What is Deep Learning and How It can Take Place of Human

DEEP LEARNING 

The idea to develop a truly intelligent computer: one that might understand human language and then can make its inferences and decisions on its own.

It will become obvious that such an effort would require no less than Google-scale data and computing power.
Deep learning copies the activity of neurons in the neocortex, 80 percent of the brain where thinking occurs. It learns, in a very real sense, to understand patterns in digital representations of sounds, images, and other types of data.
The fundamental idea that software can process is the neocortex’s large array of neurons in an artificial “neural network” which is decades old, and it has led to as many disappointments as breakthroughs. But due to improvements in mathematical formulas and increasingly powerful computers scientists can create layers of virtual neurons very easily.

Building a Brain

There have been many competing approaches to these challenges. One of them is to feed devices with information and rules about the world. It took lots of time and still left the system unable to deal with debatable data it was limited to controlled applications such as phone menu systems which ask you to make queries by saying specific words.
AI research, looked favorable because they attempt to simulate the way the brain works, though in a simplified manner. A program is a set of virtual neurons and then it is assigned in random numerical values. These weights determine how each simulated neuron responds.
Coders would develop a neural network to detect an object by attacking the network with digitized versions of images containing those objects or sound waves containing those phonemes. If the network isn’t able to accurately understand a particular pattern then an algorithm would adjust the weights.

The goal of this training is to get the network to continuously recognize the patterns in speech or sets of images that we humans can understand. This is the same way a child learns by noticing the details of an object.
The fact that stunned some AI experts was though, was the amount of improvement in image recognition. It was challenging for most humans. The accuracy increased above 50 percent when the software was asked to sort the images into 1,000 more general categories.

Big Data 

Giving training to the layers of virtual neurons in the experiment took around 16,000 computer processors, the kind of computing infrastructure that Google has made for its search engine and other services is marvelous. About 80% of the advances in AI can be credited to the availability of more computer powers.
Deep learning has changed the way of voice search on our devices. The layers of neurons lead to more precise training on many varieties of a sound, the device can easily recognize sound more reliably, especially in noisy environments such as on metro platforms. It is easy to understand what was actually uttered, the result it returns is accurate as well.
Everyone doesn’t think that deep learning can move AI toward something rivalry human intelligence. Some people say deep learning and AI, in general, ignore too much of the brain’s intelligence in favor of brute-force computing.

What’s Next? 

Google has already started thinking about future applications, the prospects are fascinating. It is a better image search which would help YouTube, for instance. It’s also favorable that more complicated image recognition could make Google’s self-driven cars much better. There are search and ads that underwrite it. There has been betterment from any technology that’s good and faster at recognizing what people are really looking for, maybe even before they realize it.

Everyone wants to have the exact meaning of words, phrases, and sentences that always trip up computers. In turn, it will require a more complicated way to graph the syntax of sentences. Google has already started using these kinds of analysis to improve grammar in translations. Language understanding will require devices to grasp what we humans think of as common-sense meaning. Finally, People plans to apply algorithms to help computers deal with the “boundaries in language.”

Self Driving Car 

Microsoft also has there promising research on likely uses of deep learning in machine vision. It also has visualized personal sensors that deep neural networks could use to predict medical problems. Sensors might also help a city it might feed deep-learning systems that could predict where traffic jams might occur.

It is a field that attempts something interpreting the human brain, it’s certain that only one technique won’t solve all the problems. But for now, it is the one which is leading the way in artificial intelligence. Deep learning is a really powerful metaphor for learning about the challenges of the world

SOPHIA, The Humanoid Robot

As we all know, the field for AI and robotics is very fast-growing. Now robots can perform movement like- back-flipping, practicing parkour moves, and even classical sculptures.

Sophia is  human-like robot, created by Hanson Robotics. Sophia is very advanced robot. Sophia was first activated on February 14, 2016, and her public appearance at South by Southwest Festival(SXSW) in March 2016 in Austin, Texas, US.

Sophia is able to display more than 50 facial expressions.

The founder of Hanson Robotics, David Hanson said "We're not fully there yet, but Sophia can represent a number of emotional states, and she can also see emotional expressions on a human face as well,"

According to Hanson, Sophia now has simulations of every major muscle in the human face, allowing her to generate expressions of joy, grief, curiosity, confusion, contemplation, sorrow, frustration, among other feelings. She is human-crafted science fiction character depicting where AI and robotics are heading. In other ways, Sophia is real science, springing from the serious engineering and science research and accomplishments of an inspired team of robotics & AI scientists and designers.

"In some of the work we're doing, she will see your expressions and sort of match a little bit and also try to understand in her own way, what it is you might be feeling," says Hanson.

Understanding Reinforced and Semi supervised Machine learning

Article 1:Iintroduction to Artificial intelligence and Machine learning

Article 2:Introduction To Machine Learning Algorithm
article 3:Understanding Supervised and unsupervised machine learning

Understanding Reinforced and Semi supervised Machine learning

As we already discussed the first two types of Machine learning Algorithm its time to understand next two and final types of machine learning algorithm which are:

*Semi supervised Machine learning
*Reinforced Machine Learning

Review of Supervised and Unsupervised Machine learning.

