artificial intelligence, Machine Learning and Deep Learning They are common terms in enterprise IT and are sometimes used interchangeably, especially when companies market their products. However, the terms are not synonyms and there are important differences between them.
AI refers to the simulation of human intelligence by machines. Its definition is constantly evolving: As new technologies are developed to better simulate humans, the capabilities and limitations of AI are being reexamined.
These technologies include machine learning (ML), deep learning, which is a subset of machine learning, and neural networks, which are a subset of deep learning.
To better understand the relationship between the different technologies, here is a primer on artificial intelligence, machine learning and deep learning.
What is Artificial Intelligence?
The term AI has been around since the 1950s. It describes our struggle to create machines that can challenge what has made humans the dominant life form on Earth: intelligence. But intelligence is difficult to define because what we perceive as intelligence changes over time.
Early AI systems were rule-based computer programs that could solve somewhat complex problems. Instead of hard-coding every decision the software had to make, the programs were split into a knowledge base and an inference engine. Developers input facts into the knowledge base, and the inference engine queried those facts to derive a result.
This type of AI had limitations, especially since it relied so heavily on human input: rule-based systems lacked the flexibility to learn and evolve, and are hardly considered intelligent anymore.
Modern AI algorithms can learn from historical data, allowing them to be used in a variety of applications, including robotics, self-driving vehicles, power grid optimization, and natural language understanding (NLU).
While AI can sometimes demonstrate superhuman performance in these areas, there is still a long way to go before AI can compete with human intelligence.
As of now, there is no AI that can learn like humans. That is, there is no AI that can learn from just a few examples. AI needs to be trained with huge amounts of data to understand any topic. Algorithms do not yet have the ability to transfer understanding from one domain to another. For example, if you learn a game like StarCraft, you can play StarCraft II just as quickly. But it's a whole new world for an AI, and it has to learn each game from scratch.
Human intelligence also includes the ability to attach meaning. For example, humanWe can identify humans in photos and videos. AI has acquired that ability too. But we also know what to expect from humans. We never expect humans to be 4-wheel drive and emit carbon dioxide like a car. But an AI system can't guess this unless it's trained with enough data.
The definition of AI is fluid. We were amazed when AI algorithms became so sophisticated that they surpassed the capabilities of skilled human radiologists, only to later learn of their limitations. That's why we now distinguish between current limited AI and the human-level AI we are striving for, or artificial general intelligence (AGI). All AI applications that exist today fall under limited AI (also known as weak AI), while AGI is currently theoretical.
What is Machine Learning?
Machine learning is a subset of AI and is a type of AI algorithm developed to mimic human intelligence. The other type of AI is symbolic AI or “good old” AI (i.e. rule-based systems that use if-then conditions).
Machine learning marks a turning point in AI development. Before ML, we tried to teach a computer all the variables for every decision it makes. This gives us full visibility into the process and allows algorithms to handle many complex scenarios.
In its most complex form, an AI follows multiple decision branches to find the one that leads to the best outcome. IBM's Deep Blue was designed this way to beat Garry Kasparov at chess.
But there are many things that cannot be defined by rule-based algorithms, like face recognition. A rule-based system would have to detect different shapes, such as circles, and determine how those shapes are arranged and within what other objects to compose the eyes. Even more difficult for a programmer would be to write the code to detect the nose.
Machine learning takes a different approach. It ingests huge amounts of data and detects patterns, allowing the machine to learn independently. Many ML algorithms work using statistical formulas and big data. It can be said that advances in big data and the vast amounts of data collected are what made machine learning possible in the first place.
ML algorithms used for classification and regression include linear regression, logistic regression, decision trees, support vector machines, naive Bayes, k-nearest neighbors, k-means, random forests, and dimensionality reduction algorithms.
What is Deep Learning?
Deep learning is a subset of machine learning, and while it still involves teaching machines to learn from data, it represents a milestone in the evolution of AI.
Deep learning was developed based on an understanding of neural networks, and although the idea of building AI based on neural networks has been around since the 1980s, deep learning only really took off in 2012. Just as machine learning was made possible by the vast amounts of data we generate, deep learning's adoption was made possible by the availability of much cheaper computing power and advances in algorithms.
Deep learning has enabled much smarter results than machine learning originally allowed. Take the example of facial recognition: what data do we provide to an AI to detect a face when the only information we can provide is the color of the pixels, and how does the AI learn what to look for?
Deep learning utilizes layers of information processing, with each layer learning progressively more complex representations of data: the first layer learns colors, the next learns shapes, the next learns combinations of those shapes, and finally learns actual objects. Deep learning marked a breakthrough in object recognition. Its invention led to rapid advances in AI in several areas, including NLU.
Deep learning is the most sophisticated AI architecture we have developed today. Deep learning algorithms include:
- Convolutional neural networks.
- Recurrent neural networks.
- Long short-term memory networks.
- Generative adversarial networks.
- A network of deep beliefs.
AI, Machine Learning, and Deep Learning
While machine learning and deep learning have clear definitions, what counts as AI changes over time. For example, optical character recognition was once considered AI but is no longer. However, a deep learning algorithm that is trained on thousands of handwritten characters and can convert them into text would be considered AI by today's definition.
Machine learning and deep learning are used in a variety of applications, including natural language processing, image recognition, classification, etc. These technologies help businesses augment their workforce by offloading mundane, repetitive tasks to intelligent machines, freeing employees to focus on more creative or higher-level thinking tasks.
Machine Learning and Deep Learning
Deep learning is a type of machine learning that uses complex neural networks to replicate human intelligence. Both deep learning and machine learning typically require access to advanced hardware, such as high-end GPUs, and large amounts of power. However, deep learning models differ in that they can learn faster and more autonomously than machine learning models and make better use of large datasets. Applications that use deep learning include facial recognition systems, self-driving cars, and deepfake content.
Both machine learning and deep learning are major milestones in the evolution of AI, and there will be many more as we move towards current AGI.
Similarities between AI, Machine Learning, and Deep Learning
Apart from their differences, AI, machine learning, and deep learning share similarities, including:
- All three of these disciplines contribute to creating intelligent machines.
- It makes solving today's complex problems easier than older programming methodologies.
- They use algorithms to make predictions, identify important patterns in data, and perform tasks.
All three of these disciplines use data to train models, which are fed datasets and then analyze and learn from key information, such as insights and patterns, ultimately resulting in a highly performant model that learns from experience.
Data quality and diversity are key elements in every form of artificial intelligence. Diverse data sets mitigate inherent biases embedded in training data that can lead to biased outputs. High-quality data minimizes errors and ensures model reliability. Like humans, models need to learn iteratively to improve their performance over time.
Editor's note: This feature article was written by David Petersson and expanded by Cameron Hashemi-Pour to include more information about the similarities between AI, ML, and deep learning.
