Why cognitive enhancement technologies do not need new regulation

After digging through old files from my PhD, I ran into a copy of a class presentation I worked on with Sarah Macleod and Mohammad Habibnezhad on cognitive enhancement technologies. Examples of cognitive enhancement technologies could be cognitive enhancing drugs such as Ritalin, or even whatever the heck Elon Musk is working on at Neuralink; what they have in common is that they make us better at thinking or learning. One of the potential future applications of my thesis work is the development of computer-based education that adapts to users attention. Such technologies may one day deliver education radically better than either our current MOOCs or even in-person lectures, and is therefore an example of a cognitive enhancement technology.

The presentation that we gave concerned whether cognitive enhancement technologies should be strictly regulated. We argued that cognitive enhancement technologies do not require additional regulation. Our argument was best summarized by the following chart:

Cognitive enhancement technologies can seem scary, but there already already existing frameworks to understand them. In Canada, the Food and Drugs Act governs all food, drugs, or medical devices, including any devices that modify or correct the body structure of humans. Both cognitive enhancing drugs and devices which modify the functioning of the human brain (as in Musk’s case) would therefore be governed by the Food and Drugs Act. This regulatory regime is fundamentally designed to ensure the safety of such technologies, or failing that offer sufficiently great rewards for the expected risks. If such high risk technologies are unable to offer great benefits to their users, they are likely to be banned.

I find the lower half of the matrix more interesting. When technologies are low risk to humans, we can envision them as either goods or rights. For example, there is pretty good evidence that coffee is a cognitive enhancer. There is some evidence that caffeine provides benefits to learning and memory, and even better evidence that it improves reaction time. However, the benefits of caffeine are slight. In market economies, we normally think of these sorts of things as ‘goods,’ insofar as they satisfy a consumer’s want. The main benefit of coffee is that it satisfies my desire for coffee; potential cognitive enhancement is secondary.

Sometimes however, goods can be  low risk and offer high advantages, so much so that not having them will disadvantage a person’s capabilities to be a functioning member of society. For example, access to primary and secondary education is categorically different from access to coffee. People who do not have access to primary and secondary education are severely limited in their ability to take part in society. Children who have access to education can learn skills required to participate in the economy or polity, or potentially choose to pursue tertiary education of their choosing. Those who do not have access are severely limited in their capabilities and agency and will never be able to choose how to contribute to society. This idea is better summarized by Amartya Sen and Martha Naussbaum, and is often called the capability approach to rights.

I believe that low risk, high benefit cognitive enhancement technologies may fall into this category of ‘rights’ if they truly offer large political or economic advantages to their consumers. If we imagine a radically better way to teach students using learning technology, students who used such hypothetical technology would have significantly greater capabilities in society. They could potentially learn in a fraction of a time, becoming much more productive and potentially much more competitive. People who do not have access to this hypothetical technology would likewise be severely limited in their capabilities. We would therefore consider universal access to such cognitive enhancing technologies, at least if they become adopted at a large scale. These days, there are even business models that incentivize both innovation and free access to such high benefit technologies. Such business models might serve as a starting point for such technologies as they eventually make their way to open access.

Long story short, this is why I believe that cognitive enhancing technologies do not need additional regulation. If they are high risk technologies, we have existing regulation that covers them. If they are low risk, they are either goods or rights depending on their benefits; we have exiting methods of distributing these. Black Mirror will have to wait on this one.

Reflections about PhDs on the eve of a defence

Despite my noble intentions with respect to this blog, I have been largely unsuccessful at adding content. There are many reasons for this, such as the aggressive teaching and publishing deadlines from last semester. However, a big part of the reason for this is my looming thesis defence, due to happen tomorrow at 10:00 am. I thought it would be fitting to reflect a bit on what it means to do a PhD and what the last four years have entailed, in hindsight.

Having done more than my fair share of university degrees, I can attest to how the PhD is quite different from the others. There are clear financial reasons for pursuing Masters or professional programs. However, in many disciplines PhDs often come with few financial rewards. According to the US Census Bureau, in many fields with robust professional programs (i.e. Business or Law), median earnings among PhD holders are lower than their professional counterparts (i.e. MBA, JD). When one considers that the opportunity cost of doing a PhD 3 to 7 years of productive labour, it becomes clear that the motivation for doing a PhD is often not financial.  Realistically, the PhD only equips students to do one thing: make a substantial contribution of research to the academic community in the discipline students have decided to pursue. Though some PhDs also require students to gain teaching experience, this is not mandatory in all PhD programs.  When it is mandatory, students could expect to spend hundreds of hours teaching a course or working as a teaching assistant. This is small compared to the thousands of hours spent cultivating research.

The best analogy of a PhD that I have yet encountered was written by Tad Waddington in Lasting Contribution. His quote reads:

The last step of the [education] process is to contribute to knowledge, which is unlike the previous steps. Elementary school is like learning to ride a tricycle. High school is like learning to ride a bicycle. College is like learning to drive a car. A master’s degree is like learning to drive a race car. Students often think that the next step is more of the same, like learning to fly an airplane. On the contrary, the Ph.D. is like learning to design a new car. Instead of taking in more knowledge, you have to create knowledge. You have to discover (and then share with others) something that nobody has ever known before.

