Revolution of Artificial Intelligence in Science

AI revolutionizing science!


Big data is a term that describes the large volume of data — both structured and unstructured. Big data has its footprints in almost all the fields of science from biology, astronomy, social science, etc. In biology large databases of genomes and proteins; in astronomy — petabytes of data from sky surveys; in social science — millions of posts and tweets which go around the internet. The flood of data can bring out human insight and analysis, which can make sense only with the help of advances in computing.

Scientists from different areas of science are trying to use Artificial Intelligence (AI) to unleash a revolution which is often in the form of Artificial Neural Network on data.

Unlike the earlier systems of AI, this system such as “deep learning” has a distinct feature — which can learn on their own from large data sets which are used for training to see patterns which are impossible for humans.

AI is not only transforming science; it is with us revolutionizing our every day tasks. It is speaking to us via our smartphones, driving for us in the roads as driver-less cars which can lead to mass unemployment.

Neural networks can’t explain their thinking like a student: The computations which lead to the outcome are hidden. So it has given rise to a new field called “AI neuroscience” — which is to open up the black box of neural networks. This can bring confidence over the insights which are yield of neural networks.

Knowing the mind behind the machine is very important to expand the role of AI in science. So many experts are making AI to design and carrying out experiments as well as interpreting results — which can open up the prospect of fully automated science. The tireless apprentice may soon become a full-fledged colleague.


Artificial intelligence — Key terms

What do people mean by Artificial Intelligence (AI)? The term has no boundaries as there can be multiple definition to explain what is AI. When AI was introduced at 1956, in a seminal workshop at Dartmouth College, it was taken broadly to mean making a machine behave in ways that would be called intelligent if seen in a human.

An important recent advance in AI has been machine learning, which shows up in technologies from spellcheck to self-driving cars and is often carried out by computer systems called neural networks. Any discussion of AI is likely to include other terms as well.

Some AI terms

ALGORITHM A set of step-by-step instructions. Computer algorithms can be simple (if it’s 3 p.m., send a reminder) or complex (identify pedestrians).

BACKPROPAGATION The way many neural nets learn. They find the difference between their output and the desired output, then adjust the calculations in reverse order of execution.

BLACK BOX A description of some deep learning systems. They take an input and provide an output, but the calculations that occur in between are not easy for humans to interpret.

DEEP LEARNING How a neural network with multiple layers becomes sensitive to progressively more abstract patterns. In parsing a photo, layers might respond first to edges, then paws, then dogs.

EXPERT SYSTEM A form of AI that attempts to replicate a human’s expertise in an area, such as medical diagnosis. It combines a knowledge base with a set of hand-coded rules for applying that knowledge. Machine-learning techniques are increasingly replacing hand coding.

GENERATIVE ADVERSARIAL NETWORKS A pair of jointly trained neural networks that generates realistic new data and improves through competition. One net creates new examples (fake Picassos, say) as the other tries to detect the fakes.

MACHINE LEARNING The use of algorithms that find patterns in data without explicit instruction. A system might learn how to associate features of inputs such as images with outputs such as labels.

NATURAL LANGUAGE PROCESSING A computer’s attempt to “understand” spoken or written language. It must parse vocabulary, grammar, and intent, and allow for variation in language use. The process often involves machine learning.

NEURAL NETWORK A highly abstracted and simplified model of the human brain used in machine learning. A set of units receives pieces of an input (pixels in a photo, say), performs simple computations on them, and passes them on to the next layer of units. The final layer represents the answer.

NEUROMORPHIC CHIP A computer chip designed to act as a neural network. It can be analog, digital, or a combination.

PERCEPTRON An early type of neural network, developed in the 1950s. It received great hype but was then shown to have limitations, suppressing interest in neural nets for years.

REINFORCEMENT LEARNING A type of machine learning in which the algorithm learns by acting toward an abstract goal, such as “earn a high video game score” or “manage a factory efficiently.” During training, each effort is evaluated based on its contribution toward the goal.

STRONG AI AI that is as smart and well-rounded as a human. Some say it’s impossible. Current AI is weak, or narrow. It can play chess or drive but not both, and lacks common sense.

SUPERVISED LEARNING A type of machine learning in which the algorithm compares its outputs with the correct outputs during training. In unsupervised learning, the algorithm merely looks for patterns in a set of data.

TENSORFLOW A collection of software tools developed by Google for use in deep learning. It is open source, meaning anyone can use or improve it. Similar projects include Torch and Theano.

TRANSFER LEARNING A technique in machine learning in which an algorithm learns to perform one task, such as recognizing cars, and builds on that knowledge when learning a different but related task, such as recognizing cats.

TURING TEST A test of AI’s ability to pass as human. In Alan Turing’s original conception, an AI would be judged by its ability to converse through written text.