Can machines think like a human? It was just a dream before the great technological revolution, but now it’s the biggest reality of our lives. The internet and technology side by side has made this world a global village. Thanks to Comcast deals. I always stay updated with the news and views of the world.
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The latest AI can think like a human
Machines were never so smart, but now they are made so smart that they can actually think for themselves. Algorithms have been developed that can amend themselves in order to ensure efficiency. How cool is that? In order to understand the machines using AI, we need to know more about deep learning.
Deep learning is basically a sub-field of machine learning which basically operates the algorithms inspired by the structure and function of the brain generally called “artificial neural networks.” Deep learning is the important part of the conversation when it comes to artificial intelligence, big data, and analytics. It promises great development in automation and self-teaching systems which are going to revolutionize the industry.
Deep Learning is used by Google
Due to the wide scope and efficiency of deep learning techniques, Google is using it in its voice and image recognition algorithms. It is also being used by researchers at MIT. Netflix and Amazon use it to help you decide what you want to buy or see next.
Deep Learning will be as good as its data
Once a deep learning researcher was asked what he’d like for Christmas. He answered, “More labeled data sets would be great.” Nerd jokes aside, the lack of training data on deep learning has made its understanding a real problem. Deep learning basically relies on millions and millions of examples to tell the algorithm what to look for exactly. It needs all the samples necessary for it to identify a dog voice from a cat voice, or to identify the humanoid things strolling on the street. Deep learning is as good as the data it is being trained on.
DeepMind’s latest AI machines think like humans
In the latest issue of Science, DeepMind revealed an algorithm that displays the first step in transfer learning. When it was shown as a series of 2D images, the algorithm actually recognized the 3D environment and was efficient enough to predict the news and views of the scene. The deep neural net actually translates the generative query network, which can analyze 2D views thoroughly via a simulated camera. This simulated camera controls the digital robotic arm to navigate the 3D environment in the 2D image. This application in robotics is a potential step towards the revolution of deep learning. When researchers peeked into the AI brain, they found the network also captured the essence of each 3D scene. And in those 3D scenes, the internal structure represented the meaningful aspects of that particular scene.
Deep learning rejuvenated the entire field of machine learning. It leads to facial recognition, voice mimicking systems, machine translation, AI-based cancer diagnosticians, self-driving cars, and more. But instead of all the success, it still requires a human brain to operate it. Deep learning relies on the artificial neural network with layers and layers of neurons. The neurons of an artificial intelligence network receive the input from multiple peers, perform the calculations it requires, then forwarding the output to the neurons. This neuron system helps to solve the problem by making the locally necessary choices at each stage with the intent of finding a global finest.
The encoding and decoding network of DeepMind’s new GQN is more human-like. An encoder network will analyze visual inputs and agitate the data into replicas of a scene. It actually forms a mathematical interpretation of the description of the scene, and every additional observation from a neural net adds to the richness of that interpretation or representation. The network can encode the high-level information or details. Then there’s the decoder. This actually interprets the representations encoded and presents solutions for the specific task. In this environment, several encoders work with several decoders to generate an array of solutions for a particular problem.
It is like giving a child a basic understanding of what a pizza is like. Even if he doesn’t know how to describe it, he will have a picture in mind which tells exactly in which category it falls. Of course, these machines are at a disadvantage. Unlike a human eye, they can’t see the scenes in 3D. Rather, they capture the environment in 2D. Deepmind’s team remedied this problem by rendering 3D environments from 2D images by using smart algorithms.