According to some experts, in 2018, natural language and audio processing (NLP for short) are the two areas that AI excels most at. It seems fairly obvious that machine audio processing would be good when you look at the number of US homes with a smart speaker device like Amazon’s Echo, Apple Home Pod, or Google Home (nearly 50%). That’s a lot of people with a device whose sole purpose is to collect data on what you’re saying and translate it into usable commands for you, and into monetizable data for the company that builds them.
“Hey, Siri!”
— me, just now
In fact, if you read that out loud right now, there is a good chance that your phone just pinged at you wondering what you want from it. With so many devices listening constantly to our every conversation and vocal machination, is it any wonder that they’re starting to understand us better than we understand ourselves?
In my home, I have Apple Home, and by proxy,
Are we doomed? Probably not… Yet. Duplex is still very limited according to Google. All it can do is book appointments for you. But people got so scared that they’d be talking to a machine and not know it that the outcry forced Google to hamper its own system. Google has announced that Duplex will now announce itself as a computer at the beginning of every phone conversation so as not to creep people out. To me, this kind of defeats the purpose of the software to begin with. But hey, it’s probably bad form to test your AI on a bunch of unsuspecting hair salon and restaurant employees without them knowing about it.
This software is getting really good at understanding and communicating with humans via voice. This is mainly because it has a TON of really good data collected by millions of devices around the world. It’s also because engineers, mathematicians, and programmers have made some serious breakthroughs in Deep Learning in the past 10 years and Apple and Google have tens of billions of dollars invested into making it work.
This weekend I’ll be learning from MIT Professor Regina Barzilay about what it means for machines to understand something. We’ll also be covering which NLP problems have been solved, where progress is being made and tasks that are still very difficult for computers to solve. Hopefully, I’ll come back with a better understanding of how it all works (I think it has something to do with phonemes and triphones) and what we can do with it. Once I do, I’ll report back here and let you know how close we are to the robopocalypse.