Accenture research shows full 84% of C-suite executives believe they must leverage Artificial Intelligence (AI) to achieve their growth objectives. Yet 76% acknowledge they struggle when it comes to scaling it across the business. Having the right framework in place for "Industrializing ML" is a key component of scaling AI in the enterprise. Join us for a glimpse into the world of Industrialized ML as it comes to life at Navy Federal Credit Union using Databricks Unified Analytics Platform.
Google's influence in our lives is overwhelming, which is perhaps one of the reasons the Department of Justice and several state attorney generals banded together to file an anti-trust lawsuit against the company. But just how wide is Google's reach? We decided to take a look, and the results may surprise you. Start with the fact that Google ads are all over the Internet, and despite the initial stated goal of "organizing the world's information," the Alphabet unit is designed to have more ads appear, to keep the earnings up. In its most recent earnings, Alphabet reported $38.30 billion for Google.
Facebook has developed an artificial intelligence capable of accurately translating between any pair of 100 languages without relying on first translating to English, as many existing systems do. The AI outperforms such systems by 10 points on a 100-point scale used by academics to automatically evaluate the quality of machine translations. Translations produced by the model were also assessed by humans, who scored it as around 90 per cent accurate. Facebook's system was trained on a data set of 7.5 billion sentence pairs gathered from the web across 100 languages, though not all the languages had an equal number of sentence pairs. "What I really was interested in was cutting out English as a middle man. Globally there are plenty of regions where they speak two languages that aren't English," says Angela Fan of Facebook AI, who led the work.
Snapchat's augmented reality dreams might be starting to look a bit more realistic. The company has been subtly improving its AR-powered Lenses every year, improving the technical odds-and-ends and strengthening its dev platform. The result is that today, more than 170 million people -- over three-quarters of Snap's daily active users -- access the app's augmented reality features on a daily basis, the company says. Two years ago, Snap shared that creators had designed over 100,000 lenses on the platform; now Snap says there have been more than 1 million lenses created. The goofy filters are bringing users to the app and the company is slowly building a more interconnected platform around augmented reality that is beginning to look more and more promising.
Millions of people have been ditching Facebook and switching to Mountain View, CA-based social media network MeWe, touted to be the ad-free future of social networking. Advised by Sir Tim Berners-Lee (the inventor of the World Wide Web), MeWe has surged to 9 million users worldwide since its inception in 2013, and has zero paid marketing ads. MeWe CEO Mark Weinstein said in his recent TedX talk that although we check our phones 150 times per day out phones are more dependent on us than we are on them. He says that we are participating in the "greatest socio-economic event in human history" – 'surveillance capitalism'. The business model of Facebook and the other current social media giants is to track, analyse, and monetise our data.
Helen Dixon, head of Ireland's Data Protection Commission, in May submitted a draft decision to more than two dozen of the bloc's privacy regulators for review, as required under the law. Eleven regulators objected to the proposed ruling, sparking a lengthy dispute-resolution mechanism, she said. The contents of the draft decision haven't been disclosed. Twitter's European operations are based in Dublin. "It's a long process," Ms. Dixon said at The Wall Street Journal's virtual CIO Network conference.
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.
Benchmarking is a crucial step in developing ever more sophisticated artificial intelligence. It provides a helpful abstraction of the AI's capabilities and allows researchers a firm sense of how well the system is performing on specific tasks. But they are not without their drawbacks. Once an algorithm masters the static dataset from a given benchmark, researchers have to undertake the time-consuming process of developing a new one to further improve the AI. As AIs have improved over time, researchers have had to build new benchmarks with increasing frequency.
Online social networks provide a platform for sharing information and free expression. However, these networks are also used for malicious purposes, such as distributing misinformation and hate speech, selling illegal drugs, and coordinating sex trafficking or child exploitation. This paper surveys the state of the art in keeping online platforms and their users safe from such harm, also known as the problem of preserving integrity. This survey comes from the perspective of having to combat a broad spectrum of integrity violations at Facebook. We highlight the techniques that have been proven useful in practice and that deserve additional attention from the academic community. Instead of discussing the many individual violation types, we identify key aspects of the social-media eco-system, each of which is common to a wide variety violation types. Furthermore, each of these components represents an area for research and development, and the innovations that are found can be applied widely.
Facebook took major steps to announce its all out committment to Chatbots. The first is a a chatbot training ground called ParlAI--a play on words which stems from its primarily French-speaking researchers. Moreover, Facebook is sharing ParlAI with the world as an open source tool. Facebook is offering the training software so that developers and researchers can use it to train their chatbot "agents."