Should Kids Still Learn to Code?  ## Metadata - Author: [[Changelog Master Feed]] - Full Title: Should Kids Still Learn to Code? - Category: #podcasts - URL: https://share.snipd.com/episode/19d0adac-bb03-403f-86ff-efdb7eaffb37 ## Document Notes ## Highlights - **AI Changing Kids Coding Education** - Jensen Huang suggests kids shouldn't learn to code anymore as AI will do it well. - This reflects a shift but adoption will be gradual and nuanced, not immediate. Transcript: Speaker 1 I don't know if it was actually at the event or in another venue, but making some comments about kids learning to code. And his comment was related to, I forget how he phrased it, but basically the just being kids shouldn't learn to code anymore because AI is going to do that fairly well. So I don't know. I don't have kids, Chris, but what is your thought as a parent? Speaker 2 As the designated father of this. Yes, exactly. On this podcast. Well, I think he's right in the long run. I think he mentioned that in the keynote. It was a little section where he covered that. He said, always in the past, we've taught our kids they need to code in recent years. And he said, now AI is changing all that. And I don't think that's news to anybody in the larger sense. We use AI for coding and technical controls and all sorts of stuff all the time. And that's just built into our ecosystem of the last few years and is increasing constantly. I have to admit that he was kind of like, we're at the moment, and I'm not quoting him, but he was kind of like, this is the moment. We're not teaching him anymore. And I'm kind of like, maybe, maybe because I know that even with us as adopters of AI technology, for coding all the time, if there's anything that we do, it has to do with that. And there's all sorts of complexities and bumps in the road and things like that. So in the large is a moving that way. Of course. I think everybody would agree with that. But I don't think that we've all just arrived there because Jensen just said it in a keynote personally. ([Time 0:02:24](https://share.snipd.com/snip/6ad53de9-ba4f-4c80-b7f8-5c2124a9d26a)) - **Encourage Dynamic Tech Learning** - Encourage kids going to college to pursue computer science with adaptability. - Emphasize dynamic learning as AI will continuously shift technology boundaries. Transcript: Speaker 1 I guess one way to rephrase the question would be at this moment, were you to have a child going into college, let's say, would you encourage them to pursue software engineering or computer Science or this sort of thing? Speaker 2 I would. I think that in the future, and actually I've spent a lot of time thinking about this topic in general, hundreds and hundreds of hours. And this is one of those things where I think we're accelerating into the future. And I think with AI capabilities year by year, making massive changes to how we live and work, humans are going to have to be fairly dynamic and how they do things. And one of those skills continues to be various technology, you know, orientations. And I don't think that's going away anytime soon, though AI will continue to change where those boundaries are. So in the spirit of we're always going to have to learn new things anyway, I don't see any problem in diving into technology encoding today with the recognition that the technology will Constantly change underfoot and you're going to have to change with it. So I, I don't think I would recommend and actually I do for Georgia State University, I'm one of their advisors in industry for their computer science school. And I would not dissuade any of those students from pursuing a computer science curriculum at this time. ([Time 0:04:07](https://share.snipd.com/snip/f0ff52a2-cc70-44b7-9820-c3e0b84d9142)) - **Shifting Data Scientist Roles** - Data scientist roles are shifting closer to software, hollowing out the middle ground. - New AI engineering roles focus on prompt narrative and reasoning chains alongside software infrastructure. Transcript: Speaker 1 Yeah, I've actually been thinking about this sort of dynamic a little bit after we've had a couple of guests on the show that are working on these systems like prompt layer and other ones Where they're managing prompts and reasoning workflows at the intersection of domain experts and software engineers. So the technical side kind of where software meets domain expertise. I was on a panel at Purdue University speaking of students and that sort of thing. And one of the questions was around this, you know, well, hey, I went into this program thinking maybe I would try to become a data scientist. Is that still a thing or should I be thinking about something else? I think one encouraging thing for people is if you're a data person, no one really has more than a year of experience architecting and building these type of generative AI systems. So in a sense, you could do one really compelling project and be ahead of most of the people trying to get these jobs. Right. So in one respect, that's really encouraging. I think it is shifting though what a data scientist means in this coming from a data scientist that has been a data scientist for 10 plus years. I think there is this kind of hollowing out of the middle where you had the three things where on one side, you had software engineering and infrastructure and that was the land of sort Of software engineers and DevOps and all that. And on the other side, you had domain experts and in the middle, you had data scientists who kind of translated the domain expertise into predictive models and machine learning and Such. The thing got handed off on the other side to integrate into software. And I think what you see is kind of this hollowing out of that middle where domain experts are getting much closer to the software side. And so I think there's kind of two maybe takeaways from that from my perspective. Either you could go into the AI engineering