Supervised Machine learning:

In supervised machine learning ,system is trained using training data or also labeled data or also classified example modal .algorithm uses these training data to take decision and show results ,this type of algorithm is very time consuming and expensive but more accurate.

Unsupervised machine learning:

In unsupervised machine learning ,system is trained without any labeled data or also classified data or also example modal. These type of algorithm is less time consuming and expensive but less accurate.

What is semi supervised machine learning?

Due to use of a lot of labeled data ,supervised learning is very time consuming and expensive,in some experiment data scientists used some labeled data with some unlabeled data and founded that it increases the accuracy of result and it was easy to implement than supervised learning. And more accurate than unsupervised learning.
         Hence they devloped a new algorithm which falls between supervised and unsupervised learning aka,semi supervised learning.
       Semi supervised learning involves human agent to render data and modals .
       

As we now know what is semi supervised learning we can now try to understand  About Reinforced machine learning..

What is Reinforced machine learning?

Reinforced learning is a technique to train a program using trial an error method or more practical way as human learns from environment using their actions.

For example if a kid does something good gets a choclate as a reward and  for doing something bad gets scolded by their parents.
      Reinforced learning is also based on same principal if system does something valuable gets reward points by agents but when it does something value less ,reward point is reduced .
System used these past experience to learn and be accurate .
        So simply this technique is more action based and trained under live environment , this topic is very eye catching for research among data scientists and programmers due to its possibility in future to create a very advanced independent system.
       In next we will discuss more about these technique and terms related to Artificial intelligence .

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Written by- Aakash deep gupta 

Understanding Supervised and Unsupervised Machine Learning

Understanding supervised and unsupervised machine learning.

Article 1:Iintroduction to Artificial intelligence and Machine learning
Article 2:Introduction To Machine Learning Algorithm
In previous Articles we Discussed, What is Machine learning ?

What is Machine learning Algorithm?

Now its time to discuss about different types of Algorithm used in Making intelligent programs ,
In general there are four types of ML algorithm :-

1.Supervised Learning.

2.Unsupervised Learning

3.Semi Supervised Learning

4.Reinforced Learning 

1. What is Supervised Learning?

Supervised Learning is a method of creating intelligent program by providing them Example data
Or Training Data which includes parameters and possible output . And these program use this Training data to generate result on Real given input.
      Its confusing? let us explain , for example you have to  classify four kinds of shape namely Square ,Rectangle ,Circle and Triangle . And these four shapes belongs to Two types of colours Blue and yellow .
         We uses parameters like radius ,sides ,colours to determine the shapes .

Square and circle belongs to blue while rectangle and triangle belongs to yellow.
Now we specified some properties

   Now we feed the example data or training data as,

A  blue circle
[Radius,blue,]

A blue Square
[Sides 4,blue]

A yellow rectangle
[Sides 4,yellow]

A yellow triangle
[Sides 3,yellow]
Now we wants to determine name of a unknown shape having known sides and colour.

Feed inputs to system
As [parameters,|possible output]
For example
   [4 sides,yellow|circle,square,rectangle,triangle]

Algorithm will check for parameters, as

It has 4 sides,
It can be square or rectangle

It has a color yellow
It can be triangle or rectangle

After calculation of mathematical values it will declare result
As Its A rectangle!

Hence ,supervised machine learning is a way in which we provide possible output and parameters to system to match it from training data .


Now we will try to understand the core concept of unsupervised machine learning.

2.What is unsupervised machine learning?

Unsupervised learning is method of creating intelligent programs by providing it Only imputs as parameters but not any example model.
      We can also say that the output of the data is unknown ,and  its algorithms responsibility to determine the shape by it self.


Let me explain using previous example .

In this case we will provide inputs in this manner
     As   [Parameters],
For example
          [4 sides ,yellow]

In this case system will use its intelligent algorithm to determine the name of shape by it self .


I hope that ,you now understands the basic concepts and difference between supervised and unsupervised machine learning .
Next article will deal with next two types
3.semi supervised learning
4.reinforced learning

Thank you
If you want to join our official telegram channel, click here

Written by Aakash deep gupta

FIVE TECH TRENDS FOR 2019

Five Tech Trends for 2019

1. Artificial Intelligence

Within a few years, analysts predict that all software will use AI at some level, according to US research and advisory firm Gartner.

2. Augmented Reality

Organizations are increasingly applying this technology across a wide spectrum of human activity from art and entertainment to commerce, education and the military.

 3. Block Chains

The fortunes of digital currency Bitcoin have drawn public attention to Blockchain technology, but this secure system for recording and verifying transactions and storing trusted records has the potential to disrupt enterprise of many kinds.

4. Automation

From convenient devices at home to industrial applications on a massive scale, automation will be a key focus of technological change, with potentially far-reaching economic and social consequences.

5. Internet Of Things

It is early days for the application of IoT strategy but it is clear that opportunities will exist for those with the technical knowledge to connect platforms as well as those with the data analytics skills to utilize the rich stream of information generated by IoT application.

