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.
From Amazon to Netflix to Pinterest, recommendation systems are the cornerstone of a majority of the modern-day billion-dollar industries. However, building recommender systems is not a straightforward task. What if we can build them in a few lines? Dropping the nitty-gritty details and concentrating on implementing algorithms with more ease is what any data scientist would like to get their hands on. Abstraction is a common trait amongst popular machine learning libraries or frameworks like TensorFlow.
The business value of natural language processing (NLP) is indisputable, and there's never been a time this technology has proven to be so useful. Just think: in the rapid shift to remote work in response to the global coronavirus pandemic, companies have leveraged NLP for everything from chatbots used to effectively onboard workers remotely, to safely interfacing with patients in healthcare settings. It's especially encouraging to see that, despite IT budgets being on a downturn (Gartner), enterprise leaders have not shied away from NLP investments. In fact, according to new research, survey respondents across industries, company sizes, and geographic locations reported increases in their organization's NLP technology budgets from 10-30% more compared to last year. With the proliferation of NLP services in the cloud, companies need not even install and manage open source NLP libraries.
Google didn't just cater to Android fans with the Pixel 5 and 4a 5G at its Launch Night In event -- it also introduced a brand new Photos editor for Android that aims to improve snapshots for everyone, not just those with the latest devices. The new Google Photos editor uses machine learning to suggest changes to pictures that you can apply with one tap. Some are simple touch-ups like Enhance, while others are flashy effects like Black and White Portrait or Color Pop. You can often see the specific changes made to a photo if you want to tweak the results. You might be happy if you prefer manual edits.
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.
Google has announced an update to its Phone app, dubbed "Hold for Me", that will allow Google Assistant to wait on hold and alert users when a human is on the line. "When you call a toll-free number and a business puts you on hold, Google Assistant can wait on the line for you," the company said in a blog post. "You can go back to your day, and Google Assistant will notify you with sound, vibration, and a prompt on your screen once someone is on the line and ready to talk." Hold for Me makes use of the search giant's artificial intelligence Duplex system, with the company saying it is able to tell the difference between a recorded message and a live voice. When the Assistant determines a human operator is back on the line, in a delicious irony, it will ask the company representative to hold while the Pixel user returns to the call.
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.
Android's machine learning renaissance is coming to the Photos app, company executives announced during Wednesday's gloriously brief Pixel 5 live stream event. To start, Google plans to augment its already useful image auto-enhance feature with machine learning algorithms that can further improve those enhancements based on the specific image you're working on. Users will be able to apply brightness, contrast and portrait effects with a single tap to start with Enhance and Color Pop filters being rolled out in a few months. And for photographers that prefer to edit their shots manually, Google reorganized the editor layout into a scrollable bar across the bottom of the screen. The company is also offering an AI-based lighting feature, dubbed Portrait Light, that can apply varying levels and differing positions of light and shadow to a photo you've already taken.
How to understand the history of artificial intelligence in the popular press in five easy steps - 1. This technology is amazing! 2. We thought it was amazing, but it's actually terrible! We've moved on to something else. 5. Repeat. I've seen this for data mining, big data, machine learning and deep learning. What's the next AI technology that will be run through the cycle?