Understanding the definitions and differences of ML and AI can be daunting, so I’m always on the look-out for well written content. This post from codementor.io does a really nice job, and I have attempted to further summarize below. Check out their site for the full post.
“AI is the concept in which machine makes smart decisions whereas Machine Learning is a sub-field of AI which makes decisions while learning patterns from the input data.
Artificial Intelligence is about acquiring knowledge and applying them to ensure success instead of accuracy.
The Four types of Artificial Intelligence are:
Lacks historical data
Completely reacts to a certain action
Reinforcement learning where a prize is awarded for any successful action and penalized vice versa
Past data is kept adding to the memory
Theory of Mind
Yet to be built as it involves dealing with human emotions, and psychology
The future advancement of AI
Machines could be conscious, and super-intelligent
Three of the most common usage of AI
Computer Vision such as Face Recognition
Natural Language Processing like Amazon’s Alexa or Apple’s Siri
What is Machine Learning?
Machine Learning is a state-of-the-art subset of Artificial Intelligence which let machines learn from past data, and make accurate predictions.
In Machine Learning, the concept of neural networks plays a significant role in allowing the system to learn from themselves.
Machine Learning is mostly about acquiring knowledge and maintaining better accuracy instead of success.
A sub-field of Machine Learning is Deep Learning. However, Deep Learning requires enormous computational power and works best with a massive amount of data. It uses neural networks whose architecture is similar to the human brain.
Machine Learning could be subdivided into three categories –
Both the input feature and the corresponding target variable is present
Only the input features are present
The algorithms need to find patterns
Rewarded with a prize for every correct move and penalized for every incorrect move”
“We see all this activity only continuing to accelerate. The world will give us more open sourced and commercially available machine intelligence building blocks; there will be more data; there will be more people interested in learning these methods; and there will always be problems worth solving. We still need ways of explaining the difference between machine intelligence and traditional software, and we’re working on that. The value of code is different from data, but what about the value of the model that code improves based on that data?
Once we understand machine intelligence deeply, we might look back on the era of traditional software and think it was just a prologue to what’s happening now. We look forward to seeing what the next year brings.”
“When it comes to deep learning frameworks, TensorFlow is one of the most preferred toolkits. However, one framework that is fast becoming the favorite of developers and data scientists is PyTorch.
PyTorch is an open source project from Facebook which is used extensively within the company.
PyTorch focuses on simplicity and accessibility. It can be used by a diverse set of users ranging from researchers to academicians to a developer. PyTorch uses a technique known as dynamic computation that makes it easy to train neural networks. TensorFlow is based on static computation that executes the code only after the graph of operations is generated.”