Both governments and tech companies alike are investing billions into developing artificial intelligence, which means there are some really exciting things happening in a field that has the potential to disrupt everything we know about being human.
If you’re looking to be on the cutting edge of developments in nearly every field then now is the time to get involved with AI and its sub-disciplines and related fields like machine learning, deep learning, robotics, and data science.
What is artificial intelligence?
Artificial intelligence is notoriously hard to define and the term currently lacks a universelly agreed upon definition. Instead we’ve ended up with an already complex topic that can mean different things to different people.
Some people consider AI to be artificial life forms that have super intelligence and are smarter than humans. Although, we’re not actually at this point of artificial intelligence – at least not yet.
Other people classify many of the commonly used data processing technology that’s currently out there as fulfilling the requirement to be considered a form of AI.
A pretty widely agreed upon definition of artificial intelligence is that it represents the use of a computer to model or replicate ‘intelligent’ behavior (such as the kind we might exhibit as humans), using algorithms to learn and perform this intelligent behavior with minimal intervention from us.
One #dadjoke definition is that AI should be thought of as “cool things that computers can’t do,” but this definition is pretty ambiguous.
Another way of defining artificial intelligence might be something that can act autonomously and adapt to its environment. So a system that can perform tasks in a complex environment without receiving constant guidance from a user while learning and improving from experience could be considered to be artificially intelligent. An example of this is an algorithm that learns how to accurately identify pictures of cats by looking at millions of photos of cats.
The most basic break down of the term artificial intelligence would probably be something like: “intelligence that is made or produced by humans rather than occurring naturally.”
But of course, this definition then opens up a whole different can of worms – for example, what exactly is intelligence and how should it be defined?Moreover, how can you determine whether something is truly intelligent (i.e can do things on its own) or is simply imitating or copying intelligence?
Even in humans, the term intelligence isn’t something that researchers have been able to satisfactorily quantify. There is no singular metric or dimension that denotes higher or lower intelligence and unlike something quantifiable like weather in different cities, you can’t compare one person’s intelligence with another and definitively say that person A has more intelligence than person B. Scientists have attempted this with metrics like IQ (intelligence quotient) scores, but these results are limited in what can actually be done with them.
If it’s hard to quantify intelligence in humans, imagine how hard it is to do for machines. Would you say that the algorithm that beat the 18 time world Go champion is more intelligent than algorithms used for self-driving cars? It doesn’t really make sense to compare the two and it would be hard to pin down in exactly what way one algorithm was or wasn’t more intelligent than the other.
This is further complicated by the fact that difficult tasks like complex math problems are incredibly easy for a computer, whereas what seems easy to us, like walking around, is incredibly hard for a computer to do without continual input. Moreover, there are a whole range of things about AI that even those working on the cutting-edge of research still don’t fully understand.
Hollywood is partly to blame for this confusion with the production of slick movies and TV shows that feature friendly humanoid servants or tyrannical robots intent on taking over the world and ridding it of us pesky humans (oh hey, Westworld).
At the same time, the field itself is relatively young and moving at such a fast pace that it can be hard to keep up, even if you’re following the latest happenings in the world of AI.
Because of this, it’s easy to think that AI is fictional. In fact, a lot of the time we don’t even realize that some of the systems we’re using multiple times a day are, in fact, AI.
From Alexa, Siri, and Google Assistant, to Facebook, Netflix, and email spam filters, to your car, cellphone or the last plane you flew in – these things (and so, so much more) all now use artificial intelligence in one way or another.
John McCarthy, who coined the term ‘Artificial Intelligence’, later commented that “as soon as it works, no one calls it AI anymore“, which is probably true. Since these services are now mainstream talking about AI can feel like something from the future when in reality it’s all around helping us do our daily tasks. For example, AI can now carry out simple tasks like creating a customized playlist (just for you!) on Spotify or trolling Twitter, to complex ones like discovering new uses for existing drugs or searching for extraterrestrial life.
Regardless, artificial intelligence is set to be a game changer and although scientists and researchers might have a hard time agreeing on a definition, the AI revolution is certainly underway.
Artificial Narrow Intelligence (ANI)
Artificial narrow intelligence, or ‘Weak AI’, refers to AI that specializes in one task, like playing chess or answering your search query. It’s unable to do anything beyond the singular task it was designed to do and lacks the ability to move laterally between tasks. Everything that we know of as AI today is ANI and, in most cases, tasks that have been outsourced to ANI systems tend to outperform humans in both efficiency and endurance.
Artificial General Intelligence (AGI)
Also known as ‘Strong AI’ or ‘Human-Level AI’, artificial general intelligence is a computer that can do anything intellectually that a human can. Based on Professor Linda Gottfredson’s description of intelligence, AGI would be able to “reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience.” Basically, it would be as smart as we are. Scientists are hard at work on creating AGI – they haven’t cracked it yet but they promise that it’s coming soon.
