Difference Between Artificial Intelligence, Machine Learning and Deep Learning

Difference between Artificial Intelligence, Machine Learning and Deep Learning

Table of Contents

Put your hands up if you’ve ever been perplexed by the difference between Artificial Intelligence(AI), Machine Learning(ML), and Deep Learning(DL).

Artificial Intelligence has grown exponentially in the domain of information technology. 

Despite AI being a trending topic, Machine learning has the power to bring transformative changes across industries.

Finally, Deep learning, which is driving today’s AI explosion hailed as the ultimate future technology all over the place.

Key Takeaways

When a system accomplishes tasks consisting of a set of predefined rules which solve issues (algorithms), this “intelligent” behaviour is referred to as Artificial Intelligence.

Machine learning aims to allow computers to learn on their own utilizing available data and generating accurate forecasts.

While DL can find the characteristics to be utilized for categorization automatically, ML expects these characteristics to be given manually.

Difference Between Artificial Intelligence, Machine Learning and Deep Learning

Difference Between Artificial Intelligence, Machine Learning and Deep Learning

Usually, these three terminologies are used interchangeably.

To clearly articulate the dissimilarity between these three closely-related terms, you can see in the above image of three concentrical coaxials, Deep Learning is a subset of ML, which is also a subset of AI.

Interesting?

Massive leaps in automation can make AI biased based on the data inputs and assumptions programmers make.

For instance, Brain Chips like Elon Musk’s Neuralink will carry an immense impact on humanity and integrate AI with humans to treat neurological disorders like Alzheimer’s, Parkinson’s, spinal cord injury, and blindness. 

AI converges with Nanotechnology and allows people to control computers and prosthetics without physical interaction. 

All-encompassing concepts of Artificial Intelligence will erupt if we can control things with our minds and communicate through brain signals. Evolving universal translators will remove all language barriers, and voice recognition will be ubiquitous.

What is Artificial Intelligence?

1956 Dartmouth conference was when AI gained its name by John McCarthy along with Alan Turing, Allen Newell, Herbert A. Simon, and Marvin Minsky. Alan proposed that since people use accessible knowledge and reason to solve issues and make decisions, then why can’t robots do the same?

Also Read: Artificial Intelligence And The Future Of Humans

AI – Mimicking the intelligence or behavioural pattern of humans or any other living entity. 

Fantastic machines with all of our perceptions (and possibly more), human reasoning, and indeed the ability to think such as us – that’s what we refer to as “General AI”. We have all witnessed all these robots in movies as both a friend (C-3PO) and a foe (The Terminator).

“Narrow AI” are technologies that can perform certain activities better than Humans. Image categorization on even a site such as Pinterest and facial recognition for Facebook are instances.

Some aspects of human intelligence can be seen in these devices. But how? Where would that intellect originate? This brings things to a whole new ring, machine learning.

What is Machine Learning?

Machine learning originated in the imaginations of the early AI community, while algorithmic techniques developed throughout time, featuring inductive logic programming as well as decision tree learning. 

ML – A technique by which a computer can “learn” from data without using a complex set of rules. This method is dependent primarily on training a model with datasets. 

The efficacy or efficiency of a machine learning solution is often determined by the type and qualities of both the data and the execution of said learning algorithms, which are explained briefly in Sect. 10 Most Popular Types Of Machine Learning Algorithms.

It turned out that computer vision was amongst the finest application areas of machine learning for several years. However, it still needed a significant amount of hand-coding to get the work completed. There may be a reason why computer vision or image detection couldn’t compete with people until recently: they were too fragile and prone to error.

Time, persistence and the consideration of the correct learning algorithms made all the difference in Machine learning.

What is Deep Learning?

Artificial neural networks, a further initial machine-learning algorithmic technique, emerged and generally vanished throughout the decades.

Studies of the Neural Networks are inspired by the biology of Human brains – these are all interactions between neurons. However, unlike the real brain, where every neuron inside a given physical distance can link to any other neuron, artificial neural networks contain definite layers, interconnections, and data propagation paths.

So Deep Learning is a technique to perform machine learning inspired by our brain’s own network of neurons.

Each neuron provides a weighting towards its input based on how right or wrong it is in relation to the job at hand. The sum of such weightings affects the actual outcome.

Deep learning-trained robots can now recognize images smarter than people in specific instances, ranging from animals to detecting cancer markers in blood and tumours in MRI scans. AlphaGo, developed by Google, learnt the strategy and prepped for its Go match by competing with itself over and over again.

Deep learning has facilitated numerous beneficial concepts of machine learning, and hence the study of AI as a whole. Deep learning splits down jobs in such a manner that almost all forms of machine assistance appear to be conceivable.

Conclusion

Hope you now understand the distinction between AI, ML, and DL.

Driverless automobiles, better preventative medical treatment, and even better movie suggestions all seem to be available now or in the near future. In comparison to ML, DL demands considerable equipment and massive volumes of learning algorithms to get reliable results.

AI is both present and future. AI may potentially reach the sci-fi state we’ve always anticipated with Deep Learning’s assistance. I’ll accept a C-3PO if you have one. You are free to keep your Terminator.

Frequently Asked Questions (FAQs)

1. Is Deep Learning the same as AI?

Deep Learning is a branch of machine learning which trains a model using massive amounts of data plus advanced methods. Deep learning is a subset of machine learning, which in turn would be a part of AI. In other terms, AI is deep learning, however, deep learning isn’t really AI.

2. Should I learn Deep or AI-first?

Unless you want to work in domains like computer vision or AI-related automation, you need to first study AI. Nevertheless, machine learning might be a great place to start. In reality, machine learning is a subfield to artificial intelligence.

3. Is AI or ML better?

Considering several of the characteristics associated with defining the distinction between AI and ML, we may infer that AI does have a broader scope than ML. AI is indeed a goal-oriented area with a built-in intelligence platform. Furthermore, we cannot argue that AI is meaningless without ML algorithms.

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