8 Cassandra

Intelligence transfer through habits

Since elementary school, I have gone to school with friends smarter than me. It has been a laborious battle to accumulate knowledge to impress them. At one point, on the weekends, I sat in the middle of the floor with a circle of open textbooks, shifting clockwise from one book to another, so that even as I came to be bored with one subject matter, I could maintain a constant rate of learning. I developed heuristics for studying such as minimize the amount of material you need to memorize in favor of deriving concepts from previously learned concepts and notice big ideas applicable to multiple domains. Whenever I discovered and adopted a new learning heuristic—it rapidly accelerated my rate of learning—much more so than picking up a one-off fact about something like the xylem in plants (the conduit for transporting water in plants, by the way).

As I moved onto even more difficult proving grounds, the people around me became even smarter and seemed to have better heuristics than me. One friend has amassed a network of dense and compact knowledge, in contrast to the superficial and thin veneer of knowledge that is easy to accumulate through rote memorization, by using analogies and even stretched analogies to link new material with previously acquired knowledge. Another friend is one of the best programmers in the world as ranked by international competitions—and from working with her on joint programming assignments, I noticed that she reflects continuously about the bigger picture of the problem (and change plans early if something better comes up) even in the midst of working on gritty implementation details.

From personal observation, some of their heuristics (let’s call them good learning habits, if they employ the heuristic over a long period of time) were major factors for how they were able to accumulate so much intelligence! As I got more involved in the field of intelligence augmentation, I asked my dear friend of ten years and involuntary subject of my one-way academic rivalry, if he had any habits responsible for making him smart. He offered this rule, always be thinking. He noted that even at MIT, there were instances he would hear people say, “I am so tired. I don’t want to think right now.” “No!” he says, “You always want to be thinking. Thinking is fun!” He said that he keeps a short list of problems on hand that he would think about for fun during all the little extra margins between events of his day. Apparently, my friend’s rule has been derived and employed by the great physicist, Richard Feynman, as well.

A recent heuristic that I have personally adopted and have found to accelerate the rate of learning or at least to give the illusion of control over it is, if you aren’t learning fast enough, learn another way. If what you are doing is not working, then you need to switch algorithms or twiddle the parameters. It makes no sense that if you are not achieving your desired output to wait, hope, and pray for variance and noise to push you over the edge. It is time to swap out your algorithm for another—any other—even if the potential algorithm does not appear to be better than the one you currently use. Your current algorithm has been empirically validated to not work, so science tells you that you should test another one. This heuristic has helped me a lot in grad school thus far, because whenever I get stuck because it does not afford me the opportunity to stay stuck. I always have the option of changing my routine (and as a computer science major, I like the idea of programming and optimizing my life.)

These word-of-mouth testimonials about heuristics begs the scientific question—can we measure the use of these heuristics in a controlled manner? Yes, we can!  Research in this area that I will name “intelligence transfer through habits” is not new and highly cross-disciplinary. Education researchers have for more than fifty years studied what they call “teaching thinking” in which they attempt to teach kids to think like a physicist, mathematician, or historian [1]. Another education researcher by the name of Costa has even drafted a list of sixteen good habits of mind [2]. Costa’s rules are more general but very much in the flavor of the heuristics that I presented earlier. Unfortunately, as noted by David Perkins of the Harvard Graduate School of Education [1], research in the area of teaching children how to think has fallen out of popularity with the emergence of rival camps of educational philosophy, in combination with the fact that short-term benefits of teaching students how to think are less observable than rote “back to the basics” repetition of the three R’s.

In another discipline entirely, behavioral biologists have greatly advanced the study of habits. They have noted that habits may account for much of unconscious behavior and that we only limited mental capacity to make conscious decisions  [3].  In addition, there has been work on how to form and break habits [4].

For particular domains such as computer science and mathematics, experts have worked to actively consolidate the heuristics that have worked for them and have used them to publish books such as The Pragmatic Programmer [5] for computer science, How To Solve It [6] for math, and Getting Things Done [7] for efficiency. None of these rule compilations have been systematically tested but they seem to work—again by testimonials from those I respect and their overwhelming sales volume . The act of learning from an expert offers hints as how to proceed for intelligence transfer via habits.

—————–

The end-goal of this research related to the problem of intelligence augmentation—how to amplify the natural born intelligence of humans—from the vantage point of interfaces for transferring “intelligence” from one person to another. One likely way to transfer “intelligence” is to transfer habits. Applications could be built upon wearable platforms like Glass or smartwatches to just-in-time prompt people to execute a particular habit at the appropriate situation.

For this class project, I hope to settle a few matters of scientific curiosity. While it is possible to ask people to adopt an arbitrary collection of habits, would we get a different result by asking people to adopt someone else’s habits? My hypothesis is “Yes!” due to nature of humans as social beings. In fact, I believe that asking people to adopt someone else’s habits would facilitate adoption and retention rates. The basis of this conjecture is evolutionary. Humans have evolved to mimic others in our social group through our body posture and facial muscles. We have higher cognitive functions that facilitate admiration and emulation of those we respect. In addition, there may an emotional or social component of using something originating from another human being. And lastly, there may be a factor of the tried-and-true success narrative that will boost results based on rationality or placebo effect.

