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Is technology advancing at a faster rate than we humans can keep up with? Well yes. This year alone there were a lot of advancements, one after another and it was hard for us to keep up. It seemed like every day we were learning something new and were on our toes.
With these advancements, the conversation around Artificial General Intelligence (AGI) is becoming more and more frequent. It was once a conversation of science fiction, which we saw in movies and books, in which those storylines were a bit far-fetched and unrealistic.
But in the year 2023 in particular, that has changed drastically. The public has a big interest in AI and how it is going to shape the future. Generative AI systems such as ChatGPT have swept the world off their feet, with some loving it, and some concerned about job replacements.
This comes back to the topic of AGI. But what is AGI?
Artificial General Intelligence (AGI) is a machine that can perform any type of intellectual task, the same way a human can.
With that being said, the big question on a lot of people’s minds is how close we are to actually achieving AGI and what will happen when we do.
This is what this blog will go through, so buckle up and enjoy learning about our potential future…
So we know that AGI is an AI system that can perform any intellectual task that a human being can. This means that machines will have to possess human-level intelligence, without any help. The groundwork for AI began in the early 1900s, with many stating that achieving AGI would complete the ultimate goal of the AI legacy.
It’s not to say that AI systems currently don’t possess the ability to perform tasks at a highly accurate level, better than humans. However, there is something that is missing with AI systems, and that is their general-purpose ability. This means that they lack the ability to adapt to new situations in a quick manner, without the need for instructions.
We human beings have adapted over many years and survived through different situations. Our general-purpose ability links to survival, this is why we’re so good at it.
There have been a lot of recent developments that have shaped the technology world, one in particular is Generative AI systems such as ChatGPT. I’d like to state that Generative AI and Artificial General intelligence have their similarities, but they are different. Generative AI is a deep learning model that has the ability to 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 finish you at a chess game, but the same AI system will not be able to tell you about what’s currently happening in world politics. This is because it is limited to a specific domain, and that’s all.
As we mentioned, AGI lacks general-purpose ability, which is also what Generative AI lacks – as that is not its purpose. Generative AI will aid AGI in its journey, but it is important to note that they are not the same.
So we understand that we haven’t exactly achieved AGI, but where are we currently 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 machine learning method that teaches computers to do what comes naturally to humans. It trains an algorithm to predict outputs, given a set of inputs.
The use of large amounts of data on sophisticated neural networks has allowed AI systems to be able 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 industry to aid the birth of AGI.
Alongside this approach, there has also been an increase in reinforcement learning. The aim of reinforcement learning is to train a model to return an optimum solution by using a sequence of solutions and/or decisions that have been 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 performs closer to the goal, a positive reward is given; however, if the model performs further away 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, during the progression of anything, you will come across challenges that you need to overcome. In the matter of research and development, the major challenge that AGI is facing is the ability to build a system that can understand the input context and adapt to it the same way humans do. Researchers are looking into new ways that an algorithm can think more in a creative manner to overcome this. For example, some researchers are looking at the possibility of intelligent AI systems that go through continual learning throughout their lifespan.
Based on this, are we even anywhere near AGI?
As you can imagine, it is not simple 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 these hardware are not cheap either. So you can imagine how many weeks and months it takes to build an accurate and robust AI system with the amount of time, data, and other resources that go into it.
It is difficult to say because the experts of AGI have mixed opinions. Some say that AGI could be achieved in the next few years, whilst others believe that we still have decades’ worth of work left.
The only thing that can determine how close we are to AGI is the rate of technological advancements that come through. The more advanced current and new technological systems get, the closer experts are to finding the missing parts of the puzzle. The more breakthroughs we see in the tech world, the closer we are to AGI.
Another aspect that governments and organizations are taking into consideration now more than ever is the ethical implications of such AI systems to society. Pushing a narrative on AGI could lead to disastrous consequences of not being able to understand and control these AI systems.
With that all being said, we are seeing more and more organizations pumping more money into the tech industry. Many are jumping on the bandwagon to meet up with the competitive market, and others are trying to create a completely new market.
The answer to this blogs question is that we will have to wait and see what technological advancements will come out in the near future to have a better understanding of how close we really are to AGI.
Nisha Arya is a Data Scientist, Freelance Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.