In this post, we are going to go through some of the easiest ways you can learn AI. I'm gonna break down for you how you can simply learn AI. There's some tips here I got covered for you.
All right, let's get into it. To start, there are many different areas within AI that you can focus on, depending on what your end goal is and interests.
For example, there is the technical understanding, things such as really understanding the fundamental concepts, programming concepts, and then also to really using AI for a problem-solving. Now, this doesn't always need to be falling under technical, but just to keep it simple.
Then we have the societal impact, so this are things covering such as ethics and bias, jobs and the future of work, privacy and security. I know its of interest to a lot of you, especially around the ethics with AI and the bias that it can create. This is a whole other area within artificial intelligence that you can spend time focusing on.
And then there's this third category, which is really more generic, depending on more specialties, where it'd be really having a strong understanding as to using different AI tools, or maybe you are more so understanding AI's current limitations, different things like that.
All right, we just went through three big buckets at a high level as to where you can put your focus when you are learning about AI. And already, it seems kind of like a lot. So let's break down what each bucket really entails, because that will give you a good sense as to where you are most interested and should put your focus in learning artificial intelligence, what relates most to you, and kind of the next steps into how to really learn and understand AI that best relates to what you are working on, your job, and the future.
Learning technical concepts of AI
Let's start by going back to bucket number one, more of the technical concepts. And just hold on a sec, even if you are a non-technical person, you're going to hear this. Let's cover some of the basics within artificial intelligence, different terms that you should be aware of and know what they mean. They oftentimes get thrown around interchangeably when in reality, they are pretty different. Artificial intelligence, we really use, a lot of times, incorrectly. We just kind of use it as this term to cover anything and everything with, you know, under AI's kind of umbrella, when in reality, there is so much more to it. Let's start out, actually, with a few of the basics.
Machine learning
What exactly is machine learning? Think of it as training algorithms to learn patterns from data and make predictions or decisions. So you're feeding this algorithm some data and based on that, it is learning and getting trained to make better decisions and predictions. And this learning can happen through various methods, through supervised learning, unsupervised learning, and reinforcement learning.
Now, we're not going to dive into those three today, but just save this post if you are interested in it or write these down and you can, I mean, machine learning, we could make for a whole series of posts, essentially.
Deep learning
So deep learning, you can think of it as an advanced subset of machine learning. So deep learning uses multi-layered neural networks to analyze various factors of data. So you can think of it, deep learning is a really good task or tool for things such as image and speech recognition, really complex tasks. Then we have a subset within deep learning, which is neural networks. I know, we can get complex fast, but this is just very high level to at least give you an understanding as to what these different terms that people throw around actually mean. Neural networks are inspired by the human brain. I mean, when you think neural, obviously the brain, so just kind of to keep that in your back pocket.
Neural networks though are a series of algorithms that mimic the operations of the human brain to recognize relationships within a set of data. And they are key, a foundational part to deep learning, which we just went through.
Artificial general intelligence
the last one I wanna cover is something that we often hear get thrown around, especially in the news or especially on social media, which is AGI or artificial general intelligence. And I think this gets thrown around because of the what ifs around it. Like what if this actually happened with AI and kind of gives this very futuristic, kind of fear mongering social post it can oftentimes. So this is a concept where AGI has the ability to understand, learn and apply new concepts to solve different problems on its own. And it's really similar to how a human brain works.
Okay, so still under topic number one, technical concepts or technical as a whole. Obviously there's the entire programming section of this too, where there are so many different courses you can take, gone are the days where you would have to go to school for computer science and learn everything the traditional way in order to become a machine learning engineer.
Now, don't get me wrong, I'm not discrediting that form of education, it is key and especially for very specific roles. But I don't want you to get discouraged that if you didn't go to school for computer science, you can't explore these options. The good thing is there are so many courses online. Here are some courses though, that really start with the basics, the foundation I just covered and work their way up. The first one I want to come up with, is a Stanford course that is on machine learning, covered by or hosted by Coursera.
