This week in our educational series, we will be focusing on the next big technology trend that you need to know about – Artificial Intelligence!  As usual our focus will be to cut through the jargon and help you understand how AI will affect the property industry.

What is AI, and why should we care?

A Basic Definition ? 

Artificial Intelligence (AI) is the concept of having machines “think like humans” — in other words, perform tasks like reasoning, planning, learning, and understanding language.

Why Should We Care?

While no one is expecting parity with human intelligence today or in the near future, AI has big implications in how we live our lives. The brains behind artificial intelligence is a technology called machine learning, which is designed to make our jobs easier and more productive.

AI in everyday use… 

Almost everyone who has a computer, smartphone, or other smart device is already using AI to make life easier:

  • Amazon echo, Google Home, Apple Siri and other voice command devices.
  • Facebook recommends photo tags using image recognition
  • Product recommendations. For example “movies you might like” on Netflix,
  • Chatbots on many websites which make it seem like you are conversing with a real human being.
  • SatNav systems suggests optimal driving routes using a combination of predictive models, forecasting, and optimization technique
  • These are all example of using machine learning algorithms

Does AI seems a little too futuristic a term for you? Most people will think of some intergalactic film like 2001:A Space Odyssey and up until recently, you would be laughed out of any meeting if you mentioned this as a solution.

Today, AI is one of the hottest words in business and industry. AI technology is critical to the digital transformation taking place today as organizations position themselves to capitalize on the data being generated and collected. So how has this happened so quickly?  This is mainly due to the big data revolution itself. The huge amounts of data has led to intensified research into ways it can be processed, analyzed and used.

Machines being far more suited than humans to this work, the focus was on training machines to do this in as “smart” a way as is possible. This has led to breakthroughs and advances that are showing their potential to generate tremendous change. From healthcare to self-driving cars to predicting the outcome of legal cases, people are beginning to chat more and more about the endless opportunities available.

AI in a nutshell: To build machines which are capable of thinking like humans. An example of basic AI is a computer that can take 1000 photos of cats for input, determine what makes them similar, and then find photos of cats on the internet. The computer has learned, as best as it can, what a photo of a cat looks like and uses this new intelligence to find things that are similar.

Applied vs Generalised AI:

Research and development work in AI is split between two branches.
Applied AI – which uses these principles of simulating human thought to carry out one specific task.
Generalized AI – which seeks to develop machine intelligence that can turn their hands to any task, like a person.

A great overview of the History of AI:

Examples of Artificial Intelligence:

For Research Purposes: Specialised AI is already providing breakthroughs in fields of study from quantum physics and medicine.

For Industry: It is used in the world of finance for uses ranging from fraud detection to improving customer service by predicting what services customers will need.In manufacturing, it is used to manage workforces and production processes as well as for predicting faults before they occur.

 For the Consumer: More of the technology we are adopting into our everyday lives is becoming powered by AI – from smartphone assistants like Apple's Siri and Google's Google Assistant, to self-driving and driverless cars.

All of these advances have been made possible due to the focus on imitating human thought processes.

Terms you NEED to know!

(Special Thanks to The Next Web)

 Autonomous

Simply put, autonomy means that an AI construct doesn't need help from people. Driverless cars illustrate the term “autonomous” in varying degrees. Level four autonomy represents a vehicle that doesn't need a steering wheel or pedals: it doesn't need a human inside of it to operate at full capacity. If we ever have a vehicle that can operate without a driver, and also doesn't need to connect to any grid, server, GPS, or other external source in order to function it'll have reached level five autonomy.

Anything beyond that would be called sentient, and despite the leaps that have been made recently in the field of AI, the singularity (an event representing an AI that becomes self-aware) is purely theoretical at this point.

Algorithm

The most important part of AI is the algorithm. These are math formulas and/or programming commands that inform a regular non-intelligent computer on how to solve problems with artificial intelligence. Algorithms are rules that teach computers how to figure things out on their own. It may be a nerdy construct of numbers and commands, but what algorithms lack in sex appeal they more than make up for in usefulness.

