Jarvis Mark 2
So far this year, I've built a simple AI that I can talk to on my phone and computer, that can control my home, including lights, temperature, appliances, music and security, that learns my tastes and patterns, that can learn new words and concepts, and that can even entertain Max. It uses several artificial intelligence techniques, including natural language processing, speech recognition, face recognition, and reinforcement learning, written in Python, PHP and Objective C. In this note, I'll explain what I built and what I learned along the way. Further, most appliances aren't even connected to the internet yet. It's possible to control some of these using internet-connected power switches that let you turn the power on and off remotely.
But often that isn't enough. For example, one thing I learned is it's hard to find a toaster that will let you push the bread down while it's powered off so you can automatically start toasting when the power goes on. I ended up finding an old toaster from the 1950s and rigging it up with a connected switch. Similarly, I found that connecting a food dispenser for Beast or a grey t-shirt cannon would require hardware modifications to work. Music is a more interesting and complex domain for natural language because there are too many artists, songs and albums for a keyword system to handle. The range of things you can ask it is also much greater.
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Lights can only be turned up or down, but when you say 'play X', even subtle variations can mean many different things. Consider these requests related to Adele: 'play someone like you', 'play someone like adele', and 'play some adele'. Those sound similar, but each is a completely different category of request. The first plays a specific song, the second recommends an artist, and the third creates a playlist of Adele's best songs. Through a system of positive and negative feedback, an AI can learn these differences. The more context an AI has, the better it can handle open-ended requests.
At this point, I mostly just ask Jarvis to 'play me some music' and by looking at my past listening patterns, it mostly nails something I'd want to hear. If it gets the mood wrong, I can just tell it, for example, 'that's not light, play something light', and it can both learn the classification for that song and adjust immediately.
It also knows whether I'm talking to it or Priscilla is, so it can make recommendations based on what we each listen to. In general, I've found we use these more open-ended requests more frequently than more specific asks. No commercial products I know of do this today, and this seems like a big opportunity.
To do this, I installed a few cameras at my door that can capture images from all angles. AI systems today cannot identify people from the back of their heads, so having a few angles ensures we see the person's face. I built a simple server that continuously watches the cameras and runs a two step process: first, it runs face detection to see if any person has come into view, and second, if it finds a face, then it runs face recognition to identify who the person is.
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Once it identifies the person, it checks a list to confirm I'm expecting that person, and if I am then it will let them in and tell me they're here. This type of visual AI system is useful for a number of things, including knowing when Max is awake so it can start playing music or a Mandarin lesson, or solving the context problem of knowing which room in the house we're in so the AI can correctly respond to context-free requests like 'turn the lights on' without providing a location. Like most aspects of this AI, vision is most useful when it informs a broader model of the world, connected with other abilities like knowing who your friends are and how to open the door when they're here. The more context the system has, the smarter is gets overall.
One thing that surprised me about my communication with Jarvis is that when I have the choice of either speaking or texting, I text much more than I would have expected. This is for a number of reasons, but mostly it feels less disturbing to people around me. If I'm doing something that relates to them, like playing music for all of us, then speaking feels fine, but most of the time text feels more appropriate. Similarly, when Jarvis communicates with me, I'd much rather receive that over text message than voice. That's because voice can be disruptive and text gives you more control of when you want to look at it.
Even when I speak to Jarvis, if I'm using my phone, I often prefer it to text or display its response. This preference for text communication over voice communication fits a pattern we're seeing with Messenger and WhatsApp overall, where the volume of text messaging around the world is growing much faster than the volume of voice communication.
This suggests that future AI products cannot be solely focused on voice and will need a private messaging interface as well. Once you're enabling private messaging, it's much better to use a platform like Messenger than to build a new app from scratch. I have always been optimistic about AI bots, but my experience with Jarvis has made me even more optimistic that we'll all communicate with bots like Jarvis in the future. To enable voice for Jarvis, I needed to build a dedicated Jarvis app that could listen continuously to what I say. The Messenger bot is great for many things, but the friction for using speech is way too much.
My dedicated Jarvis app lets me put my phone on a desk and just have it listen. I could also put a number of phones with the Jarvis app around my home so I could talk to Jarvis in any room. That seems similar to Amazon's vision with Echo, but in my experience, it's surprising how frequently I want to communicate with Jarvis when I'm not home, so having the phone be the primary interface rather than a home device seems critical. Another interesting limitation of speech recognition systems - and machine learning systems more generally - is that they are more optimized for specific problems than most people realize. For example, understanding a person talking to a computer is subtly different problem from understanding a person talking to another person. If you train a machine learning system on data from Google of people speaking to a search engine, it will perform relatively worse on Facebook at understanding people talking to real people.
