The Five Generations of Artificial Intelligence

 

Introduction

Artificial intelligence has evolved, changed, and grown over time, marked by distinct generations. Each phase brings forth advancements, challenges, and paradigm shifts that shape the landscape of AI. In this blog, we will explore the five generations of AI, from the beginning to the latest developments.

First Generation: Handmade AI

In the initial phase of AI, known as the first generation, intelligent systems were mainly crafted by humans. During this period, AI could not teach itself and relied on expert knowledge to solve decision-making, optimization, or search problems. The capacity for abstract thinking was limited at this stage.

Imagine a basic AI system from the first generation as a helpful assistant for a mail-order company. Its job is to figure out the best way to pack items in a big order to save on shipping costs. It analyzes things like how many packages to use and what sizes to make them, all to ensure the shipping expenses are as low as possible when the items are sent out for delivery.

Second Generation: Statistical Learning

In the second generation of AI, chess computers like the famous Deep Blue emerged. These computers are fed with the rules of the chess game and can independently calculate the best moves among all possibilities. A significant milestone occurred in May 1997 when Deep Blue achieved a historic victory by defeating the reigning Chess World Champion Kasparov.

The triumph was made possible through substantial computing power and specialized hardware. Getting good at the game of Go, popular mainly in Asia, took nearly 20 years after computers conquered chess. Go is trickier than chess because there are way more possible moves. It has too many options in a giant puzzle. Just looking at all the possible moves doesn't easily tell you the best one.

But in March 2016, a program called AlphaGo did something amazing. It beat the world champion, Lee Sedol. It did this by using smart techniques from deep learning, clever computer tricks called deep neural networks. The second generation of artificial intelligence is often called statistical learning. Although the technology behind it has been around for a while, it started gaining traction particularly after 2012.

This era of AI has brought us things like speech recognition and machine translation, and everyday assistants like Siri, Alexa, and Google Assistant. Besides the familiar achievements, AI has quietly become statistically better than humans in less known areas like lip reading. People often refer to this as statistical superiority in the second generation of AI because it excels in solving problems involving uncertain decisions, in situations where there's no clear right or wrong answer right away. Fixed rules become crucial for statistical systems. Without these rules, understanding and explaining the system's behavior would be much trickier.

For instance, researchers have shown how neural networks can be tricked by specially created patterns. In one example, a machine might see a cheetah in an image that appears as random noise to a human. It shows the issues of interpreting AI decisions in situations where certainty is elusive.

Third Generation: Explainability and Generative Models

In the third generation of artificial intelligence, the focus for researchers is not just on creating a system that can achieve a desired result but also on developing the ability to explain how it arrives at that result. Transparency is a key consideration in this context, aiming to make the reasons behind AI decisions understandable and transferable into a form that humans can comprehend.

Instead of bombarding the AI with lots of cheetah pictures to learn from, researchers have tried a different idea. They show the AI how to paint a cheetah. Then, instead of asking, "Does this look like a cheetah?", they ask, "If you were painting a cheetah, could it turn out something like this picture here?". This way, they are encouraging the AI to be creative and recognize cheetah-like features, not just memorize a bunch of examples.

Fourth Generation: Artificial Intuition

Now, let's talk about the fourth generation of AI called artificial intuition. It empowers computers to recognize threats and opportunities without explicit instructions, much like how human intuition allows us to make decisions without detailed guidance.

Just five years ago, the idea of artificial intuition seemed impossible. But now, big companies like Google, Amazon, and IBM are actively working on solutions. Some companies have even started putting it into practice, bringing this concept to life.

How does artificial intuition make sense of new data when it has no past information to guide it? The trick lies in the data itself. When given a current set of information, the advanced algorithms of artificial intuition can spot connections or unusual patterns among the data points. But it doesn't happen magically. Instead of using a numbers-based model right away, artificial intuition starts with a more descriptive model. It looks at the data and figures out a language that captures the overall arrangement of what it sees.

This language involves various math concepts like matrices, Euclidean space, linear equations, and eigenvalues. If you think of the big picture as a massive puzzle, artificial intuition can essentially see the completed puzzle from the get-go and then work backward to fill in missing pieces based on how different parts connect.

Artificial intuition is versatile and can be used in almost any industry, but it's making big progress in financial services. Major global banks are adopting it to uncover advanced financial cybercrimes like money laundering, fraud, and ATM hacking. Detecting suspicious financial activity is like finding a needle in a haystack with countless transactions and connected details. Artificial intuition, with its complex math algorithms, quickly pinpoints the five most important parameters related to the activity and presents them to analysts for further investigation.

The Next Generation of AI

Lastly, we have the next generation of AI, where we have supervised learning changing the game, especially in natural language processing. Thanks to a recent breakthrough called the Transformer, which was introduced by Google about three years ago, NLP has made remarkable advancements.

Currently, the most common approach in AI is supervised learning. In this method, AI models learn from datasets that humans carefully organize and label into specific categories. It's called supervised because human supervisors get the data ready beforehand.

While supervised learning has led to great strides in AI, like in self-driving cars and voice assistance, it has some drawbacks. Labeling thousands or millions of data points manually is not only expensive but also a big task. The need for humans to label data before machines can use it has become a major obstacle in advancing AI in the digital age.

Keeping our data private is a big challenge since data is crucial for artificial intelligence. Privacy concerns can limit AI's progress. A solution to this is privacy-preserving AI, where AI learns from data without exposing it. One promising method for this is called Federated Learning.

Usually, we gather all the training data in one place, often in the cloud, to teach AI models. But for many reasons, like privacy and security, much of the world's data can't be moved to a central location. This makes it hard for traditional AI methods.

Federated learning helps AI learn from data without moving it all to one place, addressing these privacy challenges. Instead of needing all the data in one place to train a model, federated learning keeps the data where it is spread across different devices and servers on the edge. Multiple versions of the model are sent out, one to each device with training data, and they're trained locally on their specific data only. The resulting model parameters, not the actual training data, are sent back to the cloud. When we put together all these mini models, we get one comprehensive model that acts as if it learned from the entire dataset simultaneously.

Now, we're in an exciting time for understanding language, thanks to Transformers. OpenAI recently introduced GPT-3, a super powerful language model that can do amazing things like write poetry, create code, compose business memos, and even write articles about itself.

GPT-3 is the latest and biggest in a series of similar language models, like Google's BERT, OpenAI's GPT-2, and Facebook's RoBERTa. What makes these models special is the technology behind them called the Transformer. This innovation allows language processing to happen all at once for every part of a text, not one after the other.

Transformers use a smart feature called attention, which helps the model understand how words are related, no matter how far apart they are. It figures out which words and phrases in a passage are the most important to focus on. This technology is a game-changer in the world of language AI.

Big AI companies like Google and Facebook are already using transformer-based models. But many other organizations are just starting to use this technology in their products. OpenAI plans to let people use GPT-3 through an API, which could lead to a bunch of new startups creating cool applications with it. Transformers are going to be the basis for a whole new set of AI abilities, starting with understanding language.

Conclusion

Even though the last decade was pretty exciting for AI, the next one might be even more amazing. AI has made a lot of progress since the 1950s when Allan Turing first talked about it, and it's not slowing down. The AI we've seen so far is just the beginning.

If you've made it this far, let us know what you think in the comments section below. For more interesting topics, make sure you watch the recommended video that you see on the screen right now. Thanks for reading!