Bridging the gap between learning and real-time adaptation with limited AI memory

Discover how Limited Memory AI enhances decision-making by retaining short-term data, bridging the gap between learning and real-time adaptation in AI systems.

Written by Tyler von Harz

Artificial Intelligence (AI) varies in complexity and ability, ranging from basic systems that respond to specific commands to more advanced ones that might one day think like humans.

Today, one practical and widely-used type of AI is called “Limited Memory AI.”

Limited Memory AI plays a vital role in practical applications because it can make more sophisticated decisions by learning from historical data.

Limited Memory AI is particularly relevant with current technology as it bridges the gap between simple automated responses and more complex decision-making processes required in dynamic environments.

What is actually limited memory AI?

AI systems can be categorized based on their capabilities:

• Narrow AI is designed to perform specific tasks and is the only form of AI that we have achieved so far. Examples include voice assistants like Siri and Alexa, self-driving car technology, and Netflix’s recommendation engine.
• General AI aims to simulate human intelligence, enabling machines to perform a variety of tasks that require human-like cognitive abilities.
• Super AI refers to a level of artificial intelligence that surpasses human intelligence across all fields, including creativity, decision-making, and emotional understanding.

Limited Memory AI refers to artificial intelligence systems that can learn from and retain information over a short period to make decisions or predictions.

Unlike simple reactive machines, which cannot use past information, limited memory AI incorporates data from recent inputs to improve its responses or actions.

However, it does not maintain a long-term memory, which distinguishes it from more advanced AI that simulates human-like memory processes.

“Limited memory AI can learn from past data to make decision, but their memory is short-lived”

Within the broader AI ecosystem, limited memory AI serves as an essential middle ground.

It supports applications that require adaptation to new data, albeit temporarily, thus offering a more dynamic response than basic AI models.

For instance, in autonomous vehicles, limited memory AI utilizes recent data like speed, distance, and environmental conditions to make immediate navigational decisions, which are crucial for safety and efficiency.

These systems typically involve machine learning models that are trained using historical data loaded into memory in a batch-wise or incremental manner.

This data is used just long enough to make immediate decisions and is then discarded or replaced by newer data. The transient use of data helps these AI systems remain fast and efficient, particularly in applications where real-time processing is critical.

Limited memory AI examples

Limited Memory AI is integral to several modern technologies, notably in autonomous vehicles and virtual assistants.

In autonomous driving, these AI systems continuously process and temporarily store environmental data to navigate safely. They adapt by learning from immediate past experiences, like the speed and proximity of nearby vehicles, to make real-time navigation decisions.

Similarly, virtual assistants like chatbots utilize Limited Memory AI to enhance user interactions by analyzing recent user input.

Since they can deliver personalized responses and remember user preferences over short periods, they are limited in the capability of their memory.

Is ChatGPT limited memory AI?

ChatGPT is based on the Generative Pre-trained Transformer architecture. It can be considered a type of limited memory AI.

However, it aligns more closely with models that maintain an ongoing, albeit limited, data retention capability. This capability allows ChatGPT to generate responses based on the immediate context provided by a conversation.

Like many large language models, it does not retain information beyond an individual session, reflecting its limited memory functionality.

The versions of ChatGPT that support memory at the time of this writing — such as 4o — would not be considered limited memory AI.

Lots of people, find it also difficult to distinguish the difference between Gen AI vs LLMs, or the difference between ChatGPT and other popular AI models, I recommend checking our Pieces blog to read more stuff like that.

How limited memory AI works

The fundamental aspect of Limited Memory AI is its ability to retain temporary data that is crucial for immediate tasks. This data is stored in a volatile memory format, which means it can be quickly accessed and modified but is not designed for long-term retention.

The storage architecture often involves a sliding window mechanism, where data enters into memory, stays for a set period during which it is processed, and then is discarded as new data comes in.

Limited Memory AI systems utilize data structures like buffers or caches that hold onto the recent inputs and outputs for a short duration.

For example, an autonomous vehicle might store recent sensor readings that reflect current traffic conditions and use this data to navigate effectively.

Limited Memory AI heavily relies on deep learning techniques, particularly neural networks, designed to identify patterns and make predictions based on the temporary data stored in the system. Each node in a neural network acts like a neuron in the human brain, processing inputs based on their weighted importance and passing on the output to the next layer.

Neural networks in Limited Memory AI are typically trained using backpropagation, which adjusts the weights of the connections between nodes based on the error rate of the output compared to the desired result.

Training happens continually as new data is processed, allowing the system to improve its accuracy and efficiency over time.

However, the training only adjusts the model based on the currently available data in the temporary memory, which is then discarded to make space for new data.

What are the advantages of limited memory AI?

• Efficient data processing — Handles real-time data efficiently by storing only recent and relevant information.
• Adaptive learning — Improves responses over time based on the latest data, enhancing decision-making accuracy.
• Cost-effective — Reduces computational and storage costs by limiting data retention.
• Scalability — Easier to scale in dynamic environments as it does not require extensive historical data storage.
• Real-time decision making — Ideal for applications needing immediate responses, like autonomous driving or interactive chatbots.

What are the drawbacks of limited memory AI?

• Data relevance — Must continuously update algorithms to discard outdated information and prioritize new data.
• Dependency on quality data — Performance heavily relies on the quality and relevance of the data fed into the system.
• Complexity in training — Requires sophisticated techniques to balance between memory usage and algorithm performance.

Sources

1. Review of Algorithms for Artificial Intelligence on Low Memory Devices, PETAR RADANLIEV AND DAVID DE ROURE, Department of Engineering Sciences, University of Oxford, Oxford OX1 3QG, U.K https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9502714
2. AI-enhanced collective intelligence, Hao Cui and Taha Yasseri https://arxiv.org/pdf/2403.10433