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Follow us on Twitter at Practical AI FM. And now onto the show. Well, hello, everyone. This is Daniel Whitenack.
I'm joined by my co-host here, Chris Benson. And today we're going to do another one of our news and update shows for you and just kind of update you on some of the goings-on in the AI community, some things that have caught our attention this week. And then also we're going to give you some more learning resources. Again, we're trying to make AI practical for you, so getting some of those learning resources out, I think, is super useful.
And I know I've already appreciated getting some of those links from Chris. So I'll kind of start us off this week. Is that right, Chris? Absolutely.
Go for it, Daniel. Awesome. Well, I saw a couple of things for you guys out there that like, you know, maybe Kaggle challenges or other challenges and that sort of thing. There were a couple of challenges or RFPs that drew my attention this week.
The first is this AI for Earth request for proposals from National Geographic. So first of all, it was interesting to me that National Geographic was putting out a request for proposals related to AI, which I think is super cool. But also it's a big passion for me in terms of sustainability and the environment. So if you're at all interested in, you know, the environment and using AI for good in that sense, definitely check out this link.
I think you have until maybe October to submit proposals. And, you know, it says that the proposals will get you, I think, request up to 200 grand. Maybe if you're part of a research organization or maybe you're a grad student or something might be a good link for you. The other one is Intel AI is putting on this AI interplanetary challenge, which sounds pretty epic.
And, you know, the subheading is super explorer mission, which, you know, there's a lot of great words there. But essentially, in my understanding, this is a way to kind of solicit proposals for space related applications of AI. And I think if you win, then you get lunch with Bill Nye and some other people. So this is a super fun one.
Maybe less of a barrier than the National Geographic one in terms of expectations. But I think this would be a good one for everyone to explore. So, yeah, those are pretty cool. I was pretty excited to see both of those.
So I ran across an article in Neuroscience News entitled, Artificial Intelligence Can Predict Your Personality By Simply Tracking Your Eyes. And that caught my attention. I know. I know that caught my attention because, you know, going back to past conversations, you know, how kind of invasive AI could become in certain use cases.
So I read that. And there's a university. It's the University of South Australia had done this process where they had 42 people that participated in this. And they gave them personality surveys.
And apparently it was one of the standard. And I'm not familiar with them, but one of the standard personality surveys that kind of covers all aspects. And then they actually had them. They monitored their eye movements, not in the lab, but they apparently wanted advice and went around through their daily lives.
And it ended up tying together the way you use your eyes and the types of movements you have with your personality and how you might behave in certain scenarios compared to other people, which is a little bit creepy. You know, we were talking about in the last news episode. We talked about, you know, the law enforcement or government monitoring, you know, using different types of AI techniques. And so this caught that morbid fascination for me in terms of that thought.
So it was very interesting. They didn't take it farther than that. Maybe fortunately. I got to the end of the article and kind of wiped my brow in relief.
But I just thought I'd pass that on. We can put the link in the show notes in the interesting but slightly creepy category. Yes, definitely in that category. It's funny to me because now it's like, well, we can't use Facebook data anymore post Cambridge Analytica and GDPR and all that stuff.
But maybe there's hope for the creepy personality detectors out there using webcam data or something like that. That's pretty interesting, though, I have to say. So the next one I found is it was a Fast Company article. And I think this is just like awesome.
You know, I have a passion for applying AI to good. And this article highlights this what they call creative coder. I actually don't know his association. I think this was kind of a hobby project for him.
Correct me if I'm wrong in our community on Slack. But his name is Abhishek Singh. So sorry if I pronounced that wrong. But he basically built a sign language interface to the Amazon Alexa API or the Amazon Alexa, I should say.
So I think this is awesome. It's, you know, making this tech accessible to a whole other community that was, you know, totally left out of that technology before. So he basically has this set up to where, you know, it will actually there's a camera that's watching you do sign language and you can sign language something. It's interpreted to text, which I think is sent to Alexa and the other API.
And then you get the response. So this is, I think, just super cool. I mean, not even in the realm of smart speakers, but in the realm of, you know, making more tech like this accessible to people with disabilities like us. You know, maybe they're deaf or they need to use sign language.
I know that there's been other AI applied in a similar fashion for, like, blind parents, helping them understand their environment for maybe they're seeing kids. And so I just think this stuff in this category is super useful. And just an encouragement from my end to any of you out there who are kind of exploring how to apply AI and how you what projects you might work on. I encourage you at least consider doing something, you know, in this realm.
