Rohan Bhasin

The Artificial Intelligence era has started only a while ago and it is drastically augmenting and amplifying our lives But in this sphere of development, the developers have had a big problem to test their AI. This is where great minds like Elon Musk step in and create adroit and efficient resources to test the Intelligence that we create. His vision and aspiration led to the start of OpenAI, a free open source library and a company specializing in research. They create toolkits for Deep Learning developers like us to deploy models and make AI play games. This helps us to analyze the power of the intelligence by simulating game environments.  OpenAI provides enthusiasts with 2 toolkits to work with Gym - Toolkit for comparing and developing deep reinforcement learning algorithms... Universe - A collection of gym environments to train and measure an AI's general intelligence... Today we are going to write a simple script in python to help build a ...

Artificial Neural Networks, Seems to be a pretty novel approach to solve big scale problems. It actually works and mimics a Human brain and thinks as a mannered Human, full of consciousness and sophisticated Intelligence.  I trained a neural net to play Tic-Tac-Toe at 3 different modes: Random, Easy and Smart. So what does this mean?? The program uses a genetic algorithm to evolve neural networks to play tic tac toe with some intelligence.  It's written in pure JavaScript and runs completely in your browser.  It utilizes web workersto parallelize the work, and local storage to save your progress. How does it work?? First, we generate every tic tac toe game state where the next move has more than one valid option, a mere 4,298 states.  We then use an AI that plays a perfect game to determine the "right" move or set of equivalentmoves to make in each case.  Perfect means the AI 1) never loses, 2) tries to win as quickly as poss...

Using an Arduino I created a bot that detects obstacles in a 25 cm radius. The code rotates the geared DC motor using the Motor Driver Shield anti-clockwise. This allows the chassis to rotate and move away from the obstacle. This project may drastically change the way we look at Car automation.  #include <AFMotor.h> //import your motor shield library #define trigPin 12 // define the pins of your sensor #define echoPin 13  AF_DCMotor motor1(1,MOTOR12_64KHZ); // set up motors. AF_DCMotor motor2(2, MOTOR12_8KHZ); void setup() {   Serial.begin(9600); // begin serial communitication     Serial.println("Motor test!");    pinMode(trigPin, OUTPUT);// set the trig pin to output (Send sound waves)   pinMode(echoPin, INPUT);// set the echo pin to input (recieve sound waves)   motor1.setSpeed(105); //set the speed of the motors, between 0-255 motor2.setSpeed (105);   } void loop() {    long du...

Recommender systems are everywhere. Amazon recommends buys we like, google recommends searches, Youtube recommends systems and Facebook recommends people. All of these implementations describe Recommender Systems. There are 2 types of Recommender Systems. Collaborative Systems - Systems that recommend information based on what you like and what the others like. Content Based Systems- Based on what you have viewed This is a simple ML based recommender System in python. It uses the LightFM library as the dataset import numpy as np from lightfm import LightFM from fetch_lastfm import fetch_lastfm data = fetch_lastfm() model = LightFM( loss = ' warp ' ) model.fit(data[ ' matrix ' ], epochs = 30 , num_threads = 2 ) # Get recommendationns function def get_recommendations ( model , coo_mtrx , users_ids ): n_items = coo_mtrx.shape[ 1 ] for user in users_ids: # TODO create known positives # Artists the model pred...