Artificial General Intelligence – Pro et Contra

Head, Board, Machine Learning, Algorithm
AGI

“There is a thin domain of research that, while having ambitious goals of making progress towards human-level intelligence, is also sufficiently grounded in science and engineering methodologies to bring real progress in technology. That’s the sweet spot.” – Yann LeCun

Mind Map

Before we delve into benefits of AGI, let us look at what is AGI first :

Artificial General Intelligence is based on four key principles:

  • Essence of intelligence is thought i.e.., rational deliberation which is necessarily sequential
  • Ideal model of thought is logical inference based on concepts
  • Perceptions is at a lower level of thought
  • Intelligence is based on ontology

Computers with AGI can think, comprehend, learn and apply AI techniques to solve real life challenges. AGI can handle unfamiliar problems. It is referred to as deep or strong AI. The capabilities of AGI are listed below:

  • Sensory perception
  • Fine motor skills
  • Natural Language Understanding
  • Natural Language Processing

Now let us look at the benefits of AGI:

  • AGI can provide solutions to world’s problems related to health, hunger, and poverty.
  • AGI can automate processes and improve efficiencies in the companies
  • Without manual supervision, AGI can execute tasks
silhouette of man standing on rock near body of water during daytime
Singularity

AGI has problems and disadvantages as mentioned below:

  • Singularity can be the effect of AGI
  • AI can be destructive to mankind
  • It can be a weapon for human extinction
  • AGI can evolve without any rules or principles

Note : Singularity is a hypothetical point in time when technological growth becomes uncontrollable and irreversible, resulting in uncontrolled changes to mankind.

“The robot has some objective and pursues it brilliantly to the destruction of mankind. And it’s because it’s the wrong objective. It’s the old King Midas problem. We’ve got to get the right objective,” he explains, “and since we don’t seem to know how to program it, the right answer seems to be that the robot should learn – from interacting with and watching humans – what it is humans care about.”Stuart Russell

 “Golden Touch” myth is about the old King Midas Problem. King Midas, a rich and greedy king in Ancient Greece, acquired the ability to change all that he touched into gold. But hardly had he started that everything was transformed into gold, including his daughter.

Solution for singularity is to develop a simulation tool to simulate AGI machine with different techniques. This simulation will help in predicting the AGI behavior against mankind.

Augmented, Reality, Virtual, Glasses
Simulation

Regression

There are different methods of Regression used in machine learning. The different techniques are listed below:

  1. Linear Regression
  2. Polynomial Regression
  3. Ridge Regression
  4. Lasso Regression
  5. Non Parametric Regression
  6. K-Nearest Neighbor Regression
  7. Kernel Regression

The types of the regression is dependent on the number of explanatory variables such as single (simple) and multiple.

Regression types

                      

 In the next section, linear regression is discussed in detail.

 Linear Regression

Linear Regression

                                  

Linear Regression is very popular modeling method.  This method consists of dependent and independent variables. Dependent variables are continuous. Independent variables are continuous and discrete. In linear regression, independent variables (Z) and dependent (W) variables are used for identifying relationship between them. The relationship used is a straight line which is a best fit. It is also referred as linear regression.

It is represented by an equation W=mZ + c + err, where c is intercept, m is slope of the line and err is error term.  To predict the value of a variable, the function W is used.  The linear regression has single independent variable.

Multiple linear regression has more than independent variables. If there are more than one independent variable, multiple linear regression addresses the finding the fit for the line which relates the dependent variable and independent variables.

Least Square method is used for finding the fit for multiple linear regression technique. The method tries to minimize the sum of the squares of the differences from each point to the line.  The deviations are squared and added to ensure that the positive and negative values are not cancelled out.

Code Snippet : Linear_Regression.py

Instructions for Running the Code

pip install numpy

pip install tensorflow

python linear_regression.py

Output of the code Execution