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Get started at Hired.com slash Practical AI. Welcome to Practical AI, a weekly podcast about making artificial intelligence practical, productive, and accessible to everyone. This is where conversations around AI, machine learning, and data science happen. Join the community and select with us around various topics of the show at change.com slash community.
Follow us on Twitter at Practical AI FM. And now onto the show. So this is Daniel, and I have Chris, my co-host here with me, who is an AI expert and specializing in deep learning. How's your deep learning been going, Chris?
It has been going 100 miles an hour. This field is moving so fast and so many new things are happening that I'm trying to keep moving forward and keep my head above water. How about you, Daniel? Awesome.
Yeah, it's been crazy. It's been good. I've been doing a lot of data munging and cleaning this last week, which has been fun and working a little bit with graph databases. So maybe at some point we'll bring that into the show.
But I'm glad that you mentioned that there's a lot going on. This is, for our new listeners, this is one of two different formats that we're doing in the show where we're going to just kind of give some news and updates that we've seen in the community and also provide some resources for those starting out in AI or wanting to level up in AI, some learning resources that we found out there. So, yeah, I'm excited to talk about what's going on in the community. What did you see going on this week, Chris?
So there are so many news stories that we're getting hit each week, and just picking a few is really kind of the hardest part of this. But I get asked all the time about kind of where AI is going in global scale, you know, in terms of different countries. You know, everyone always asks about the U.S. and China and how Russia fits in.
And so I came across a Medium post this past week called Artificial Intelligence Strategies. And it's actually, it kind of maps out what the known strategies are for a bunch of different countries out there and kind of puts them on a timeline. And we'll share the link in the show notes. But it's really cool, and it has some great graphical stuff.
But it kind of starts off with an overview of national AI strategies and gives you a table of contents, which is a couple of dozen countries. And then each of those kind of has a one or two paragraph blurb about what those countries are doing. And it's just a great single point to go to and say, you know, hey, what is China doing? What have they announced?
What are they interested in? And you can go down to China and find out. But then there's many others as well, such as right below China is Denmark. And so I'll leave it to our listeners to go explore that for the show notes.
But it was a great starting point if you want to understand how AI is being seen at the nation-state strategic level. How about yourself? I mean, I'll tell you what China is doing with AI. They're identifying all those people at their pop concerts with facial recognition.
They're people with, I forget, have you seen those stories? It's like at the one, I forget the singer's name, but they've like nabbed, I forget how many at this point, but at his concerts for like whatever it is, outstanding warrants or that they're, you know, they're looking for them or however that works in China. But that's what I know China's doing with AI. You know, it creates this huge issue of the ethics of how to use these technologies.
And, you know, China is approaching that in that way, which I certainly am not comfortable with. But then, you know, more recently, Amazon, you know, with some of the stuff in the U.S. about spatial recognition with law enforcement. And it was a big uproar a week or so about that.
And so I think you were seeing the populations of all these different countries having to react to this rapid onslaught of this new technology and how each of these governments is choosing to use it with or without oversight. It's a fascinating time in terms of understanding how we're moving forward from an ethical standpoint. And I think that's certainly going to be a show coming up where we're going to talk about that in some great depth. Yeah, for sure.
And, you know, to go along with that, one of the things I saw was that the ACLU called for a moratorium on government use of facial recognition technologies. Yes. Which goes right along with what you're saying. And I just think it's interesting that and I think one of the actually the guests on the changelog podcast is talking about this, how there's kind of this this spectrum of how people perceive AI on one side.
You know, they they think that it's kind of so amazing that it can do everything. And that's, you know, a really awesome thing that's going to solve all our problems and automate everything. And on the other side, people, you know, think it can do more than it can and just creates a lot of creepiness out of out of that. Where in both respects, it needs to be, you know, those expectations need to be tailored back somewhat.
But I think there is this kind of problem of setting expectations for even what AI is capable of. But then certainly once technologies like this come out, there's definitely a lot of conversations that need to happen around around the use of them, especially by government entities. I agree. I mean, unlike the highly educated listeners of our podcast, because they're here learning about this kind of stuff, as are we.
I think the vast majority of people out there are hearing this in the news every day, but they don't have any basis upon what to evaluate that. And there's so much education that needs to happen, even while this field is just racing forward at light speed. It's really creating a lot of social, cultural and economic turmoil in terms of our lives changing so fast. So it's definitely something we need to we need to dig down into on an upcoming episode.
For sure. So another article that I saw that came from Space.com was that the actual first robot that is using AI flew in space recently. And it is interesting. It is called, I'm assuming I'm pronouncing it right, Simon or it's C-I-M-O-N.
Simon. Simon. Simon. We'll say Simon or Simone to make things not awkward.
There you go. Somebody can tweet us or hop on to our Slack and correct us in terms of what that is. But, you know, my first reaction when I saw this was, really, this was the first? I would have expected it a long time ago that, you know, maybe some deep learning, you know, from a CNN standpoint might have been used.
But apparently, you know, as of the morning of June 29th, which is, you know, not too far back, a couple of weeks back as we record this, it was a small, it says a small robot endowed with artificial intelligence launched a two-day trip to the International Space Station. So apparently. What does it do? Does it just wake them up?