If there's a way for you to do it and there's time for you to do it, it's awesome to see this. First of all, I love that application of it. It's a fairly obvious one that does so much good. And I think there's so many other opportunities for similar applications, whether it be Alexa or other platforms.
And in general, I definitely join with you on the aspiration of using these tools in AI to do good. I am actively looking at using AI for animal advocacy causes that I'm so passionate about. And so maybe in a future episode, we can talk a little bit about how we how we get into that in terms of our own aspirations for AI plus good. So I'm looking forward to that conversation.
That'd be great. We'll have to arrange a Twitch stream where we live code some examples. Excellent. OK.
OK. So I one of the things that I have been talking about a lot with people lately is how AI is impacting digital transformation. And it's changed the nature of it. And that seems to be becoming a more and more popular thing for people to try to understand the implications of.
And I ran across a Forbes article entitled, Think You Know How Disruptive Artificial Intelligence Is? Think Again. And the basic idea there is they're kind of saying, you know, people talk about job displacement and automation and stuff like that. But that really the effect of AI over time is really going to be driving digital transformation throughout organizations.
And so they kind of finish up with the idea of it's not about a job. It's about how an entire business is set up and how it achieves its function and how it serves its customers. And they describe it as digital transformation 2.0, rise of the fully automated business. And beyond the article itself, I just find this a really fascinating topic.
And not only in the way it reshapes technology, but in the way that it's reshaping business itself. And some jobs are automated way, but new, totally new jobs that we're seeing come into existence are coming in. And that as you are combining these technologies with the humans that make up this business, how do you organize all that together going forward to best serve the customer need? And so I've seen more and more of these types of articles and probably will continue to share some.
But I think the intrinsic change that business is now entering will be a pretty interesting topic for us for some time to come. Yeah, that's great. And I'm thinking about I know next week we're going to have Mike Bugimbe join us, who I've talked to before about how he kind of changed, in essence, a lot of his company's perception around how decisions are made and thinking about that in terms of data and this whole new realm of artificial intelligence and algorithms. And so I'm excited to hear his perspective on some of those things and I think that will be really good.
That'll be a great conversation. Yeah, the last one that I wanted to draw people's attention to was this article titled The Mythos of Model Interpretability. I know I've talked to a lot of different people. We've even talked on the show before, I think, with the guests from Muta about, you know, what really is model interpretability.
I think a lot of people are skeptical about this idea of model interpretability. But I think that this article really kind of it's a pretty long article. I'll kind of give that context. But it dives a lot into details about how we think about model interpretability, how, you know, where it comes up in our decision making and, you know, why we should be thinking about interpretability, maybe where we shouldn't be thinking about interpretability.
I love certain of the statements like an interpretation may prove informative even without shedding light on a model's inner workings. So there's a lot of great perspective here, I think, about kind of stepping back from all these discussions around model interpretability and looking at that field kind of in that idea as a whole. So I definitely recommend reading through that, especially in light of a lot of things coming out like GDPR, which we talked about on another episode, which has connections to model interpretability. We all need to understand a little bit more about that.
So I'd recommend this article. I'm looking forward to reading that after we stop recording. That's my next thing. So my final article that I want to draw is going back to a topic we've alluded to a little bit.
But it was really who wrote it that caught my attention. It was in The Atlantic. And it's called How the Enlightenment Ends. And it's going down the dark path about the dangers of AI to humanity.
And I know there are lots of different perspectives on that from different people. But it was written by Henry Kissinger. And for those who may be younger in our audience and aren't familiar with him, once upon a time, Henry Kissinger, who's now a very old man, was one of the world's premier guys in terms of diplomacy. His expertise in foreign affairs and such was just world-renowned.
And he was our secretary of state, I believe, back in the Nixon. He opened up China back in that day. And he has had a company ever since that was one of the top companies in the world in the space. And so even though he is not a technologist by any stretch, he is a brilliant thinker.
And he kind of starts off the article saying that he was almost about to walk out of this talk. They turned toward AI, and he didn't have any particular interest, but he happened to catch the beginning of it. And it started him thinking. And so he sat through the rest of that presentation.
