The Bill may have unintended consequences for safety by failing to consider that the FAA currently limits operations above 400 feet AGL. By establishing an arbitrary 350-foot limit above real property, the Bill would confine drone operations to a narrow 50-foot corridor, which would result in increased congestion, move many operations closer to manned aircraft than needed and may lead to an increased risk of accidents.

The Bill is over-broad in its attempt to achieve privacy – it does not even distinguish between drones that have cameras and those that do not. The Bill is also ineffective, for instance it does not restrict operators from capturing images and videos of private property that are taken from a vantage point just outside of the owner’s property line. And since Congress has mandated the FAA to integrate drones into the national airspace, the Bill may trigger a constitutional challenge based on federal preemption.

California has existing laws that can be applied to drones, including trespass and anti-paparazzi laws. Nevertheless, if California lawmakers are resolved that current laws are insufficient to protect against the risks posed by drones, they should dispense with the current Bill and introduce legislation that is narrowly tailored and likely to be effective in achieving its purpose.

A discussion with Ryan Calo on Google, your personal data, and the consumer robotics market

Can someone tell me: What’s Google doing, making robots? Or at least: What is it doing, making such acquisitions?

As you probably heard, in the first two weeks of December last year, they bought some pretty heavy companies (house specialties in parentheses):

• Schaft (Navigation)
• Industrial Perception (Vision)
• Redwood Robotics (Manipulation)
• Meka Robotics (People-safe designs)
• Holomini (Navigation)
• Bot & Dolly (Camera tracking)
• Boston Dynamics (Navigation)
• DeepMind (General AI & abstracted reasoning)

This list covers AI, robotic vision, and a famous four-legged ‘bot that can bust into a sprint that could beat Usain Bolt. So it’s safe to say that the focus is on navigation and vision.  This is nice for the roboticists who are waiting for market validation in the consumer space. Google is providing that. They’re also providing us with a fleet of robots that can navigate, see, and use the network.

What’s Google’s motivation?

Presumably, as a publicly-trade company, this is intended to make Google more money.

They did a pretty good job last year. Sitting between Apple and Coca-Cola, they’re the second most valuable brand in the world. Paid clicks were up approximately 31 percent over 2012, and as of year end, Google employed 47,756 full-time employees. They also, in 2013 had advertising revenues of approximately $50 billion.

Google exchanges personal information for online services. Expanding their horizons from online tools, a few years ago, Google gave Android away to handset makers for the same reason Google gave free web search and email away to you and I: to collect personal information and display other information back. This is Google’s job and their profits show that they are very good at that.

It makes sense, then, that Andy Rubin, who headed up the Android effort, is now heading up Google’s robotics effort. After all, robots – like smartphones – are not just a platform for providing products and services; they are also a platform for collecting data.

Personal information as currency

Nothing is free, so if you aren’t paying cash, you’re paying with your personal information. And that information, from 2013, was worth $50B … so it stands to reason that Google’s motive for getting into robotics is that it offers yet another means of harvesting more information about you. Google will be using robots to collect and display personal information, just like Android did, and before that, email and web searches did. If Rubin has moved on from phones to robots, it’s probably because he thinks robots offer the greater potential in terms of opportunity.

There are three reasons why this makes sense:

1. Robots will give Google access to the physical world for a reduced cost. Like Apple, they are seeking to produce advanced hardware with companies that have proven track records for such manufacturing, and Google is evidently partnering with FoxConn to make this happen.
2. Robots will allow Google access to home and business information with unprecedented fidelity of information. By providing us with robots that are cheap (if not free; think of how smartphones as subsidized), Google can eventually build data conduits into our homes that will allow the company to know what we read, what dog food we buy, what we wear, and when we turn on the Nest thermostat (and, yes, the business model is the same for Nest as it is for robotics: more consumer data).
3. As with an open source OS for smartphones, by producing an open source OS for robots, Google will have direct access to all of the information that Android offered the company.