side, which is maybe less hardcore infrastructure, low level programming and more almost narrative writing of prompts and creating of These reasoning chains and all of that sort of thing and become amazingly good at that and rely on good software and infrastructure people on the other side. Or there's still going to be a need because everything is still software. There's still going to be a really heavy need for people that can make your chains of reasoning and hosted models and software deployments actually go well. ([Time 0:07:23](https://share.snipd.com/snip/bf9d2d94-8688-49cf-ae25-5cf3311e508f)) - **Engage in AI Project Communities** - Start learning AI by engaging in project-specific communities like Hugging Face and Langchain. - Join their Discord or Slack to ask questions and collaborate with others building AI projects. Transcript: Speaker 1 We've produced a lot of content but we've also engaged in various spheres over the years and there might be a lot of new people let's say their web developers or their back end engineers Or whatever and they're getting into the space, they're doing projects and they're kind of normal programming conference isn't or maybe has some AI topics but they're wondering is There a better place to find people that are doing these sorts of projects and that sort of thing. I know you were at one point involved in kind of the meet up space although COVID maybe had a little bit to do with the downgrade of some of those communities. Speaker 2 Yeah, it's a great point and it's evolved in interesting ways kind of what you're getting at there and to start with the last thing that you said I was for a number of years I ran the Atlanta Deep learning meet up and the phrase deep learning is kind of antiquated now as well in the learning and it kind of fell off when COVID hit but we were really the preeminent kind of AI oriented Community in the Atlanta area which is where I'm at. It's interesting as another counterpoint to go into this you and I met in a different community we met in the go language community because we were both go programmers and we were kind Of the two people thinking a lot about AI and data science in that community so it was a natural thing for us to gravitate together but interestingly if you're really focused on different Aspects of AI whether it be generative AI or other fields of AI since we've recently pointed out that not all things are just generative AI even though that's the hot thing right now. There are many vendor specific communities we have a podcast specific community here where we engage our listeners all the time and there are some platform specific communities but There is kind of a like where we met in the go community there was an overall whether whatever you were doing in the go space there was a larger community and you kind of knew all the people And all the names that were there and would follow that and be participant in it. Here we don't really have that we have many many fragmented AI communities and we'll go to hugging face for us you know to get open source models and to see what's going on there and you Know there are lots of these smaller communities but I would imagine if you're coming into the space today and you're one of those people who really want to dive into AI here in 2024 it Must be very hard to figure out what space you should be in to make all the connections and to ramp up. Any thoughts on that what would you recommend to somebody Daniel if somebody were to come into the space today? Speaker 1 Yeah it is a bit of a challenge because it is a bit fragmented and maybe we could split this up into a couple of kinds of engagement maybe one from a more technical side and one from a less Technical side. So in terms of architecting and building generative AI apps or other kind of AI apps or fine tuning models and that sort of thing I think what I would generally recommend is starting out With some sort of learning resource that is probably going to be on hugging face Langchain, LAMA index, Lance DB, one of the other vector database providers. These sorts of projects have really good tutorials and guides associated with them. Starting out more projects related like those are you know trusted projects in the AI space and trusted platforms in the AI space and then looking at one of those projects like let's Say you go and you find a guide that is you know setting up multi-modal RAG system to search over videos or images with LAMA index or something like that right. Well a lot of these projects not all of them but a lot of them have some type of forum or chat kind of interface that the community around those projects gathers in so oftentimes it's either Discord or Slack or a forum type of thing. So I think if you start kind of in those spaces like a LAMA index or a Lance DB and look at a guide that is something similar to what you want to build you try going through the guide but you Also look to see if those projects have a discord associated with them or a Slack channel associated with them and go ahead and log into those and you know it's okay to lurk for a while but As you're going through your example and you know you don't understand this or you're getting that error just go ahead and be brave and put something in those spaces. I've generally found them to be fairly welcoming. For example if you go to LAMA index there's a community page and you'll see right away join discord. There's many other of these spaces so like our friends over at the Layton Space podcast have a very very active discord server that they're running. ([Time 0:14:12](https://share.snipd.com/snip/94be599a-3084-4041-9461-3ed719c0e7b4)) - **Grow AI Networks Socially** - Build AI community friendships by attending in-person or virtual AI events. - Follow podcast guests and trusted AI professionals on social media to expand your network. Transcript: Speaker 1 It might depend on people's personality and