#ai aiprojects screenrecorderpython

[samirlmch]

Python Screen Recorder

In theory, an artificial general intelligence could carry out any task a human could, and likely many that a human couldn't. 

AlphaGo's domination of the notoriously complex game of Go, can give the impression society is on the fast track to developing AGI.

Yet the systems in use today are generally rather one-note, excelling at a single task after extensive training, but useless for anything else

While machines may exhibit stellar performance on screen recorder a certain task, performance may degrade dramatically if the task is modified even slightly

Studying animal cognition and its neural implementation also has a vital role to play, as it can provide a window into various important aspects of higher-level general intelligence

YAn Lecun He believes the path towards general AI lies in developing systems that can build models of the world they can use to predict future outcomes. A good route to achieving this, he said in a talk last year, could be using generative adversarial networks (GANs)

Hard-coding morality into machines seems too immense a challenge, given the impossibility of predicting every situation a machine could find itself in. If a collision is unavoidable, should a self-driving car knock down someone in their sixties or a child? What if that child had a terminal illness? What if the person in their sixties were the sole carer of their partner?

once AI reaches human-level intelligence, it will rapidly improve itself through a bootstrapping process to reach levels of intelligence far exceeding those of any human. But in order to accomplish this self-improvement, AI systems will have to rewrite their own code. This level of introspection will require an AI system to understand the vast amounts of code that humans cobbled together, and identify novel methods for improving it. 

CAN WE ACHIEVE AGI ?

Artificial general intelligence systems are designed with the human brain as their reference. Since we ourselves don’t have the comprehensive knowledge of our brains and its functioning, it is hard to model it and replicate it working. However, it is theoretically possible--that given infinite time and memory, any kind of problem can be solved algorithmically. 

LINEAR REGRESSION FROM SCRATCH

We will build a linear regression model to predict the salary of a person on the basis of years of experience from scratch. You can download the dataset from the link given below. Let’s start with importing required libraries:

%matplotlib inline

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

We are using a dataset of 30 data items consisting of features like years of experience and salary. Let’s visualize the dataset first.

dataset = pd.read_csv('salaries.csv')

#Scatter Plot

X = dataset['Years of Experience']

Y = dataset['Salary']

plt.scatter(X,Y,color='blue')

plt.xlabel('Years of Experience')

plt.ylabel('Salary')

plt.title('Salary Prediction Curves')

plt.show()

CONCLUSION

We need to be able to measure how good our model is (accuracy). There are many methods to achieve this but we would implement Root mean squared error and coefficient of Determination (R² Score).

Try a Model with Different error metrics for Linear Regression like Mean Absolute Error, Root mean squared error.

Try an algorithm with a large data set, imbalanced & balanced dataset so that you can have all flavors of Regression.

AI COURSE

INTRODUCTION TO ARTIFICIAL INTELLIGENCE & MACHINE LEARNING https://aihubprojects.com/introduction-to-machine-learning/ 

Difference between Machine learning and Artificial Intelligence https://aihubprojects.com/difference-between-machine-learning-and-artificial-intelligence/ 

IS AI OVERHYPED? REALITY VS EXPECTATION https://aihubprojects.com/is-ai-overhyped-reality-vs-expectation/ 

BEGINNERS GUIDE TO MACHINE LEARNING https://aihubprojects.com/beginners-guide-to-machine-learning/ 

KEY TERMS USED IN MACHINE LEARNING https://aihubprojects.com/key-terms-used-in-machine-learning/ 

Performance Metrics in Machine Learning Classification Model https://aihubprojects.com/performance-metrics-in-machine-learning-model/ 

Performance Metrics: Regression Model https://aihubprojects.com/performance-metrics-regression-model/ 

LINEAR REGRESSION FROM SCRATCH https://aihubprojects.com/linear-regression-from-scratch/ 

LOGISTIC REGRESSION FROM SCRATCH https://aihubprojects.com/logistic-regression-from-scratch/ 

NAIVE BAYES ALGORITHM FROM SCRATCH https://aihubprojects.com/naive-bayes-algorithm-from-scratch/ 

DECISION TREE FROM SCRATCH https://aihubprojects.com/decision-tree-from-scratch/ 

RANDOM FOREST FROM SCRATCH PYTHON https://aihubprojects.com/random-forest-from-scratch-python/