Basics of AI

What is Artificial Intelligence?

Artificial intelligence (AI) is the theory and development of computer systems capable of performing tasks that historically required human intelligence, such as recognizing speech, making decisions, and identifying patterns. AI is an umbrella term that encompasses a wide variety of technologies, including machine learning, deep learning, and natural language processing (NLPs). 

What is Natural Language Processing?

  • What it does: NLP is like teaching computers to understand and talk like humans. It helps computers understand what we say or write and respond in a way that makes sense.
  • Example: When you ask Siri a question and she understands what you mean, that's NLP at work. It's also used for things like translating languages or checking grammar in text.

What is Machine learning?

  • What it does: ML is like teaching computers to learn from examples and make decisions on their own. It's about training computers to recognize patterns in data and make predictions based on what they've learned.
  • Example: When you use a spam filter that learns to recognize junk emails over time, that's ML. It's also used for things like predicting weather or recommending movies on Netflix.

What is Deep learning?

1. Neural Networks:

Deep learning uses artificial neural networks, which are computer systems inspired by the structure of the human brain. These networks consist of layers of interconnected nodes (neurons) that process information. These deep neural networks can automatically discover and extract features from raw data.

2. Applications:

Deep learning is used in a wide range of applications, including autonomous vehicles, virtual assistants, recommendation systems, healthcare diagnostics, finance, and more. It powers many of the advanced technologies we use daily, such as voice assistants, facial recognition systems, and personalized recommendations.


                     


What is On-Device-AI? 

On-device AI is an exciting and rapidly evolving field that allows us to run artificial intelligence algorithms directly on a local device, such as a smartphone, wearable, or even an automobile, without needing to send data back and forth to remote servers. 

This form of AI utilizes the processing power of the device’s own hardware, such as CPUs, GPUs, or specialized chips like neural processing units (NPUs), to run AI algorithms locally.

The distinctions between on-device AI and cloud-based AI 

1. Location of Data Processing: 

  • Cloud-based AI: Data is sent from the device to cloud servers, where processing occurs. The results are then sent back to the device.
  • On-device AI: All data processing is done locally on the device itself, using the device’s own hardware

 2. Privacy and Security:

  • Cloud-based AI: potential privacy risks as data travels over the internet and is stored on external servers, susceptible to breaches.
  • On-device AI enhances privacy by keeping data on the device, reducing exposure to data breaches and unauthorized access.

3. Dependency on the Internet: 

  • Cloud-based AI requires a strong and continuous internet connection to function effectively.
  • On-device AI operates independently of internet connectivity, offering reliability even in areas with poor or no internet access.


4. Speed and responsiveness:

  • Cloud-based AI: Can experience delays due to data transmission times and server processing speeds.
  • On-device AI: allows for real-time processing directly on the device, which is crucial for immediate responses needed in applications like autonomous driving and real-time health monitoring.


EXAMPLES

1. Voice Assistants
Examples: Apple’s Siri, Google Assistant, Amazon Alexa 
Some basic commands, like setting a timer, adjusting the volume, or controlling smart home devices, can be processed locally. This reduces the response time and offloads simpler tasks from cloud servers.         
2. Facial Recognition
Examples: Apple Face ID, and Android Face Unlock.
Functionality: These systems use on-device AI to analyze facial features and authenticate users quickly and securely without sending data to the cloud.

3. Keyboard Predictions and Autocorrect
Examples: Gboard, SwiftKey.
Functionality: These keyboards use AI to predict the next word you are likely to type and correct typos based on the context of your conversation, all processed locally on your device.

4. Health and Fitness Tracking
Examples: Apple Watch, Fitbit, Samsung Health.
Functionality: Wearable devices use AI to monitor physical activities, detect irregular heartbeats, and provide personalized health insights by analyzing data directly on the device.



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