When I first read this quote three years ago, it stuck with me. I had the good fortune of having pursued two master’s degrees before starting my PhD and had originally thought that the PhD would be like a more advanced version of the previous two. Looking back, I don’t believe that was the case, and agree with Waddington more than ever. If you are considering ever doing a PhD, I recommend that you should be the sort of person who enjoys spending a ridiculous amount of time and energy to make a difficult, small, yet very real contribution to human knowledge.

It’s been a pleasure to have the opportunity to pursue and cultivate interdisciplinary research which I feel truly does break down barriers between disciplines. It has been challenging and at times grueling, but also rewarding, and I believe I have come out a better person. I would like to thank everyone for supporting me through the journey. I wouldn’t do it any differently if I could do it all again.

An Interactive Demonstration of iframe

One of the courses I teach is called Information Management Systems, in Dalhousie’s Master of Library and Information Studies program. As part of this week’s lab, we focused on a Microsoft 365 Cloud Applications to explore cloud applications and web services. Cloud applications are one of the many technologies that make digital workplaces possible and we will explore two new Microsoft applications that are designed to change work processes. The first is Sway, an application designed to allow users to make high quality, speedy and interactive presentation content which is shared broadly on the internet. This post demonstrates how Sway works by sharing the presentation through an iframe, one of the HTML5 features that supports interactive media content. Feel free to explore the sample presentation below. Note: none of this content is mine, it is all curated by the Nova Scotia Archive and is borrowed strictly for demonstration and learning purposes. You can find the original source of the content here.

Why MOOCs are bad and what we can do about it

Perna, L., Ruby, A., Boruch, R., Wang, N., Scull, J., Evans, C., & Ahmad, S. (2013, December). The life cycle of a million MOOC users. In MOOC Research Initiative Conference (pp. 5-6).

In 2011, Sebastian Thurn and David Evans had the bright idea of recording their Stanford University lectures and posting them on the internet. The initiative was incredibly successful and hundreds of thousands of students flocked over the broadband highways to learn about artificial intelligence from two of the greatest minds in the field. In fact, their original initiative was so successful that Thurn later left his job to fund Udacity, today one of the most innovative and disruptive forces in university education. By 2012, it seemed that the whole world was talking about Massive Open Online Courses (“MOOCs”) and that MOOCs were on the path to transforming how teaching was done forever by providing the highest quality education to everyone, everywhere, for free.

This story has a deeply personal element for me. Then an unemployed (or at times underemployed) philosophy grad, I had to make a decision about whether to go back to school to pursue yet another graduate degree, this time in computer science. I sometimes wonder what my life would have been like if I had decided to take a year off and gorge myself on an intellectual mash of Stanford videos on machine learning. I usually conclude that it would have been for the worse. I am very thankful that I ended up going back to school because I probably learned a lot more than I would have otherwise. In late 2013 and early 2014, a number of quantitative studies were published that were like a wet blanket over silicon valley’s burning fire for MOOCs. Researchers at Penn, for instance, found that as few as 5% of MOOC registrants actually finish their courses, while only a fraction of those attain high grades. What’s worse is that MOOC users were found to disproportionately come from educated, male and wealthy backgrounds, largely in the USA. So much, then, for the fad that was the MOOC revolution. Or so the story goes.

Why do MOOCs suck so much at teaching the people that they are trying to help? One of the many reasons is that they are not well-designed. Robert Ubell from NYU has been doing e-learning a long time and thinks that MOOCs suck because they were not designed to keep users engaged, like a good teacher would. Ubell points to active learning, a theory that getting students deeply involved in the learning process will produce better outcomes. For example, active learning holds that asking students questions during a lecture would produce better results because students are more deeply engaged in the process. By involving students, we can better keep their attention, which is one of the fundamental brain mechanisms governing learning. MOOCs suck at knowing when you are paying attention. Good teachers know this by the glazed look in their students eyes as their attention drifts into the mental netherworld between the classroom and PewDiePie’s latest embarrassment.

If we had a good way to measure attention, we would have a way of improving MOOCs. The problem is that scientists do not yet have reliable ways of measuring attention through a computer. Sure we can look at clicks or scrolls, but do clicks really tell you much when you are rewarded by faking it? Alternatively, we could ask you whether you are paying attention, but this will disrupt the course experience. This is why I am looking at brain data. If we watch people’s brains, we can reliably understand when they stop paying attention, and maybe build MOOCs that teach better. It’s a bold idea, but if it works, we could develop technologies that achieve this original vision: quality education for everyone, everywhere, for free.

Getting started with WordPress

Some people think that inaugural posts are really important. I have wanted to start a blog for many years, but have always been hesitant. I think it  is because at the best of times I worry about producing high quality work and for a long time suffered with crippling writers block. I think that a lot of graduate students can relate. Scholars are notorious for overthinking things.

Over time, I have learned to manage writers block by designing an environment that forces me to do things. In the case of this blog I committed to preparing “how-to” materials for the Dalhousie University Public Scholars Program. The final result of this work is in a series of  videos on getting started with WordPress, which feature this website. This series is designed to be specific to some of the unique needs of scholars who want to start writing their own blog.