI works may have helped make me a better mathematician, but I wouldn’t expect it to solve P = NP anytime soon. 

Given my background in teaching, I was recently asked what I thought about applications of AI in education and I think it’s incredibly important that we take an abundance of precaution with its integration. This is something I’ve written about before, but I think it merits repeating that “AI needs to build a relationship of trust with all of the stakeholders in the environment”.  In our society, we depend on teachers to be “mandated reporters” for child abuse and I don’t think AI can responsibly fill this role. Without having the lived experiences of a human being, how could such an AI possibly know what symptoms are out of the ordinary?  What if it’s wrong?

Our very notion of “intelligence” is arguably shaped by the common human experience of schooling.  In my time teaching, I learned that the profession depended as much on “empathy” as it did “knowledge”.  Most of the people I’ve met who “hate math” developed this mindset in response to abusive teaching practices.  In order for AI to ever succeed in replicating good teaching, it needs to learn “how to love” in addition to “how to think” and I don’t think it ever can.  

Even my use of the word “learn” here seems inappropriate. AI doesn’t technically “learn” anything in a conventional sense, it just “statistically minimizes an objective cost function”.  Seeing as “love” doesn’t have an objectively quantified function, it therefore it’s impossible to replicate using the existing methods of machine learning.  Even if a machine were capable of expressing love, the act of replacing a human being’s job with such an AI would go against the very definition of what it means to love.

As with any new technology, AI can be used to make our lives better or worse depending on how it’s used. Let’s not lose sight of “why” we’re constructed these systems in the first place: to improve the quality of human life. Building these programs comes with a very real energy cost in a world where humans are already consuming natural resources at an unsustainable rate. If the expected pay-off for these AI systems is less than the expected environmental costs then the only responsible course of action is to not build it.  Anyone who fails to mention these costs when it comes to their AI product should be treated as nothing short of a charlatan.

I can’t shake the feeling that we’re in the midst of an “AI hype bubble” and it’s only a matter of time before it bursts. I can’t tell you not to use AI, especially if your job depends on it, but as your friend, I feel it’s important for me to speak up about the risks associated with it. 

True friends know when to call bullshit.

I didn’t become a teacher with the intention of doing it forever.  My original goal was to design educational video games, but I felt it would be presumptuous of me to build such technology without ever having set foot in a classroom.  Becoming a math teacher seemed like the fastest way for me to find out what kinds of tools schools actually needed. Now I’m not even sure I’m the same person.  

I made countless mistakes during the twelve years I spent teaching, but the one thing I think I got right was approaching it with a “here-to-learn attitude”.  Learning can only take place with the learner’s consent.  Opening one’s self up to learning a new skill means allowing oneself to be vulnerable to mistakes.  Teaching is about creating an environment where multiple learners feel comfortable with the risks of engaging in that process together. The first step is to establish a relationship of trust.

In all honesty, relationship building has never been one of my strengths so I had to make an active effort to improve on it as a teacher.  I found that the most powerful method for facilitating a student’s learning is to simply ask what they need and listen to what they say.  Really listen and trust them.  It’s incredibly difficult to learn when you’re tired, hungry, or stressed. Sometimes “taking a break” is a necessary stage in the learning process. Treating people with kindness is a prerequisite for any meaningful learning to take place.   

One of the most difficult challenges for me as a teacher was learning how to navigate spaces of trauma.  For me, mathematics is something that evokes feelings of joy but my experiences are both highly abnormal and shaped by privilege.  More often a student’s experiences with mathematics are shaped by structural forms of oppression including racism, sexism, and ableism.  Learning how to openly reflect on how I was complicit in these systems was a key factor in my growth as a teacher.  I believe students should be able to see themselves in mathematics, so I tried to actively seek out and integrate the stories of mathematicians from diverse backgrounds into the curriculum.  The self-work continues to be an ongoing process.

My goal as a teacher was to construct an environment where my students could freely “play” with mathematics.  I feel learners are entitled to the opportunity of exploring mathematics and discovering new knowledge on their own.  Often the play comes with a set of constraints that help direct it towards a specific objective, but the important qualities are that the task has a low skill floor and high skill ceiling.  There should be both an easy way for everyone to engage and the depth to encourage further exploration.  Too often we fall into a trap of erroneously thinking there’s “one right answer” in mathematics, so I make it a point to include questions with “no wrong answer”.  I found this helped to foster a culture of collaboration in the classroom because everyone’s input is of equal value in the discussion.

Exploration has limited effectiveness when you’re obligated to address very specific learning objectives, so I usually follow up with some form of direct instruction to fill in the gaps. It’s not quite as engaging, but sometimes students need a concrete example of the behavior they’re expected to model. Our brains are very efficient at mirroring actions.  I’ve found that “worked examples” can also provide a valuable resource afterwards when the student is attempting to replicate the process on their own.  As the number of examples grows, the metacognitive process of learning how to organize this information can reveal insights into its structure.

The next phase of the learning process is to engage in a cycle of formative assessment and feedback known as “practice”.  Any new skill must be practiced to be maintained.  This is one area where I think educational technology excels, because it can enable nearly instantaneous feedback to learners.  While my students often enjoyed the “gamification” of practice, it’s important to select such products carefully.  I’ve learned it’s important for developers to remember that “accuracy is more important than speed” and “some skills cannot be assessed through multiple-choice”.  As our technology improves, so will our automated feedback.  I’m particularly excited about the potential applications of “Large Language Models” in this area, but the application of Artificial Intelligence will also require a great deal of testing before it meets the ethical criteria necessary for use in the classroom.

In the reality of schooling, there’s likely to be a summative assessment stage in the learning process as well, but I tend to think this distinction is artificial.  As far as my class policies were concerned, all assessments are treated as formative where possible.  I tried to allow my students the opportunity to retake assessments as often as needed to the extent I was able. This is another aspect of teaching I found heavily supported by technology.  The combination of algorithmic question generation with automated feedback made it possible for me to focus on the broader picture provided by the data over time.  

If anything, I tend to look at summative assessment data as a tool for self-reflection.  As a student, summative assessments provide me with a form of external validation that I have in fact learned what I set out to learn.  As a teacher, the relation between assessment data and my own performance was always a little bit fuzzy but the process of looking back at that data and asking questions about what I could do differently was an essential part of my personal self-improvement.  I think it’s important to not put too much stock in any one assessment and instead use multiple data sources like observations and interviews to help triangulate areas for growth.  

The final stage in the learning process is to teach what you have learned to someone else.  I think we sometimes overlook this stage because it starts a new cycle of learning, but there are subtle differences between having a skill and being able to teach that skill to others.  Through attempting to teach math, I often found myself seeing old concepts in a new light.  My knowledge of geometry and data analysis grew deeper each time a student asked me “why?”.   Sometimes the most powerful phrase in the classroom is “I don’t know. How can we find out?”.  Likewise, I’m thankful that I had co-workers that were more knowledgeable about teaching than myself and capable of sharing that expertise.  I’m hopeful that I’ll be able to use what I’ve learned about teaching to help others as well.

I’m not necessarily looking for another “teaching job” but the act of teaching has become inseparable from how I learn.   Even if no one reads what I write, the act of putting my thoughts into words has power in it.  No matter where I go or what I do, I will learn new things and attempt to teach them to others.  We face a critical moment in society where we need to recognize the true value of the skills that teachers can bring to an organization. Every organization must learn to grow and teachers are experts on learning how to learn.