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A.I. in Ghost Hunting Devices

Updated: Jul 13, 2023

Artificial Intelligence, also known as AI, is becoming more and more prevalent in our everyday lives. It can be found in many of our smart devices. But can AI be designed into ghost hunting equipment without needing the internet? Well, the answer is not simple. Yes, it is possible if you have a ton of money to spend, but it's not affordable for most people.


Let's first understand how AI works in our smartphones. When you use AI on your smartphone, it doesn't actually perform the computations on the device itself. Instead, it sends your inputs to a computer in the cloud (a remote server) that runs the AI algorithms. The AI calculates the answer and sends it back to your smartphone to display the result. This is why your smartphone requires internet access to use AI.


 

Yes, it is possible if you have a ton of money to spend, but it's not affordable for most people.

 

The main reason why affordable handheld devices don't have on-board AI without internet access is because it would cost more money than most people can afford. There are several factors that contribute to this:


  1. Limited Processing Power: AI algorithms need a lot of computational power to perform complex tasks like understanding human language, recognizing images, or learning from data. Handheld devices have limited processing power compared to larger computers, so fitting the necessary hardware for efficient AI processing into a handheld device at a low cost is a big challenge.

  2. Memory and Storage Constraints: AI models require a large amount of memory and storage to work effectively. Storing the large datasets and trained models within a handheld device is difficult and expensive, as it requires a significant amount of memory capacity. Memory and storage components are costly, and it's challenging to integrate them into a compact handheld device while keeping costs low.

  3. Energy Efficiency: AI algorithms consume a lot of energy, which quickly drains the battery of a handheld device. Running complex AI tasks on a handheld device requires a powerful processor, which consumes more energy. Balancing processing power with energy efficiency is crucial to provide a good user experience. Achieving optimal energy efficiency while keeping costs low is a difficult trade-off.

  4. Custom Hardware Requirements: Some AI applications, particularly those involving deep learning and neural networks, need specialized hardware like graphics processing units (GPUs) or application-specific integrated circuits (ASICs). These custom hardware solutions are expensive and may not be feasible within a limited budget for a handheld device. Designing custom hardware for AI while keeping costs low is a significant challenge.

  5. Training and Updates: AI models often require training using large datasets, which is computationally intensive and time-consuming. Doing this training on a handheld device is not feasible due to limited resources. Additionally, AI models often benefit from periodic updates to improve their performance and accuracy. Without internet access, it becomes difficult to update and refine AI models, limiting their effectiveness.

  6. Connectivity and Data Access: AI applications often rely on real-time data or cloud-based services to enhance their capabilities. Without internet access, the AI functionalities of a device are limited, reducing its potential usefulness.


Even with advancements in technology, designing a handheld device with AI capabilities without internet access for an affordable price, say under $500, remains a difficult challenge. Overcoming the limitations of processing power, memory, energy efficiency, custom hardware, and data access within such cost constraints requires significant innovation and cost optimization.


Therefore, as of 2023, there are no cost-effective ways to have a stand-alone AI device that fits in the palm of your hand without relying on cloud-based resources. Developing portable AI without internet access is a formidable challenge.


Edge AI

In recent years, artificial intelligence (AI) has made significant strides, enabling groundbreaking advancements in various industries. One notable development is the emergence of edge AI, which brings AI capabilities directly to non-connected devices. By embedding AI models onto chips, edge AI overcomes the limitations of connectivity and cloud reliance. However, this is still an expensive technology and out of reach for most consumers. But, if AI were to find its way into handheld ghost hunting devices it would be through edge AI. But at a steep price.


AI computations can be computationally intensive, leading to increased power consumption and reduced battery life in edge AI devices. Running AI tasks on a resource-constrained device such as a portable handheld would shorten the life of the battery.


Achieving optimal performance in edge AI implementations while keeping costs low is a challenging trade-off. Balancing the computational resources, memory requirements, and hardware specifications to meet the desired performance levels within a cost-effective framework can be complex and may involve compromises in terms of accuracy, speed, or functionality.

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