Artificial Learning, Machine Learning and Deep Learning: Know The Difference

Artificial Learning, Machine Learning and Deep Learning: Know The Difference

Welcome to 2025!

Meet Susan. Your friend, assistant, manager and everything you need in your routine. She keeps your emails organized, schedules your meetings. Sees to your basic home needs and replenishes your stock of groceries whenever it’s about to finish. She can be your best friend when you are low. She paints and writes poetry when you need a bit of Art and Literature to unwind.

That sounds a lot like Samantha, voiced by Scarlett Johansson in HER. You possibly can’t have missed that beautiful movie. Well, at least you must have heard of it.

Susan or Samantha are – simply put – more refined and technological version of Siri, Cortana. Google Assistance, or Google DeepMind.

Tech companies – large and small – are racing to make AI a part of everyday life. The digital world is full of buzz words that seemed real only on reel – Artificial Intelligence, Machine Learning, Data Crunching, Deep Learning, Reinforcement Learning. Big words with little or no meaning in the real word, not so long ago.

Let’s understand the three most frequently heard terms these days: “Artificial Intelligence”, “Machine Learning” and “Deep Learning”.

What’s The Difference?

Since there is no standard definition for any of these terminologies, they are often used rather loosely as inter-changeable terms. Hence, distinguishing them gets all the more difficult. Moreover, the generally understood meaning of these terms has evolved over time. What was meant by AI in 1960 is very different than what it means today.

The easiest way to think of their relationship is to visualize them as concentric circles.

  • AI — the idea that came first and is the largest circle
  • Machine Learning — which blossomed later as the middle circle
  • Finally, Deep Learning — which is driving today’s AI explosion, as the smallest and inner-most circle.

See Also: An Insight into 26 Big Data Analytic Techniques

From Spark to Lightening

AI paved the way for research Labs soon after the coining of the term at Dartmouth Conferences in 1956. While it took decades before technology could catch up with our imagination, we appear to be finally on the cusp of an AI revolution, with more venture capital investment, more big tech companies getting involved in R&D, and more everyday use of AI in our lives.

Let’s walk through how an AI Spark turned into an AI Lightening.

ARTIFICIAL INTELLIGENCE: Machines in-built with Human Intelligence

Artificial Intelligence is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. It is the field of study looking for ways to create computers that are capable of intelligent behavior. A machine is deemed ‘intelligent’ if it can do things normally associated with human intelligence.

We have seen these machines in movies as the good guys (C-3PO in the Star Wars series) or the bad guy (the cyborg assassin in The Terminator).

To qualify as artificially intelligent, a machine should be able to do a few basic things like:

  • Natural language processing (i.e. communicate with no trouble on a given language)
  • Automated reasoning (using stored information to answer questions and draw new conclusions)
  • Machine learning (the ability to adapt to new circumstances and detect patterns).

It is common knowledge that some facets of Human Intelligence can be exhibited by AI. Now the question is where does that Intelligence come from. That’s where Machine Learning comes from.

Read Also: The Rise of Machines: Real World Applications of A.I.

MACHINE LEARNING: Approach to achieve Machines with Human Brain

Machine learning is an artificial intelligence discipline geared towards the technological development of human knowledge. It explores the development of algorithms that learn from given data. An algorithm is a series of steps to accomplish a task. Machine Learning came directly from minds of the early AI crowd, and the algorithmic approaches included decision tree learning, clustering, reinforcement learning, association rule learning and the likes.

These algorithms are able to learn from past experience (i.e. the given data) and teach themselves to adapt to new circumstances and perform certain tasks. Machine learning allows computers to handle new situations via analysis, self-training, observation and experience.

Machine learning has facilitated the continuous advancement of computing through exposure to new scenarios, testing and adaptation, while employing pattern and trend detection for improved decisions in subsequent (though not identical) situations.

Though there were so many algorithms, none achieved the ultimate goal of AI. There’s a reason computer vision and image detection didn’t come close to rivaling humans until very recently. Earlier, it was too brittle and prone to errors.

Time, and the right learning algorithms made all the difference.

DEEP LEARNING: Techniques to train the Machine’s Brain

Another algorithmic approach from the early machine-learning crowd. Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. It involves a particular kind of mathematical model that can be thought of as a composition of simple blocks of a certain type, in a multi-layered structure and where some of these blocks can be adjusted to better predict the final outcome.

The word “deep” means that the composition has many of these blocks are stacked on top of each other, and the tricky bit is how to adjust the blocks that are far from the output, since a small change there can have very indirect effects on the output.

Deep Learning tries to emulate the functions of inner layers of the human brain, and its successful applications are found in image recognition, language translation, or email security. Deep Learning creates knowledge from multiple layers of information processing. The Deep Learning technology is modeled after the human brain, and each time new data is poured in, its capabilities get better.

How Deep Learning Has Improved AI?

Deep Learning has enabled many practical applications of Machine Learning and by extension the overall field of AI. Driverless cars, preventive healthcare, even better movies recommendations, are all here today or on the horizon.

Deep Learning could be a key puzzle piece leading to the creation of smarter, more human-like AI. Deep learning could improve all facets of AI, from natural language processing to machine vision. Think of it as a better brain that’ll improve how computers learn. It could enhance virtual assistants like Siri or Google Now to deal with requests they’re not familiar with. It could process videos and generate short clips summarizing the content.

Who knows, maybe one day everyone will have their own versions of Samantha!

Parina Hassani

Parina Hassani is working as a Research Analyst at Systweak Softwares. She researches on the Future Era of Technology. She brings to us this new future face of technology and how it would change our world. Beyond this she has an inclination for fiction novels, exploring different cuisines, anchoring and, confectionery and dessert cooking.

Leave a Reply

Your email address will not be published. Required fields are marked *