Machine learning

The meat and potatoes of AI is machine learning — in fact it's typically acceptable to substitute the terms artificial intelligence and machine learning for one another. They aren't quite the same, however, but connected.

Machine learning is the process by which an AI uses algorithms to perform artificial intelligence functions. It's the result of applying rules to create outcomes through an AI.

Black box

When the rules are applied an AI does a lot of complex math. This math, often, can't even be understood by humans (and sometimes it just wouldn't be worth the time it would take for us to figure it all out) yet the system outputs useful information. When this happens it's called black box learning. The real work happens in such a way that we don't really care how the computer arrived at the decisions it's made, because we know what rules it used to get there. Black box learning is how we can ethically skip “showing our work” like we had to in high school algebra.

Neural network

When we want an AI to get better at something we create a neural network. These networks are designed to be very similar to the human nervous system and brain. It uses stages of learning to give AI the ability to solve complex problems by breaking them down into levels of data. The first level of the network may only worry about a few pixels in an image file and check for similarities in other files. Once the initial stage is done, the neural network will pass its findings to the next level which will try to understand a few more pixels, and perhaps some metadata. This process continues at every level of a neural network.

Deep learning

Deep learning is what happens when a neural network gets to work. As the layers process data the AI gains a basic understanding. You might be teaching your AI to understand cats, but once it learns what paws are that AI can apply that knowledge to a different task. Deep learning means that instead of understanding what something is, the AI begins to learn “why.”

Natural language processing

It takes an advanced neural network to parse human language. When an AI is trained to interpret human communication it's called natural language processing. This is useful for chat bots and translation services, but it's also represented at the cutting edge by AI assistants like Alexa and Siri.

Reinforcement learning

AI is a lot more like humans than we might be comfortable believing. We learn in almost the exact same way. One method of teaching a machine, just like a person, is to use reinforcement learning. This involves giving the AI a goal that isn't defined with a specific metric, such as telling it to “improve efficiency” or “find solutions.” Instead of finding one specific answer the AI will run scenarios and report results, which are then evaluated by humans and judged. The AI takes the feedback and adjusts the next scenario to achieve better results.

Supervised learning

This is the very serious business of proving things. When you train an AI model using a supervised learning method you provide the machine with the correct answer ahead of time. Basically the AI knows the answer and it knows the question. This is the most common method of training because it yields the most data: it defines patterns between the question and answer.

If you want to know why something happens, or how something happens, an AI can look at the data and determine connections using the supervised learning method.

Unsupervised learning

In many ways the spookiest part of AI research is realizing that the machines are really capable of learning, and they're using layers upon layers of data and processing capability to do so. With unsupervised learning we don't give the AI an answer. Rather than finding patterns that are predefined like, “why people choose one brand over another,” we simply feed a machine a bunch of data so that it can find whatever patterns it is able to.

Transfer learning

Another spooky way machines can learn is through transfer learning. Once an AI has successfully learned something, like how to determine if an image is a cat or not, it can continue to build on it's knowledge even if you aren't asking it to learn anything about cats. You could take an AI that can determine if an image is a cat with 90-percent accuracy, hypothetically, and after it spent a week training on identifying shoes it could then return to its work on cats with a noticeable improvement in accuracy.

Opposing Expert Views – The Future and AI

Is artificial intelligence good or bad? We don't know for sure but most people will agree that artificial intelligence is inevitable.
There are people on both sides: People who believe AI can potentially destroy our future and lives. The other group of people is fairly optimistic and believes AI can help us in solving most problems that exists today.
Here are two very opposing views!

Join us next week for for Part 2 – 
AI applications in Real Estate

Next week we help you understand the applications of AI in Real Estate.

Check out our blog this week and next, our contributors have been writing about AI!

  • Anthony Slumbers
  • Cleo Folkes
  • Heylandlord
  • Angels Media
  • Restb.ai
  • Ask Porter

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