PixelProf

I appreciate the comment, and it's a point I'll be making this year in my courses. More than ever, students have been struggling to motivate themselves to do the work. The world's on fire and it's hard to intrinsically motivate to do hard things for the sake of learning, I get it. Get a degree to get a job to survive, learning is secondary. But this survival mindset means that the easiest way is the best way, and it's going to crumble long-term.

It's like jumping into an MMORPG and using a bot to play the whole game. Sure you have a cap level character, but you have no idea how to play, how to build a character, and you don't get any of the references anyone else is making.

This is a very output-driven perspective. Another comment put it well, but essentially when we set up our curriculum we aren't just trying to get you to produce the one or two assignments that the AI could generate - we want you to go through the motions and internalize secondary skills. We've set up a four year curriculum for you, and the kinds of skills you need to practice evolve over that curriculum.

This is exactly the perspective I'm trying to get at work my comment - if you go to school to get a certification to get a job and don't care at all about the learning, of course it's nonsense to "waste your time" on an assignment that ChatGPT can generate for you. But if you're there to learn and develop a mastery, the additional skills you would have picked up by doing the hard thing - and maybe having a Chat AI support you in a productive way - is really where the learning is.

If 5 year olds can generate a university level essay on the implications of thermodynamics on quantum processing using AI, that's fun, but does the 5 year old even know if that's a coherent thesis? Does it imply anything about their understanding of these fields? Are they able to connect this information to other places?

Learning is an intrinsic task that's been turned into a commodity. Get a degree to show you can generate that thing your future boss wants you to generate. Knowing and understanding is secondary. This is the fear of generative AI - further losing sight that we learn though friction and the final output isn't everything. Note that this is coming from a professor that wants to mostly do away with grades, but recognizes larger systemic changes need to happen.

100%, and this is really my main point. Because it should be hard and tedious, a student who doesn't really want to learn - or doesn't have trust in their education - will bypass those tedious bits with the AI rather than going through those tedious, auxiliary skills that you're expected to pick up, and use the AI was a personal tutor - not a replacement for those skills.

So often students are concerned about getting a final grade, a final result, and think that was the point, thus, "If ChatGPT can just give me the answer what was the point", but no, there were a bunch of skills along the way that are part of the scaffolding and you've bypassed them through improper use of available tools. For example, in some of our programming classes we intentionally make you use worse tools early to provide a fundamental understanding of the evolution of the language ergonomics or to understand the underlying processes that power the more advanced, but easier to use, concepts. It helps you generalize later, so that you don't just learn how to solve this problem in this programming language, but you learn how to solve the problem in a messy way that translates to many languages before you learn the powerful tools of this language. As a student, you may get upset you're using something tedious or out of date, but as a mentor I know it's a beneficial step in your learning career.

Maybe it would help to teach students about learning early, and how learning works.

Yeah, I knew freelance folks who provided long term support with such complicated setups. The base daily rate plus hourly with a monthly retainer and weekly on call fees. Wild.

Or, hourly = extremely high paid contract work.

Education has a fundamental incentive problem. I want to embrace AI in my classroom. I've been studying ways of using AI for personalized education since I was in grade school. I wanted personalized education, the ability to learn off of any tangent I wanted, to have tools to help me discover what I don't know so I could go learn it.

The problem is, I'm the minority. Many of my students don't want to be there. They want a job in the field, but don't want to do the work. Your required course isn't important to them, because they aren't instructional designers who recognize that this mandatory tangent is scaffolding the next four years of their degree. They have a scholarship, and can't afford to fail your assignment to get feedback. They have too many courses, and have to budget which courses to ignore. The university holds a duty to validate that those passing the courses met a level of standards and can reproduce their knowledge outside of a classroom environment. They have a strict timeline - every year they don't certify their knowledge to satisfaction is a year of tuition and random other fees to pay.

If students were going to university to learn, or going to highschool to learn, instead of being forced there by societal pressures - if they were allowed to learn at their own pace without fear of financial ruin - if they were allowed to explore the topics they love instead of the topics that are financially sound - then there would be no issue with any of these tools. But the truth is much bleaker.

