Algorithms modeled loosely on the brain have helped artificial intelligence take a giant leap forward in recent years. Those algorithms, in turn, have advanced our understanding of human intelligence while fueling discoveries in a range of other fields. MIT founded the Quest for Intelligence to apply new breakthroughs in human intelligence to AI, and use advances in AI to push human intelligence research even further. This fall, nearly 50 undergraduates joined MIT's human-machine intelligence quest under the Undergraduate Research Opportunities Program (UROP). Students worked on a mix of projects focused on the brain, computing, and connecting computing to disciplines across MIT.

At first glance, the two rows of portraits at the top of this article just look like a bunch of average-looking people. The catch is, none of them exist. All of these faces are fakes, put together by artificial intelligence. To be more precise, these faces are created by a generative adversarial network (GAN) developed by Nvidia, using deep learning techniques to produce realistic portraits out of a database of existing photos. Head over to the This Person Does Not Exist website to see for yourself: every time you refresh the page, you get a new face.

These days, you hear a lot about machine learning (or ML) and artificial intelligence (or AI) – both good or bad depending on your source. Many of us immediately conjure up images of HAL from 2001: A Space Odyssey, the Terminator cyborgs, C-3PO, Data from Star Trek, or Samantha from Her when the subject turns to AI. And many may not even be familiar with machine learning as a separate subject. The phrases are often tossed around interchangeably, but they're not exactly the same thing. In the most general sense, machine learning has evolved from AI. In the Google Trends graph above, you can see that AI was the more popular search term until machine learning passed it for good around September 2015.

The security and robustness of deep neural networks(DNNs) architectures is one of the most important areas of research in the deep learning field. The native complexity of neural networks and its lack of interpretability makes them vulnerable to many forms of attacks. Some of the most sophisticated and scariest forms of attacks on DNNs are generated using other neural networks. Adversarial neural networks(ANNs) are often used to generate numerous attack vectors on DNNs by manipulating aspects such as the input dataset of the training policy. Protecting against adversarial attacks is far from being an easy endeavor as the attackers are always mutating and evolving.

AI safety research needs social scientists to ensure AI succeeds when humans are involved. That's the crux of the argument advanced in a new paper published by researchers at OpenAI ("AI Safety Needs Social Scientists"), a San Francisco-based nonprofit backed by tech luminaries Reid Hoffman and Peter Thiel. "Most AI safety researchers are focused on machine learning, which we do not believe is sufficient background to carry out these experiments," the paper's authors wrote. "To fill the gap, we need social scientists with experience in human cognition, behavior, and ethics, and in the careful design of rigorous experiments." They believe that "close collaborations" between these scientists and machine learning researchers are essential to improving "AI alignment" -- the task of ensuring AI systems reliably perform as intended.

GPT2 represents a major advancement in what's known as unsupervised learning. With most neural networks, the training consists of supervised learning. That means you have to feed in labeled data sets and evaluate the outcome to tune the various processing nodes until the network functions as intended. Unsupervised networks like GPT2 can assimilate large volumes of data without human involvement. Many researchers believe this is key to the future of AI, and OpenAI just showed that it can work and produce impressive results.

OpenAI's researchers knew they were on to something when their language modeling program wrote a convincing essay on a topic they disagreed with. They'd been testing the new AI system by feeding it text prompts, getting it to complete made-up sentences and paragraphs. Then, says David Luan, VP of engineering at the Californian lab, they had the idea of asking it to argue a point they thought was counterintuitive. In this case: why recycling is bad for the world. For decades, machines have struggled with the subtleties of human language, and even the recent boom in deep learning powered by big data and improved processors has failed to crack this cognitive challenge.

Deep learning approaches have been established as the main methodology for video classification and recognition. Recently, 3-dimensional convolutions have been used to achieve state-of-the-art performance in many challenging video datasets. Because of the high level of complexity of these methods, as the convolution operations are also extended to additional dimension in order to extract features from them as well, providing a visualization for the signals that the network interpret as informative, is a challenging task. An effective notion of understanding the network's inner-workings would be to isolate the spatio-temporal regions on the video that the network finds most informative. We propose a method called Saliency Tubes which demonstrate the foremost points and regions in both frame level and over time that are found to be the main focus points of the network.

Scientists at the University of Washington have released an interesting tool for studying the ultrasonic vocalizations of rats. It is a convolutional neural network capable of analyzing and categorizing rat calls. Studying rat vocalizations is time-consuming for a human. For one thing, most squeaks that rats make are above the 20kHz threshold of human hearing. So analysis requires slowing recordings down by as much as a factor of 20 to hear the highest vocalizations. This also means that one hour of audio becomes 20 hours of analysis.

Note: This post is part of a broader work for predicting stock prices. The outcome (identified anomaly) is a feature (input) in a LSTM model (within a GAN architecture)- link to the post. Options valuation is a very difficult task. To begin with, it entails using a lot of data points (some are listed below) and some of them are quite subjective (such as the implied volatility -- see below) and difficult to calculate precisely. As an example let us check the calculation for the call's Theta -- θ: Another example of how difficult options pricing is, is the Black-Scholes formula which is used for calculating the options prices themselves.