What is the meaning of the word understanding? This was a question posed during a particularly enlightening lecture given by Dr. Anupam Basu, a professor with the Department of Computer Science Engineering at IIT Kharagpur, India.
Understanding something probably relates to being able to answer questions based on it, maybe form an image or a flow chart in your head. If you can make another human being comprehend the concept with the least amount of effort, well that means you do truly understand what you are talking about. But what about a computer? How does it understand? Continue reading →
[This entry has been edited for clarity. An example given discussing the similarity of words in French and English was incorrect. The following sentence has been removed: “The next question addressed by Bhattacharya was the ambiguity that may arise in languages with similar origins, for example in French ‘magazine’ actually means shop while in English, well it is a magazine.”]
Today is June 14th, so I am 14 days into summer school; 7 more days left, and we are all already feeling saddened by the idea of leaving Kharagpur soon. In India, an IIT is a dream for 90% of the 12th graders who join IIT coaching classes. The competition is high so not everyone gets in. I’m one of those who didn’t get in. So when I saw there was an ACM Summer School opportunity at the largest and oldest IIT in India, obviously I grabbed it. By sheer luck, I was selected to actually attend the school. Over the course of 21 days, we have been tasked to learn about machine learning and natural language processing. Continue reading →
Artificial Neural Networks (ANNs) are used everyday for tackling a broad spectrum of prediction and classification problems, and for scaling up applications which would otherwise require intractable amounts of data. ML has been witnessing a “Neural Revolution”1 since the mid 2000s, as ANNs found application in tools and technologies such as search engines, automatic translation, or video classification. Though structurally diverse, Convolutional Neural Networks (CNNs) stand out for their ubiquity of use, expanding the ANN domain of applicability from feature vectors to variable-length inputs.
Almost two decades ago I saw in the arcades the futuristic fighting game “Rise of the Robots”. As a youngster I was imagining what the future of computing and robotics could be. The game ended up not being that great, regardless of that it wasn’t visually very realistic, but instead, it relied on the gameplay and partly on the player’s imagination for the immersion. Hence, around this time, I was dreaming of tablets (from Star Trek) and completely autonomous robots that would help us with everyday tasks (like terminators, without the killing part of course and maybe the Jetson’s robots?).
Yet the future was not exactly what I was expecting. After all this experimentation and technological progress, it seems that people hyped with Chatbots (or chatterbots) instead!Continue reading →
Intelligent Systems, Artificial Intelligence, Smart Recommenders, Machine Learning and the list of endless fancy words that popup here and there over websites will always have a mystery behind. Over the past few years, we have witnessed great advancements in computer systems. Computers can now take over tasks that we, humans, never thought a computer would be able to do – including tasks that no human brain can efficiently and quickly perform such as looking through thousands of text files and drawing connections between them, reading millions of medical papers and connecting genes to potential diseases. The latter is the job of IBM Watson’s Discovery Advisor, a tool for researchers.
This way it seems that many researchers around the world strive to build computers that can substitute humans completely. The question that arises is: are we going to see computer brains that completely mimic human brains? In our post today, we cover some basics of the research in this direction trying to figure out an answer for the million cells question ..