Artificial intelligence (AI) has come a long way since its inception, with technologies like machine learning and natural language processing revolutionizing various industries. However, despite the significant advancements in AI, there is still a long way to go before we can achieve true AI. That's where Artificial General Intelligence (AGI) comes in.
What is AGI
AGI stands for Artificial General Intelligence, which refers to the ability of an artificial intelligence system to perform any intellectual task that a human can do. AGI is a level of intelligence that is equivalent to or beyond human intelligence, and it is designed to learn, understand and perform any cognitive task that a human can perform.
Unlike narrow AI systems, which are designed to perform specific tasks such as playing chess or recognizing speech, AGI is designed to learn and apply knowledge to any task that is thrown at it. AGI is able to think abstractly, reason, plan, solve problems, comprehend complex ideas, learn quickly, and even communicate effectively in natural language.
AGI in AI revolution
AI is divided broadly into three stages: artificial narrow intelligence (ANI), artificial general intelligence (AGI) and artificial super intelligence (ASI).
Source: UBS, as of 15 August 2016
The first stage, ANI, as the name suggests, is limited in scope with intelligence restricted to only one functional area. ANI is, for example, on par with an infant. The second stage, AGI, is at an advanced level: it covers more than one field like power of reasoning, problem solving and abstract thinking, which is mostly on par with adults. ASI is the final stage of the intelligence explosion, in which AI surpasses human intelligence across all fields.
At present, we are at the stage of Artificial Narrow Intelligence (ANI), and Rodney Brooks, co-founder of iRobot and roboticist at MIT, predicts that AGI will not be developed before the year 2300. While the development of AGI may take years, there has been significant progress in AI advancement, particularly in generative AI, which includes Dall-E 2, Midjourney, Deep Dream Generator, and Big Sleep.
According to reports from the industry, the global market for AGI is projected to reach a value of approximately USD 144.2 billion by 2026, with a Compound Annual Growth Rate (CAGR) of 41.6%. AGI refers to a machine that can perform tasks and activities at the same level as humans. It is important to note that as there are no existing AGI systems or technologies, any potential uses, such as assisting with mundane human activities, remain speculative.
AGI vs AI: what is the difference
The key difference between AI and AGI lies in their scope and versatility. AI is a more narrow and focused form of intelligence that is designed to perform specific tasks. AGI, on the other hand, is a more general and versatile form of intelligence that is designed to perform any intellectual task that a human can do.
Specifically, Artificial Intelligence (AI) is a broad term that encompasses any machine or system that can perform tasks that typically require human intelligence, such as problem-solving, learning, perception, and decision making. AI systems are designed to perform specific tasks, such as playing chess, recognizing speech, or identifying objects in images.
On the other hand, Artificial General Intelligence (AGI) is a higher level of intelligence that is designed to perform any intellectual task that a human can do. Unlike AI, AGI is designed to learn and apply knowledge to any task that is thrown at it. AGI is able to think abstractly, reason, plan, solve problems, comprehend complex ideas, learn quickly, and even communicate effectively in natural language.
Examples of AGI
As of now, there is no true example of Artificial General Intelligence (AGI) that exists in the world. Current AI systems are considered to be narrow or weak AI systems, which are designed to perform specific tasks and are not capable of generalizing to new tasks or understanding the world in the same way that humans do.
However, there are ongoing efforts and research towards developing AGI, and there have been some notable achievements in the field. Some of the notable examples of AGI research are:
OpenAI's ChatGPT-4: GPT-4 is a language model developed by OpenAI that is capable of generating human-like text. The model has been trained on a vast amount of text data and can perform a wide range of language-related tasks, such as translation, summarization, and question-answering.
Google's AlphaGo: AlphaGo is an AI system developed by Google that defeated the world champion in the complex game of Go. The system uses a combination of deep learning and reinforcement learning to learn the game and make strategic moves.
IBM's Watson: Watson is an AI system developed by IBM that is capable of natural language processing and can answer questions posed in natural language. Watson has been used in various applications, including healthcare and finance.
The future towards AGI
While Google's BARD and other similar platforms and tools may appear to be AGI due to their advanced capabilities, they are actually language models created from large datasets. In March of this year, a petition was launched called "Pause Giant AI Experiments: An Open Letter," which has received over 27,565 signatures and calls for a six-month ban on AI systems that are more powerful than GPT-4. Recently, US President Joe Biden called for a meeting with major players in the AI industry, including Microsoft, Google, and OpenAI, to discuss product safety and potential risks.
GPT-4 is considered a significant step towards achieving AGI, and it is expected to arrive much earlier than Rodney Brooks' predicted timeline of 2300. It could potentially arrive as soon as 2027, 2030 or 2050.
Bottom up
Developing AI systems towards the AGI stage in the AI revolution requires developers to train a large amount of data for AI models, especially for the Generative AI one. These datasets not only require a large quantity but also need to be of high quality in the process of collecting and annotating. Pixta AI is a leading company that provides Data sourcing and Data annotation services at a low price with high-quality, thereby expanding opportunities for accessing AI and AGI for both small and medium-sized businesses, not just letting the AI industry to be dominated by large corporations.
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