If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Since ancient times, humans have been interested in finding systematic approaches to reasoning and logical thinking. Now, we want to make machines "think" like us and endow them with the reasoning ability that, unfortunately, we don't quite understand ourselves. But, why do we need machines that can deconstruct truths and validate reasons like we do? One of our most recent AI-related posts discusses the story of an AI system that can detect skin cancer more accurately than dermatologists. No doubt, this is a big deal in that an early diagnosis is one of the most effective methods for providing successful cancer treatments.
Neural Networks is one of the most popular machine learning algorithms at present. It has been decisively proven over time that neural networks outperform other algorithms in accuracy and speed. With various variants like CNN (Convolutional Neural Networks), RNN(Recurrent Neural Networks), AutoEncoders, Deep Learning etc. neural networks are slowly becoming for data scientists or machine learning practitioners what linear regression was one for statisticians. It is thus imperative to have a fundamental understanding of what a Neural Network is, how it is made up and what is its reach and limitations. This post is an attempt to explain a neural network starting from its most basic building block a neuron, and later delving into its most popular variations like CNN, RNN etc.
Facebook doesn't make magnetic resonance imaging machines and has no plans to start. But that hasn't stopped researchers at the social networking giant's Montreal artificial intelligence lab from working with NYU School of Medicine to make MRI scans faster and more accurate. Facebook celebrated the one-year anniversary of the Montreal lab Thursday by highlighting the MRI project and some other research work. The company also announced that the AI lab will move into a new space that can accommodate up to 80 people, which gives the team room to expand from the 20 or so researchers currently working there. Joelle Pineau, an associate professor at McGill university and the lab's head, said that medical imaging was a good fit for the kind of "fundamental research" on artificial intelligence that Facebook hired her to do.
Developing a new drug can cost billions of dollars and take a dozen or more years to bring to market. Two Israeli researchers have applied artificial intelligence (AI) and deep learning to shave time and money off the drug-discovery process. Instead of searching for the appropriate molecules to use in a new medicine, as is done today, they enabled a computer to make smart predictions without human guidance. Shahar Harel and Kira Radinsky at the Technion-Israel Institute of Technology fed into their computer system hundreds of thousands of known molecules as well as the chemical composition of all FDA-approved drugs up until 1950. Aided by AI, the computer came up with new potential molecules by making sometimes unexpected correlations from within this massive sample.
Robert Jones was driving home through the pretty town of Todmorden, in West Yorkshire, when he noticed the fuel light flashing on the dashboard of his car. He had just a few miles to find a petrol station, which was cutting things rather fine, but thankfully his GPS seemed to have found a short cut – sending him on a narrow winding path up the side of the valley. Robert followed the machine's instructions, but as he drove, the road got steeper and narrower. After a couple of miles, it turned into a dirt track, but Robert wasn't fazed. After all, he thought, he had "no reason not to trust the satnav".
A few years ago I had written an article about how technology is literally obliterating jobs and how to skill yourself up for this new economy. In this article I want to provide you with a renewed peek into what I believe is the road to success, should you want to be part of the artificial intelligence economy - which some are equating to the revolution nearly 120 years ago with electricity. But first, let's take a look at what digital disruption or the increased pervasiveness of new technology is doing to the job market. Last year, AP (Associated Press) ran a 3-part big story on "Technology and permanent job destruction," very real and a very chilling narrative of what is happening in the job market right now. We have never witnessed at jobless economic recovery ever!
Today's artificial intelligence systems, including the artificial neural networks broadly inspired by the neurons and connections of the nervous system, perform wonderfully at tasks with known constraints. They also tend to require a lot of computational power and vast quantities of training data. That all serves to make them great at playing chess or Go, at detecting if there's a car in an image, at differentiating between depictions of cats and dogs. "But they are rather pathetic at composing music or writing short stories," said Konrad Kording, a computational neuroscientist at the University of Pennsylvania. "They have great trouble reasoning meaningfully in the world." To overcome those limitations, some research groups are turning back to the brain for fresh ideas.
During the heydays of pulp sci-fi, Robert A. Heinlein penned a now forgotten short novel titled Waldo. The parable broadly speculated on how robotics and automation would eventually come to shape the lives and the landscape of the future. Almost a century later, Heinlein's work reads like a prophesy, foretelling the 21st century's rapid march towards adopting machines to do men's work. Look around and you'll find myriad examples. Robots are putting together cars on the assembly line and acting as companions for the disabled.
Artificial intelligence (AI), and the subfield of machine learning (ML), study the processes and practicalities of enabling machines to skilfully perform intelligent tasks, without explicitly being programmed for those tasks. Recently, AI systems have neared or surpassed human performance in several tasks, such as game playing and image recognition , but these have typically been quite narrow and focused domains. Nonetheless, AI in its various forms is today successfully applied across a large range of domains and for challenging tasks, ranging from robotics, speech translation, image analysis and logistics to its ongoing use in designing molecules. Since the 1960s, medicinal chemistry has applied AI in various forms and with varying degrees of success to the design compounds. Supervised learning, where labeled training datasets are used to train models is extensively applied.