Artificial intelligence, machine learning, and deep learning are some of the biggest buzzwords in the tech industry right now. The craze around these concepts has never been greater, and the world’s biggest enterprises are investing massive sums of money in these fields. The global AI market is valued at $87 billion today, the machine learning market at $15 billion, and the deep learning market at a relatively paltry but still staggering $12 billion! To learn more about how artificial intelligence, machine learning, and deep learning differ from each other, keep reading!
Some of the most important people in the tech industry are convinced that these innovations are going to change the world. Google’s CEO Sundar Pichai has even gone so far as to say that “AI is probably the most important thing humanity has ever worked on.” Bill Gates, on the other hand, believes that “inventing a breakthrough in machine learning would be worth ten Microsofts.” And he knows a thing or two about the value of Microsoft!
As a result of their ubiquity in today’s tech lingo, terms such as Artificial Intelligence and machine learning are often thrown about interchangeably. The three aforementioned concepts are quite similar. But there are certain intrinsic differences among all three of them. If you want to pursue a career in tech, you must know how these concepts differ from each other. You can definitely go about it just to satisfy your curiosity!
Understanding AI, ML, and DL
1. Artificial Intelligence
AI is defined as the simulation of human intelligence processes by machines, particularly computers. Applications of AI include robotics, natural language processing, big data analysis, etc.
2. Machine Learning
ML is a subset of artificial intelligence. It aims at training software systems to accurately predict outcomes without being explicitly programmed to do so. It does so by relying on historical data related to inputs similar to the ones the system is presently considering.
3. Deep Learning
DL is a subset of machine learning itself. It aims to train machines to obtain knowledge in the same way that humans obtain knowledge. Deep learning algorithms are based on the structure of the human brain itself and are hence called artificial neural networks.
So, to put it very simply, artificial intelligence is a broad overarching field encompassing both machine learning and deep learning. Machine learning is a subset of artificial intelligence, while deep learning is a subset of machine learning itself. Got it? Great!
Now, let us dive further into the differences between these three fascinating fields of research!
Difference Between Artificial Intelligence and Machine Learning
As explained above, artificial intelligence is a very broad field, of which machine learning is only one aspect. In fact, when people talk about “artificial intelligence,” they are mostly referring to the part of artificial intelligence that deals with machine learning. However, AI is much more than just that! Technologies such as machine learning, deep learning, neural networks, natural language processing, and computer vision are all parts of AI.
Essentially, an AI system can recreate human cognitive functions, such as learning or solving problems. This can be achieved through any of the various technologies mentioned above or even a combination of some or all of them! Machine learning is, in fact, one of the most commonly used technologies to design artificial intelligence systems. This has led to the two terms being used interchangeably, and a great deal of confusion has arisen. But it is still simply one of the techniques used to achieve the “goal” of artificial intelligence.
Think of it in terms of publishing. Your “goal” is to print a text. Now you can do this through printing at a printing press, printing from an electronic printer, publishing it as a PDF in ebook format, or even woodblock printing! In the same way, machine learning is simply a “means,” with the end being AI, i.e., a system that can think and act like a human. Check out the guide on Machine Learning vs AI to know for more information.
How Does Machine Learning Work?
You can better understand the difference between the two by learning how machine learning works. Machine learning algorithms seek to identify patterns in data given to them. For example, a dataset of fifty students might be given to the algorithm.
The amount of time each student spent studying in the week before a test is mentioned, alongside their grades on that test. The algorithm detects the pattern that all the students who studied above four hours a day got an A+ on the test, all the students who studied above three hours a day got an A, and so forth.
Then, if another dataset is supplied, which consists only of students and the time they spent studying before the test, the algorithm can predict that all the students who studied more than four hours a day will get A+.
This is a simplistic example, but you get the gist. An important component of machine learning algorithms is optimizing the model. In the above analogy, the model is “students studying above 4 hours a day get A+ grades.” If further datasets are supplied, it might turn out that, in fact, only students who study more than four and a half hours a day will get A+.
The algorithm then adjusts its model accordingly. The more data the algorithm gets, the more accurate its model will become. In other words, the algorithm improves its accuracy over time, i.e., it learns. Hence the name “machine learning”!
Types of Machine Learning
Machine learning can be of two types: supervised and unsupervised. Supervised machine learning revolves around data that has already been labeled or classified. Unsupervised machine data revolves around what is unlabelled, i.e., completely raw. Because of the requirement that the data be partially pre-processed, supervised machine learning requires more human intervention than its counterpart. It is used to make predictions, while unsupervised machine learning is generally used to find relationships and patterns within datasets.
Natural language processing is another AI technique mentioned above. While machine learning looks for patterns in data to predict outcomes, natural language processing aims to train computers both to understand human speech and synthesize realistic-sounding human speech. It interprets written text and converts them into commands a computer can understand. While they are very different concepts, they are often used together in pursuit of the larger goal, namely, designing artificial intelligence systems!