A couple of weeks ago, while attending RoboMadness at my old stomping grounds of SRI, the CTO and EVP of iRobot, Dr. Paolo Pirjanian, made a curious statement on stage. He outlined a ‘holy trinity’ for robotics as: vision, navigation, and the cloud, and pointed out that, “navigation is key” because it will build environment maps that will allow problem solving. So for example, a 3D environment map will allow the indexing of objects, as well as conversational interfaces for voice and gesture control. If a book is out of place and you want your robot to put it back, then the idea of “back” needs to be indexed somewhere. That somewhere is the cloud, so this information will, then, attach to the cloud.

All that personal data …  all those clouds …

Here we have an example of mapping, navigation and cloud robotics, which, if I try to look at it objectively, sounds a lot like what Google appears to be doing.

Is surveillance the new business model for consumer robotics?

In my desperation to get to the bottom of this I called up Ryan Calo, for he once wrote that “robotics combines, for the first time, the promiscuity of data with the capacity to do physical harm; robotic systems accomplish tasks in ways that cannot be anticipated in advance; and robots increasingly blur the line between person and instrument.”

Professor Calo researches the intersection of law, robotics and the Internet. His work on drones, driverless cars, privacy, and other topics has appeared in many famous publications and he serves on a number of advisory boards, including the Electronic Privacy Information Center (EPIC), the Electronic Frontier Foundation (EFF), and others.

I threw these ideas at him and he took difference with the angle. Here was Calo’s response:

The frame is right, though I would quibble with characterizations of profit – there is a potential for win-win and it’s not necessarily a transgression of privacy. I would love that.  In fact I kind of wish that Google and Facebook would consider creating a robot platform for the home. The robotics industry really needs a personal robot in much the same way as a personal computer. We need something that is very versatile – something we could run all kinds of different code on – a platform or PC that is open to 3rd party innovation.

Then what are these companies that collect our data, like Google or Facebook, up to?

They want to bring in the Internet to more places – to where it is hard to bring it.  Amazon wants robots that can shave seconds, or fractions of a second, in the time it takes to get a package from the warehouse. This is how they’re thinking of using robots.

So in other words, with regards to these companies, so far they are leaving a lot on the table and it may be that, cynically, they end up building this platform that personal robotics needs and their motivation is to get a look inside the home, but that doesn’t seem to be the case. I understand there are things like Nest that give some perspective on the home, so it is a natural fit for Google’s other enterprises. But it is a closed platform that does one thing.

Isn’t that why Google has traditionally offered more services? To get more user information?

No, it’s to get more users. Google doesn’t just rely on users, but also on inventory – it needs a place to display those ads. They want more screens. They created email to bring more people into the fold. But I don’t think Google does everything they do just to get more information. They want to go to advertisers to say, “We have X users” and that increases the value.

You could explain a lot by saying, “They think that smart objects of various kinds are the future and they want to use all these new screens.” Are they building driverless cars to collect personal information? Well, they’ve been doing that with people. Maybe they want drones to fly around for the same reason. But Google appreciates that contemporary life is shifting. Once you have driverless cars. the screen can be as big as you want so that Google can own that screen. If everyone is being driven by cars, maybe Google will have a credible map so that people have to build on that infrastructure. They want to be the ecosystem.

Mr Calo and I have more discussions ahead, but for now, like all good underlings, we’ll just have to keep an eye on the surveillance system to see when it wakes up.

If you liked this article, you may also be interested in:

• DARPA’s Gill Pratt on Google’s robotics investments
• Robots Podcast: Privacy, Google, and big deals
• Google’s robot acquisitions likely cost less than $100 million
• AFK (or, Robotic free speech, and what you can do to help)
• Robot love: Spike Jonze’s new sci-fi film ‘Her’ may be closer to reality than you think
• The death of search (or, My dysfunctional relationship with Siri)

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Link to audio file (33:53)

What does it mean to have giants like Google, Apple and Amazon investing in robotics? Since last December, Google alone has acquired a handful of companies in robotics, home automation and artificial intelligence. This can be pretty exciting for robotics. But what exactly is the internet giant planning to do with this technology? Is there something we should be worried about? If there is, what can we do about it?

Experts have been actively talking about this in the media, including through Robohub’s recent focus series on Big Deals in Robotics.