In the case of Jarvis, training an AI that you'll talk to at close range is also different from training a system you'll talk to from all the way across the room, like Echo. These systems are more specialized than it appears, and that implies we are further off from having general systems than it might seem. On a psychologic level, once you can speak to a system, you attribute more emotional depth to it than a computer you might interact with using text or a graphic interface. One interesting observation is that ever since I built voice into Jarvis, I've also wanted to build in more humor. Part of this is that now it can interact with Max and I want those interactions to be entertaining for her, but part of it is that it now feels like it's present with us. I've taught it fun little games like Priscilla or I can ask it who we should tickle and it will randomly tell our family to all go tickle one of us, Max or Beast.
I've also had fun adding classic lines like 'I'm sorry, Priscilla. I'm afraid I can't do that.' My experience of ramping up in the Facebook codebase is probably pretty similar to what most new engineers here go through. I was consistently impressed by how well organized our code is, and how easy it was to find what you're looking for - whether it's related to face recognition, speech recognition, the or iOS development. The open source packages we've built to work with GitHub's Atom make development much easier. The build system we've developed to build large projects quickly also saved me a lot of time.
Our open source AI text classification tool is also a good one to check out, and if you're interested in AI development, the whole GitHub repo is worth taking a look. One of our values is 'move fast'. That means you should be able to come here and build an app faster than you can anywhere else, including on your own. You should be able to come here and use our infra and AI tools to build things it would take you a long time to build on your own.
Building internal tools that make engineering more efficient is important to any technology company, but this is something we take especially seriously. So I want to give a shout out to everyone on our infra and tools teams that make this so good.
He announced at the start of the year that he wanted to build a simple AI that could control his home, including his lights, temperature, appliances, music and security. He also wanted it to 'learn his tastes and patterns, learn new words and concepts, and even entertain Max' (his daughter.) And now he has published a explaining how he did it. Zuckerberg's Jarvis uses several artificial intelligence techniques, including natural language processing, speech recognition, face recognition, and reinforcement learning, written in Python, PHP and Objective C. For example, he said it's hard to find a toaster that will let you push the bread down while it's switched off so he had to find a 1950s toaster and rig it up with a connected switch. Elsewhere, he had to modify a food dispenser to feed his dog Beast and a t-shirt cannon to deliver his iconic grey shirts.
'For assistants like Jarvis to be able to control everything in homes for more people, we need more devices to be connected and the industry needs to develop common APIs and standards for the devices to talk to each other,' Zuckerberg continued. Emc unisphere download. 'I programmed Jarvis on my computer, but in order to be useful I wanted to be able to communicate with it from anywhere I happened to be. That meant the communication had to happen through my phone, not a device placed in my home,' he said.
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He began by building a bot to communicate with Jarvis 'because it was so much easier than building a separate app'. He now texts the Jarvis bot and it can translate audio clips into commands. In the middle of the day, if someone arrives at his home, Jarvis also texts him an image to tell him who's there, or it can text him when he needs to go do something. 'I have always been optimistic about AI bots, but my experience with Jarvis has made me even more optimistic that we'll all communicate with bots like Jarvis in the future.' Facebook Zuckerberg said the most difficult part for him to work on was face recognition 'because most people look relatively similar compared to telling apart two random objects.'
He used Facebook's research, and the work it has done on facial recognition from photos, to help improve Jarvis. He paired this software with cameras which he installed on his front door showing multiple angles.
From this, he built a simple server that continuously watches the cameras and runs a two-step process: first, it runs face detection to see if any person has come into view, and second, if it finds a face, then it runs face recognition to identify who the person is. Once it identifies the person, it checks a list to confirm Zuckerberg is expecting that person, from his calendar for example, and will let them in and tell him that they've arrived. 'In the longer term, I'd like to explore teaching Jarvis how to learn new skills itself rather than me having to teach it how to perform specific tasks. If I spent another year on this challenge, I'd focus more on learning how learning works,' Zuckerberg continued.
'Finally, over time it would be interesting to find ways to make this available to the world. I considered open sourcing my code, but it's currently too tightly tied to my own home, appliances and network configuration. If I ever build a layer that abstracts more home automation functionality, I may release that. Or, of course, that could be a great foundation to build a new product.'