Great students are using these tools in astounding ways to learn, to grow, to explore. Other students - not bad necessarily, but ones with pressures that make education motivated purely by extrinsic factors than intrinsic - have a perfect crutch available to accidentally bypass the necessary steps of learning. Because learning can be hard, and tedious, and expensive, and if you don't love it, you'll take the path of least resistance.

In game design, we talk about not giving the player the tools to optimize their fun away. I love the new wave of AI, I've been waiting for this level of natural language processing and generation capability for a very long time, but these are the tools for students to optimize the learning away. We need to reframe learning and education. We need to bring learning front and center instead of certification. Employers need to recognize this, universities need to recognize this, highschools and students and parents need to recognize this.

This should be interesting to play with. Does anyone know of any Copilot-like VS Code extensions that provide similar UX but hook into a custom local/remote server? I would love to write my own pipeline and context builder for it, but I haven't written a VS Code extension before and a starting point would be good.

I haven't worked with any of the current code-based open models so I don't really know how it compares, excited to hear thoughts from others on this.

When I teach story points (not in an official Agile Scrum capacity, just as part of a larger course) I emphasize that the points are for conversation and consensus more than actual estimates.

Saying this story is bigger than that one, and why, and seeing people in something like planning poker give drastically differing estimates is a great way to signal that people don't really get the story or some major area wasn't considered. It's a great discussion tool. Then it also gives a really rough ballpark to help the PO reprioritize the next two sprints before planning, but I don't think they should ever be taken too seriously (or else you probably wasted a ton of time trying to be accurate on something you're not going to be accurate on).

Students usually start by using task-hours as their metric, and naturally get pretty granular with tasks. This is for smaller projects - in larger ones, amortizing to just number of tasks is effectively the same as long as it's not chewing away way more time in planning.

You check the clock. You check again, because you didn't actually read the time because you were too absorbed in the process of checking the clock that you forgot to check the clock.

You check the clock again. You have a new email. You consider checking the clock again, but give up and accept your fate because checking the clock a (second? Third? Tenth? First?) time is just too much right now, you're already running late anyways so it was kind of all procrastinating in the first place. You don't even know what you were supposed to be checking it for. Just wait and see, it's probably not that important. Maybe you'll check the clock and see if it sparks your memory.

You check the clock. You finally see the time. The bus drives past you.

The ways we cope and overcome, and the ways we don't.

Hmm... Nothing off the top of my head right now. I checked out the Wikipedia page for Deep Learning and it's not bad, but quite a bit of technical info and jumping around the timeline, though it does go all the way back to the 1920's with it's history as jumping off points. Most of what I know came from grad school and having researched creative AI around 2015-2019, and being a bit obsessed with it growing up before and during my undergrad.

If I were to pitch some key notes, the page details lots of the cool networks that dominated in the 60's-2000's, but it's worth noting that there were lots of competing models besides neural nets at the time. Then 2011, two things happened at right about the same time: The ReLU (a simple way to help preserve data through many layers, increasing complexity) which, while established in the 60's, only swept everything for deep learning in 2011, and majorly, Nvidia's cheap graphics cards with parallel processing and CUDA that were found to majorly boost efficiency of running networks.

I found a few links with some cool perspectives: Nvidia post with some technical details

Solid and simplified timeline with lots of great details

It does exclude a few of the big popular culture events, like Watson on Jeopardy in 2011. To me it's fascinating because Watson's architecture was an absolute mess by today's standards, over 100 different algorithms working in conjunction, mixing tons of techniques together to get a pretty specifically tuned question and answer machine. It took 2880 CPU cores to run, and it could win about 70% of the time at Jeopardy. Compare that to today's GPT, which while ChatGPT requires way more massive amounts of processing power to run, have an otherwise elegant structure and I can run awfully competent ones on a $400 graphics card. I was actually in a gap year waiting to go to my undergrad to study AI and robotics during the Watson craze, so seeing it and then seeing the 2012 big bang was wild.

Yeah I probably should have added the /s to that one.