In today’s episode, AJung Moon talks with Avner Levin, a privacy and cybercrime expert, about Google’s recent acquisition of companies. Levin sheds light on why we should be concerned about the recent series of acquisitions by the big companies from privacy and cybercrime perspective. He also discusses whether the existing privacy policies are ready to handle what may lie shortly ahead of us in the future — the future of the Internet of Things, or perhaps Google-branded robots.

Avner Levin
Avner Levin is the Chair of the Law & Business Department, as well as the Director of the Privacy and Cyber Crime Institute at Ryerson University in Toronto, Canada. His research interest is in regulation and protection of privacy with respect to technology within Canada and worldwide. In the interview, he advocates for more discussion of privacy issues to take place, not just within the companies that (do/will) hold our data, but by governing bodies.

Links:

• Download mp3 (15.5MB)
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS
• Avner Levin’s Website

 

If you liked this article, you may also be interested in:

• Google’s robot and artificial intelligence acquisitions are anything but scary
• Will companies like Google and Amazon usher in a new era for robotics?
• The robots behind your Internet shopping addiction: How your wallet is driving retail automation
• What do Google, Amazon, IBM, Apple, Microsoft and all the airlines and car companies want?

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

 

Let’s say a white box showed up on your doorstep and you open it up and find a little humanoid robot made by Google. A GoogleBot! The brightly-colored pamphlet says that the little disk of a device will vacuum your floor, all for the same cost as your Gmail account: free.

Since you already use Google’s search engine, have a Gmail address, and use GoogleDocs, Voice, Earth, Wallet, wear a Google Glass and have been driving around one of those autonomous cars you’ve been selected as a beta tester! Whee! A free robot!

So what do you do with this thing?

Vacuum the floor, of course. Off it goes, bumping into walls, building its little map as it sucks up Doritos crumbs and dust bunnies.

Google’s greatest hits, like Search, Email, Docs, Voice, or Translation, are founded on, or fodder for, Natural Language Processing technologies (NLP).  NLP and other semantic technologies are also why Facebook and, in some cases Apple, has provided us with such great services. They analyze what we write and say.

Meanwhile, others are further ahead of this linguistic learning curve. The National Security Agency (NSA) of the United States was using primitive forms of NLP at least a decade before Google was using it for search.  This is common with technologies: Government agencies get theirs first, then the rest of us get to use the stuff later, whether it is global maps of the earth (1650s), satellite photograph (1950s), or GPS (1970s).

Anyway, there are four NSA data collection programs: MAINWAY, MARINA, NUCLEON and – specifically for Internet content, and the most important source of collection – the PRISM project, which by now we’ve all heard about.

On June 6 of 2013 The Washington Post and the Guardian reported that the system draws data from Google, Yahoo, Microsoft and other Silicon Valley giants, keeping the folks in Fort Meade, Maryland busy mining what is the richest depository of personal information in all human history. Evidently the NSA is sucking up 2.1 million gigabytes of data every hour of every day. All information is stored, all communication is monitored, and the fact that someone is peeping in on you has been held secret.

This, of course, has caused some concerns.

These technologies can be used in a predictive manner, as well.  What you want, what you think, and what you will do can be predicted with a 93% rate of accuracy, and that includes where you are now and where you will be in twenty-four hours.
We move across a daily landscape of emotions just as we move across a daily landscape of city streets. Semantic analysis (or, more accurately, probabilistic latent semantic analysis, and other technologies that look at our trends) shows where we’ll be and what we are likely to think and feel.

In early December of 2007, Marissa Mayer, now CEO of Yahoo and then the vice president of Google’s Search Products and User Experience division, showed how Google Trends can be used to predict presidential elections. She showed how Google Trends accurately predicted George W. Bush’s win over John Kerry in 2004, and also pointed out how Trends accurately predicted Nicolas Sarkozy’s win in May of 2007, over Segolene Royal in the French elections.

These trickles of valuable personal data add up to rivers of money.

Valuable enough to grow a company into the size of Google, or valuable enough to justify a $10 billion annual NSA budget, those little strings of text add up to lots and lots of real money.
Let’s not forget this Google robot that has shown up on our doorstep.  Our big hockey-puck robot (or “connected system” as it is more fashionably called) is buzzing around the floor doing the job for free.  This concept is now a prototype and has been operating for three years.