I have designed two experiments to test this hypothesis. The first experiment attempts to measure if habits are more likely to stick coming from another person. I plan to:

• Give people super long list of vocab words to learn
• Measure the number of words learned (3 groups) and rule adoption rate / persistence (2 groups)
• Experimental groups:

–      Control: Ask them to maximize the number of words they learn that week

–      Ask them to follow some rule in order to maximize the objective

–      Tell them some rule is someone else’s habit: then ask them to follow some rule in order to maximize the objective

The second experiment aims to test if adopting someone’s habits change you as a person. I plan to:

• Ask people to self-evaluate their personality
• Help people to encode their daily habits
• Ask people to wear someone else’s habits for one week
• Ask them to re-evaluate their personality

References

1. Perkins, D.  40 Years of Teaching Thinking: Revolution, Evolution, and What Next? 
2. Costa, A. L., & Kallick, B. (2000). Describing 16 habits of mind.
3. Bargh, J. A., & Chartrand, T. L. (1999). The unbearable automaticity of being. American psychologist, 54(7), 462.
4. Duhigg, C. (2013). The Power of Habit: Why We Do what We Do, and how to Change. Century.

7 Cassandra

I was inspired by the Klopfer and Sheldon paper in which the authors transitioned students from playing a game about environmentalism to authoring their own environmentalism game. The act of having students develop their own game was valuable both for content creation specific to the students’ geographical location and as a learning process. I really admired this approach for its pragmatic value– fewer resources are required because students can be relied upon to generate content as part of the learning process!

This was the first time I have thought of user-based content generation from the context of learning, and I am excited about the scalability of the idea. In addition, to requiring fewer “expert” users and “teachers, I think that the idea may be powerful from a motivational perspective. Requiring users to generate content that will be used by other users, may motivate them to yield higher quality content. Beyond the environmentalism game example, I think the technique could be used more generally in the form of teaching. I frequently hear TAs or professors say that you really learn material when you have to teach it. What if we could apply the same concept to regular students? Imagine a student is learning a subject and given questions probing their understanding of the material in the traditional manner. Imagine that the student is told that with some X% probability, their answer will be given to another student learning the same material when the other student is stuck and, in keeping with the quiz show “Who Wants To Be a Millionaire”, phones a friend for help. In this scenario, the student may be motivated to learn the material more carefully for the sake of potentially helping a classmate down the road. The student may be more motivated in this group learning scenario than individualized learning. Humans are social beings after all, and user-based content generation during learning may be useful for more than content creation.

Assignment 5 – Cassandra

Structuring Information for Peer-to-peer Learning

(aka. swapping out your brain for someone else’s)

Models

Google’s mission statement is to organize the world’s information, but rendering knowledge in searchable form is only part of that problem. The other part of the problem is to provide structure to information. As Ashby framed in the end of his 1956 book [1], intelligence amplification is mainly a “selection” problem. We are simply presented with much too information to utilize. Some of this content is wrong or low quality. In contrast, high quality content is both factually correct and reusable in the sense that one high quality idea can be used to beget many new secondary ideas. This type of high quality content with a large impact is a model. In fact, the objective of the entire scientific discipline attempts to take vast amounts of downstream observations and organize the observations in such a way as to be explained by or inferred from a minimal number of upstream models. The quality of a model is judged by both its ability to explain current observations as well as to make predictions for the future. The structure provided by these upstream higher order models (and not the downstream observations) comprise intelligence.

Some of Google’s information is indeed about these models, but it represents information about models equally and in the same fashion as other information. It may be of interest to instead treat models as a separate and more powerful class of knowledge when indexing world knowledge.

Life Models

There are many classes of models. Scientific models are the subject of academic research. There are also implicit life models that each individual assumes. These individual models can arise from (1) conscious deliberation, or they might simply be (2) implicit habits picked out through life. Indeed, a considerable part of variance in cognitive intelligence seems to arise from a person’s deliberate selection of a world model and their daily habit patterns. These two types of models, conscious models and habitual defaults, may serve as vessels for transferring intelligence models from person to person.

As a user scenario, imagine an application for a wearable like Glass that lets you “wear someone’s habits”. If you find a daily routine that works well for you, you can share it as an effective habit schedule for other people to try. Alternatively, you can try out the habit schedules of others by downloading them and having Glass prompt you with just-in-time situational alerts for executing a particular habit. This device would allow you to be more productive by emulating people you respect. Or it might let you “live the life of someone else” as an interesting costume change. Or it might serve as an therapeutic mechanism for breaking out of daily habit loops.

This type of device also has interesting implications for collective intelligence. By crowd-sourcing questions to such as “what are the most effective patterns for daily life?” and “which belief frameworks are powerful?”, we can curate a haul of answers from the world’s population. Each person on the planet has either explicitly or implicitly reached a personal answer to these questions. Our only task is to render their answers into forms easily adoptable by other persons.