What is it? Is it like an assistant? It's a small flying sphere, and it has a kind of a cartoon-like face on the front of it. That's super creepy.
It is pretty creepy, actually, in the pictures of it. And apparently it is able to propel itself around in the ISS and through little puffs of air and interacts with the astronauts. And so, you know, we finally got into that moment of 2001. Futurism is there now.
So I imagine this is going to be, you know, just pervasive in space missions going forward. Not only this one particular robot, but probably many, many to come. Yeah. It might be the first thing that has motivated me not to go into space, so I don't have to stare at that face for, like, months on end.
But you can just be up there with an entire crew of seaman robots. It's just you and the spacecraft, and all you do for the next six months is interact with them. Yeah, I'll pass on that one. I don't know if that falls into this category.
And speaking of kind of governments and nations' strategy around AI, I saw that DJ Patel, who was the first chief data scientist of the U.S., I'm not entirely sure what he's up to now, but him, along with Hillary Mason and a couple others, came out with an article about doing good data science. And I think it's a good read for everyone. It brings up a lot of good things and talks about, basically, that we need to have the space and the time to address the ethical questions that are coming up in data science and AI work, and, you know, share those at conferences and be open about that side of the work. So if that's something you're interested in, it's definitely a good read, and by some of the leaders in the field, for sure.
Definitely. I'm very, you know, we keep talking about ethics and AI. So far, it's more of an ancillary topic in our episodes, and I know we're going to have it as a primary topic coming up. But I ran across that Google's CEO posted an article called AI at Google are Principles, which I was happy to see because I think this is something that most companies need to be framing in terms of how they're approaching using these technologies from an ethical standpoint and their objectives and such.
And I thought I'd take two seconds and run through what he put, kind of highlight. The article is longer, and people can go read it, but he said under objectives for AI applications to be socially beneficial, avoid creating or reinforcing unfair bias, be built and tested for safety, be accountable to people, incorporate privacy design principles, uphold high standards of scientific excellence, and finish with be made available for uses that accord with these principles. And he also goes on and lists some things that they would not do. He talks about their long-term approach, but I was just happy to see that they were actually thinking their way through it and publishing it so that so many other organizations can kind of follow suit and put forward their own objectives and hopefully put them out there so people can see what they are.
And we can continue to have this conversation so that AI can continue to be used for wonderful things like feeding people in Africa, which was a previous episode of ours. Yeah, for sure. And it definitely fits into the same spirit of that other article about doing good data science and publishing those thoughts in blog posts and other things, and definitely a good read. So the other thing that we for sure want to do in these news and updates episodes is to share some learning resources with people.
Maybe you're starting out in AI or you just want to keep yourself fresher or learn new things. We want to definitely give you and help expose some of those resources. And the first one that I found recently was that Bloomberg published this free online course in machine learning fundamentals. So you can view it, I think, as a series of lectures on YouTube, but they kind of have their own site now that you can go to and kind of run through these episodes in order, and they have extra resources and things there.
I haven't been through it, but it seems really useful, and people seem positive about it. That sounds like a great one. And likewise, Google has their machine learning crash course that uses TensorFlow APIs, and that is freely available when they have, like these others, we can put the link in the show notes. And they describe it as a self-study guide for aspiring machine learning practitioners.
And so that's also one of many great free resources out there where people can get their hands dirty on these. And then on a slightly different tangent, I have a lot of conversations with people that are trying to figure out how they're going to do the computation side. And we use cloud services, obviously, from the major providers. And in some cases, maybe we're lucky and we can afford some pretty good deep learning-oriented hardware with GPUs or TPUs or whatever.
But there is a YouTube video by Siraj Raval, if I'm pronouncing his name correctly, who is kind of one of the better-known luminaries in the deep learning space. He does a lot of YouTube videos and courses, and he's fairly well-known by the rest. But he did a best laptop for deep learning video. And it was a good thing that if you're on a budget, it was a great way of kind of saying, okay, how can I get into this?
And if I'm going to build a system or if I'm going to buy one, what are those trade-offs? And it was just a good basic thing. I suspect that on the hardware side, we're going to see a lot more of those types of recommendations as people get more and more into this, as this space becomes more accessible to people. Yeah, that's great.
I have always, you know, thinking about what laptop is next for me. I can watch those videos all day. I definitely, you know, after seeing Kelsey Hightower demo from Pixelbook, I've got my eyes set on those, although you're probably not going to train too many neural networks on them. But yeah, that's always a fun one to watch.
So keep your eyes out for more episodes like this, where we share some things going on in the community. If you have suggestions for things you would like to talk about, or maybe links that you think are relevant, join our community. You can go to changelog.com slash community. You can ping us on Twitter or Slack.
All those links are there. There's people already discussing things in our Slack channel. And so join the community. We'd love to talk to you.
Stick around for next week, actually, on the topic of learning. We're going to have Jared Lander with us, who is really big in our community. And he's going to talk a little bit about that. But even more, we've kind of asked him to give us a little bit of an overview of the landscape of AI techniques and how certain things like deep learning fit into that.
So I think that'll be great to hear from one of the experts in the field. So thanks for finding some interesting stuff. And I'll talk to you next week. Talk to you later, Daniel.
Have a good one. 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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