I still get asked the question and sometimes by people who thought they knew until Machine Learning and AI came along.
Well the answer is simple enough and hasn’t changed at all:
An Algorithm is still a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer.
“The code written by programmers is a collection of algorithms.
If the file exists open it, of not create an empty file of that name and present it to the user.” That is a simple algorithm written by a programmer.
Luckily the majority of algorithms are not made up of complex looking geeky maths formulae but common sense and even programming languages are mercifully becoming more human friendly.
The answer therefore is, dont be in awake of algorithms they’re just instructions to solve a problem of some sort. They’re not magic either and don’t assume that they are clever or reliable.
Deep learning is first of all a buzzword. That means it may not have a precise and widely accepted meaning. It is a form or use of artificial intelligence as is machine learning from which deep learning evolved.
OK we started with out with simple stuff like Data mining this became more sophisticated a section of it developed into Machine learning which as it became more sophisticated became deep learning.
Machine Learning fundamentally is the ability to parse data, learn from it, and then make a determination or prediction about something in the world using algorithms. So rather than a human writing software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms so that it is able to analyse the situation and write the code.
Just like human analysts it needs help at times because it is not a subject matter expert. Understanding the subject matter is the tricky bit, after that the algorithms become rather easy. As a programmer I struggled with these same problems. Where ML tools and great analysts gain an advantage is when they take everything they are told or see with a pinch of salt and dig deeper. Many humans are lazy to do this, but machines do it every time relentlessly and come up with those very awkward questions that eventually solve big problems.
Today ML has mastered things like looking at people through a camera lens, recognising them and classifying their mood. All good fun, but very dangerous to rely upon.
Neural networks were played with and discarded many times by the AI community but have begun to make a comeback and are now at the root of some of the more exciting recent glimpses of potential advances in ML
The “Explained series” is planned to build into a trustworthy collection of explanations and commentaries that can be trusted to tell the story straight without any bias and attempt to make the subjects accessible to the layman. The latter is not always easy as some of these terms refer to genuinely complex subject matter, while others are simply too vague to pin down (there’s another word for that). There is also a limit to how far I can go in explaining every term when there are a lot of them, so I have to sometimes rely on your initiative to right click the offending word and look it up.
If you want an answer on something and you can’t find it easily, please use the comments section to just ask and I will appreciate not having to research the next topic.