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Is technology advancing at a faster rate than we humans can keep up? Well yes. This year alone there was a lot of progress one after another and it was difficult for us to keep up. It seemed like every day we learned something new and were on our toes.
With these advances, discussions around artificial general intelligence (AGI) are becoming more and more frequent. It used to be a science fiction conversation, which we saw in movies and books, in which the plots were a bit far-fetched and unrealistic.
But especially in 2023, the situation has changed radically. There is a lot of public interest in AI and how it will shape the future. Generative AI systems like ChatGPT have turned the world upside down, with some loving it and others worrying about job replacements.
This comes back to the topic of AGI. But what is AGI?
Artificial general intelligence (AGI) is a machine capable of performing any type of intellectual task, in the same way as a human.
That being said, the big question on many people’s minds is how close we actually are to reaching AGI and what will happen when we get there.
That’s what this blog is going to be about, so buckle up and have fun learning about our potential future…
So we know that AGI is an AI system capable of performing any intellectual task that a human being can perform. This means that machines will need to possess human-level intelligence, without any assistance. The foundations of AI began in the early 1900s, and many have argued that achieving AGI would achieve the ultimate goal of AI’s legacy.
This is not to say that AI systems do not currently possess the ability to perform tasks with high precision, better than humans. However, AI systems are missing something, namely their ability to be versatile. This means that they do not have the ability to adapt quickly to new situations without the need for instructions.
We human beings have adapted over the years and survived different situations. Our all-around ability is tied to survival, which is why we’re so good at it.
Many recent developments have shaped the world of technology, including generative AI systems such as ChatGPT. I would like to clarify that generative AI and artificial general intelligence have their similarities, but they are different. Generative AI is a deep learning model that can generate content such as text and images, based on the data it was trained on.
To give you an example, an AI chess program will most likely end you in a game of chess, but the same AI system will not be able to inform you about what is currently happening in world politics . This is because it is limited to a specific domain, and that’s it.
As we mentioned, AGI lacks general-purpose capability, which generative AI also lacks – because that is not its focus. Generative AI will help AGI on its journey, but it is important to note that they are not the same.
So we understand that we haven’t exactly reached AGI, but where are we now and what’s in the works?
Research and development
There have been years and years of research into deep learning, which is a subfield of machine learning. It is a method of machine learning that teaches computers to do what is natural for humans. It trains an algorithm to predict outputs, given a set of inputs.
Using large amounts of data on sophisticated neural networks has enabled AI systems to tackle complex tasks such as natural language processing (NLP) and image recognition. There is a lot of learning and improvement happening in the deep learning sector to facilitate the birth of AGI.
Reinforcement learning
Along with this approach, there has also been an increase in reinforcement learning. The goal of reinforcement learning is to train a model to return an optimal solution using a sequence of solutions and/or decisions created for a specific problem. In order for the model to choose the right solution/decision, a reward signal is put in place.
If the model approaches the goal, a positive reward is given; however, if the model performs further from the goal, a negative reward is given. Machine learning models learn by understanding their environment and receiving feedback based on their actions.
Adaptable AI Systems
Naturally, as you progress through anything, you will encounter challenges that you must overcome. When it comes to research and development, the major challenge facing AGI is the ability to build a system that can understand the input context and adapt to it in the same way that humans do. Researchers are investigating new ways for an algorithm to think more creatively to overcome this problem. For example, some researchers are investigating the possibility of intelligent AI systems that engage in continuous learning throughout their lives.
Based on this, are we really close to AGI?
Hardware limitations
As you can imagine, it is not easy to build these amazing AI systems. They require a lot of computing power, which has pushed the development of specialized hardware such as GPUs and TPUs. And this stuff isn’t cheap either. So you can imagine how many weeks and months it will take to build an accurate and robust AI system with the amount of time, data and other resources devoted to it.
It’s difficult to say because AGI experts have divided opinions. Some say the AGI could be achieved within the next few years, while others believe we still have decades of work left.
The only thing that can determine how close we are to AGI is the pace of technological progress that is being made. The more current and new technological systems advance, the more experts are able to find the missing pieces of the puzzle. The more breakthroughs we see in the technological world, the closer we get to AGI.
Another aspect that governments and organizations are taking into consideration more than ever is the ethical implications of such AI systems for society. Advancing a discourse on AGI could have disastrous consequences if we fail to understand and control these AI systems.
That being said, we are seeing more and more organizations pumping more money into the tech industry. Many are jumping on the bandwagon to take on the competitive market, while others are trying to create a completely new market.
The answer to this blog question is that we will have to wait and see what technological advancements appear in the near future to better understand how close we really are to AGI.
Nisha Arya is a Data Scientist, Independent Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing data science career advice or tutorials and theoretical knowledge around data science. She also wants to explore the different ways in which artificial intelligence is/can benefit the longevity of human life. A passionate learner, looking to expand her technical knowledge and writing skills, while helping to mentor others.
