“Deep Learning, on the other hand, is a very young field of Artificial Intelligence that is powered by artificial neural networks.
It can be viewed again as a subfield of Machine Learning since Deep Learning algorithms also require data in order to learn to solve tasks. Although methods of Deep Learning are able to perform the same tasks as classic Machine Learning algorithms, it is not the other way round.
Artificial neural networks have unique capabilities that enable Deep Learning models to solve tasks that Machine Learning models could never solve”
“‘In a recent Northeastern University and Gallup survey that found 71 percent of Americans feared the surge in AI would cause more job loss than gain.’
Computer scientist John McCarthy coined the phrase “artificial intelligence” in 1956,
Algorithms are mathematical formulas that amount to a set of processing instructions — akin to a recipe — that aim to solve a specific problem.
Finds patterns in a large amount of data. Machine learning comes in three forms: supervised, unsupervised and reinforcement learning.
Utilizes a web of computation models called “neural networks” that are designed to mimic human brains.
Natural language processing
NLP technology uses machine-learning algorithms that tag parts of speech and the relationships between words to analyze the meaning in text and audio.
Uses deep learning models to manipulate photos and videos to create realistic images of people doing or saying something they never did
Predictions are all over the map about whether technology will usher in new work to offset the loss of jobs to artificial intelligence in upcoming years.
A recent Oxford Economics report that found 20 million manufacturing jobs will be lost by 2030
On more positive note, a 2018 report from World Economic Forum — a nonprofit composed of the world’s 1,000 top companies — predicts that while 75 million jobs will be displaced by automation, it will generate 133 million new roles
A growing number of AI experts and politicians agree that the advancements in AI have outpaced government regulation.
Although the next stop remains unknown, the AI train isn’t stopping anytime soon. As World Economic Forum Founder and Executive Chairman Klaus Schwab aptly summarized the Fourth Industrial Revolution, ‘There has never been a time of greater promise or greater peril.'”
“A recent global study co-sponsored by IBM and the National Retail Federation found that two in five retailers are already using intelligent automation (via deep learning or machine learning), and adoption is expected to double in the retail industry by 2021.”
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”