Though iRobot swatted the ConnectR remote-controlled domestic robot in the start of 2009, ending the product line, the concept refused to die. A year later, in March of 2010, the concept was picked up by a group of Swedish hackers who launched a project similar to what I outline above, named GåågleBot (pronounced “GoogleBot”). These Roomba hackers dissected the robot and inserted various items such as a camera, a Wi-Fi card, a small computer, and a good squirt of the AJAX programming language. After sewing it all back up, the thing still maintained operations as a normal Roomba.

The GåågleBot can also read. It comes with optical character recognition (OCR) software so that you (or someone else) can photograph and translate text that happens to be lying around the house, drop it into a database, and run searches on that text. GåågleBot source code and instructions are available for download.

A vacuum is just a vacuum, but these days it is part of what is commonly being called The Internet of Things.  Smart Houses, Connected Systems, or Robots … the basic idea is that we use networked computers in objects around us.  If we consider the Internet of Things as a physical extension of the Internet we find a new method emerging – as precise as existing analytics systems – that can measure how we each live and spend our money.  If you purchase a product, like a watch, and it communicates via a WiFi or Bluetooth network, then whoever or whatever it is communicating with will know where you are, who or what is nearby, your name, movements, past, present and, by prediction, future. Other objects near it will identify themselves and as you synchronize a device with a user account, and the overlapping data increases the relative value of both datasets. Your identity and the identity of you live with will be known by the company you bought it from, of course, and with it will come all of your purchases, likes, dislikes, relationships, and documents that are hanging heavy with fruit to be plucked by the pinchers of NLP and semantic analysis.

Meanwhile, the robot Google sent you rolls around on the floor, sucks up dust, and blinks a network indicator. It seems so humble.  But this is how Google, Yahoo, PRISM, the NSA, and therefor the US Government collects data that can be potentially very dangerous information. All from a little vacuum robot.  After all, dangerous bombs often come in humble packages.

Now, I don’t mean to sound like the Unabomber here, so I will point you to someone that has thought about this topic far more, and whom I am parrotting, and that is Bruce Schneier.  He has written a nice article on this topic, which you can find here.

Let’s not be paranoid. This vacuum, like an email service, may come in handy, and it doesn’t cost anything.  So we pull the robot out of the box, flip it on, poke in a password, and go back to eating Doritos.

Now let’s consider another possibility, which is that Google has mailed us not a vacuum hockey-puck, but a little humanoid robot that can walk and grab stuff. GoogleBot is an android that comes up to your knee, just a little guy with big feet and a cute head, kind of like NAO. So you set it down and it begins to explore the house. Sure, for the first few days it is a touch disturbing as it waddles around the house and reads the books on your shelves, and runs optical character recognition on all of your magazines, and seems to be studying the contents of your refrigerator and cupboards and dog food labels and clothing labels, and very carefully watches your television. Once you even see it touching your computer and you chase it away.  But eventually it settles down and you think, “That was kind of cute.” Then, finally you get used to it. It does the laundry and feeds the dog, and along the way, GoogleBot gets another cluster of data about your life – and you get your dishes done.

This system may be financed with the same model that Google uses today for things like email, search, voice and other services.  Since Google is publicly traded we can see that Amazon paid out $55m in 2011 to advertise for books or whatever.  So they would want to know what you buy, what you read, and what you have on the coffee table.  Home Depot paid out almost as much, so they would want to know what you have in the yard. State Farm and Progressive insurance companies paid Google nearly $90m, so they would have a great interest in knowing your health habits. All this cascades down into a long list of retail information, health, hygiene, and habits that covers what you are reading, what’s in your yard, what’s in your home, your refrigerator, etc. And so – to be useful – your GoogleBot should be busy dusting your bookshelves, washing your dishes, doing your laundry, and being an active participant in your personal health by cleaning the shower, toilet, and floor.

If someone types in “Self-Employed Health Insurance” and clicks on an ad, Google earns about $43. There are dozens of small robots that cost less than that, which means a home care companion robot of about $500 would amortize GoogleBot’s production cost and shipping in just a few weeks, and become wildly profitable after that robot had spent a year in your home.