And then he started going to many of the world's top AI luminaries and asking them their thoughts in different ways. And so he has really landed personally in the same space as Elon Musk and others who are warning us of the dangers in the long term to humanity. And he kind of walks through a process that really spans a historical narrative starting with the Enlightenment roughly 500, 600 years ago and talks about how humans have developed technically through that period and where he thinks AI will go. And he ends in a very dark place.
It's a cautionary note that basically says let's be very careful in terms of in the years ahead as new AI develops how we implement that AI. And so there are many articles similar to this out there where people are warning us of such things. But it really, like I said, Henry Kissinger is one of the greats of the 20th and early 21st century and certainly a great living thinker today. And that it may be paused a little bit.
And as someone who tends to myself tends to celebrate AI and all its possibilities going forward, I do give a little bit of thought to Mr. Kissinger's perspective there. Any thoughts, Daniel? Yeah, definitely.
So I'm glad you pointed it out. I'm looking forward to reading it. I kind of wonder, I mean, this isn't really the case with Elon Musk necessarily, but I think there's this kind of balance between, you know, for the people that think that AI and the hype around AI, you know, that AI can currently do more than it can actually do, then people kind of either hype up AI to thinking, you know, oh, it's going to do all these amazing things to other people who kind of go down the darker path, like you were saying. At least in my opinion, in reality, I think we're at a point where our expectations around AI need to be kind of moderated in a certain way.
But I also appreciate, you know, the fact that we as practitioners of AI need to be understanding how the influencers in our world are thinking about AI. And also, you know, how we as AI practitioners can better communicate and, you know, impress upon them the proper expectations around what AI can do and the proper way to go about thinking about AI ethics and where we should, you know, obviously that is an important thing that I don't want to shy away from, but I think it also has to be kind of wrapped in this cloak of proper expectations. So, yeah, it's very, very interesting. I agree.
I think the, in general, I think I would agree that the current state of deep learning and AI technologies today doesn't feel very threatening to me. You know, there are certainly use cases we talked about, the Chinese government identifying people and stuff, but it definitely doesn't have that. I leave a tiny door open in the back of my mind to some future development in AI, you know, that's beyond where we're at today, well beyond where we're at today, you know, in terms of what could happen decades or even centuries down the road. But I think we get far outside of the practical when we get to that.
And so I absolutely agree with you that the reality check is pretty important. What's possible today and in the foreseeable future. Yeah, and I think, yeah, the human element here is really important. I was just having a conversation with someone on a Slack channel about does, you know, AI have morality?
And, you know, my thought around that was, I mean, similar to other tech, I think the morality of the creators is what infuses any sort of morality in the technology in the same way that, you know, certain technology can be used for good to automate emails and all those things, but it also could be maybe used for bad and like phishing scams and all those things. And that really comes back to the root in the creators of that technology. And so I think we need people thinking about this and pushing us. So we also need, you know, people with a head, you know, looking towards the ethics of what we're doing as the creators of AI, which is especially a technology which has kind of a more subtle infusion of the creator's morality and fairness and bias into it than maybe other technologies.
I agree. And I guess I'll finish by saying as we touch on ethics again in an upcoming episode, we will have an ethics expert related to AI on. And so that will be a good surprise coming. That's a much anticipated episode.
We've already had a lot of requests for that. So now we'll kind of go. That was the news that caught our attention over the week. Definitely let us know in our Slack channel.
You can join us on changelog.com slash community and join our Slack channel and let us know what news articles you're finding interesting from the week related to AI. But before we finish off, as always, we want to give you a few learning resources to help level up your skills in practical AI and maybe help you be more productive as practitioners of AI or maybe learners or students of AI. One of those that I found this week was this article called Fast, Flexible Pandas, I think is the title. Sorry, Fast, Flexible, Easy, and Intuitive, which are all good things, I guess.
Yeah, and I know, so I've been guilty in the past of maybe slamming pandas on a few occasions. I definitely think that... And just to clarify, we're not talking about the animals, right? We're not talking about...
Correct, this is the Python package called Pandas, which is a kind of data munging and manipulation package that kind of organizes data into what's called data frames and series and other things. I just wanted to save you the hate mail on that, sorry. Yeah, I appreciate that. Yeah, I have nothing wrong with pandas in general.