The task can be phrased in a variety of ways. How can we share our individual brain structure with others? How can we share our world models with others? How can we share our patterns with others? Two different approaches are proposed in the next section.

Implementation

a) The Logical Approach

Perhaps the most obvious approach is to condense our individual philosophies into their purest most form and explicitly express them. In this case, the technical challenge is to design simple descriptive languages [__] for models of this type. Interested parties may wish to swap out their current model for a new one, consciously and deliberately reprogramming themselves.

Such a descriptive language for specifying world models will likely require both what and why components. The what-component can be modeled after rule-based logic systems [__] of the style “WHEN [EVENT A], DO [THING X]” The why-component that converts rules into reasons can be used for building hierarchical trees that can be further used converting reasons to higher-order value systems.

In a continuation of the habit-based example above, some may want to adopt the morning routine of Mr. Rogers, the beloved actor from the children’s show Mister Rogers’ Neighborhood. Mr. Rogers’ morning routine [4] can be summarized as “waking up at 5 a.m.; praying for a few hours for all of his friends and family; studying; writing, making calls and reaching out to every fan who took the time to write him; going for a morning swim; getting on a scale; then really starting his day”. The first few lines of a Mr. Rogers daily program could be written as:

(what) WHEN 5AM, DO wake up. % (why) “Because time is precious”

(what) WHEN DONE, DO pray for friends and family % (why) “Because I believe in God” AND “I love my friends and family”

Interchanging models for those of others will causes us to swap out our routinized defaults for a new set of defaults and will likely result in a cascade of effects. It may be particularly advantageous to replicate models of the people that you admire, for instance, an established researcher in your discipline. It may be fun to adopt the models of one of our ancestors, for instance, to live a day as your grandmother would and capture her behavioral and life lessons. It may be a literal way of experiencing “life in someone else’s shoes”, and feel more connected to either strangers or people that you know and care about.

b) The Chaos Approach

The alternative approach is to give up on rationalizing behavior and instead attempt to infer method from madness. Consider the following design of a file system designed for brainstorming. First note, that it is possible to infer the content of someone’s brain from her computer file system, particularly when the computer is used for storing and organizing ideas (as in the case of a researcher). It is possible to organize the file system in such a way that it can be used for brainstorming if specific structural rules are followed. My personal file system is organized in such a way to facilitate brainstorming by having two searchable folder ‘mentors’ and ‘me’. The ‘mentors’ folder contains plain text versions of the most inspiring research papers. The ‘me’ folder contains subfolders with plain text ideas of my own and plain text notes inspired by the content of presentations and meetings.

The logic of this organization is inspired by two research papers with powerful ideas: “What Would They Think” [2] and “Remembrance Agent” [3]. First, the idea of the Remembrance Agent to look at what you are reading and writing and propose past content based on related keywords. Second, the idea of What Would They Think is to show the affective reactions of respected mentors when reading new content. We can combine the two ideas to address Ashby’s “selection problem” identified in the first section of this paper– that a large component of intelligence involves the selection of the most useful information out of the large pool available. The proposed file system for brainstorming searches keywords against the ‘mentors’ and ‘me’ directories to bring up relevant material as well as near-miss material that are less relevant but serve to bring in tangential ideas.

The important idea here is not my file system itself but that if multiple users organize their file systems in the manner described, the file system becomes an external representation of an individual’s mind. File systems then become interchangeable in such a way that I can brainstorm with someone else’s personal ideas and mentor preferences. The file system brainstorm serves as an information dump of someone’s mind that has not been distilled into logical rules but instead persisted as the chaotic pile of quirks and nuances that embody ourselves.

Proposed Artifacts

To recap, the artifacts proposed within this paper were:

1. Wear someone else’s habits wearable for REPROGRAMMING SELF WITH SOMEONE ELSE’S HABITS
2. Remembrance Agent + What Would They Think file system for BRAINSTORMING WITH SOMEONE ELSE’S MIND

Conclusion

Both proposed systems propose the augmentation of intelligence facilitate the spread of powerful habits and ideas, giving us the framework to swap out our minds for the more preferred and intelligent minds of others. The proposal is only for systems to provide additional structure and transparency to knowledge that the world already has. Control remains firmly in the hands of each individual who has suddenly been endowed with the capability of self-intelligence augmentation.

References

1. Ashby, W. R. (1956). An introduction to cybernetics. Taylor & Francis.
2. Liu, H., & Maes, P. (2004, January). What would they think?: a computational model of attitudes. In Proceedings of the 9th international conference on Intelligent user interfaces (pp. 38-45).
3. Rhodes, B., & Starner, T. (1996, April). Remembrance Agent: A continuously running automated information retrieval system. In The Proceedings of The First International Conference on The Practical Application Of Intelligent Agents and Multi Agent Technology (pp. 487-495).
4. Hattikudur , Mangesh. 15 reasons Mr. Rogers was the best neighbor ever. http://edition.cnn.com/2008/LIVING/wayoflife/07/28/mf.mrrogers.neighbor/index.html

 [Slides from class]