Because our personal information is coveted by both corporations and governments, it seems inevitable that this home service robotics model will arrive some day in the coming decade.

Hopefully the GoogleBot or YahooBot or whateverBot will come with bulletproof SHA privacy switches built in, switches we can trust. Hopefully privacy options will allow us to put curtains on the parts of our lives we consider personal. That way we don’t have to stuff the robot in the freezer when the girlfriend comes over.  And perhaps even revenue options will let us collect money for the information Google collects about us (Google still wins, we just pay less usage tax). Or perhaps these are added features that we will have to pay for, like in-app purchases today.  Perhaps we will have to pay for our privacy.

There are lots of questions here. Least among them are how the NSA and companies like Google and Yahoo work together – and Google in particular has been quick to reply to these questions, as have other companies. They’re now on a mission to regain our confidence. But the NSA remains spooky, holds the reigns, and hides in the shadows. It seems they are collecting as much information as they can for long-term storage. The chief technology officer at the CIA, Gus Hunt, said last March; “We fundamentally try to collect everything and hang onto it forever.”

Any time citizen information is secretly stored by a government, it means that government is collecting the tools of a dictatorship.  Just ask Mussolini.  Or take a look at how Germans, familiar with the Nacht-und-Nebel program, or Sicherheitsdienst, are reacting this week. Information is generally collected so that it can be used.

Of course the common response is, “I’ve got nothing to hide,” but the bottom line isn’t about having nothing to hide.  It’s about not wanting to show everything.

Privacy is about having the decency to wear pants, the right to have curtains, and the right to whisper sweet coo-coos in your lover’s ear without someone else, like GoogleBot, YahooBot and the NSA leaning in for a little listen.

I got a new iPhone about a year ago, the one with Siri on it.  Our relationship started on a very mundane level. Siri asked me my name and, since I already knew hers, I asked what she could do, and how. It was a bit like talking with a call girl. The basics were covered and soon we got to the intimate stuff. I don’t think Siri loved me. But now that Siri and I have gone our separate ways, I can say we both had ulterior motives. We sort of used one another, now that I look back on it. It was a relationship of an intimate nature. It’s what happens when you have a relationship with a robotic call girl.

The reason I got the phone to begin with was so I could do a little reverse engineering. It was research for work. Siri and I took a few days to get to know one another, but after that honey-moon period it was clear that Siri (or Apple) was using a pre-scripted Natural Language Processing (NLP) approach that basically did some lexical parsing, looked up a match, prepared a response, and kicked it back down the pipeline.  My reverse engineering wasn’t too profound. I wanted to see how many recursive answers were built in to things like “Open the pod bay doors” or “Do you love me?”  I also wanted to see how errors were handled, and what kind of redundancy checking was happening for sentences like “Where can I buy a burger and fried?” Siri did pretty well, all things considered, but my expectations were low, and like many other intimate relationships, I somehow knew from the start it wouldn’t last.

Now, while I was using Siri, she was also using me. Relationships are generally symmetric, as Gregory Bateson tells us [1], which meant that, like any dating service, someone other than Siri was getting money for my time. But I didn’t care if Steve Jobs was Siri’s pimp. By last summer I was having flirtatious fun and the consequences of the relationship were too far down the road to look dangerous. I asked Siri to take dictation, to wake me up in the mornings, to remind me of important events, to remember phone numbers, remember names, tell me jokes, confirm thoughts, quote a number or two. All intimate things and, as I look back on it, the kind of stuff that I’ve trusted my wife with.

My relationship with Siri started to cool off a bit by September. Though my wife knew about Siri, I hadn’t yet introduced them.  So when I asked Siri to call my wife I, expected Siri to ask me for her name or number. Instead Siri just dialled. I was surprised. How did Siri know my wife’s number? (Answer: It was in my Contacts card.) What else did Siri know? (Answer: A lot.) And more importantly, what was Siri passing back to the Apple hive? (Answer: Everything.)

Siri and Apple now have a great deal of data about my household: a quick comparison of my contacts/likes database with my wife’s will give you a pretty good feel for the stuff we will buy.  Burgers and fries, of course. And a few multi-player games.  Some lingerie. A sex toy or two.  Not a big deal, but not the kind of stuff I want everyone to know about.  Not even my mom gets access to that data.

