Computers are good at following processes i.e. a sequence of steps to execute a task. If we give a computer, steps to execute a task the computer should easily be able to complete it.

The steps are nothing but Algorithms. An algorithm can be as simple as printing two numbers or predicting who will win elections in the coming year!

So, how can we accomplish this?

Let’s take an example of predicting the weather forecast for 2019. First, we need a LOT of data. Let’s take the data from 2006 – 2018.

Now, let's divide this data in an 80:20 ratio. 80% of the data is going to be our labeled data, and the rest 20% of the data is going to be our test data.

**Reminder: We have the output for the entire 100% of data which has been acquired from 2006 – 2018.**

What happens once we have collected the data? We are going to feed the labeled data i.e. the 80% of data into the machine. Here, the algorithm is learning from the data which has been fed into it.

Next, we need to test the algorithm. We feed the test data, i.e. the remaining 20% of the data to the machine. The machine gives us the output. Now, we cross verify the output given by the machine with the actual output of the data and check for its accuracy. Once we check for accuracy and we are not satisfied with the model, we tweak the algorithm to give us the precise output or at least somewhere close to the actual output. Once we are satisfied with the model, we then feed the data to the model so that it can predict 2019’s weather forecast. Doing things that "require thought" or reasoning, like playing chess or solving integrals are things that computers can already do.

This misunderstanding about what intelligence really is has cost us 60 years and a million man-years of banging our head against the wall.

Deep learning is the currently most popular expression of an alternative path to a "better kind of AI". Artificial Intuition is a special branch of Deep Learning tailored at the understanding text.

The easiest way to know whether you are dealing with classical (futile) or modern AI is whether the system requires you to supply *any* models of the world (MOTW). Any MOTW means the AI is limited to operate in the domain specified by the MOTW and is therefore not **general** intelligence. Also, anything with a MOTW is typically not designed to extend that model; this is a *very difficult* task.

Better to start from a Model of the Mind (MOTM) or a Model of Learning. These can be derived either from neuroscience (difficult) or from epistemology (much easier). A well done MOTM can then learn anything it needs to know to solve problems in any domain.

The main problem for most is to find what's called "a domain-independent method for determining saliency". In other words, all intelligence, natural or artificial, has to spend most of their time answering the question "what matters".

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