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The Most Amazing Artificial Intelligence Milestones So Far

Artificial Intelligence (AI) is the hot topic of the moment in technology, and the driving force behind most of the big technological breakthroughs of recent years.

Since the dawn of computing in the early 20th century, scientists and engineers have understood that the eventual aim is to build machines capable of thinking and learning in the way that the human brain – the most sophisticated decision-making system in the known universe – does.

1956 – The Dartmouth Conference With the emergence of ideas such as neural networks and machine learning, Dartmouth College professor John McCarthy coined the term 'artificial intelligence' and organized an intensive summer workshop bringing together leading experts in the field.

ELIZA represented an early implementation of natural language processing, which aims to teach computers to communicate with us in human language, rather than to require us to program them in computer code, or interact through a user interface.

However, it was important from a publicity point of view – drawing attention to the fact that computers were evolving very quickly and becoming increasingly competent at activities at which humans previously reigned unchallenged.

This was significant because while Deep Blue had proven over a decade previously that a game where moves could be described mathematically, like chess could be conquered through brute force, the concept of a computer beating humans at a language based, the creative-thinking game was unheard of.

2012 – The true power of deep learning is unveiled to the world – computers learn to identify cats Researchers at Stanford and Google including Jeff Dean and Andrew Ng publish their paper Building High-Level Features Using Large Scale Unsupervised Learning, building on previous research into multilayer neural nets known as deep neural networks.

2015 – Machines “see” better than humans Researchers studying the annual ImageNet challenge – where algorithms compete to show their proficiency in recognizing and describing a library of 1,000 images – declare that machines are now outperforming humans.

Since the contest was launched in 2010, the accuracy rate of the winning algorithm increased from 71.8% to 97.3% - promoting researchers to declare that computers could identify objects in visual data more accurately than humans.

2016 – AlphaGo goes where no machine has gone before Gameplay has long been a chosen method for demonstrating the abilities of thinking machines, and the trend continued to make headlines in 2016 when AlphaGo, created by Deep Mind (now a Google subsidiary) defeated world Go champion Lee Sedol over five matches.

Although Go moves can be described mathematically, the sheer number of the variations of the game that can be played – there are over 100,000 possible opening moves in Go, compared to 400 in Chess) make the brute force approach impractical.

While human operators currently ride with every vehicle, to monitor their performance and take the controls in case of emergency, this undoubtedly marks a significant step towards a future where self-driving cars will be a reality for all of us.

Build the Artificial Intelligence for detecting diabetes using Neural Networks and keras.

Deep learning is the most important part of artificial intelligence which takes a huge amount of data and provides the solution to any problem.

Deep learning used to be a part of machine learning but because of the availability of high computing power and data now it achieved its own place in the field of artificial intelligence.

The simple neural network is a mathematical model which simply takes Xn as inputs, multiply inputs with weights Wn and add bias b and pass it from activation function and returns Y as output as shown in below.

The purpose of using activation function in the neural network to get the smooth loss function means loss function has to be a convex function or concave function then we can apply gradient descent on it.

We will use Pima diabetes dataset and built a neural network which will predict if a patient is diabetic or not given inputs X like Glucose, blood pressure, skin thickness, etc.

Here we are using one input layer with 12 neurons, one hidden layer with 8 neurons and one output layer with 2 neurons.

Sigmoid takes all negative values and maps them to zero and relu is also similar activation function which can be defined as y = max(0, x).

Currently, most used neural networks are CNNs (Convolution neural networks) and RNNs (Recurrent neural networks) which are specifically used for computer vision and for sequential data like time series data respectively.

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