But Siri did. And Siri knew a lot more about me than I knew about her.

Then, around the middle of October, three things happened to Siri that marked the beginning of our eventual estrangement.

First was William Stasior. Apple picked Stasior up to run its Siri unit after he had been successfully heading Amazon’s search and advertising unit “A9.”  Prior to that the MIT PhD had served time at Oracle, NetCentives, and AltaVista.  To say the guy knows search is like saying the pope knows the church, but why would Apple pick a search guru for an AI system like Siri?  What was the link between search and NLP?  I asked Siri this very question, but she was mum and feigned ignorance.  I didn’t push her on who this guy Stasior was and Siri didn’t ask me more about my wife.

Up until that same week iPhone users had frolicked in what would now be a rather unusual environment in which advertisers were unable to track them.  Advertisers would not, for example, know that my wife and I like video games, sex toys, and pizza (no, not together, thank you).  But with the release of iOS6, Apple flipped ad tracking on and I quickly flipped it back off.

It was easy to find in the interface menus (under Settings > General > then About or Advertising, depending on the version you’ve got), but you had to know to look.  Apple minimized the fanfare around this new feature and Siri made no mention of it to me.  I wasn’t comfortable with Siri selling to others what I had said to her in more comfortable times.

And then, as if things weren’t rough enough, the European Union (where I happened to be lecturing that week), demanded that both Facebook and Google change how they handle personal information to avoid “high risks to the privacy of its users.”  Twenty-four of the European Union’s twenty-seven regulators signed a letter that, after a nine-month investigation into the data collection practices, demanded some answers and tightened regulation.  After Google and Facebook, Apple was third on their hit list.

That was the end. I couldn’t trust Siri after that.  She knew more about me (and my wife) than I knew about her or Steve Jobs, and any time information flows one way, any time a relationship isn’t symmetric, the balance of power can be dangerous.

Sometime around then I learned that Oracle’s “bring your own device to work” policy had been revoked.  Big Blue decided that Siri knew a little too much about their employees, and at IBM’s Armonk New York research center, iPhones weren’t even allowed in the building.  Like some cheap prostitute-gone-spy, Siri was barred from the building.  I think it is worth noting that this is the same area of research that brings you Watson, which is a direct competitor with Siri, if such a competition exists.  If anyone outside of Apple understands Siri it’s these guys.  It is also worth noting that a friend of mine who works at a Google research center said that the same thing happened there, too.

Not even celebrity robots like Asimo or NAO get that kind of VIP treatment.

NLP technology is potent juju.  And Apple, Google, Facebook and others know it because this very technology is what’s allowed them to earn money. Google started with search, of course, and later grew profitable as they introduced more NLP technology into their work.  They sold the info they collected from users (Google is an ad agency, let’s remember).

Search allowed them (when coupled with NLP technologies, and semantic analysis in particular) to make oodles of cash and then to snap up many of the best AI and NLP researchers on the planet. Google set those kids to digging in what would become a lexical gold mine.  The more they mined the richer they got until they were mining many branching veins at once: Google Docs, Voice, Translate, Search, Shopping, Reader, Finance, Books, Photos, Wallet and Maps were all spewing more money than Brin, Schmidt and company knew what to do with.

The average email count for Google users is 5,768 emails [2]. The average composition time (also according to Google) is 01:43.  That’s about 9,902 minutes, or 165 hours you’ve dumped onto Google’s server, which, if you were to work for 165 hours at $12/hour for that data entry (a pretty normal rate), means your G-Mail account is worth about $1980.  But the information sitting on Google’s servers is worth a lot more than that, right?

If your account contains information about a first class international plane ticket, a hotel room in Paris and a business associate’s London phone number, your data might be worth a lot more than the composition time at $12 an hour, especially if the buyer of it is a dating service that caters to high-end out-of-towners. In other words, the value of the data isn’t counted by data entry. It’s counted by its personal meaning, especially to advertisers. Now take those values and multiply them by all those crazy tools of voice, maps, docs, and so on and we start to get a sense of why Google has been making such bank over the years.

