AI is the future, and it's also the past. Not just in the sense of having been developed in previous years and decades, but also in the sense of being capable of recreating human history. This power was highlighted vividly by a study published at the end of August by researchers from University College London and Duke University, who managed to use artificial intelligence to create separate representations of two images that had been painted on both sides of a single panel. More specifically, they used X-ray imaging techniques to produce a combined representation of the outer panels of the famous 15th Century Ghent Altarpiece painting. Because the resulting image was a combination of two images superimposed on each other, it was previously hard to analyze.

GARY MARCUS: The dominant vision in the field right now is, collect a lot of data, run a lot of statistics, and intelligence will emerge. And I think that's wrong. I think that having a lot of data is important, and collecting a lot of statistics is important. But I think what we also need is deep understanding, not just so-called "deep learning." So deep learning finds what's typically correlated, but we all know that correlation is not the same thing as causation.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Last week, the Allen Institute for Artificial Intelligence (AI2) introduced Aristo, an artificial intelligence model that scored above 90 percent on an 8th grade science test and 80 percent on a 12th-grade exam. Passing a science test might sound mundane, if you're not familiar with how deep learning algorithms, the current bleeding edge of AI, work. After all, AI is already performing tasks such as diagnosing cancer, detecting fraud and playing complicated games, which are much more complicated than answering simple science questions about the moon and squirrel populations. But despite its fascinating achievements, deep learning struggles when it comes to tackling problems that require reasoning and commonsense.

If you've ever attempted to learn Thai, you can assume that this Southeast Asian language is extremely difficult -- if not the most difficult -- for machines to also understand. Thai is a character-based language with numerous quirks that disrupt natural language processing algorithms. Because of these quirks, leading NLP engines fail to understand Thai beyond the surface-level, causing an underwhelming customer experience. First, the language consists of several types of interjection words in a single sentence. Many of these words do not carry any meaning relevant to the sentence's intent; these words are most often used to indicate emotion or an expression of politeness.

Virtual Reality (VR) is opening up exciting new frontiers in the development of video games, paving the way for increasingly realistic, interactive and immersive gaming experiences. VR consoles, in fact, allow gamers to feel like they are almost inside the game, overcoming limitations associated with display resolution and latency issues. An interesting further integration for VR would be emotion recognition, as this could enable the development of games that respond to a user's emotions in real time. With this in mind, a team of researchers at Yonsei University and Motion Device Inc. have recently proposed a deep-learning-based technique that could enable emotion recognition during VR gaming experiences. Their paper was presented at the 2019 IEEE Conference on Virtual Reality and 3-D User Interfaces.

Founded in London in 2010 and acquired by Google in 2014, AI company DeepMind Technologies Ltd. reportedly reduced the amount of energy required to cool Google's data centers by 40 percent. DeepMind reported these results in July 2016, however, the company claims that it first began applying machine learning two years prior to improve energy usage. Specifically, a set of data center operating scenarios and parameters were used to train a system of neural networks. The neural network "learned" how the data center functioned and began identifying opportunities for optimization. Google claims that data was pulled from thousands of sensors located in the data centers.

Indian Institute of Technology Hyderabad researchers have developed a method by which the inner workings of Artificial Intelligence models can be understood in terms of causal attributes. 'Artificial Neural Networks' (ANN) are AI models and programs that mimic the working of the human brain so that machines can learn to make decisions in a more human-like manner. Modern ANNs, often also called Deep Learning (DL), have increased tremendously in complexity such that machines can train themselves to process and learn from data that has been supplied to them as input, and almost match human performance in many tasks. However, how they arrive at decisions is unknown, making them less useful when the reason for decisions is necessary. This work has been performed by Dr Vineeth N Balasubramanian, Associate Professor, Department of Computer Science and Engineering, IIT Hyderabad, and his students Aditya Chattopadhyay, Piyushi Manupriya, and Anirban Sarkar. Their work has recently been published in the Proceedings of 36th International Conference on Machine Learning, considered worldwide to be one of the highest-rated conferences in the area of Artificial Intelligence and Machine Learning.

Your RSVP on the meetup page will not count as an RSVP to the event. Join us as we discuss Custom Machine Learning Recipes for the Enterprise, and an AI-enabled Smart Pricing approach to a manufacturing use-case. We came up with machine learning based smart pricing approach to tackle 100,000 spare parts in which the solution was implemented in real-time and currently in use to help the pricing team. The same solution was adopted in 5 different countries with minimal work. This project is one of the top 4 projects in Nasscom under Engineering innovation.

Last year the credit reporting agency Equifax announced that malicious hackers had leaked the personal information of over 143 million people after their system was hacked, and while that's concerning it's long been known that if a hacker wants to access your online data by simply guessing your password then there's a high chance you'll be toast in less than an hour. Now though after a recent announcement there's even more bad news for users – scientists at the Stevens Institute of Technology in New Jersey have found a way using Artificial Intelligence (AI) to create a program that, when combined with existing hacker tools, took just minutes to figure out more than a quarter of all the passwords from a set of more than 43 million LinkedIn profiles. And perhaps yours was one of them…? Despite this concerning turn of events though the same researchers say the technology may also be able to be used to beat hackers at their own game by helping users measure the strength of their passwords. "The new technique could also potentially be used to generate decoy passwords to help detect breaches," says Thomas Ristenpart, a computer scientist who studies computer security at Cornell Tech in New York who wasn't involved with the study, which is something that another team accomplished last year by creating a bot that tells you when your accounts have been hacked even when the companies being hacked didn't know, or didn't fess up to the fact.

Deep neural networks are the go to algorithm when it comes to image classification. This is partly because they can have arbitrarily large number of trainable parameters. However, this comes at a cost of requiring a large amount of data, which is sometimes not available. I will discuss One Shot Learning, which aims to mitigate such an issue, and how to implement a Neural Net capable of using it,in PyTorch. This article assumes some familiarity with neural networks.