Really starts as to what is machine learning and then breaks or works its way down to the nitty gritty. So this is course whether you are technical or not, you can learn a lot from it. Coursera, they offer so many amazing courses.
Their deep learning specialization is actually a series of five courses to help you understand deep learning and it will help you also not only understand deep learning and you'll get more into the specifics around building, training and deploying neural networks. So it really starts getting into the details, especially around deep learning.
Learning Societal Impact of Artificial Intelligence
All right, let's jump into the second column, which is societal impact. So this is, if you are more interested in AI learning. When we start learning about AI and really getting into that world, one thing you will quickly come to realize is there is so much to learn. And oftentimes that can make you feel so intimidated.
You just back away from it and be like, ah, this isn't for me. That's why I really wanted to break it down to these three columns. And then within them, there is still so much to learn. So really, you have to focus or hone in one thing. And then from there, you'll just over time continually gain information and educate yourself on others. Don't try and learn everything at once.
All right, so under societal impact, we have ethics and bias in AI algorithms. And it's no secret, there's been tons of conversations around this that there are some issues or challenges when it comes to bias within AI. And AI generated images such as the ones that Dolly creates can raise ethical concerns about bias, discrimination, privacy, and even job displacement.
I mean, when we look at when AI was first starting to be able to generate art, I think that was a huge concern and still is. And there's this really fine line or friction we are facing right now as to who sets the regulations, where does the line stop? And also too, when the regulations are set, the line keeps on changing as technology is literally changing, it seems like on a day-to-day basis. And I think this is really interesting AI ethics for anyone who is really passionate about legal, psychology, anything around the more the human brain, anything like that, I think you will really find AI ethics really interesting and something to start looking into as a place to start learning about AI. And there is such a need for AI ethicists, people who study this really carefully because AI is the worst it will ever be, meaning it's continuing to grow and get better and better. And we need to start putting regulations and really thoughtful considerations into place as to what this will look like moving forward. Then we have jobs or the future of work.
And you know, this is an area that I love to explore, mainly because it's so interesting to me how AI will impact jobs not only today, but in the future, as well as how we learn different skills, our learning will change and even our skills will change too because of AI.
Now, what can you learn under here? For me, one thing that I really focus on is understanding as to how AI impacts us currently and then what it can do in the future, looking ahead. So this kind of, you have to put your futurist cap on or futurist hat on where you're making assumptions based on the data that is available today as to what it could look like moving forward. And then taking that and educating others as to where to put their time and skills when learning about AI, as to how it will help them grow in their career.
Learning generic AI or Specific AI tools
All right, the third pillar we have is what I called kind of the generic or beyond the basics where it's more specialized areas. This could be things such as maybe you're passionate about AI, but you really want to focus on some AI tools. This could literally be you becoming an expert and learning more so about what AI tools are offered, what are the best AI tools for specific things. All right, and on that note, you wanna know something really cool is you can now use the GPT store, which just released to actually educate yourself on all of these topics as well. I recently was on the GPT store and I found Consensus and I was going through their custom GPT and it is really impressive actually. It goes through, it says your AI research assistant. So you can search 200 million academic papers from Consensus. So you'll get science-based answers and it will help you draft content with accurate citations.
So I thought this was really cool. I just stumbled across it and I've been using it recently to understand different concepts within artificial intelligence and it's worked really well for me because you know what's coming from actual scientists. There's over 200 million academic papers, that're located in there. So it's a great way as well to start your learning.
All right, at the end of the day, if you are interested in learning AI take it step by step. We went through some key concepts today. Also, I will shared with you some key courses to really lay that foundation and then from there you can kind of branch off into different areas that we went through.
Thank you all for reading. I hope you found this post educational, inspirational as to where to put your focus based on your interests. Hit that share button and I'll see you all soon. Thanks everyone.
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