The Wikidata project was kicked off around a year ago by the German chapter of Wikimedia, which is still steering its gradual development. For Wikipedia, the advantage is simple and powerful — if there’s a central, machine-readable source for facts, such as the population of a city, then any update to that data can be instantly reflected across all the articles in which the facts are included.

To posit a morbid example: a singer may have dozens or even hundreds of language versions of her Wikipedia entry and, if she were to die, the addition of a date of death to the Wikidata database would immediately propagate across all those versions, with no need to manually update each one (yes, I can also see how this might go horribly wrong).

Indeed, Wikidata is now being used as a common data source for all 286 Wikipedia language versions. Here’s the under-development “item” page for Russia, if you want to see what Wikidata looks like in practise.

But the really interesting thing with Wikidata is that it’s not just for Wikipedia – although it’s worth remembering that its API is still under development, the database can be used by anyone as it is published under a Creative Commons 0 public domain dedication. Here’s how Wikidata project director Denny Vrandečić put it in a statement:

“It is the goal of Wikidata to collect the world’s complex knowledge in a structured manner so that anybody can benefit from it, whether that’s readers of Wikipedia who are able to be up to date about certain facts or engineers who can use this data to create new products that improve the way we access knowledge.”

There are already some pretty cool (if bare-bones) examples of what people can do with Wikidata. One is GeniaWiki, which is trying to map the family relationships between famous people (the first and so far only example is that of the Bach family), while a Tree of Life project is trying to put together a viable, Wikidata-based “taxonomy of all life”.

It’s worth noting that the initial funding for Wikidata’s development has come from Google, the Gordon and Betty Moore Foundation, and the Allen Institute for Artificial Intelligence. Ultimately, Wikidata is precisely the sort of venture that is needed to feed the nascent semantic web and AI movement.

It’s far from the only venture in this space – I’d also recommend keeping a close eye on Google’s Knowledge Graph, which powers Google Now, and Wolfram|Alpha, which partly powers Siri – but all these (often intertwined) projects are essentially trying to do the same thing: to turn facts into something that machines can understand.

And that, in conjunction with advances in natural language processing and machine learning, will ultimately help us converse with machines. These are the building blocks of artificial intelligence and the future of search, and Wikidata’s very permissive license should act as an open invitation to anyone dabbling in this space.

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Tempo’s new app uses many of the same artificial intelligence technologies that went into Siri to generate a smart calendar that infers appointment details and context from your other social media and messaging services. Tempo parses all of the data in a customer’s email accounts, address books and LinkedIn and Foursquare profiles in the cloud using Amazon Web Services. That’s where it ran into problems.

The Tempo AI team

According to CEO Raj Singh, it takes a huge amount of computing resources to bring new customer online. Its platform must initially cull through all of the data in the customer’s various email and social media accounts. Once the customer is on-boarded the burden on the AI lessens, though it does reprocess all of that data on a regular basis – any time new email or contact data is added to system, Tempo can generate new semantic links between new data and old.

“There is just generally a ton of CPU to make all of this work; processing data takes time and we don’t get a network effect, since we have to process each individual’s data,” Singh said in an email. “We re-process data constantly; to be semantically relevant and contextual, we’re constantly re-processing, this is very expensive (it’s like Google constantly re-crawling)”

Tempo was not only surprised by the sheer volume of new registrants – last week Tempo estimated it had a backlog of more than 100,000, but it now believes that number is conservative – it was also caught off guard by the amount of data each customer had. Singh said the average customer is linking 2.5 email accounts to their calendar. Tempo’s servers are getting slammed in both directions: they’re processing more new customers than expected and each new customer has much more information than anticipated.

That led to Tempo’s decision to put a halt to new activations for the next few days. It will finish parsing all of the current email accounts for those who have successfully registered, and it has submitted to Apple an update to its iPhone app that contains a built-in reservation system (right now the app simply won’t let you sign up). Once the reservation system is in place, it will begin allowing new customers in gradually as CPU resources allow. (Update: the new version of the iPhone app is now live on iTunes.)

Of course, since Tempo’s platform is hosted in AWS, it could simply buy more CPU time to get over the hump. Singh said he wouldn’t go into the details for competitive reasons of how Tempo is managing its backend, except to say that the amount of computing resources it needs to overcome the backlog would be very expensive. Plus, once Tempo brings all of these new customers on board, the demands on its servers will drop considerably.