When some of the world’s biggest brains throw around phrases like “human extinction” and “worst thing to happen to humanity in history” they are probably referring to artificial superintelligence. Nick Bostrom, a leading AI thinker defines superintelligence as “an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom, and social skills.” One of the reasons people are alarmed at the prospect of superintelligence is that machines could potentially go from just a little bit smarter than humans to trillions of times smarter in a matter of days or even hours. Because they would be so much smarter than we are, we’d have no way of understanding why or how they did what they did. There’s debate about how quickly (if at all) scientists will be able to create ASI but the prospect of it is something that keeps some of the smartest people in the world up at night.
Why artificial intelligence matters
As one NYTimes article so succinctly put it, AI “can probably do less right now than you think. But it will eventually do more than you probably think, in more places than you probably think, and will probably evolve faster than powerful technologies have in the past.”
Although AI isn’t as powerful as all the hype around it would imply (yet), it is already causing substantial changes to the way we work and make our money.
Automation has been and continues to be the first harbinger of this shift, with actual robots already replacing dangerous and repetitive work, while smart tech means you don’t even have to make your own coffee anymore.
The scale of job displacement from automation is going to increase rapidly as the technologies in AI and its surrounding fields get better and better. Some estimate that from 75 million to 375 million people might need to switch jobs and learn new skills as a result of these technological transitions. To put that into context, approximately 375 million people speak English as their first language. That’s a lot of people.
Ultimately, artificial intelligence matters because one way or another it is going to affect you. AI means change and this change may just be the biggest one’s humanity’s ever seen, in terms of scope, disruption, scale, and speed. Almost certainly bringing with it uncertainty for many and opportunity for some.
Imagine having a time machine and being able to go back in time. You’d know everything you know now but it hadn’t happened yet. There are some things out there that don’t require a time machine to give you the insider tip and the growth of artificially intelligent machines is almost certainly going to be one of them.
AI & its subfields
The majority of topics covered on Elle Knows Machines fall either within the remit of computer science (making computers do things), mathematics (the language that describes the universe), or data (specifically, how to make machines utilize large amounts of it and then do useful things with it).
This site primarily focuses on the top-level categories of artificial intelligence and data science and in many ways these can be thought of as siblings – different but with a shared lineage and significant cross over.
AI is a relatively broad term that encompasses a number of different sub-fields while being tangentially related to others, either directly or indirectly.
Below is a visual conceptualization of how the different areas fit together:
One of the terms that you’ve probably heard the most about in relation to AI is machine learning.
Machine Learning (also referred to as ML) is a sub-field of artificial intelligence and refers to the area or discipline concerned with developing algorithms that make computers ‘learn’ a task or function without being explicitly programmed to do so.
The overall goal of these algorithms is to improve their performance in a given task as they receive and process more data over time, ultimately helping AI systems to become more adaptive to complex environments and a wider range of known and unknown scenarios.
Deep Learning is a sub-field of machine learning and consists of specific machine learning techniques known as neural nets – basically algorithms inspired and influenced by the structure and function of the human brain.
The ‘deep’ in deep learning refers to two things: the complexity of the mathematical model used to run these algorithms and the high levels of computing power now available that have allowed researchers to utilize such complex math. These two things combined are delivering results that appear to be significantly different from previous attempts.
Sitting alongside machine learning and deep learning are fields which bridge the gap between the digital world and the physical, allowing intelligent machines to sense and interact with stimuli in the real world environment around them.
If the AI is the brain, then these areas are effectively the eyes, ears, arms, and/or legs, utilizing sensors to sense things from the environment and actuators to react to information and allow physical action.
The field of robotics is probably the best known of these. It involves the building and programming of machines so they can do things in the physical world, increasingly utilizing a combination of artificial intelligence and machine learning, alongside sensors and actuators to complete tasks with near or full autonomy.
Robots are basically containers for AI and should probably be thought of as the physical manifestation of artificial intelligence (narrow or otherwise) that allow movement around and/or interaction with the physical world.
Sometimes robots look like humans and sometimes they don’t, but getting a robot to do things that humans can in a natural way is one of the holy grail tasks of this branch of study and draws from sub-areas and related disciplines such computer vision, natural language processing, and cognitive modeling.
There are some remarkable developments happening in the field of robotics right now, some of which you can see in this video:
Natural language processing
Although you may not know it, odds are you’ve already come across a well established form of natural language processing (NLP) through using a system like Alexa or Siri, or Google Assistant.
When you ask any of these systems a question, be that verbally or through typing, the software is using NLP to analyze and synthesize natural (aka human) language and speech.
This is a really important and exciting element of AI because it’s helping computers communicate with humans in their own language, whether that’s English, Bengali, or Mandarin.
Computer vision is another area of artificial intelligence that’s currently growing at an incredible speed.
Not surprisingly, computer vision is about getting computers to be able to ‘see’ on par with human vision.
What began as a summer project assigned to a grad student, the challenge of true computer vision, has turned out to be incredibly hard to crack. Still, researchers have been making some major developments in the field. One example of computer vision can be found on the iPhone X, which lets you unlock your phone with your face or get emojis to mimic your expressions with Animoji.
But what about data science?
When it comes to making machines intelligent, data is a big deal and while large amounts of it are ultimately the fuel of data-hungry AI, the actual field of data science (collecting data and then obtaining actionable knowledge and insight from them) needs to be considered separately – find out how here.