The Limitations of Current AI: A Closer Look at the Computational Bottleneck

Translator

As artificial intelligence (AI) continues to advance, it is essential to examine the fundamental limitations of current AI models. This article will delve into the computation-centric approach used in AI and highlight the potential shortcomings of this methodology. Moreover, it will explore the concept of Associative Memory, a novel approach inspired by the human brain, and discuss its implications for the development of more efficient and effective AI systems.

Current AI models are built upon a mathematical formula based on regression to the mean and weighted partial derivatives. While this approach has led to remarkable progress in machine learning, it is not representative of how the human brain processes information. The brain stores patterns in memory, which are retrieved and compared with input patterns to match or update partially or fully. This process is characterized by partial activation and associative search, making it more akin to a blend of the analytical skills of Sherlock Holmes and the search capabilities of Google.

The brain's memory structure can be understood as an internet-like network with two intersecting forms: horizontal and vertical, associative and hierarchical. This unique architecture enables humans to learn, solve problems, and act quickly, despite their slower neural processing speed. Unfortunately, most AI systems do not incorporate this associative memory approach, relying instead on more straightforward algorithms.

The development of AI systems that mimic the human brain's associative memory capabilities would require a significant shift in the current computational paradigm. This would involve creating learning algorithms that approximate pattern review and search, as well as generate computationally efficient engrams. While some researchers have made progress in this area, such as Walter Fritz and his General Learner project, this concept remains largely unexplored.

The ethical implications of this emerging technology are far-reaching. As AI systems become increasingly autonomous, they will need to rely on more accurate and efficient associative memory mechanisms to make decisions and interact with humans. The development of these systems could have significant consequences for various industries, from healthcare and finance to transportation and education.

In conclusion, while AI has made tremendous progress, its limitations in computation are a significant obstacle to achieving more intelligent and efficient systems. By exploring the concept of Associative Memory, researchers and developers can create more effective AI models that better mirror the human brain's ability to learn and process information. The potential benefits of this approach are significant, and it is essential that we prioritize its development to ensure that AI systems are both more intelligent and more ethical.