เกี่ยวกับตอนนี้
On this week's episode of Inside Outside Innovation, we sit down with Alyssa Simpson Rochwerger and Wilson Pang, authors of the new book, Real World AI: A Practical Guide for Machine Learning. We sit down and talk about some of the biggest misperceptions about AI, as well as some practical advice on how to tackle creating, building, and maintaining AI projects. Let's get started.
Inside Outside Innovation is the podcast to help you rethink, reset and remix yourself and your organization. Each week, we're bringing the latest innovators, entrepreneurs, and pioneering businesses, as well as the tools, tactics, and trends you'll need to thrive as a new innovator.
Interview Transcript with Alyssa Simpson Rochwerger and Wilson Pang, Authors of Real World AI: A Practical Guide for Machine Learning
Brian Ardinger: Welcome to another episode of Inside Outside Innovation. I'm your host, Brian Ardinger. And as always, we have another amazing set of guests. Today we have Alyssa Simpson Rochwerger and Wilson Pang, authors of the new book called Real World AI: A Practical Guide for Machine Learning. Welcome to the show.
Wilson Pang: Thank you, Brian. Really excited to be here.
Brian Ardinger: We are excited to have you both here. This is an exciting world of technology and new trends that are happening. AI is obviously on the forefront of a lot of people's minds. And I'd love to get your input on what do you really mean when you say real-world AI and how does that differ than people's perceptions out there?
Alyssa Simpson: I think one of the things that we wanted to address with this book is sort of the in-between space between, you know, the hype and maybe what you read about AI and the headlines, or what you see AI or very smart futuristic systems depicted in the movies. And then, you know, also a very academic or technical approach to machine learning that you may have come across a textbook or learned in school.
And this is kind of the middle reality, right? Is, you know, what are real companies who are using machine learning based technology? How are they using it? You know, what struggles are they having? What successes are they having? And then how does it work in the real world. In the reality that we all live in and share and products that you probably use frequently in your everyday life?
Brian Ardinger: Well, I think there are a lot of misconceptions about what AI even is. Maybe Wilson, can you tell us a little bit about some of the myths or misconceptions about AI that people commonly struggle or fumble over?
Wilson Pang: There's a few major common misconceptions. Number one, it's really, a lot of people think AI, they think is the kind of a machine can do whatever human can do, right? This is called Artificial General Intelligence. Basically, AI can repeat human. So that's happening in those small ways but it's far away from the reality. In reality, what AI, all the real-life AI, is really the application, which can be taught or learn to carry a specific task without being programmed to do so
So, the machine can learn from data. And then performing some tasks. So that's kind of a like what real world AI is. So that's number one misconception. Number two misconception is that people are thinking to build an AI, the team needs to spend a lot of their time to tune the model, work on the model, and get the best performance. It is more related to the signs or the model tuning.
In reality, science is a big part of AI, for sure. But for the AI team, they spend a majority of their time, on data. Need to collect the right data, clean up the data, make sure the data has all of the representatives and also make sure that data has quality. And then the data complete the magical part is to really improve the AI performance. So those are the two major misconceptions. I hope everyone can really learn and understand that.
Brian Ardinger: You know, you often see two sides coming out, when you talk about AI. You have one side of the spectrum where everybody talks about AI as a panacea of opportunity. And it's going to change the world for the better. On the other side, you have the folks that think AI is a massive danger and a menace and a lot of unknown repercussions. Where do you guys fall on that spectrum?
Alyssa Simpson: I'll argue all sides of that one. I think both are true and neither are true. As with anything, machine learning and AI technology is disruptive. It already has changed the world as we know it and will continue to be an incredibly powerful and disruptive technology in almost every industry. On the other side, it's just technology, right? And there's a lot of things that are powerful and it's only as powerful as what you put it towards and how you shape it.
And in other ways, it's incredibly brittle and really narrow as Wilson was referring to, this is not a magic eight ball. It's not generalized. AI often is very narrow and specific, and only is able to accomplish a fairly narrow and specific tasks that you train it for, if you have all the data available to train it really successfully to perform that task.
And so, you know, what we see in reality is companies having a lot of success. If they are able to find a use case that this technology can perform really well and do things that previously were undoable before and open up new streams of opportunity.
Brian Ardinger: Where are some of the industries that are getting the most out of AI right now. And where do you see the trends moving, when it comes to folks capturing the benefits of AI?
Wilson Pang: AI only become a buzzword in recent years. But in reality, AI has been there for a long time and high-tech industry who has big company like Google, Facebook, all those tech giants. They have been using AI for a long time. And AI has been used to optimize their price sprints, to help you find the right product, to give you a better recommendation. To show you a better content.
So those have been there for a while. The usage of those AI technologies is pretty mature, and that's also almost embedded in every part of their product. So, for high-tech industry, very mature. Meanwhile, in recent years, it's also a lot of other industries, they are catching up. Like the finance industry. The medical industry. All kinds of industries are catching up.
So, you can see the train of AI is already, the adoption of AI has become much broader. And also, the speed to adopt AI is also a leverage spot. Meanwhile, as you mentioned earlier, AI can really bring on a lot of benefits. Can also create a lot of harm. So, with this even wider adoption, we really need to make sure people understand how to use AI in the responsible way. So that way we can get the benefit part, but meanwhile not really doing a lot of damage to the society.
Brian Ardinger: Yeah, you definitely hear that. And you hear the use case scenarios when it goes bad and that's oftentimes hyped up. I actually just read this morning. I think it was a fast company article talking about how the New York City Council is considering new rules meant to curb bias in hiring when it comes to AI and putting AI in hiring practices and that. What's your take on some of those challenges that you're seeing in that particular space.
Alyssa Simpson:...
ในตอนนี้
สำเนาบทสนทนา 🔗
ตอนอื่นๆ ใน PODCAST นี้
