The Magic of AI: How Computers Can Learn and Think

AI is predicted to be the most influential technology in 2023. We’re not even halfway through 2023, and it appears that we’ve advanced so far in the AI world that it’s difficult to imagine doing some tasks without the assistance of an AI tool, such as ChatGPT, Bard, Bing’s AI, and so many others.

But, while using these tools, have you ever wondered how these tools, or AI in general, work behind the scenes?

Let’s take a look at the engineering that goes into creating these amazing and powerful tools, and learn about a few different types of AI.

What is AI?

AI or Artificial Intelligence is not some magical system. It’s a set of computer algorithms that can learn and improve. It updates its output, using the feedback received from the previous output.

We train these algorithms on a large set of data, and with the help of the feedback loop, it generates an extensive knowledge base.

It can then use its knowledge base to generate unique results, just like ChatGPT, Bard, Bing’s AI, etc.

How does AI learn?

Let’s try to understand this with an example of game-playing.

I’m sure you’ve played the legendary snake game before. Now the job is to make AI play the snake game.

To accomplish this, we’ll let AI play the game itself with some set of rules.

Every time it loses, we’ll provide it with some feedback.

The feedback received is in the form of penalties. If it doesn’t perform the correct set of movements while playing the game and loose, it’ll receive a penalty.

Therefore, it tries to minimize the penalty after each loss during the training, generating an extensive knowledge base, using which it can play the game without fail.

How does an AI algorithm look?

Let’s continue with the previous example of game-playing.

If trained well, it can play the snake game almost without fail.

But how does it do that? How does an AI algorithm looks like🤔

You think of the algorithm as many if-else statements used conditionally to perform game movements in the correct order. It can predict future situations (traps) using its knowledge base and make movements accordingly.

Looks simple, right?

Yes, the concept is straightforward. The tricky part is the implementation and training.

To make it fast, data scientists do various implementation optimizations.

Also, the accuracy of an AI-generated response directly depends on its model. The larger the training data, the better the accuracy 👌

Types of AI Models

We can create different types of models based on the needs, such as models to generate text or images, models to analyze visuals in real-time, and so on.

Let’s take a look at some of the most influential models in recent years.

GPT

With the launch of ChatGPT and GPT-4, this model is currently one of the most buzzed-about topics in the community.

GPT is an abbreviation for Generative Pre-trained Transformer. It is a family of language models that are used to generate text-based data.

These are pre-trained on massive amounts of data and, as a result, are very effective in producing accurate results most of the time. We can fine-tune them further to meet specific requirements.

GPT-4, the most recent version of the GPT, was recently released by OpenAI. It has been trained with one trillion parameters and is ten times more powerful than its predecessor GPT-3.5.

Stable Diffusion

Have you ever heard of MidJourney or DALL-E 2? If not, you must check out these amazing tools.

Essentially, these are AI tools that you can prompt and it will turn that prompt into an image.

Amazing, isn’t it?

Of course, it is. But what powers these amazing tools?

These tools use a technique called Diffusion. To put it simply, we use the diffusion technique to convert the Gaussian noise to an image that matches our prompt.

Stable Diffusion is an open-source model that uses deep learning and diffusion to convert text to an image.

The above tools, however, do not necessarily use Stable Diffusion but are based on a similar technique we just discussed.

Codedamn’s JARVIS

AI is making significant gains in every industry, including medicine, architecture, and research. However, the most significant sector that it is influencing is education, particularly when it comes to learning to code.

And when it comes to learning to code, we can’t forget about Codedamn, one of the most advanced learn-to-code platforms.

With artificial intelligence on the rise, Codedamn has a step ahead with its next-generation JARVIS assistant. It’s an AI-powered personal assistant available inside your Codedamn Playgrounds to whom you can ask questions and clarify doubts without leaving your browser or switching tabs.

It uses OpenAI‘s latest GPT-4 Model, so you can already guess how powerful it is.

It can read and analyze all of the code you’ve written in your Codedamn Playground, as well as answer your questions and solve bugs.

Imagine learning from a course, coding along, having AI resolve your doubts and bugs, receiving AI-generated Code Reports, and so much more, all on a single platform, without ever leaving your browser. That’s Codedamn for you.

Sign Up today at https://codedamn.com

Summary

AI is the most talked-about technology in 2023. Every day, we see new and amazing tools arrive. However, these tools are supported by some excellent engineering.

It is just a set of computer algorithms that are fed a large amount of data using which they generate a large knowledge base. This knowledge base is then used to generate unique outputs.

There are numerous types of AI models available, each with its own set of use cases. For example, GPT, Bard, and Bing’s AI are text-based language models used to generate textual data from our prompts, Stable Diffusion is a model that is used to generate images from a text prompt.

Nowadays, these tools have become such an integral part of our daily lives that it is impossible to imagine doing a task without them.

In the near future, we might see AI-based systems will start replacing, not all, but a significant number of human jobs.

This article hopefully provided you with some new information. Share it with your friends if you enjoy it. Also, please provide your feedback in the comments section.

Thank you so much for reading 😄