Nights on the moon can last up to 350 hours. That creates some big technical challenges as NASA's Artemis program gears up to send people back to the moon. In addition to issues like extreme temperature changes, one of the biggest difficulties presented by lunar night is the loss of solar power. For long-term habitation to be viable, NASA needs to find a sustainable solution. To that, it's launching a $5 million prize in its Watts on the Moon Challenge, which is being launched in collaboration with crowdsourcing platform HeroX.
From his home base on the Hawaiian island of Kauai, Anton Andryeyev is running Twitter's efforts to chase Russian bots and other rogue actors off the platform. A year ago, he traded his office in the company's San Francisco headquarters for this tropical home office two thousand miles away, surrounded by standup paddle boards and a monitor large enough to see his entire 25-person engineering team all at once. Andryeyev's remote office represents a sweeping experiment in the future of work: allowing white-collar workers to work from anywhere, forever. Corporate America has long been defined by physical offices. But in a few short weeks, the pandemic upended that as thousands of companies mandated their employees work from home.
The National Institute of Standards and Technology (NIST) is launching the Differential Privacy Temporal Map Challenge. It's a set of contests, with cash prizes attached, that's intended to crowdsource new ways of handling personally identifiable information (PII) in public safety datasets. The problem is that although rich, detailed data is valuable for researchers and for building AI models -- in this case, in the areas of emergency planning and epidemiology -- it raises serious and potentially dangerous data privacy and rights issues. Even if datasets are kept under proverbial lock and key, malicious actors can, based on just a few data points, re-infer sensitive information about people. The solution is to de-identify the data such that it remains useful without compromising individuals' privacy.
The videos i this article will blow your mind... and they are already out of date. Soul Machines is on the cutting edge of building commercial AI avatars that can appear on a computer screen, and even in 3D, to simulate face-to-face engagement. The face in the main image of this article is one of their 3D avatars and they are already being deployed in banks and energy companies to inform and serve customers. With names such as Jamie (ANZ Bank), Will (Vector Energy), Ava (Autodesk), and Sarah (Daimler Mercedes Benz), they are connecting with customers, replicating human emotion, providing the right answers and asking insightful questions. Many call centre workers in affluent countries have been'off-shored' to lower cost countries, and now those roles are set to be outsourced to AI bots.
In the ever-expanding world of computer hardware and software, benchmarks provide a robust method for comparing quality and performance across different system architectures. From MNIST to ImageNet to GLUE, benchmarks have also come to play a hugely important role in driving and measuring progress in AI research. When introducing any new benchmark, it's generally best not to make it so easy that it will quickly become outdated, or so hard that everyone will simply fail. When new models bury benchmarks, which is happening faster and faster in AI these days, researchers must engage in the time-consuming work of making new ones. Facebook believes that the increasing benchmark saturation in recent years -- especially in natural language processing (NLP) -- means it's time to "radically rethink the way AI researchers do benchmarking and to break free of the limitations of static benchmarks." Their solution is a new research platform for dynamic data collection and benchmarking called Dynabench, which they propose will offer a more accurate and sustainable way for evaluating progress in AI.
Russia's biggest technology company enjoys a level of dominance that is unparalleled by any one of its Western counterparts. Think Google mixed with equal parts Amazon, Spotify and Uber and you're getting close to the sprawling empire that is Yandex--a single, mega-corporation with its hands in everything from search to ecommerce to driverless cars. But being the crown jewel of Russia's silicon valley has its drawbacks. The country's government sees the internet as contested territory amid ever-present tensions with US and other Western interests. As such, it wants influence over how Yandex uses its massive trove of data on Russian citizens. Foreign investors, meanwhile, are more interested in how that data can be turned into growth and profit. For the September/October issue of MIT Technology Review, Moscow-based journalist Evan Gershkovich explains how Yandex's ability to walk a highwire between the Kremlin and Wall Street could potentially serve as a kind of template for Big Tech.
As many of you will know, artificial intelligence is a passion of mine. I believe in its potential to boost productivity, solve problems, and make the world a better place. For me, it's more than just talk; I am building an entire business around AI and I stand with the users and creators of AI who see its potential and the exciting places it can take us. But not everyone is like us. Despite growing body evidence to the contrary, many people still see AI as a dark force; a development to be feared instead of celebrated.
"What in the name of Paypal and/or Palantir did you just say about me, you filthy degenerate? I'll have you know I'm the Crown Prince of Silicon Valley, and I've been involved in numerous successful tech startups, and I have over $1B in liquid funds. I've used that money to promote heterodox positions on human enhancement, control political arenas, and am experimenting with mind uploading. I'm also trained in classical philosophy and was recently ranked the most influential libertarian in the world by Google. You are nothing to me but just another alternative future. I will wipe you out with a precision of simulation the likes of which has never been seen before, mark my words."
Researchers at Facebook AI recently introduced and open-sourced a new framework for self-supervised learning of representations from raw audio data known as wav2vec 2.0. The company claims that this framework can enable automatic speech recognition models with just 10 minutes of transcribed speech data. Neural network models have gained much traction over the last few years due to its applications across various sectors. The models work with the help of vast quantities of labelled training data. However, most of the time, it is challenging to gather labelled data than unlabelled data.
Understanding individuals' feelings are fundamental for organizations since clients can communicate their feelings and sentiments more transparently than ever before. By automatically analyzing customer feedback, from study reactions to social media discussions, brands can listen mindfully to their clients, and tailor products and services to address their issues. Sentiment analysis is a machine learning method that recognizes polarity (for example a positive or negative thought) within the text, whether a whole document, paragraph, sentence, or clause. Marketing is ending up being one of the artworks most disrupted by the digital revolution. A lot to the aversion of customary marketing proponents and maybe to the pleasure of technologists, it is presently a lot about codifying the whole knowledge chain – catching the abundance of digital data, sorting out it, applying algorithms to process it and taking care of back noteworthy decisions to different functions– all in real-time, with end to end automation, and at lightening quick speed.