Facebook's billion-plus users speak a plethora of languages, and right now, the social network supports translation of over 45 different tongues. That means that if you're an English speaker confronted with German, or a French speaker seeing Spanish, you'll see a link that says “See Translation.”
Yesterday, Mark Zuckerberg announced that Facebook is hiring 3,000 people to work on its community operations team, which reviews images, videos, and posts that users report. These new hires will join the 4,500 existing employees in an effort to minimize the reach of future events like the shooting of Robert Goodwin. It's a considerable-but-essential investment for Facebook, but it leads us to a basic question: Can't this job be automated?
Language is all about repetition. Every word you're reading was created by humans, and then used by other humans, creating and reinforcing context, meaning, the very nature of language. As humans train machines to understand language, they're teaching machines to replicate human bias.
On Thursday, Google announced that its Home smart hub device can now recognize and identify up to six different users by the sound of their voice. It's an inevitable—but crucial—step in the development of smart home virtual assistants. The new skill means that different people in a household will be able to ask the Google Assistant questions about what's on their calendar, or what their commute looks like, and the Home device will know who is speaking to it and give tailored responses. It'll make it a more streamlined experience for families sharing a smart home speaker hub.
The future of bots is sitting in thousands of documents folders, waiting to be born. At least, that's the premise of Albert, a bot and bot-creation tool from NoHold, which released a pro version on Monday. The premise behind Albert is straightforward: upload a document, and then ask the Albert-generated bot to answer questions with information based on that document. Albert is a product of the modern era of chatbots, but Albert's origins are, by tech standards, positively ancient: the key work dates back to a patent filed in 1999.