Indian Institute of Technology Madras students were part of an international research project led by a Queen's University Belfast Researcher in the U.K. who has developed an innovative new algorithm to make Artificial Intelligence (AI) fairer and less biased when processing data. Dr. Deepak Padmanabhan, Researcher at Queen's University Belfast and Adjunct Faculty Member at IIT Madras, has been leading an international project, working with Ms. Savitha Abraham and Ms. Sowmya Sundaram, PhD Students, Department of Computer Science and Engineering, IIT Madras, to tackle the discrimination problem within clustering algorithms. Companies often use AI technologies to sift through huge amounts of data in situations such as an oversubscribed job vacancy or in policing when there is a large volume of CCTV data linked to a crime. However, while AI can save on time, the process is often biased in terms of race, gender, age, religion and country of origin. Dr. Padmanabhan said that AI techniques for exploratory data analysis, known as'clustering algorithms', are often criticised as being biased in terms of'sensitive attributes' such as race, gender, age, religion and country of origin.

When some people think of machine learning and artificial intelligence, their thoughts go immediately to Skynet or devices taking over humankind.

Speech recognition is an acute area of interest for Apple, whose cross-platform Siri virtual assistant is used by over 500 million customers worldwide. This past week, the tech giant published a series of preprint research papers investigating techniques to improve voice trigger detection and speaker verification, as well as language identification for multiple speakers. In the first of the papers, a team of Apple researchers propose an AI model trained to perform both the task of automatic speech recognition and speaker recognition. As they explain in the abstract, the commands recognized by speech-based personal assistants are usually prefixed with a trigger phrase (e.g., "Hey, Siri"), and detecting this trigger phrase involves two steps. The AI first must decide whether the phonetic content in the input audio matches that of the trigger phrase (voice trigger detection), and then it must determine whether the speaker's voice matches the voice of a registered user or users (speaker verification).

People share millions of photos and videos on Facebook every day, creating some of the most compelling and creative visuals on our platform. Some of that content is manipulated, often for benign reasons, like making a video sharper or audio more clear. But there are people who engage in media manipulation in order to mislead. Manipulations can be made through simple technology like Photoshop or through sophisticated tools that use artificial intelligence or "deep learning" techniques to create videos that distort reality – usually called "deepfakes." While these videos are still rare on the internet, they present a significant challenge for our industry and society as their use increases.

Realistic music generation has always remained as a challenging problem as it may lack structure or rationality. In this work, we propose a deep learning based music generation method in order to produce old style music particularly JAZZ with rehashed melodic structures utilizing a Bi-directional Long Short Term Memory (Bi-LSTM) Neural Network with Attention. Owing to the success in modelling long-term temporal dependencies in sequential data and its success in case of videos, Bi-LSTMs with attention serve as the natural choice and early utilization in music generation. We validate in our experiments that Bi-LSTMs with attention are able to preserve the richness and technical nuances of the music performed.

Mozilla Research is developing Servo, a parallel web browser engine, to exploit the benefits of parallelism and concurrency in the web rendering pipeline. Parallelization results in improved performance for pinterest.com but not for google.com. This is because the workload of a browser is dependent on the web page it is rendering. In many cases, the overhead of creating, deleting, and coordinating parallel work outweighs any of its benefits. In this paper, we model the relationship between web page primitives and a web browser's parallel performance using supervised learning. We discover a feature space that is representative of the parallelism available in a web page and characterize it using seven key features. Additionally, we consider energy usage trade-offs for different levels of performance improvements using automated labeling algorithms. Such a model allows us to predict the degree of parallelism available in a web page and decide whether or not to render a web page in parallel. This modeling is critical for improving the browser's performance and minimizing its energy usage. We evaluate our model by using Servo's layout stage as a case study. Experiments on a quad-core Intel Ivy Bridge (i7-3615QM) laptop show that we can improve performance and energy usage by up to 94.52% and 46.32% respectively on the 535 web pages considered in this study. Looking forward, we identify opportunities to apply this model to other stages of a browser's architecture as well as other performance- and energy-critical devices.

In November, the musician Grimes made a bold prediction. "I feel like we're in the end of art, human art," she said on Sean Carroll's Mindscape podcast. "Once there's actually AGI (Artificial General Intelligence), they're gonna be so much better at making art than us." Her comments sparked a meltdown on social media. The musician Zola Jesus called Grimes the "voice of silicon fascist privilege."

At a recent meeting of the World Economic Forum, someone asked Stuart Russell, a professor of computer science at the University of California, Berkeley, when superintelligent artificial intelligence (AI) might arrive. He loosely estimated it to be within his children's lifetime, and then he emphasized the Chatham House rules of the meeting and that his conjecture was "strictly off the record." But, he writes in his new book Human Compatible: Artificial Intelligence and the Problem of Control, "Less than two hours later, an article appeared in the Daily Telegraph citing Professor Russell's remarks, complete with images of rampaging Terminator robots." Hyperbole by many media outlets has made it challenging for experts to talk seriously about the dangers of artificial superintelligence--a technology that would surpass the intellectual capabilities of humans. Nonetheless, many experts have written books on the subject.

I love LDA but I'm a firm believer that it doesn't have many practical/successful applications in the real world I am actually published on a couple of papers regarding applying LDA at large scale, while working at a large company. It doesn't seem to be a super common approach, and it appears to be really easy to get it wrong. Coupled with the fact that there are other approaches that are about as good, numerically, well, yeah, not a lot of people bother. Doesn't mean it can't work. Anyhow- after we applied a few tricks, the coherence of the topics produced was much (qualitatively) better than clusters being produced by other standard clustering/co-clustering approaches, albeit quantitatively worse.