Written by- Aayush Sourav

UNDERSTANDING MACHINE LEARNING ALGORITHM

Understanding Machine learning Algorithm

Article 1:Iintroduction to Artificial intelligence and Machine learning

As We Already know, What is Machine Learning  in previous article  or subset of Artificial Intelligence .

          We can now proceed to more complex topics in this Series . The next topic we are going to discuss is Machine learning Algorithm .which deals with the method and logic's behind these intelligent systems.

                But before machine learning algorithm we  must understand what this term Algorithm actually means in detail .

What is Algorithm?

In definition :

Algorithm is the set of Rules To be followed to solve a particular class of problems .

In general:
Algorithm is the steps involved in a process , which must be taken to find the solution of a particular types of  problem ,

For example :
Your mother asks you to bring milk from your uncles house so that she can prepares sweet from it and again asks you to deliver it to your uncles house.

Now let us convert it into Algorithm and a Program logic.

Step 1:
Take a empty bottle and go to uncles house.
Step 2:
Fill the bottle with milk
Step 3:
Travel to your home ,and give it to your mother.
Step 4:
Sweet is prepared by your mother
Step 5:
Deliver it to your uncles house.

What ? We were not talking about milks and sweets but Algorithm isn't it?
Now see the program version of the example.

Step 1:
You created a empty variable(bottle) and gave a message to user to give input(milk)

Step 2:
You took the input (milk) into variable(bottle)

Step3:
You presented it to processor (mother) it used the logic (Prepare the sweet)

Step 4:
Input (milk)  is now converted into output (sweet).

Step 5:
You gave the output (sweet) to user (uncle)
Explanation:
Let us assume we are making a program to check the no to be prime or not  We first creates a variable to store the number. Then we prints a message to input a number ,after that we apply the logic on number and prints the result to user
         All the individual steps involved is nothing but algorithm.

In programming the Algorithm is represented using Flow charts.

Now you understands the Term Algorithm . Now we will discuss ML algorithm.

What is Machine learning Algorithm?

Machine learning algorithm is the set of rules or methods of programming.
 in which program is written in a way that ,it can take inputs and can give the desired output without changing the program codes. It can use the input data to maximise the accuracy every time we provide  data.
      Or we can say that ,the steps and method we use to make program that is ,capable of receiving input , and changing  its code according to inputs data to increase the accuracy of desired output is known as machine learning algorithm.

The four types of Machine learning Algorithm are
1.Supervised learning

2.Unsupervised learning

3.semi supervised learning

4.Reinforced learning

We well discuss each of them in detail in next articles.

Thank you

Written by Aakash deep gupta

Beginner's Guide To Artificial Intelligence and Machine Learning : Introduction

beginner's Guide To Artificial Intelligence and Machine learning :Introduction

We cant deny the fact that nowdays sci-fi movies are more popular than any other genre also these amazing movies played a crucial role in growth of sci lovers . According to some theory   the master piece Interstellar inspired astrophysicist to research about black holes resulting in more proved theories.
       I am sure if you are a sci -fi fan you  definitely know at least a something about  Machine learning . This article is about to introduce you to machine learning ,its application and its dangers.

What is Machine learning?

Machine learning is a branch of Algorithm and programming in which we try to develop some advance system that can analyse data and can learn by it self .
       To be familiar with machine learning you may know what intelligence actually means and how brain works so lets begin.

What is intelligence ?

Intelligence in human beings is defined as

the ability to acquire and apply the knowledge

Acquiring knowledge is all about to collect the needed information and ,applying means to take decision on the basis of past experience ,learning and already Developed logic's.
Some examples are:


1.learning 

2.taking decision based on past good or bad experience.

3.mastering the technique to control your flow of emotions also termed as emotional intelligence .

Now you have a basic understanding of human intelligence so now we will discuss how brain works for intelligence.

How brain works?

In Human brain there's a kind  of cells cells that carry data we acquire called as neurons . These neurons have different kind of data each and when we try to learn something new or any mental activity, these neurons interact with each other to make new connections .
       You can understand it as ,we have all letter in different boxes (existing neurons ),and we take some letters and build some words and (some more neurons ) after that we connect those words and form  sentence (new connection)

Machine learning which is also called A subset of Artificial intelligence ,is based on similar principal .

Basic Theory

In machine learning or Artificial intelligence The program is build in a way that every time we feed data ,program build more accurate connection ,until it reach the most accurate result or it learns.

Why we need Artificial Intelligence?

The limit of computer is ,it can only solve those problems that human did already . In another word all the problem solved by computer are actually feeded by some intelligent programmer ,computer is actually not solving the problem but following the instructions.

But in artificial intelligence ,computer can try new logic's by themselves and can solve more complex problem a human cant even think of . Which gives it a value . A more interesting thing is it can analyse data and can conclude with accurate and valid results.

It was a basic introduction to artificial intelligence ,in next articles we will discuss Application ,terms related to AI , how it actually works and Dangers of AI .
Article 2:Introduction To Machine Learning Algorithm
Thank you.

Written by Aakash deep gupta