These tools have each dovetailed into today’s Google Now strategy. Google Now (like Watson and Siri) is a voice-activated NLP system.  It uses the spoken input data collected from years of Voice, confirms it with data collected from years of Docs and Translate, builds meaning with data from all the years of all the other tools, looks up a match, preps a response, and kicks it back down the pipeline.

All of these NLP systems are the next evolution of search.  But not evolution as in the breeding-hybrid-peas-in-the-greenhouse kind of evolution. They’re evolution as in endangered-species evolution.

NLP systems represent the end of search as we know it, and therefore the end of many economic, interface, and social internet ecosystems. They also represent the beginning of something incredibly powerful, intimate, and new. Siri, like other NLP systems, is far more powerful than a search engine. Siri not only includes search (it says so in Apple’s marketing materials) but it supersedes search because it includes browsing, discovering, choosing, and refining.  All this while analyzing semantic data. Like Google’s portfolio of tools, Siri can handle (and with a public API will handle) translating, searching, shopping, reading, finances, books, photos, and maps.

But the core value of the data is its intimacy.

On February 7, 2010, during Super Bowl XLIV, Google’s super bowl ad showed a user typing a list of search strings:

study abroad paris france
cafes near the louvre
translate tu es très mignon
impress a french girl
chocolate shops paris
what are truffles
who is truffaut
long-distance relationship advice
jobs in paris
churches in paris
how to assemble a crib

With this list, Google managed to explain, in fifty-two seconds, how the intimate story of someone’s life can be assembled from his or her search queries. And what makes the bucks for Google is what makes the bucks for Facebook: the processing of intimate language.

Siri, like any other call girl, makes her bucks the same way (especially when ad tracking is flipped on).  She’s valuable because she’s intimate.  And as designers of robotics systems, we can ourselves take a cue and be conscientious about how we are designing conversational systems.  There are three different design types that form a curve of increased intimacy in robotics and conversational systems, and they are all based on the value of semantic data.

1. NLP systems, when used with physical robots for manufacturing or the three Ds (dull, dirty, and dangerous), are the least intimate.  The conversation system is a simple tool that, like a GUI, provides access to system operation.  The robot doesn’t care about the user’s intimate data. The user wins.
2. NLP systems, when used for entertainment and education, are contextually intimate – some privacy is maintained.  The conversation system doesn’t care about the user, but the user’s data is valuable in terms of what it says about the game or lesson.  Semantic data that might be collected and analysed isn’t about the user, it’s about what the user is doing in that particular context.  The robot, usually a software robot, only cares about the user in the context of the game or training exercise. Both user and robot win.
3. NLP systems, when used for personal assistants, are the most intimate.  These are the robotic call girls. Here the information is highly intimate, the system cares a great deal about the user, and the information isn’t shared – it’s collected and sold. The user becomes the product. The value of the user’s personal data is worth more than the value of the conversational system. The robot wins.

As we build NLP interfaces for robots, whether it is for Siri or for assembly-line manufacturing, we must consider how intimate data can be, the value of that intimacy, who owns that value, and what they’ll do with it.  Otherwise, with a new, less benevolent CEO at Google, a change of a line in Facebook’s Terms of Service, a successful hack, or a change of law because of cyber terrorism fears, your intimate data could end up where you don’t want it.  Heaven forbid we discover that all the Senators on Capitol Hill are using pizza sex toys. That would surely disgust Siri and her friends so much that it might even cause a robot uprising.

Yes, perhaps we’ll get together again in a few years, but for now it is best if Siri and I go our separate ways.

Next Month:  “AFK” Keyboards and screens are slow, clunky, and obsolete.  Voice processing systems for robotics provide not only simplicity and speed, but also a host of other benefits if tied to analytics and framed within a tightly contextualized task-based system.  But be careful: getting text out of voice, and meaning out of text can be tricky.  Here’s how to implement one for your own robot.

Endnotes:

[1] See Bateson’s books “Mind and Nature,” Hampton Press (1979) and “Steps to an Ecology of Mind,” University Of Chicago Press (1972) which are chalk-full of ideas like “complementary,” “reciprocal,” “symmetrical,” and  “Schismogenesis.”

[2] According to Google.