8 January 2021 • 4 min read
Artificial Intelligence (AI) vs. machine learning (ML) and deep learning (DL)
Although the three terminologies are typically used interchangeably, they no longer pretty refer to similar things. Let's look into the three buckets of computer science and compare artificial intelligence vs. machine learning with a little deep learning grasp.
Therefore, is there a distinction between artificial intelligence vs. machine learning and deep learning?
Here is a figure that tries to visualize the relationship between them and how they relate to each other:
Figure 1: Artificial intelligence vs. machine learning and deep learning
As you can see above three concentric circles, deep learning is a subset of machine learning, which is additionally a subset of artificial intelligence.
So, artificial intelligence is the all-encompassing idea that at the beginning erupted, then observed with machine learning aid that thrived later, and ultimately deep learning that is promising to boost artificial intelligence advances to some other level.
Let's dig deeper so that you can recognize which is higher for your particular use case: AI, ML, or DL.
As the title suggests, artificial intelligence can be loosely interpreted to mean incorporating the human brain into machines. Artificial intelligence is a constellation of several different technologies that enable machines with human-like intelligence levels to feel, understand, act, and learn. For this reason, everyone's definition of artificial intelligence is different: AI isn't just one thing.
Making things a little bit more complicated, I need to mention that AI can be divided into two groups.
Most of what we use in our day-to-day lives is narrow AI, which conducts a single task or a collection of related jobs.
Here are a few examples:
Such systems are robust, but the field of play is narrow: they tend to concentrate on driving performance. However, narrow artificial intelligence has immense transformational power with the right application, and it continues to change how we work and live.
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An Artificial General Intelligence (AGI) would be the intelligence of a machine capable of understanding the world and any human being, and with the same potential to learn how to perform a massive range of tasks with extremely high efficiency. AGI doesn't exist and has featured in science-fiction stories for more than a century, but researchers and companies claim it is possible to build one soon.
Artificial intelligence is the broader thinking that consists of the whole thing from Good Old-Fashioned AI (GOFAI) to futuristic applied sciences such as deep learning. Do you remember deep blue compute capable of playing chess? It was not artificial intelligence and machine learning, neither reinforcement learning. It was a brute force search approach/science. It was Good Old Fashioned artificial intelligence (ai).
Machine learning (ML) intends to allow machines to research utilizing the furnished records and make correct predictions. The learning part is essential here. Machine learning is a subset of ai; it's genuinely a method for completing AI.
Machine learning entails giving data to the algorithm and permitting it to research more remarkable about the processed information. It's a learning process conducted by the algorithm. Give no data, and ML will find it hard to act.
Imagine a store with ai and machine learning-powered scanners that identify the kind of fruit, based on its attributes:
However, the closing row offers the weight and texture solely, and there is no data about a kind of fruit. By the way, data scientists are critical here, as they design the table structure. Wrong structure can mislead business operations. ML will not go beyond that, so the boundaries in which the algorithm operates should always be valuable for business profits.
AI and machine learning can be seasoned to "guess" whether the fruit is an orange or an apple. It's the learning from the data part and decision-making job. Programming conducted by the software engineer team, scientists can now unveil the most critical part - deciding.
After the algorithm is fed with the data, it will examine the different traits between an orange and an apple. The algorithm can automatically learn, but that would be just an algorithm. Machine learning is different. It can make decisions.
Machine learning-powered systems (a subset of ai), which learn from data, will be able to fill up thousands of rows (executing millions of tasks) with a blink of the eye speed. It's is the most exciting part of the science - automation, possible thanks to pattern recognition and computer science.
AI and ML systems can provide prompt responses, which means the difference between fruits can be grasped very quickly. The more tasks are performed, the more knowledge is accumulated by artificial intelligence, machine learning techniques.
For this reason, big data is critical for ai and ml technologies. The machine learning algorithm can learn very fast, and the data pile size doesn't matter. Regarding our example, the more fruits are scanned by the scanner, the better the computer program's decision-making process will be.
It is crucial to have smart data scientists and intelligent programming in place to fix the puzzle. By smart, I mean people who understand the importance of the business purpose of AI systems.
Machine learning algorithms are cataloged into:
Supervised learning is divided into
Unsupervised learning into:
- clustering (finding data clusters of comparable objects, such as a pool of customers buying specific products daily, customers complaining about the same feature, customers clusters buying similar items),
- affiliation (finding frequent sequences of objects, let's say the app can learn that you usually take UBER and stop for a Starbucks on the way)
- dimensionality discount (prognostication, characteristic collection, and function extraction)
I think this is the right moment to mention that human intelligence relates to adaptive learning and experience. It does not always depend on pre-fed data like the ones required for ML. In my opinion, artificial intelligence and machine learning will always co-exist with humans. Human intelligence is capable of inventing fantastic hardware and software, which then form a computer system or complex systems. If you look at the following examples from this perspective, it becomes evident that ai machine learning supports human behavior, letting us increase efficiency and productivity.
A well-known and typical example of machine learning in the real world is image recognition. Data science through data scientist hands can help a lot here.
Image recognition examples from the real-world:
Machine learning is often commonly used inside an image for facial recognition. The system will recognize commonalities and match them to faces utilizing a database of individuals.
Machine learning ai can be a word-for-text translation. A computer program can transform speech (another great medium of data) and voice captured live into text files. Intensities on time-frequency bands may also segment the voice.
Examples of ai powered understanding of speech:
Applications such as Google Home or Amazon Alexa are among the widespread applications of ai type of recognition. This is not the only reason why data science has become so popular.
Machine learning can help to diagnose diseases. Many doctors use speech-recognition chatbots to identify patterns in symptoms or image recognition to find COVID.
Examples of real-world medical diagnostics and problems approach:
Machine learning may divide the available data into groups. Afterward, analysts will measure the likelihood of a fault when the classification is complete.
Examples of predictive analysis:
From unstructured data, machine learning can extract structured information. Organizations collect vast amounts of consumer data. ML algorithms automate the process of explaining datasets for predictive analytics tools.
Application examples:
These procedures are usually repetitive, but machine learning can decode a vast amount of data and check for patterns, which then science or business can use for specific improvements.
As in the past mentioned, deep learning knowledge is a subset of machine learning; it's undoubtedly an approach for realizing machine learning. In different words, deep learning is the subsequent evolution of machine learning.
Deep learning algorithms are roughly stimulated through the patterns discovered in the human brain. Like we use our brains to find patterns and classify several sorts of information, deep learning algorithms can be taught to accomplish identical duties.
Our brain stores unstructured data and still approach vast problems. Artificial neural networks (ANNs) are algorithms that intend to imitate the way our brains make decisions.
Deep learning can routinely find out the points to be used for classification. Machine learning requires these facets to be described manually.
Source: John White, MSys Training
As you can see, there is a significant difference and crystal clear relationship between AI and computer algorithms.
One of the most common answers ai and ml, and neural networks world is "it depends." When should we use a neural network? It depends. When is the reinforcement learning algorithm suitable? It depends. Artificial intelligence and machine learning offer a massive variety of benefits, but strategists need to recognize which approach serves business best with the help of science. Machines, networks, and data science create a compelling combination, which needs to serve business eventually.
Understanding the difference between ai and machine learning and deep learning helps to make better decisions. Each pool of algorithms drives different results and demands different types, sizes, and quality of data input.
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