How To Join The Applied AI Revolution


Have you ever wondered whom to thank for some of the modern conveniences you might have started taking for granted, like Siri, Cortana or Alexa (assuming you agree these are conveniences)? The people at the Association for Computing Machinery (ACM) decided to thank Geoffrey Hinton, Yoshua Bengio and Yann LeCun in April of this year by honoring them with the Turing Award for their contributions to deep learning and neural networks. These contributions are put to use every time you log into your smartphone using fingerprint or facial recognition or when you use Google Photos or a voice assistant, and likely every time you use Amazon, Netflix, Facebook or Instagram. The advances in automatic language translation and autonomous cars in recent years arguably wouldn't have progressed as rapidly had it not been for the contributions of these three researchers. All of that is still an understatement of their contributions to artificial intelligence (AI).

DEF CON 2019: Researchers Demo Hacking Google Home for RCE


LAS VEGAS – The Tencent Blade Team of researchers demonstrated several ways they have developed to hack and run remote code on Google Home smart speakers. The hacks center around what is known as a Magellan vulnerability, which can be used to exploit the massively popular SQLite database engine. Here at a session at DEF CON on Thursday, the researchers shed light on their work "breaking" Google Home. What made the talk unique wasn't necessarily that Google Home smart speakers could be compromised using Megellan – that was public news in Dec. 2018 – rather it was how the hack was pulled off. On stage Tencent researchers Wenxiang Qian, YuXiang Li and HuiYu Wu laid out the evolution of their research.

How Bias Distorts AI (Artificial Intelligence)


When it comes to AI (Artificial Intelligence), there's usually a major focus on using large datasets, which allow for the training of models. What may seem like a robust dataset could instead be highly skewed, such as in terms of race, wealth and gender. Then what can be done? Well, to help answer this question, I reached out to Dr. Rebecca Parsons, who is the Chief Technology Officer of ThoughtWorks, a global technology company with over 6,000 employees in 14 countries. She has a strong background in both the business and academic worlds of AI.

10 jobs in Artificial Intelligence for people with 5 years of experience


By Aditi Sharma The IT and ITeS sector accounted for 7.7% of India's GDP in 2017 and is one of the fastest growing sectors in the country. The government's initiative to boost this sector by creating the National Institution for Transforming India (NITI) Aayog will give a boost to the IT sector. Here Artificial Intelligence (AI) will be the key driver to boost research and development in the sector. Here are 10 jobs available in AI for people with less than 5 years of work experience. Check these and apply as well: Analytics and AI Specialist - The role of an Artificial Intelligence specialist is to enhance the operations within the company.

10 Best Books to Learn Data Structure and Algorithms in Java, Python, C, and C


The current edition of this books is the 3rd Edition and I strongly suggest that every programmer should have this in their bookshelf, but only for short reading and references. It's not possible to finish this book in one sitting and some of you may find it difficult to read as well, but don't worry, you can combine your learning with an online course like Data Structures and Algorithms: Deep Dive Using Java along with this book. This is like the best of both world, you learn basic Algrotihsm quickly in an online course and then you further cement that knowledge by going through the book, which would make more sense to you now that you have gone through a course already.

The Best (And Scariest) Examples Of AI-Enabled Deepfakes


There are positive uses for deepfake technology like making digital voices for people who lost theirs or updating film footage instead of reshooting it if actors trip over their lines. However, the potential for malicious use is of grave concern, especially as the technology gets more refined. There has been tremendous progress in the quality of deepfakes since only a few years ago when the first products of the technology circulated. Since that time, many of the scariest examples of artificial intelligence (AI)-enabled deepfakes have technology leaders, governments, and media talking about the perils it could create for communities. The first exposure to deepfakes for most of the general public happened in 2017.

Building Better Deep Learning Requires New Approaches Not Just Bigger Data


In its rush to solve all the world's problems through deep learning, Silicon Valley is increasingly embracing the idea of AI as a universal solver that can be rapidly adapted to any problem in any domain simply by taking a stock algorithm and feeding it relevant training data. The problem with this assumption is that today's deep learning systems are little more than correlative pattern extractors that search large datasets for basic patterns and encode them into software. While impressive compared to the standards of previous eras, these systems are still extraordinarily limited, capable only of identifying simplistic correlations rather than actually semantically understanding their problem domain. In turn, the hand-coded era's focus on domain expertise, ethnographic codification and deeply understanding a problem domain has given way to parachute programming in which deep learning specialists take an off-the-shelf algorithm, shove in a pile of training data, dump out the resulting model and move on to the next problem. Truly advancing the state of deep learning and way in which companies make use of it will require a return to the previous era's focus on understanding problems rather than merely churning canned models off assembly lines.

How the moon landing shaped early video games

The Guardian

On 20 July 1969, before an estimated television audience of 650 million, a lunar module named Eagle touched down on the moon's Sea of Tranquility. The tension of the landing and the images of astronauts in futuristic spacesuits striding over the moon's barren surface, Earth reflected in their oversized visors, would prove wildly influential to artists, writers and film-makers. Also watching were the soon-to-be proponents of another technological field populated by brilliant young geeks: computer games. It is perhaps no coincidence that during the early 1960s, when Nasa was working with the Massachusetts Institute of Technology's Instrumentation Lab to develop the guidance and control systems for Apollo spacecraft, elsewhere on campus a programmer named Steve Russell was working with a small team to create one of the first true video game experiences. Inspired by the space race, and using the same DEC PDP-1 model of mainframe computer that generated spacecraft telemetry data for Nasa's Mariner programme, Russell wrote Spacewar!, a simple combat game in which two players controlled starships with limited fuel, duelling around the gravitational well of a nearby star.

Towards Generation of Visual Attention Map for Source Code Artificial Intelligence

Program comprehension is a dominant process in software development and maintenance. Experts are considered to comprehend the source code efficiently by directing their gaze, or attention, to important components in it. However, reflecting importance of components is still a remaining issue in gaze behavior analysis for source code comprehension. Here we show a conceptual framework to compare the quantified importance of source code components with gaze behavior of programmers. We use "attention" in attention models (e.g., code2vec) as the importance indices for source code components and evaluate programmers' gaze locations based on the quantified importance. In this report, we introduce the idea of our gaze behavior analysis using the attention map, and the results of a preliminary experiment.