The future of AI
- On February 6, 2024
Many researchers work these days trying to forecast the future of AI. The fundamental requirement to continue improving AI models is to continue improving computational power. The main reasons for this coupling are:
- Handling Complex Models: AI models have become increasingly complex and capable of handling intricate tasks such as natural language understanding, image recognition, and more. These models often require a large number of parameters, and training them efficiently demands significant computational resources.
- Improved Accuracy: More computational power allows for the training of larger and more accurate AI models. These models can generalize better, leading to improved performance on a wide range of real-world tasks.
- Faster Training Times: With increased computational power, training AI models can be done more quickly. Reduced training times mean that researchers and practitioners can iterate and experiment more rapidly, leading to faster AI advancements.
Will we continue to see improvement in computational power? According to most studies computational power is going to continue improving at a faster speed than ever (i.e. exponentially) which guarantees that AI models will continue improving and we will continue to be surprised by its new capabilities every few months.
Researches analyzing the future AI divide future AI models into two groups:
1. Artificial General Intelligence (AGI) is AI that can do anything a human can.
2. Transformative artificial intelligence (TAI) is AI that constitutes a transformative development, i.e. a development at least as significant as the agricultural or industrial revolutions.
AGI and TAI are related concepts in the field of artificial intelligence, but they refer to different stages and levels of AI development:
- Artificial General Intelligence (AGI):
- AGI refers to AI systems that possess human-like intelligence and can understand, learn, and perform tasks across a wide range of domains, just like a human being. AGI is often characterized by its ability to transfer knowledge and skills from one domain to another, exhibiting general problem-solving capabilities.
- AGI systems have the potential to outperform humans in nearly any intellectual task, adapt to new situations, and reason about the world. They are not limited to specific tasks or domains.
- Transformative Artificial Intelligence (TAI):
- TAI, on the other hand, refers to a stage of AI development that is characterized by AI systems having a transformative impact on society, the economy, and various industries. TAI is not necessarily synonymous with AGI but describes the practical consequences of advanced AI systems, which could be AGI or highly specialized narrow AI.
- TAI encompasses AI systems that significantly change the way businesses operate, disrupt industries, create new job opportunities, and impact the global economy. Examples of TAI applications include self-driving cars, advanced robotics, personalized medicine, and advanced natural language processing.
In summary, AGI represents the ultimate goal of creating AI systems with human-like general intelligence, while TAI refers to the stage where AI technologies, whether they are AGI or highly specialized, bring about transformative changes in society and industry. Achieving AGI would be a significant step toward TAI, but TAI can also be driven by specialized AI technologies that have a broad societal impact.