Tempo certainly isn’t the only smart calendar app in the market. On Tuesday, Sunrise debuted a new smart calendar app, which my colleague Erica Ogg just wrote about in detail. Meanwhile, personal data search startup Cue (formerly Greplin) has been offering an intelligent calendar since June.

Because of Tempo AI’s pedigree from SRI and its associations with Siri, though, its app was always going to get a lot of attention (some rave reviews about the app also helped). Customers haven’t responded kindly to Tempo’s delays though. Of the 597 reviews on iTunes today, 435 were one-star skewerings. Tempo said that once the reservation system in place, it’s hoping it can do a better job explaining the reasons for the delay.

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Lemnios is the former Assistant Secretary of Defense for Research & Engineering and has spent the last decade thinking about the intersection of technology and the military, including issues ranging from cyber security to big data. But in a conversation on Friday, held at the end of his first week employed by IBM, he shared with me his thoughts on artificial intelligence and what he sees as the challenges standing between the tech industry and the big data revolution.

Zachary Lemnios

While some call data the new plastic or new oil, I am beginning to think of it as process in which we turn the most relevant bits about our behavior and health into digital bits that computers can understand. And then the computers do what they do best, which is parse that digital information and identify patterns that can then be acted upon. But before we can turn humanity and human behavior into machine-readable data and get the opinion of silicon brains, we have to deal with power consumption and errors.

First off, Lemnios is keen on using artificial intelligence and machine learning as ways to help computers support humans in a way that’s not usually well articulated in the AI community. After talking to some researchers in the AI community, you get the impression that true AI will teach computers to think like humans. But Lemnios’ opinion is that modeling out a human brain in silicon has value, but that remaking a brain in silicon shouldn’t be a focus.

“I look it as helping inform how we implement systems, not in vivo, but in silicon,” he said. “A silicon neuro-inspired switch will look different from bioswitch.”

His goal is to build applications that “converse” with humans and help spur new ideas and avenues of research. Maybe it’s a digital muse or a Software Socrates, but Lemnios thinks the convergence of data and cognitive computing (or AI) will deliver exactly that. But to get to that goal there are two problems he sees: One is delivering the computing required for such calculations without requiring a power plant, and the other is how to make computers autonomous enough so they can deal with flawed data.

One reason many researchers are looking at how the brain is modeled is because it manages to do great gobs of computing very efficiently, which might address the power problem. But we are far away from modeling the human brain, so in the meantime he’s planning on looking at architectures and hardware to move and process petabytes of information efficiently. The other issue is more complicated — creating a machine learning algorithm that knows when data is faulty and then knows what to do about it much like Numenta, Jeff Hawkin’s startup hopes to do.

“Data is not static .. it changes, and not only does it change but it could be ambiguous and incorrect — or it could be missing,” he said, and computers have to know how to recognize problems and then know how to work around them. He gave an example of a human finding themselves lost in a strange city with an appointment to get to. That person will very quickly figure out how to get to their appointment via directions on their smartphone or by hailing a cab. A computer in that situation might continuously reprise the faulty directions that got it lost in the first place or may not even realize it’s lost.

But if computers are going to handle translating human behavior into insights that can then become actionable market or programs, they’ll have to recognize the weird glitches produced by user error, malicious tinkering or even just random corruption in files. With IBM’s resources behind him, perhaps Lemnios can lick this problem.

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What makes Atooma different is the simplicity of its approach. It uses an IFTTT approach, which is short for “if this, then that.” You simply have to tell the app what to do in the case of a certain event. Here’s how Atooma describes it: “Create your Atooma to get any kind of tasks automagically performed on your smartphone. You can set up conditional events (IF) that trigger simple actions (DO).”

The interface to do this in Atooma is extremely intuitive. Starting with a few colored dots that represent different conditions, customize the events and then select actions in a circular user interface. It’s almost like dialing up short automaton script with an old rotary phone.

How does it work? So far, quite well for me. I created some simple scripts myself, but you can see other scripts that fellow Atooma users have created. I found one to “turn Wi-Fi off when leaving home”, which I downloaded. All I needed to do was change the event location to my house — which was simple using a maps interface — and that was it. To test it, I walked about 200 meters down the street and just like that, my Wi-Fi radio automatically shut down. I already had a script to turn it on when arriving home and Atooma did just that when I returned from the walk.

