Artificial Intelligence, Machine Learning, Deep Learning – What’s the Difference for Real Estate Pracitioners?
In Internet Technologies, the phrases artificial intelligence, machine learning, and deep learning are frequently used interchangeably. But there are significant differences between the three terms; they are not interchangeable.
Artificial intelligence (AI) describes how machines mimic human intelligence. Its definition changes frequently. The possibilities and constraints of AI are reviewed when new technologies are developed to more accurately emulate humans.
Machine learning (ML), deep learning (a subset of ML), and neural networks (a subset of deep learning) are some examples of these technologies.
Here is a primer for real estate practitioners on artificial intelligence vs. machine learning vs. deep learning to help you grasp the relationship between the various technologies.
Artificial intelligence: What is it?
Since the 1950s, artificial intelligence has existed. It shows our fight to create machines that can compete with human intelligence, which has made us the most intelligent lifeform on Earth. However, defining intelligence can be challenging because our understanding of intelligence evolves through time.
Early artificial intelligence systems were rule-based computer programs that had a limited capacity to address complicated issues. The software was partitioned into a knowledge base and an inference engine rather than having every choice hardcoded. The knowledge base would be filled with facts by developers, and the inference engine would then query those facts to produce responses.
This kind of AI was constrained, especially given how strongly it depended on human input. Rule-based systems are no longer regarded as intelligent since they lack the ability to adapt and learn.
Natural language understanding (NLU), robotics, self-driving cars, power grid optimization, and other fields can all benefit from the ability of contemporary AI algorithms to learn from previous data.
Even though artificial intelligence (AI) has occasionally outperformed humans in several areas, there is still a long way to go before AI can match human intelligence.
There is currently no AI that can learn the way humans do, that is, from a small number of examples. For AI to understand any topic, vast volumes of data must be used during training. The ability of algorithms to translate their knowledge from one domain to another is still a work in progress. For instance, after mastering the StarCraft video game, we can play StarCraft II with same ease. For AI, however, it is a completely new universe, and it must completely learn every game.
The ability to connect meanings is another quality of human intellect. Think of the term “human,” for instance. In addition to humans, AI has developed the ability to recognize humans in images and movies. But we also understand what to expect from people. We never anticipate a person to have four wheels and produce as much carbon as a car. An AI system, however, couldn’t infer this unless it had adequate training data.
The definition of AI is fluid. When AI algorithms were so advanced that they surpassed highly skilled human radiologists, we were astounded. But as time went on, we discovered their limitations.
Because of this, we now make a distinction between the current limited form of AI and the artificial general intelligence (AGI) that we are seeking. While AGI is still only theoretical, narrow AI, often known as weak AI, encompasses every application of artificial intelligence that currently exists.
How does machine learning work?
One of the AI methods we’ve invented to imitate human intelligence is machine learning, a subtype of AI. Symbolic AI, also known as “good old-fashioned” AI (i.e., rule-based systems utilizing if-then statements), is the alternative type of AI.
A major turning point in the development of AI is machine learning. Prior to ML, we attempted to teach computers all the factors that would affect each choice they had to make. As a result, the procedure was completely transparent and the algorithm was able to handle numerous intricate situations.
OpenAI ChatGPT is an example of machine learning that some real estate practitioners use regularly. The system can help write property descriptions, write blogs and make a newsletter sound professionally written. When prompted correctly, ChatGPT can reduce time spent on writing projects. Thereby, it creates efficiency for Realtors to spend more time communicating with clients.
In its most complex form, the AI would investigate a number of options before selecting the one that produced the greatest outcomes. That is how IBM’s Deep Blue chess program was created to defeat Garry Kasparov.
But there are a lot of things, like facial recognition, that we cannot categorize using rule-based algorithms. A rule-based system would need to recognize various forms, such as circles, and then establish their placement and relationship to other objects in order to define them as an eye. How to code for identifying a nose would be even more difficult for programmers!
A distinct approach is taken by machine learning. It enables autonomous machine learning by consuming massive volumes of data and spotting patterns. Big data and statistics formulas are frequently used in ML algorithms. It is debatable if our developments in big data and the enormous amounts of data we have gathered have made machine learning possible.
Linear regression, logistic regression, decision trees, support vector machines, naive Bayes, k-nearest neighbors, k-means, random forest, and dimensionality reduction methods are a few of the ML algorithms used for classification and regression.
Deep learning: What is it?
A subset of machine learning is called deep learning. It still entails letting the computer learn from data, but it represents a significant advancement in AI.
Deep learning was created based on our understanding of neural networks. Although the concept of creating AI via neural networks has been around since the 1980s, deep learning didn’t really take off until 2012.
Deep learning was made possible by the availability of much cheaper processing power and breakthroughs in algorithms, just as machine learning was made possible by the enormous amount of data we collected.
Results from deep learning were far more intelligent than those from machine learning alone. Think about face-recognition technology. Given that the only information we can provide the AI is pixel colors, what type of data should we give it and how should it learn what to look for in order to detect a face?
Deep learning employs layers of information processing, each of which gradually picks up increasingly complicated data representations. Early layers might pick up on colors, followed by layers that learn about forms, layers that learn about combinations of those shapes, and layers that learn about actual objects. Object recognition has advanced thanks to deep learning. Its creation soon made AI more advanced in a number of areas, including NLU.
Right now, deep learning is the most advanced AI architecture we have created. Convolutional neural networks, recurrent neural networks, long short-term memory networks, generative adversarial networks, and deep belief networks are only a few examples of deep learning methods.
AI versus deep learning and machine learning
While AI has evolved over time, machine learning and deep learning have defined definitions. Optical character recognition was once regarded as AI, but not anymore. However, by today’s definition of AI, a deep learning algorithm that can translate handwriting into text after being taught on thousands of examples would qualify.
Many applications, including those that conduct natural language processing, picture identification, and classification, are powered by machine learning and deep learning. Through the use of intelligent machines to complete boring, repetitive jobs, these technologies assist businesses in enhancing their staff. Employees can then concentrate on creative or intellectually challenging tasks. Machine learning vs. deep learning
Deep learning is a subset of machine learning that imitates human intellect by using sophisticated neural networks. Both deep learning and machine learning often need access to highly sophisticated hardware, such as top-tier GPUs, as well as a lot of energy to function.
Deep learning models, on the other hand, differ in that they frequently learn more quickly and independently than machine learning models, as well as having a superior ability to utilize huge data sets. Self-driving automobiles, deep fake content, and facial recognition algorithms are a few examples of deep learning applications.
Both deep learning and machine learning are significant steps forward in the development of AI. As we get closer to the current AGI, there will probably be a lot more.
AI, machine learning, and deep learning share similarities
Beyond their distinctions, deep learning, machine learning, and AI have the following characteristics:
They can more readily address today’s complicated problems than traditional programming approaches, and they rely on algorithms to make predictions, identify significant patterns in data, and perform tasks. All three fields contribute to the development of intelligent machines.
Data are used by all three fields to train their models. To evaluate and discover crucial information like insights or patterns, models are fed data sets. Through experience-based learning, they eventually develop into high-performance models.
For any type of artificial intelligence, the caliber and diversity of the data are crucial components. Diverse data sets reduce the effects of ingrained biases in the training data that could produce skewed results. High-quality data reduces errors to guarantee model dependability. A model must iteratively learn in order to improve over time, much like humans do.