Limited Memory

- Limited Memory AI

Limited memory AI is a type of machine learning (ML) model that uses historical data and pre-programmed information to make predictions and perform classification tasks. It's the most widely-used type of AI today.

Limited memory AI learns from the past and builds experiential knowledge by observing actions or data. It can look into the past and monitor specific objects or situations over time. These observations are then programmed into the AI so that its actions can be performed based on both past and present moment data. 

Limited memory AI is characterized by the ability to absorb learning data and improve over time based on its experience similar to the way the human brain's neurons connect.

Limited memory AI differs from reactive machines, which operate solely in the present moment. Reactive machines have no memory and are completely reactive and task-specific. With a specific input, you would always get the same output.

- Limited Memory AI Examples

Limited memory AI is a type of AI that has short-term memory. This allows limited memory AI to temporarily store ex eriences and use them to make action. 

Although far advanced from their predecessors, ChatGPT and other market competitors (Amazon Bedrock, Google's Bard AI, DeepMind's Chinchilla AI) are still considered Limited Memory/Generative AI machines.

Here are some examples of limited memory AI: 

• Chatbots: These chatbots use machine learning and data to respond to customers. They are often used in customer service and online interactions.
• Self-driving cars: These cars use limited memory AI to store information about other cars, such as speed, distance, and speed limits. This allows them to navigate the road and make quick decisions.
• Virtual voice assistants: These assistants, such as Siri and Alexa, are examples of limited memory AI.

Other examples of limited memory AI include: 

• Machine learning chatbots
• Visual AI tools
• Text generation tools

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- Limited Memory Machines

Limited Memory machines can retain data for a short period of time. While they can use this data for a specific period of time, they cannot add it to a library of their experiences. 

Many self-driving cars use Limited Memory technology: they store data such as the recent speed of nearby cars, the distance of such cars, the speed limit, and other information that can help them navigate roads.

Limited memory is comprised of machine learning (ML) models that derive knowledge from previously-learned information, stored data, or events. Unlike reactive machines, limited memory learns from the past by observing actions or data fed to them in order to build experiential knowledge. 

Although limited memory builds on observational data in conjunction with pre-programmed data the machines already contain, these sample pieces of information are fleeting. Self-driving cars are one of the best examples of Limited Memory systems. These cars can store recent speed of nearby cars, the distance of other cars, speed limit, and other information to navigate the road.

- Limited Memory AI Autonomous Vehicle Applications

Autonomous vehicles, or self-driving cars, use the principle of limited memory in that they depend on a combination of observational and pre-programmed knowledge. To observe and understand how to properly drive and function among human-dependent vehicles, self-driving cars read their environment, detect patterns or changes in external factors, and adjust as necessary. 

Not only do autonomous vehicles observe their environment, but they also observe the movement of other vehicles and people in their line of vision. Previously, driverless cars without limited memory AI took as long as 100 seconds to react and make judgments on external factors. Since the introduction of limited memory, reaction time on machine-based observations has dropped sharply, depicting the value of limited memory AI.