That’s just a simple — but useful — example, of course. You can fire off events if a file is downloaded or deleted (handy for an automatic upload to Dropbox), for example, take action upon incoming texts, emails or calls, or automatically send a tweet or Facebook message in a particular event.

Similar the Wi-Fi rule, I’ve already set up Atooma to turn on my Bluetooth radio when I leave home. I seem to forget to do so when going out for a drive; I’d rather use a Bluetooth headset in the car. And I noticed another user set up a rule to read incoming text messages aloud when the GPS radio detected he was traveling faster than a certain speed and presumably driving. Essentially, user actions are only limited by what events and hardware Atooma supports and your imagination.

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I spoke last week with Hirokazu Anai of Fujitsu Labs, who explained to me the thinking behind the Todai Robot project and the challenges in making it happen. At a high level, the project wants to create a system that can read and understand questions on the university’s entrance exams (Fujitsu is focused on math, while IBM is working on the history portion) and be able to answer them at a rate high enough to pass — currently between 80 percent and 90 percent for the first-stage exam, and then 30 percent to 40 percent for the second, more-difficult stage. The most-difficult problem, Anai said, is getting the robot to understand the questions and convert them into code that a computer can understand.

However, he added, trying to create a system that can carry out all the necessary calculations on a single consumer processor only makes the project more challenging. For the math questions Fujitsu is focused on, once the system converts natural language into computer-readable code, it then must choose from a collection of computationally complex computer-algebra algorithms the correct one to answer the question. If the system were running on a supercomputer, Anai said, the whole process would run a lot faster because of the increased computing power.

A workflow for a sample math question.

The Todai Robot project’s anticipated completion date of 2021 speaks to the difficulty of pulling this off, as do the issues IBM is running into trying to convert its Watson question-answering system into something capable of running on devices such as mobile phones. Building such intelligent systems is never easy — it took IBM several years to get Watson Jeopardy!-ready — and doing so on a single processor means a lot of optimization just to account for the limited capacity. Anai acknowledges it will be a tough goal to meet, although he’s confident that his team, at least, will be able to solve the math aspect ahead of schedule.

Through a translator, though, Anai said designing the exam-taking robot to run on a laptop means the project has greater utility once it makes its way into the public sphere. He noted manufacturing, insurance and even the travel industry as areas that could benefit by putting this type of decision-making power in the hands of regular users who might come across tough math problems in their day-to-day jobs. And even expanding the system to a small computing cluster could help generate answers significantly faster, still without requiring a major capital investment.

Feature image courtesy of Shutterstock user Palto.

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Group conferencing like none other

MindMeld is an iPad app (for now) that uses Facebook’s open graph and identity to help create quick audio or video conferences. Add a few people and start talking. But here is where things get interesting: As you speak (or other participants speak), the app listens and starts surfacing information pertaining to what you are talking about.

For instance, if you are talking about an upcoming meeting with, say, someone like me, then in near realtime, it would show you my Wikipedia page, surface my recent blog posts, show GigaOM location on a map, and other such information. And as fast as the topic shifts, the system brings up relevant information for that new topic. Sometime in the future, the company will be able to access data from your Dropbox or Google Docs account and when it does, Cisco’s WebEx division should reach for a proverbial bottle of migraine medicine.

I got the demo of the application at a fairly noisy restaurant in my neighborhood, and even then, it kept offering suggestions and information pretty quickly. If there was a lag, it was due to AT&T’s LTE network, which isn’t as robust as advertised, especially in San Francisco. Put this app on a WiFi – which we did – and everything from picture quality to voice latency and information being pushed to the screen was pretty flawless. Sure, it was a demo on a system used by no more than a dozen people, including all eight of the company’s employees, but I have seem many demos in my time. Someday, Siri will work as flawlessly as this app and will get an A-plus from me.

Rise of anticipatory computing

The MindMeld app has me convinced about the capabilities of Tuttle and his crew. Yes, if you looked at the company just from the perspective of this app, its ambition might seem limited. Tuttle says what matters to him is that their platform (which he expects to unveil next year) is used by other apps that can integrate it using their API. But in order to showcase his company’s grand ambition, he needed an app and hence MindMeld.

