Building blocks

To reach artificial general intelligence, researchers are working towards advancements in a few different fields.

Let's talk a bit about these fields since they're the building blocks for many types of AI.

Natural language

AIs are more useful when we can communicate with them easily. For that, they need natural language processing, or NLP.

NLP is something we use every day when we ask one of our devices for the weather, the time, or to call mom.


With natural language processing, researchers make machines understand things just like a human would. But that's harder than it sounds.

What phrases do you think would be a problem for an AI to understand.

Exactly! AIs need to be very sophisticated to understand things like figures of speech without taking them literally.


Speech recognition is excellent for getting input, but machines also need to learn from gathered data.

For that, there's a whole domain of AI called machine learning. Machine learning is such a vital step that it's often confused for AI.

Machine learning

The simple version of machine learning is this: we give a machine a lot of data regarding a topic. Let's say images of cats.

After showing it hundreds, or even thousands of cats, we then give it a test. For example, asking it to name the animal in this picture.

Answer: cat

//Output Below


We know we've succeeded when the machine can recognize a concept on a regular basis.

Representation of data

AI's also need ways of representing data. Better said, they need a way of storing information, accessing it and comparing it.

Good data representation allows AIs to make better decisions.

Many ways

But how can AIs represent decisions in a comparable way that allows them to see the pros and cons each option offers?

          / \
         /   \
        B     C
       / \     \
      /   \     \
     D     E     F

We can think of decisions as branching off into different possible outcomes. We can then represent them in something called a decision tree

Decision tree

For example, we can use a decision tree to plan a trip from Accra to Tamale. 

Which branch allows us to get there and take the least amount of decisions?

        / \
       /   \
    Kumasi   Tamale 
     / \
    /   \
Ho  Tamale

Exactly! This allows us to see which option has the fewest steps. This doesn't always mean it's the right decision though.


More complicated versions of decision trees are used in all kinds of applications. That's what AIs use to win at tic-tac-toe or chess.

What decision would lead to a winning game for X?

Exactly! AIs use similar methods to make decisions all the time.


AIs also need to come to logical conclusions through reasoning

Many instances of reasoning in AIs is done through propositional logic

Propositional Logic

We use propositional logic to simplify language into small parts and then give them values like true or false.

If I stay inside then I'll be bored.
   |___________|      |___________|
         A                  B

In this example an AI can then judge the truth value of A and B separately and then the truth value of the entire sentence.


But using things like propositional logic and decision trees requires a lot of computing power due to the level of detail.

Current hardware limitations mean it would take very long to reach any conclusion.


For that reason, researchers are also looking into making AI that can have human-like intuition

AI intuition is still a theory, but it would help machines solve uncertain situations where there isn't enough data, or there's too much.


As researchers make progress on all of these fronts, AGI becomes more of a reality. 

And as soon as that becomes a reality, we'll quickly move towards the next step, artificial superintelligence.

Super AI

Artificial narrow intelligence can be impressive, while artificial general intelligence will probably change the way we live. 

But both can't compare to the future of AI, artificial super intelligence. Let's see what would be needed to reach that level.


The path toward super intelligence will probably be a way shorter than the one from narrow to general intelligence.

Let's add ANI, AGI, and ASI into this timeline.

AI development timeline:
ANI                  AGI 
 |____________________|_ ...

Notice how ASI is right after AGI? There are a few reasons for this.

Field advancements

Researchers are continually gaining ground in all domains of AI, from machine learning to reasoning, or knowledge representation.

Computational power

So what are some steps necessary for advancing AI technology? One of the first steps is to improve computational power.

Luckily, the computational power of hardware is accelerating at a steady and exponential rate.

Moore's law

It's so steady that it's even given rise to a theory known as Moore's law.

The law says that computational power doubles every two years. This law has been very accurate so far.

The brain

The next step is improving software. One of the main ways of doing that right now is by mimicking the brain through neural networks.

Neural networks mimic the brain by storing small amounts of information in each neuron.


Neurons activate from Input to Output. In this diagram, it's from left to right, while they look for information that matches the input.

Input  ->   ...   ->  Output 

That's it! Let's look at another example.

Neural networks

Neural networks can be complicated, but here's a simplified version: They split information into a lot of smaller pieces.

Can you guess the number at the end of this network?

  • 9

Exactly! We can see that the combined two neurons form the number 9.


When we get input in the first column, we then compare it to the neuron in the next column and the next column after that.

We do this until we get to the output layer and find the best match. This system is similar to that of the human brain.


There's only so much we can do to develop AI. After we provide the hardware and advanced software systems like neural networks...

... AI will have to carry on the work by itself through evolution. That is where genetic algorithms come into play.

Genetic algorithms

Genetic algorithms work via survival of the fittest. A set of algorithms are created to solve a problem. 

The most successful ones are carried over to the next generation and crossed over to create better versions of the algorithm.


Eventually, one of them is successful in not only solving the problem but doing so in an optimal way. That will be the fittest algorithm.

It's through techniques like these that AGI will eventually develop into ASI.


AI will then be able to evolve by itself faster than we could ever create it. Researchers call this stage an intelligence explosion.

It's when the switch from artificial general intelligence to superintelligence will happen almost instantaneously. 


We're not sure if ASIs will be a good or bad step for humankind.

Nick Bostrom, a leading AI expert, calls it an "extremely difficult problem with an unknown time to solve it". 

The important part is you know about it now, and you can also help humankind figure that out.