Applications of Machine Learning in Artificial Intelligence
Some of the fields in which machine learning has been used to design artificial intelligence systems are:
- Retail: Retailers use machine learning and artificial intelligence to design recommendation engines and allow customers to search with images instead of written searches.
- Banking: AI systems are used in fraud detection and predicting risks on a particular investment.
- Cybersecurity: AI systems are invaluable cybersecurity tools. They can easily churn large amounts of data to detect anomalous behavior or traffic that humans might overlook.
- Transportation: Transportation organizations, including those in the public sector, use AI tools to improve their efficiency in various ways. These include analyzing passenger behavior to identify areas suitable for investment and forecasting traffic patterns to identify the quickest and most efficient routes.
- Marketing and sales: In the advertisement and sales sector, AI tools are used to analyze data from customers to create personalized offers, forecast sales, gauge customer sentiment, etc.
These are just some of the countless practical applications of machine learning and artificial intelligence. In tandem, there is virtually nothing they can’t achieve!
Differences Between Machine Learning and Deep Learning
So far, we’ve covered the differences between artificial intelligence and machine learning, with the latter being the subset of the former. Now, we will move on to the differences between machine learning and its own subset, namely deep learning.
As their respective names suggest, both machine learning and deep learning revolve around computer networks’ “learning,” i.e., improving their performance over time without requiring human intervention. The key difference between the two methods is in how computers learn.
As mentioned above, deep learning is the subset of machine learning which revolves around artificial neural networks. These are algorithms that are inspired by the structure and workings of the human brain itself – hence the name. Deep learning is intended to teach machines to think in the same way humans do – with the same nuance and intuition, which are often lacking in other, more conventional forms of AI.
The biggest difference between deep learning, and the broader concept of machine learning, is the degree of human intervention required in each case. Conventional machine models do improve at their job over time as they ingest more data, but they still require a large amount of help from humans.
For example, if the machine learning algorithm makes an inaccurate prediction, then its developer has to intervene and make some adjustments. The algorithm will only make the prediction; it cannot determine the accuracy of the prediction on its own and will need a human to do this for it.
Deep learning networks, on the other hand, can (theoretically) determine the accuracy of their predictions on their own. If the predictions they returned are inaccurate, they will determine what changes need to be made to the model. Then, they will implement the changes.
Thus, the machine will “fix” itself. The optimal deep learning algorithm hence requires no human intervention once it has gotten off the ground. Check out Machine Learning vs Deep Learning guide to know more.
Types of Deep Learning
Like machine learning, deep learning networks are basically of two types: convolutional neural networks and recurrent neural networks. Convolutional neural networks (CNNs) are designed specifically for image processing and object detection. They are commonly used in computer vision, that subset of AI which teaches machines to process visual data. Facial recognition technologies are a good example of computer vision techniques in action.
Recurrent neural networks, on the other hand, have integrated feedback loops that enable the algorithm to “remember” prior data points. They use this memory of past events to decide how to act in the present or even in the future. This form of deep learning is very close to conventional machine learning but essentially a “better” version of it.
Do remember that deep learning is a subset of machine learning, which is itself a subset of artificial intelligence. So, all the differences between AI and machine learning also apply to AI and deep learning. Deep learning is just one of many techniques used to design artificial intelligence systems. It is used in many of the broader fields, such as computer vision and machine learning, which form integral parts of AI.
Applications of Deep Learning
Some of the applications of deep learning tech are as follows:
- Virtual assistants: Virtual assistants like Alexa and Siri rely on deep learning to interpret the commands you give in natural human speech. They also use deep learning to learn about your preferences in various matters, such as music or dining.
- Entertainment: Wimbledon has used deep learning techniques to analyze audience sentiment, player expressions, etc., to identify key moments to turn into highlight packages. By efficiently identifying the moments that audiences will most want to see, they have saved time and money.
- Self-driving cars: MIT is currently using deep learning techniques to design driverless cars that can operate even without pre-programmed maps!
Conclusion
Despite all the advances which have already been made, the fields of AI, machine learning, and deep learning are still in their infancy. The market value of all three of these fields is predicted to grow exponentially shortly. So, if you get into these fields now, you will continue to reap benefits for a long time!
You don’t have to take my word for it; just listen to Microsoft CEO Satya Nadella – “AI is the defining technology of our times. AI and machine learning are being infused into every experience deeply, and we’re going to see a lot more of that.”
But before you can dive into making breakthroughs in AI, you’ll first need to understand the differences between AI, machine learning, and deep learning. In that vein, we hope you enjoyed this article on how artificial intelligence, machine learning, and deep learning differ from each other!