Tuttle pointed out that unlike other semantic efforts that analyze usage history, their approach is to look at the past 10 minutes and then anticipate what users might need in the next 10 seconds. “We have a predictive model that changes second to second and surfaces relevant information without searching,” says Tuttle. He calls it “anticipatory” computing, and as far as I am concerned, “predictive” is the future direction of computing.

Tuttle started the company two years ago to develop a platform that would “continuously pay attention to what happens in your life and pick up ambient information and then start to surface relevant information.” Why? Because be believed that our computing habits were going from being desktop bound to completely mobile, and that would essentially mean a different usage behavior.

Sensors, data and mobile = complexity

With more devices and more sensors coming into our lives, the amount of data being generated will reach a point where the machines need to start anticipating our needs. Search as a way to access information doesn’t and won’t work — mostly because search can only respond to questions we ask. Also, if most of our computing is shifting to devices that are always with us, the idea of how to compute also has to change.

Tuttle, who did his Ph.D studies at the AI Labs (Artificial Intelligence Labs) at Massachusetts Institute of Technology (MIT), says the idea for the company came when he was fine-tuning his last company, Truveo. That company, which used speech-to-text technology and helped search video streams, has become one of top video search engines. Fast forward to today, the emergence of faster networks, cheaper (and reliable) cloud compute platforms and newer technologies has made it possible for Tuttle and his co-founder Jheeta to develop Expect’s platform.

They don’t go into great detail about their infrastructure, but say that at any given time during a call on MindMeld, there are multiple processing threads going, and they are pushing and pulling data over the network at a pretty rapid clip. They currently have based their system on Amazon’s EC2 and have built both voice and data communication layers in addition to using speech-to-text technology from a partner. Translation: they are using Nuance’s technology. For data (information) they tap sources such as YouTube, Yelp and Google.

Tuttle isn’t the only one thinking about anticipatory computing. Google recently launched Google Now, and it wouldn’t surprise me to see something similar to Expect’s approach show up when Google Glasses become mainstream. And other startups are working on making anticipatory computing a reality and coming up with new techniques that would simplify everyday computing tasks.

PS: Here are two links to videos that introduce you to the company and the app. 

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Vicarious wants to build a series of algorithms that mimic the way the mammalian brain processes and applies information — in short it wants to build software that will grant computers intelligence. The first concrete product the Union City, Calif.-based startup aims to build is a human-like object recognition system, but this is something that co-founder and CTO Dileep George estimates is three to four years away. Apparently the long time frame is just fine with investors, and what makes Vicarious such an audacious bet.

CEO and Co-Founder D. Scott Phoenix explains that the company isn’t focused on commercialization anytime soon as a means to preserve the research into building a truly robust set of intelligence algorithms, as opposed to an industry specific algorithm that leads to limited artificial intelligence — some kind of idiot savant. “We will continue working on solving the core problem.” Phoenix says. “I think it has held back AI when others have tried and found something that works well in a particular domain and then they refine that. Then the tech gets more narrow over time.”

The human brain is computing’s Mt. Everest

Building computer hardware or software modeled on the human brain is the kind of big tech problem that Peter Thiel, a former PayPal executive and a partner with Founders Fund has called on entrepreneurs to do. In this case he’s putting money where his mouth is. And the brain as a computer is like the Mt. Everest of computer science problems. When compared with CPUs or even newer forms of silicon brains, the brain is a far more efficient processor. From a Scientific American article comparing the human brain to IBM’s Watson AI project:

So a typical adult human brain runs on around 12 watts—a fifth of the power required by a standard 60 watt lightbulb. Compared with most other organs, the brain is greedy; pitted against man-made electronics, it is astoundingly efficient. IBM’s Watson, the supercomputer that defeated Jeopardy! champions, depends on ninety IBM Power 750 servers, each of which requires around one thousand watts.

Thus in both hardware and software the search for a silicon brain has absorbed researchers. “We want to help humanity thrive,” says Phoenix. “Human progress is limited by the number of people and their training to solve big problems, so by understanding the core algorithms that produce intelligence we can build computers that are 30 billion times faster and dramatically increase the rates of problem solving on behalf of humanity.”

To build a better AI you don’t need to map the brain.

There are countless research efforts seeking the same thing as Vicarious, but they are going about it in different ways. For example, both IBM and HP are trying to build out a silicon version of the brain in order to create neural computers capable of processing information in different ways– more akin to how humans do it. IBM actually showed off the first chips capable of cognitive computing last year.

