This ability to adaptively react to situations, and improve over time, is intelligence. The adjustment of your rules based on experience is learning.

Understanding the different elements of AI: 10 key definitions

1. Algorithm: sounds like a fancy word, right? Well, there’s nothing new or innovative about it. An algorithm is simply a recipe for dealing with a problem. It’s a set of steps to be followed. You could apply a personal algorithm - a fixed series of actions and responses - to how to perfectly deal with your mornings.

2. Rules / rule-based systems: rules are just the core algorithms that define an AI system. All interface programming - how you use a website, or your phone, or any digital device - is based on rules. The earliest version of Artificial Intelligence was an attempt to make an intelligent machine by giving it so many rules that it would be able to deal with literally every possible scenario. This is referred to as a "zero learning" system, for obvious reasons. It was basically running a massive list of algorithms, just regurgitating canned responses. Of course, life presents us with infinite possibilities, so this approach didn’t work out so well:

3. Data: is information. In the case of an AI system, it’s all the information it’s picking up from its surroundings, and about the information itself (e.g. trends in the information, outliers, etc).4. Model: is the version of the world an AI system is designed to interpret and focus on. E.g. a machine that sorts everything it sees - its visual information - into red objects, and green objects. It is a red-green sorting computer vision model. It sees the world in red and green.5. Machine learning: remember when we defined what intelligence was, and how learning made it possible by adjusting the rules with experience? Well, that’s why machine learning goes hand in hand with Artificial Intelligence! (It is not the same thing as Artificial Intelligence, though it is often incorrectly used interchangeably.) A "zero learning" network applies fixed algorithms to respond to situations, so it never learns. A machine learning system applies algorithms, but also checks the outcomes, and based on that adjusts its algorithms. Machine learning basically means using algorithms to adjust the system's original algorithms, based on the outcomes of the system. (Cue Inception meme.)Example:Rollie the alarm clock could have an algorithm that adjusts the length and volume of its alarm based on how effectively it woke the user over the past month.6. Training / training set: the process of teaching a system by supplying its algorithm data to learn from. Most machine learning systems today are started by feeding in a set of information that the system can calibrate itself on. (See: Supervised Learning in the Further Reading section at the end.)7. Neural Networks: a set of algorithms attempting to recreate (on a very limited scale) the dense interconnections of the human brain by densely interconnecting many simultaneously running AI models.8. Deep learning: a type of machine learning where multiple layers of algorithms are layered over each other, with the output from one cascading into the next. For example, when the output of a neural network is fed into the input of another, it becomes a deep neural network.9. Computer vision: one of the most immediately impactful uses of AI involves making sense of the data in images. Computer vision is the field dedicated to interpreting images and videos. It drives everything from automatic photo-tagging on Facebook, to how self-driving cars get around (and avoid killing people).10. Natural language processing (NLP): the other primary application of AI is in understanding the ideas and intent of language. NLP is a core part of AI and has been around since the advent of the earliest computers. Most recently, deep learning has been applied to NLP to astonishing results. Take this recent case, for example:

Most of the above concerns go hand in hand with the concept of Emergent Artificial Intelligence, which is the idea that Artificial Super Intelligence might unexpectedly, rather than deliberately, arise one day out of the increasingly complex AI systems we’re creating. This concept comes from the theory of Emergence, which is the idea of larger/ more complex things emerging from smaller/ less complex ones. Life on Earth is an example.

So even if we know that an AI made a bad decision, we don’t know why or how, so it’s tough to build mechanisms to catch bias before it’s implemented. The issue is especially precarious in fields like self-driving cars, where each decision on the road can be the difference between life and death. Early research has shown hope that we’ll be able to reverse engineer the complexity of the machines we created, but today it’s nearly impossible to know why any one decision made by Facebook or Google or Microsoft’s AI was made.

On one hand, AI today is nowhere near achieving super intelligence. Tech expert Ken Nickerson labels todays AI as more akin to AIS: artificial idiot savants. They are infinitely better than humans at a single or handful of tasks, but are otherwise totally useless, even compared to an average human child. He even goes so far as to suggest that the entire industry is on the wrong path, specifically that attempting to copy human intelligence so literally is no different from calling planes artificial birds and creating literal replicas, as early inventors did.