what opportunities kind of present themselves to those people. There are a good number of events that are gradually happening like our friends over at the MLOps community have had a series of online virtual events I know there was a AI engineering Event out on the west coast there are events like hugging face I think is doing some type of hugging face tour with demos at various locations in person so that's a really great place to Kind of meet face to face with people and interact and build relationships in terms of personalities and that sort of thing one thing you could do is look at our previous episodes of this Podcast and go and even if you don't look listen to all the episodes which of course you should because they're all great or maybe some are better than others but they're they're all pretty Good I think but but you could look at the guests from the previous podcast and go to LinkedIn go to Twitter go to blue sky whatever your favorite social is and see if you can find some of Those people on those platforms and those are trusted people that you know we've met over the years and so in an online sense you could start following them and see who they are kind of Reposting and interacting with online and that's kind of ([Time 0:21:04](https://share.snipd.com/snip/2435ef11-4138-4bcd-98f8-bff5b8411956)) - **AI Accelerates Non-Tech Workflows** - Chris Benson used AI on a live call to generate presentation talking points and VBA code for PowerPoint. - This cut about 5-8 hours of work but required manual finishing, showing AI boosts productivity for non-technical users. Transcript: Speaker 1 People within an organization and then leading by example and kind of in a one-on-one sort of way rather than a top-down directive of we shall now do things this way right understood Speaker 2 I actually have an example to illustrate one of the things that you were talking about there in my own employment we have both access to chat gbt and we also host internal models internal Open source models which we love because that way you don't have to worry about the if you're sending proprietary information out things like that and so this morning in my day job was Working with a team of people there's a presentation that has to come out of that a PowerPoint presentation that has to come out as one of the deliverables of that and as we were having A group discussion sharing screen was able to type in some of the things that we wanted to talk about into the model generated dynamically while we were on a group call I generated a set Of talking points to the various issues that we were addressing basically kind of a presentation within the prompts if you will was then able to turn that presentation into VBA code Visual basic for applications code where it embedded the content in that VBA code then I was able to open up PowerPoint right there copy and paste out of the prompt this is totally non-technical What we're talking about here go to the tools tab down to macros and open the visual basic editor in PowerPoint which is available to everybody paste it in the code as a new module and ran It and it produced our PowerPoint for our team right there the whole thing took five minutes to get a 30 page PowerPoint set up now there was a lot of manual tweaking to be done afterwards And adding some graphics and stuff like that but we probably cut you know five eight hours worth of work out of our workflow by tossing the critical ideas into the prompt turning them Into that code and copying and pasting it doesn't take a developer to do that that anybody could do that so that's one of many possible use cases where you're you're using it you haven't Replaced any of the workers but you're accelerating everybody's productivity dramatically at that point and saving a lot of time to do it so as I've been thinking about how we how we Get more people in the world to use these technologies to their benefit I think having a number of different kind of typical persona you know use cases like that that many people might Need so there you know there's your PowerPoint strategy folks is you know right now and whatever job you're in you can do that if you have access to one of these larger models and then the Other thing I wanted to dive into is it was it's interesting the emotional quirks people are worried about everything from will this take my job if I start using it and make me irrelevant They wonder who's watching when I do this can my boss see if I stumble if I'm struggling with something who in my company is aware of what I'm doing so there's a kind of a lot of a fud fear uncertainty Doubt associated with the use of the tools and I think part of that is just kind of in these trainings that you are alluding to earlier to be able to to have discussions with people about Their fears and see if you can get some interest in uptake by going right at the thing that's holding them back and a lot of times people think it's technical it's not the PowerPoint thing You can have zero technical training and go do that if you just know to open up that single thing in the PowerPoint deck and type in the and not type in but copy and paste ([Time 0:28:14](https://share.snipd.com/snip/092e5fd1-bd87-49fb-b8ae-f252b004da3b)) - **Ease AI Adoption With Safety** - Introduce AI tools where employees feel safe to reduce fear and uncertainty. - Start with use cases that don't involve sensitive data to let users build intuition about AI behavior. Transcript: Speaker 2 The code that was produced at the prompt so I think that there are hundreds or thousands of opportunities along this line that people could take advantage of any thoughts on what you Speaker 1 Might do in that way it's interesting that you bring up the fear and uncertainty piece because there are a lot of misconceptions and it doesn't often work to just straight up invalidate Those so somehow I think if you're thinking about this adoption in your