And actually, I have nothing bad to say about the pandas package either. It's amazing, and I use it most days, I think. I love it. I think I've been guilty a little bit in the past of probably using poor pandas skills or patterns and blaming the slowness or the lack of good results in terms of performance on pandas when it's actually been my kind of poor use of pandas.
I think this article lays out some good patterns that you can use when you're selecting data, when you're looping through data, when you're working with daytime data and other things. I still don't think pandas is obviously right for every single use case, but I think it's incredibly powerful, just an amazing project, and I think this gives you some good patterns to use with it. Sounds good. I'm looking forward to that one.
I ran into an article this week. It was on Medium, actually, called An Introduction to Gradient Descent Algorithm, and it was by a lady named Sarah Iris Garcia, and we'll put a link in the show notes to her post, and she basically talks about gradient descent, which finds parameters that minimize the cost function, which is error in prediction, and she kind of takes you through what a gradient is and then talks about the learning rate associated with that gradient and talks about what big learning rates versus small learning rates do and what the implications of those are in your training and then continues on with a working example and talks about the various steps in gradient descent and some of the variants to that. And so the reason this drew me in was gradient descent is really one of the very first things you learn when you step into the world of deep learning, and if you're new to the field, you may not be familiar with it and you may need to ramp up, and some of us who've been in this for a while kind of take it for granted, but it's one of those fundamental building blocks that you need to learn in those early days, and so I wanted to put this article out there so that people could get a start here, especially considering how well she puts the introduction together. Awesome, yeah, that's a great resource.
And the last one that I have, and I think you have one more, but I found this link to a newly released kind of package of e-books, but one e-book particularly focused on Vim, so the editor Vim, which you can, if you're in a terminal on some Unix machine, or you can use Vim to edit various code or text documents or whatever it might be, I actually use Vim as my primary code editor, and I definitely feel like I've not mastered Vim. I know a lot of people give Vim a hard time because you get into it and then you can't figure out how to get out of it or whatever other jokes you might have about Vim, but I think it's useful for everybody to learn a little bit about Vim because maybe you are SSH-ing into a machine where you're running a cron job or whatever it might be, and you want to be able to edit some script or something on the machine in a quick way right there in the terminal. Vim is a great choice for that, even if you don't use it as your primary editor like I do, which you should. But I won't get into that, but I think this is definitely for people that maybe struggle with knowing how to kind of, when they're SSH-ing into a machine and they want to modify stuff, this is a great resource to kind of level up your skills on that front and be a little bit more effective in that way.
So I wish that I was using Vim as my primary editor, and for years I keep trying to, and then of course I run into a situation where I get frustrated and I roll back to one of the other editors out there that I keep trying, and certainly when I SSH-ing, it's what I'm using. So maybe this is my path forward, Daniel. Yeah, well, I've definitely got a ways to go. I know that some Vim masters probably cringe when they watch me scroll through various parts of the document or something in a non-efficient way.
So I'm looking forward to learning a few things here too. Okay, well, I'm definitely going to dive into that one. So the last thing that I am introducing today for learning is O'Reilly has an article called Introducing Capsule Networks. And to give people a quick background is Capsule Networks are, I guess, an invention by Jeffrey Hinton, who is one of the luminaries in the deep learning world.
And it is what you might think of as an alternative to convolutional neural networks. And it's a really hot topic right now. There's a lot of interest in it. But what this article does is it kind of takes you through Capsnets, which is what they're called for short, and it differentiates them with convolutional neural networks and talks about some of the different ways and places that you might use them.
It talks about the differences in architecture and approach, strengths and weaknesses, and kind of gives you a thorough introduction so that if this feels like it's one of the architectures that you're interested in for your use case, that you can then take it forward and learn more about it. But I've been looking for a really good intro to this, and I thought this was a good way of dipping your toe into it and deciding if it's something that you want to do further. Any thoughts on Capsule Nets? Awesome.
My only comment is that I haven't gone through the article yet, but it looks like there's some really great figures in there to kind of help visually walk through some of the concepts. So I think if you're interested in the subject, it might be a good starting place to jump off from. So definitely take a look at that. Great.
Well, I appreciated all the stuff you found this week, Chris. As always, it's an exciting week in AI, and I'm excited to talk to you next week to interview Michael Gambay. So we'll talk to you next week. Sounds good.
Daniel, have a good one, and talk to everyone later. All right. Thank you for tuning in to this episode of Practically High. If you enjoyed the show, do us a favor.
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