Artificial intelligence is everywhere, and people across industries are now aware of it, but what is artificial general intelligence, and why are people beginning to talk about it?
These were the exact thoughts that came to my mind when I heard of artificial general intelligence or AGI. So, what is it, and why is this a big deal according to some?
Let’s find out.
What is Artificial General Intelligence (AGI)?
Have you seen those videos or read blogs where you are warned about the evil power of artificial intelligence and that the technology is going to take over the world and eliminate non-compliant humans, while all you get is rather less-intelligent GPT, which is no way near threatening?
Well, that scary technology can be artificial general intelligence. As of today, the subject of artificial general intelligence is thought to be a hypothetical form of AI that can think, process, and act like a human. A real, conscious, self-aware, logical human.
Today, artificial general intelligence (also sometimes referred to as strong AI) is a mere concept and yet to be created, but even the idea of AGI happening a few years, or decades from now is exciting. Imagine if you could ask AI to do something. It just doesn’t apply an algorithm to get the work done but critically assesses the situation to ensure you get the right output—isn’t that amazing?
The existing forms and models of AI are no way near to what AGI is, and AI engineers and developers are working to make it a reality—but it might take years or decades to see it in action.
AGI Vs. AI: Are These Similar?
A lot of people around you (and me) might not understand the difference between AGI and AI—but the fact of the matter is both these terms are confusing, at least till AGI is publicly introduced and becomes common.
AGI, or artificial general intelligence, is a subset of AI but an advanced version of it. The AI in practice today, or the models that are being widely used, are often trained or real-world data that has been cleansed and refined to omit any inaccuracies and are expected to perform a single or a set of tasks. However, there’s a big limitation to achieving maximum efficiency, and that is: all these models are often limited to a single context and cannot analyze data, and hence, cannot have human-like intelligence. It performs certain defined actions and follows the already laid out rules in the algorithm to steer its actions and processes, which are also in a specific environment.
The case with artificial general intelligence is starkly different from what we know and understand about AI (or weak AI). The idea of having an AGI system means that it will not necessarily have a set of rules or pre-defined environments to function appropriately. This means it will take a more logical, problem-based approach to arrive at a solution—and not follow some generic path already fed into the weak AI model. This also entails that AGI is set to be more flexible, hence making it acceptable in various industries and applications for an increased number of tasks.
What Are the Capabilities of an AGI System?
When implemented, AGI should be able to carry out a range of tasks that no current system is able to perform. No doubt that the systems in place today are increasingly intelligent and solve a variety of problems for humans, but they are still not at the level of human or general intelligence.
Here’s some of what we can expect from an artificial general intelligence system:
- It will be able to perform/execute abstract thinking tasks, opening new doors of research.
- It will have the transfer learning capability, which means more diverse applications.
- It will be able to make decisions, not just by using an algorithm, but by using common sense.
- It will have contextual or background information, which will enable better outputs or decision-making.
Artificial General Intelligence Examples & Use Cases
AGI isn’t real yet, at least for the public. No system currently exists that works and performs with human-like abilities. However, there are a few applications of weak or narrow AI development that take it near to what will be found in artificial general intelligence, or strong AI, with better accuracy and efficiency and without the limitations and constraints of limited memory.
Let’s have a look at some of the AGI examples and use cases:
· Fully Autonomous Vehicles
Fully autonomous vehicles or self-driving cars are a form of AI that is near to what an AGI system will be like. These self-driving models/systems approximate or sometimes far surpass human intelligence and depict AGI-like characteristics. The systems are pre-trained with data to identify objects, human and non-human, and made in a way that all regulations and safety measures are well in place.
· IBM Watson
There are some calculations (for space missions, super-complex engineering, and military-tech purposes) that normal computers cannot handle—that’s where supercomputers like IBM Watson take the front seat and utilize their incredible power and AI capabilities to perform those impossible calculations.
· Large Language Models
For the last couple of years, we have experienced a significant increase in the use of large language models or LLMs. Generative Pre-trained Transformers, or GPT for short, are publicly released models by OpenAI that reflect the incredible capability of these AI models. It is true that these models often produce outputs that are inaccurate and very basic, but they hint at what could be made if further advancements are made possible.
These are some use cases where artificial general intelligence can be incorporated, and if done appropriately, it can massively impact the output and improve practical usefulness and efficiency.
Benefits of Artificial General Intelligence
One might wonder that if it is such a game-changing technology, what are the benefits of artificial general intelligence, and why does it not have the hype it deserves?
While I might not be able to answer the latter part of the question, I can certainly list some of the probable benefits of artificial general intelligence against real-world problems and what could be achieved if AGI becomes a reality. Let’s have a look:
· Cure of Chronic Illnesses
Scientists, for a long time, have been trying to find practical and effective cures for multiple chronic illnesses like cancer. This will be a huge stride toward making a better and safer world.
· Expedited Space Exploration
Space exploration is often hindered by a plethora of limitations, but with the advent of artificial general intelligence, it could be made possible, and who knows, we might be able to experience something revolutionary.
· End to Global Warming
Global warming is among the world’s most pressing concerns. The practical use of AGI could be the gateway to devise intelligent, practically viable, and effective policies to reduce the global carbon footprint, adopt more sustainable practices across different sectors, and put an end to global warming—which is now a looming threat.
