Artificial Intelligence And Machine Learning
. Artificial intelligence can be loosely defined as the science of mimicking human behavior. Machine learning is the specific subset of AI that trains a machine to learn. The concept emerged from pattern recognition and the theory that computers can learn without being programmed to complete certain tasks. Things like cheaper, more powerful computational processing, the growing volumes of data, and affordable storage has taken deep learning from research papers and labs to real life applications. However, all the media and hype surrounding AI, has made it extremely difficult to separate exciting futuristic predictions from pragmatic real-world enterprise applications.
John Giannandrea is Apple’s senior vice president of Machine Learning and AI Strategy . He said in an interview “Find me something where we’re not using machine learning.”
So let’s see how can he say that !!
What is Apple’s AI strategy?
Apple used to do most of its work behind closed doors. That has changed in recent years, as machine learning powers numerous features in Apple’s devices and Apple has increased its engagement with the AI community.
let’s take an handwriting example :
Giannandrea made the case that Apple is best positioned to “lead the industry” in building machine intelligence-driven features and products. “We made the Pencil, we made the iPad, we made the software for both. It’s just unique opportunities to do a really, really good job.” said Giannandrea .
Machine learning is used to help the iPad’s software distinguish between a user accidentally pressing their palm against the screen while drawing with the Apple Pencil, and an intentional press meant to provide an input. It’s used to monitor users’ usage habits to optimize device battery life and charging, both to improve the time users can spend between charges and to protect the battery’s longterm viability. It’s used to make app recommendations.
When big tech companies talk about artificial intelligence today, they often mean machine learning. Machine learning is a subset of AI. Many lauded gadget features — like image recognition — are driven by a subset of machine learning called “deep” learning.
Next Example is of Siri:
Siri, which is perhaps the one thing any iPhone user would immediately perceive as artificial intelligence. Machine learning drives several aspects of Siri, from speech recognition to attempts by Siri to offer useful answers.
Savvy iPhone owners might also notice that machine learning is behind the Photos app’s ability to automatically sort pictures into pre-made galleries, or to accurately give you photos of a friend named Dhruv when his name is entered into the app’s search field.
Image signal processors (ISP):
Phones have long included image signal processors (ISP) for improving the quality of photos digitally and in real time, but Apple accelerated the process in 2018 by making the ISP in the iPhone work closely with the Neural Engine, the company’s recently added machine learning-focused processor.
Some of the ways that Apple uses machine learning in its recent software and products:
There are many new experiences that are powered by machine learning.
- language translation
- On-device dictation
- New features of health, like sleep ,hand washing, heart health and things like this.
- App predictions or keyboard predictions.
- AI cameras
All of these things benefit from the core machine learning features that are built into the core Apple platform.
Apple performs machine learning tasks locally on the device
On hardware like the Apple Neural Engine (ANE) or on the company’s custom-designed GPUs (graphics processing units).. The Neural Engine is an octa-core neural processing unit (NPU) that Apple designed to handle certain kinds of machine learning tasks.When Apple has talked publicly about the Neural Engine, the company has shared performance numbers, like 5 trillion operations per second in 2018’s A12 chip.
They have target CoreML from any of the popular machine learning things, like PyTorch or TensorFlow, and then you essentially compile down your model and then you give it to CoreML.
There are many things to discuss about implementation of AI and ML by Apple , so yes “Find me something where we’re not using machine learning.” said Giannandrea is correct .
Here i have taken example of one popular brand using AI and ML , but there are many more brands using AI and ML , now day’s it’s very common ,There are 5.11 billion unique mobile users worldwide in 2019, and 2.71 billion of them use smartphones. 100 million people have started using smartphones in the past year. 52% of the world’s population are mobile internet users. There will be 2.87 billion smartphone users worldwide in 2020.