The workshop marked the official beginning of AI history.
Read: [Everything you oughta know about narrow AI]
What are the requirements of AGI?
Defining artificial general intelligence is very difficult.
These are the kind of functions you see in all humans since an early age.
This is a challenge that requires the AI to have an understanding of physical dynamics, and causality.
It should also be able to reason about counterfactuals, alternative scenarios where you make changes to the scene.
Challenge 3: Enter a random house and make a cup of coffee.
Add some milk and sugar.
This challenge will require the AI agent to have a general understanding of houses structures.
It should have basic knowledge such as the following: Food items are usually found in the kitchen.
The kitchen is usually located on the first floor of the home.
Coffee is stored in the cupboard.
Milk has to be kept in the refrigerator.
Most humans solve these and dozens of other problems subconsciously.
The following are two main approaches to AI and why they cannot solve artificial general intelligence problems alone.
Symbolic AI is premised on the fact the human mind manipulates symbols.
We have mental representations for objects, persons, concepts, states, actions, etc.
Symbolic AI systems made early progress.
Computer programming languages have been created on the basis of symbol manipulation.
But symbolic AI has some fundamental flaws.
It only works as long as you’re free to encode the logic of a task into rules.
But manually creating rules for every aspect of intelligence is virtually impossible.
They also required huge efforts by computer programmers and subject matter experts.
Another problem with symbolic AI is that it doesnt address the messiness of the world.
An example is detecting objects in an image.
Computers see visual data as patches of pixels, numerical values that represent colors of points on an image.
Each object in an image is represented by a block of pixels.
Consider, for instance, the following set of pictures, which all contain basketballs.
In some of them, parts of the ball are shaded with shadows or reflecting bright light.
In some pictures, the ball is partly obscured by a players hand or the net.
Yet in others, the lines and writings appear in different angles.
And the balls size changes based on how far it is from the camera.
The complexity of the task will grow exponentially.
Then, you train the AI model on many photos labeled with their corresponding objects.
But does deep learning solve the general AI problem?
Neural networks have so far proven to be good at spatial and temporal consistency in data.
But they are very poor at generalizing their capabilities and reasoning about the world as humans do.
But it does not understand the meaning of the words and sentences it creates.
Neural networks lack the basic components youll find in every rule-based program, such as high-level abstractions and variables.
That is why they require lots of data and compute resources to solve simple problems.
In a nutshell, symbolic AI and machine learning replicate separate components of human intelligence.
When will artificial general intelligence become reality?
But most agree thatwere at least decades away from AGI.
Today, there are various efforts aimed at generalizing the capabilities of AI algorithms.
Some scientists believe that the path forward ishybrid artificial intelligence, a combination of neural networks and rule-based systems.
Other scientists believe that pure neural internet-based models will eventually develop the reasoning capabilities they currently lack.
But whats for sure is that there will be a lot of exciting discoveries along the way.
you’ve got the option to read the original articlehere.