IBM’s Watson

IBM also has another effort at AI, although a much less literal one than the hardware efforts. Watson takes loads of text on a certain topic and then has algorithms that help it detect the probability of a relevant response when people ask questions of that material. IBM is building a new business model around offering Watson as a service to help in the medical and financial fields.

Google also is delving into research that ties into artificial intelligence and machine learning. A recent research paper on training a computer to “recognize” an image of a cat without outside supervision is a type of AI. And while George of Vicarious explains that its research is different because it is broader and will be capable of learning from moving images as opposed to stills taken from videos, the core idea is related.

There are plenty of other companies attempting to offer at least the veneer of artificial intelligence from Apple’s Siri technology to startups such as ai-one, which is building a software development kit to add AI to other apps. And plenty of other companies are using the fruit of cheaper access to lots of data to make programs and predictive models that look like intelligence.

But computers today rely on people to tell them what to do — that’s what programming is for — but giving them the ability to recognize patterns and then relate those patterns to an understanding about how the world works frees them from the constraints of programming. Of course, once they have that freedom it’s unclear what that means for computer science, programming and the current job market. It’s also unclear how far that freedom can really take a computer. Just giving it intelligence won’t mean it can “think” for itself.

Either way, Vicarious is a startup playing in a field with giants, with a big idea about changing the world.

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Actually, ai-one calls its technology “biologically inspired intelligence” because it learns like the human brain does, detecting patterns and learning new data sources automatically without requiring developers to feed it new analytic models. Better yet, according to company literature (it has a SlideShare page very much worth checking out for more details), it takes less than a day to train developers on ai-one and less than a day to build an application. The company’s goal is to “embed intelligent computing capability in every device,” ai-one President Tom Marsh told me during a recent call.

What is a holosemantic data space!?!

At the highest level, ai-one’s technology — which powers its suite of products — works by creating a virtual brain (ai-one calls is a holosemantic data space) that processes files as they stream into the system. Marsh said the brain finds associations “between every byte pattern and every other byte pattern … in the entire corpus,” which allows it to work in various spaces. Ai-one calls the smallest unit of information in any given field the “data quant,” and that’s what it analyzes. For text files, those are words; for genomes, it’s the DNA base pairs.

Ai-one’s first product, called Topic Mapper, is a set of libraries and an API for text analysis (there’s also a genomics-focused version). Text analysis is the foundation for everything from LinkedIn’s People You May Know feature to Siri’s, which converts speech to text before analyzing it and providing an answer.

Marsh showed me a demo application that shows what Topic Mapper can do. In just seconds, the app had ingested 49 data files from the World Cup soccer website, processed them and created an XML file that ranked all the words by which ones it thought were the most-important. It also created a graphical representation on which related words stem from highly relevant words and create clusters. “Your brain does this really brilliantly,” Marsh said, but it can be difficult to write algorithms that do so.

Interestingly, that application didn’t use natural-language processing, which made the results of the demonstration all the more impressive, but also highlighted its shortcomings. Marsh also showed me the results from an attempt to analyze tweets from last year’s SEMTECH conference that ranked URLs and date stamps fairly highly even though they’re not words. Because it was just analyzing the byte patterns and didn’t know anything about language, the app didn’t know the difference between a time such as 12:21 and a term such as “schema.org.”

Using a simple browser-based tool called BrainBrowser that ai-one created, Marsh showed what happens when Topic Mapper meets natural-language processing. He was able to process the text of a website while filtering out specific parts of speech, resulting in a perusable list of relevant keywords. Developers could use this ability to easily create apps for finding related people, places and things with a high degree of accuracy. And what I saw is just a demo to show the guts of BrainBrowser — tied to industry-specific ontologies and/or dressed up under a pretty user interface, you could have a very specialized or broadly accessible application.

Sensors networks or image recognition could be the killer app

Despite what its technology can do, though, Marsh said ai-one is still waiting for someone to develop the killer app that propels it into the bigtime. “Our challenges are more commercial at this point than they are technical,” he said.

Maybe the products on its roadmap will help out. For starters, there’s the updated version of Topic Mapper slated for release in June that will integrate 64-bit, multithreaded processing and scale from 4 gigabytes per instance to 18 exabytes per instance to make it far more powerful and able to handle far larger datasets. There’s also Graphalizer, which is expected for release in 2013 and tunes the ai-one technology to detect patterns from streaming data such as that from sensor networks and financial trading systems.