organization and you're working with people to some degree you kind of have to find an entry point where there's Less of this fear and uncertainty because I think all of us that are working with these models and have been working with these models recognize that working with generative AI models Prompting them integrating them into your workflow it isn't often what you expect it to be getting into it and you kind of have to build up your own intuition of oh this is kind of how this Model behaves and this is kind of how this model behaves and this is kind of how the prompting works and oh this you kind of have to build up some of that intuition before you get a sense of How they operate but you're never going to build up that intuition if you just focus on the use case that people have some fear over like generating putting in customer information into Into the interface or something like that so I think to some degree you have to find some use cases where people are able to safely interact with these models and it could be private chat Interface that you allow people to use it could be a local chat interface like LM studio or something like that that you encourage people to use because it's local and there's nothing Going anywhere and you can tell people oh yeah this is fine and then they get a sense of the models and you can go from there so I think it's about finding that foothold to some degree one Of the interesting things that I've found is people sort of expect these models they get disillusioned when they ask like a search engine like question into these models and they just Don't find what they need and so that's kind of some of that intuition that I was talking about like these models operate slightly different than a search engine that's a great point And so everyone kind of had to build up a little bit of intuition I think when they learned about you know how to google things I think there is a like there is a skill of how to google things Right there is and so there is a similar intuition I still you know I'm sure you've run into this many times like people ask questions in a business context and you're like why didn't you Just google that well maybe they don't have the intuition around how to like properly I've definitely seen this before properly search the internet to find answers and self-serve Themselves actually it's interesting I found a an article this week that talked about why AI search engines really can't kill google this was from the verge of publication and so it Talks about search engines like perplexity and u.com and google jim and i and chat gpt to some degree it goes through it's really interesting article people should look it up and we'll Include it in our show notes but it talks through some of the kind of main use cases that you might have learned to do and google like navigation or something navigation questions that Don't really work so well in chat and in the current chat interfaces so there's a different sort of intuition that needs to be built up and one isn't just a drop-in replacement for the Other it's ([Time 0:31:49](https://share.snipd.com/snip/f1ec3836-27c2-496e-8163-a971c3c93ac5)) - **AI Chat Vs. Search Engines** - AI chat models and search engines have distinct strengths and require different user skills. - Effective information gathering involves combining both tools, using the best one for the task. Transcript: Speaker 1 Do and google like navigation or something navigation questions that don't really work so well in chat and in the current chat interfaces so there's a different sort of intuition that Speaker 2 Needs to be built up and one isn't just a drop-in replacement for the other it's a great point and there's a not only are they distinct skill sets but there is a superset of how you use them Together you know for their strengths along the way and you know with the notion that you know like search engines primary job you know as the article notes there is to get you to a website And when I talk like I'm still old school and I tend to I don't just go to a website that I already know through google I will actually just type it in directly because I know it and but my daughter Who is 11 she knows the website she knows where it's at but she still puts it in google to go there and her friends do that too she she uses it as a navigation tool to the point that you just Made a moment ago whereas when we're prompting you know in these models we're really seeking information in a lot of ways you know whereas the instead of getting to a website that has Information we're kind of getting the model to feed that information to us directly and I personally tend to use both it's very common for me to flip back and forth between google and A large language model and use them each for what I want or if I don't know exactly where to go for google I'll learn a bit from the model and then I'll do a deep dive on a website that's specific To what I just learned from the model and get there that way so it's an evolving landscape of tools to get these things done now yeah and I would definitely encourage people to check out Speaker 1 This article it's quite interesting they go through different types of queries like navigational queries what they call buried information queries exploration queries evergreen Information like how many weeks in a year or when is mother's day real time queries like sports scores and and that sort of thing the exploration questions that I mentioned like why were Chain saws invented it's like exploration and learning sort of thing so and they compare some of the answers from different ones so if you're struggling maybe with this intuition maybe That's a good place to jump in and and try some of those queries yourself that are there and see what comes back from the various chat GPT or jimini or u.com and those ([Time 0:35:20](https://share.snipd.com/snip/da41f769-ca98-4447-aaff-59e5d5f4a862))