Technologies Contributing to AGI Research
It is now established that AGI is still a work in progress, but there are a few technologies that are aiding scientists and researchers in taking small but concrete steps toward a full-fledged AGI system. Below are a few of the emerging technologies commonly used for this purpose:
· Deep Learning
Deep learning is among the emerging technologies that contribute to AGI research. It studies raw data and forms conclusions based on that through neural networks. It usually has multiple layers that are used to decipher and understand complex relationships. There are many incredible systems in place, built by AI experts, that are able to understand text, audio, images, and video data and process various other types of information.
· Generative AI
Generative AI, also commonly known as Gen AI or generative artificial intelligence, is part of deep learning. This technology has seen stark improvement in adoption and distribution over the past few years. It works by classifying and understanding data and producing unique outputs, including texts, images, code, audio, and videos. Generative AI models are trained with massive amounts of data, allowing them to cater to a variety of queries that are similar to human-generated content and have some form of intelligence. However, because of its limitations and its generative nature, the outputs are not always factually correct.
· Natural Language Processing
NLP, or natural language processing, is exactly what the name suggests. It is a type of AI that enables systems or models to comprehend and then generate human-like content or human language. It uses machine learning and computational linguistics to understand and make sense of data and build contextual relationships. NLP is fuelling the research for AGI in multiple ways.
· Computer Vision
For the analysis of visual and spatial data, computer vision is the right technology. The technology now has widespread applications, including self-driving vehicles that process and analyze real-time camera feeds and steer the vehicles clear of any obstacles or apply breaks on time. Computer vision is also widely implemented in multiple factories, mainly to ensure worker safety in critical areas, analyze equipment effectiveness, and at conveyor belts for classification of different objects based on their color, size, and other factors.
· Robotics
Although robotics is not directly linked to the ongoing AGI research, robotic process automation is a significant application of the AGI system, where organizations can build complete plants through specific mechanical systems that intelligently perform physical movements. For example, an AGI-powered robotic arm could perform tasks that are not currently automated, especially the ones requiring physical human touch. The sensory perception and manipulation capabilities of AGI could transform that.
Challenges in AGI Research
Like every other research, scientists and researchers face certain challenges in developing an ideal AGI system. These challenges include:
· Sensory Perception
Sensory perception today is not possible through machines or software. However, AGI aims to solve this issue. It is among the biggest challenges faced by researchers to solve the sensory perception issue and interact with the outside world or objects. Apart from the robotic capabilities and maneuverability, the system must also be able to perceive information like humans. Today’s technology needs significant advancement before we can use machines to perform sensory tasks.
· Form Connections
The weak or narrow AI models are limited because of the data they are trained on and cannot transfer the expertise of one domain to another—while humans can very often do that. This challenge of having to draw connections and transfer the knowledge of one domain to another, as well as the ability to give accurate results, even when the input is from unfamiliar data, is hindering scientists from making great strides.
· Emotional Intelligence
Another challenge in developing artificial general intelligence is the incorporation of human emotions or emotional intelligence. The ability to function as humans and possess characteristics is a blocker in building an artificial general intelligence system. The ability to respond based on emotions, among other factors, and not only text-based linguistic datasets, makes this technology stand out—however, it has yet to be built.
Ethical Considerations and the Future of Artificial General Intelligence
One thing is for sure—the benefits of artificial general intelligence are incredible and far-reaching, but there’s also a flip side to it. AGI should be approached with caution. While AGI is immensely useful, it can act as a two-edged sword if not managed and regulated properly.
The challenges we face with current AI technologies hint at even greater risks with the emergence of artificial superintelligence or strong AI—surpassing common human intellect. Without proper regulation, we may confront AI systems perpetuating harmful biases and making decisions contrary to human ethics.
The control (or the lack of it) over AGI raises questions about power dynamics. While AGI holds immense potential across various industries, it’s important for organizations and leaders to prioritize ethical considerations in its development. Ensuring that AGI and other AI technologies are geared towards useful and beneficial purposes should be a primary focus before widespread adoption.
Frequently Asked Questions about Artificial General Intelligence (AGI)
Q1: What is the difference between AGI and artificial intelligence (AI) we have today?
AI currently in use is called Narrow AI or weak AI, meaning it excels at specific tasks like playing chess or recognizing faces—and not general applications. The use of these Narrow AI models is limited.
AGI, on the other hand, aims to achieve human-level intelligence that is capable of learning and adapting to any situation. There’s no such system yet; however, researchers and scientists are working to make it a possibility.
Q2: Does AGI exist yet?
No, artificial general intelligence is still a theoretical concept.
While AI has made significant advancements, it hasn’t reached the level of general intelligence seen in humans. Some forms of AI applications are called earlier versions of AGI.
Q3: When will AGI be achieved?
There’s no definitive answer to when we will witness a fully capable AGI model or when it will be achieved. We don’t know it yet.
AI experts, scientists, and researchers have varying predictions, with some believing it could be decades away, while others say it might never be achieved.
Q4: What are the potential benefits of AGI?
AGI could revolutionize many fields, including retail, healthcare, modern warfare, security, manufacturing, education, and more. It could also handle complex tasks currently beyond human capabilities that we cannot even think of today.
Q5: Are there any risks associated with AGI?
Some experts worry about the potential dangers of AGI surpassing human control. Similar to what we have seen in movies Matrix and Terminator.
While developing AGI is important, it is even more significant to have safeguards in place to mitigate potential risks related to ethical boundaries, data privacy, and if things get out of control.