But the real game-changer might be UltraMatch, a version of the ai-one technology targeting image recognition that’s on the calendar for release in July. Aside from some projects going on inside of Google (e.g., Google Glasses), we’ve yet to really tap into the potential image recognition and matching in real time, but the possibilities are both invigorating and scary. Marsh said UltraMatch uses pixels as the data quant, and the technology has already been proven within some CSI labs that use a shoeprint-matching engine ai-one built several years ago.

Actually, Google Glasses, and Google Goggles, is a prime example of just the type of big-company innovation ai-one wants to bring to the masses. He’s impressed enough with what large software vendors are doing in their research labs, but also realizes that’s a recipe for walled gardens of innovation. If we don’t democratize access to AI techniques, he said, we’re essentially “handing the keys over to IBM and Google and letting everything run on their computers and asking, ‘Pretty please, can we have some smart computing?’.”

Feature image courtesy of Shutterstock user Sashkin.

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What CrowdControl does, essentially, is partner with crowdsourcing services like Amazon Mechanical Turk (a very close partner, in fact) to provide a layer of quality assurance between the remote workers and the client needing work done. CrowdControl provides a worker-management interface, determines pricing and — most critically — uses its software to determine whether the work product is accurate.

Here’s how I described CrowdControl’s theory and methodology back in November:

Sentiment analysis already is becoming big business for companies such as IBM and SAS that are turning their predictive analytics engines on social media streams. But CrowdControl Founder and CEO Max Yankelevich says there are two big problems in the space right now. One is that current natural-language-processing technologies are better suited to identifying keywords than they are to deciphering true sentiment. The other is that humans, whose brains are inherently better at looking at text in context and working around abbreviations and poor grammar, have a tendency to underperform.

…

To cure that problem, CrowdControl contains more than 15,000 rules to determine how accurate workers are in completing their tasks. Those rules comprise much of the company’s secret sauce, but Yankelevich explained the methods for “adjudication,” the process of judging accuracy, at a high level. A big one is called “plurality,” which entails either assessing a worker’s answer in relation to everyone else’s answer on the same question, or giving the same question multiple times and looking for the same response. Another is “gold answers:” The tester continuously inserts questions to which it knows the answer and calculates how often the worker gets it right.

The company recently completed a survey of half of Amazon Mechanical Turk workers, which should help it further refine its algorithms and processes. As Yankelevich told me in November, where workers live and what they do for a living can have a big effect on how they perform. Presently, CrowdControl says its ideal use cases are sentiment analysis, data cleansing and data normalizing, which is the process of adding consistent structure to unruly data sets.

Image courtesy of CrowdControl.

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Of course, the outage was annoying and inconvenient, and hopefully Apple learned a valuable lesson about managing a large-scale, persistent data service managed from its own server facility, and this will never happen again. But the extent of the outcry as the outage wore on, as well as the attempts on Friday to follow up and try to get to the bottom of what exactly happened, show that Siri’s effect on the mobile landscape is not insignificant.

It could be the case that Apple’s servers couldn’t handle the demand that Siri was putting on the system, as some users who contacted Apple support about the problem were told. That would indicate that Apple underestimated the scale of demand for Siri, which suggests the personal assistant is being used a lot. But even if the problem is independent of demand, the fact that the news of Siri’s going down spread as far and as quickly as it did, and elicited so much response from the user community, indicates that it is finding a place in people’s lives. Some of the media attention could be attributed to the fact that people love when a winner like Apple stumbles, but user concern seems genuine.

When Apple first announced the personal assistant software, I admit to thinking that Siri had limited value beyond triggering an initial feeling of novelty that would fade quickly. After using Siri myself, I found that it actually had a lot of real use value, even in countries where it hasn’t yet gained localization features. The indignation of users affected by the outage indicates that I wasn’t the only one who found myself leaning on Siri a lot more heavily than I expected to.

It’s not as widespread, but the outcry about Siri’s downtime reminds me of the web-wide groans that go up every time the Twitter fail whale makes one of its visits or when Tumblr takes a tumble. That’s a minor PR problem for Apple in the short term, but in the larger picture, it’s a very good thing that people miss Siri when she’s not around.

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