It's the fashionable thing to speculate on future Apple products. One idea that continues to get traction is the Apple TV, a complete TV set with integrated Apple-ly features. Supposedly to be announced this fall, after failing to appear at any event for the past three years, it will revolutionize up the TV market, cure global warming, and cause puppies and kittens to slide down rainbows in joy.

I don't buy it. I don't think Apple will make a TV. Televisions are low margin devices with high capital costs. Most of the current manufacturers barely break even.

Furthermore, the market is saturated. Pretty much anyone in the rich world who wants a TV has one. Apple needs growth opportunities. The last thing they need is a new product with an upgrade cycle at least twice as long as desktop computers. It doesn't make sense.

All that said, speculating about products is a useful mental exercise. It sharpens the mind and helps you focus when working on real products. So here we go:

If Apple Made a TV, How Would They Do It?

First let's take care of the exterior. In Apple fashion it would be pretty and slender. Either a nice brushed aluminum frame like the current iMac or a nearly invisible bezel. I suspect they will encourage wall mounting so the TV appears to just float. The current Apple set top box will be integrated, as will network connections, usb, etc. Nothing to plug in except the power cord.

Next, we can assume the current Apple TV will become the interface for the whole device. A single remote with on screen controls for everything. While I love my Roku, I hate having to use one remote for the interface and a second for power and volume.

Third, they will probably add a TV app store. I don't think it will feature much in the way of games and traditional apps. Rather, much like the Roku, there will be apps for each channel or service. The current Apple TV essentially has this now with the NetFlix and HBO apps. The only difference would be opening the store up to more 3rd party devs.

I think we can assume this will another client of your digital hub. Apple already wants us to put all of our music, videos, and photos into

So far everything I've described can be done with the current Apple TV set-top box. So why build a TV. Again, I don't think they will; but if they would need to add something to the experience beyond simply integrating

First, a camera for FaceTime. Better yet, four of them, one in each corner of the screen. Four cameras would give you a wide field of view (with 3D capture as a bonus) that can track fast moving toddler as they move around the living room. This is perfect for video chatting with the grandparents.

Furthermore, there are modern (read: CPU intensive) vision algorithms that can synthesize a single image from multiple cameras. Right now the camera is always off to the side of the screen, so your eyes never meet when you look at the person on the other end. With these algorithms the Apple TV could create a synthetic image of you as if the camera was right in the middle of the TV. Combined with the wide field of view and a few software tricks we could finally have video phones done right. It would feel like the other person is right on the other side of your living room. It could even create parallax effects if you move around the room.

Video calls are a clear differentiator between the Apple TV and regular TVs, and something that a set top box alone couldn't do. I'm not sure it's enough to make the sale, though. What else?

How about the WWDC announcement of HomeKit? An AppleTV sure sounds like a good home hub for smart automation accessories. If you outfit your house with smart locks, door cameras, security systems, and air conditioners, I can see the TV being a nice place to see the overview. Imagine someone comes to the door while you are watching a show. The show scales down to corner while a security camera view appears on the other screen. You can choose to respond or pretend you aren't home. If it's a UPS guy you can ask them to leave it on the front door.

I imagine the integration could go further. Apple also announced HealthKit. The Apple TV becomes a big screen interface into your cloud of Apple stuff, including your health data. What happens if you combine wearable sensors with an Apple TV. See a live map of people in the house, ala HP's Marauders Map. An exercise app can take you through a morning routine using both the cameras and a FitBit to measure your vitals.

A TV really could become another useful screen in your home, something more than just a video portal. I think the idea has a lot of potential. However, other than a camera and microphones almost everything I've detailed above could be done with a beefed up standalone Apple TV set top box. I still don't think a full TV makes sense.

More Beta Testing Software

This is a new future tweeting system I'm working on. It will let me write a post, send it to my blog, then link the blog from twitter, all in the *future*!

The Web is amazing for answering questions. Suppose you want to answer a question like, "what does the .JPG file extension mean", then the answer is just an internet search away. Millions of answers. However, if you stray from the common path just a tiny bit things get hairy. What if you want to get a list of all file extensions? This is harder to find. Occasionally you might find a PDF listing them, but if you are asking for all file extensions then you probably want to do something with that list. This means you want the list in some computable form. A database or at least a JSON file. Now you are in the world of ‘public’ data. You are in a world of pain.

Searching for “list of file extensions” will take you to the Wikipedia page, which is open but not computable friendly. Every other link you find will be spam. An endless parade of sites which each claim they are the central repository of file extension data. They all have two things in common:

• They are filled with horrible spam like ‘scan your computer to speed it up’ and ‘best stock images on the web’ and ‘get your free credit report now’.
• They let you add new extensions but don’t let you download a complete list of the existing ones.

What I want is basic facts about the world; facts which are generated by the public and really should belong to the public. And I want these facts in a computable form. So far I cannot find such a source for file extensions. These public facts, as they exist on the internet, have morphed into a spam trap: vending tiny bits of knowledge in exchange for eyeball traffic. These sites take a public resource and capture all value from it, providing nothing in return but more virus scanner downloads. That they also provide so little useful information is the reason I have not linked to them (though they are obviously a search away if you care).

The closest I can find to a computable file extension list is the mime type database inside of Linux distros. This brings up a second point. Every operating system, and presumably web browser, needs a list of all file extensions, or at least a reasonable subset. Yet each vendor maintains their own list. Again, these are public facts that should be shared, much as the code which processes them is shared.

File Extensions are not the only public facts which suffer the fate of spam capture. I think this hints at a larger problem. If humanity is to enable global computing, then we need a global knowledge base to work from. A knowledge base that belongs to everyone, not just a few small companies, and especially not the spammers.

Wikipedia and it’s various data offshoots would seem to be the logical source of global computable data, yet the results are dismal. After a decade of asking, Wikipedia’s articles still aren’t computable in any real sense. You can do basic full text search and download articles in (often malformed) wikimarkup. That’s it. Want to get the list of all elements in the periodic table? Not in computable form from Wikipedia. Want to get a list of all mammals? Not from them. Each of these datasets can actually be found on the web, unlike the list of file extensions, but not in a central place and not in the same format. The data offshoots of Wikipedia have even bigger problems, which I will address in a followup blog.

So how do we fix this? Honestly, I don’t know. Many of these datasets do require work to build and maintain and those maintainers need to recoup their costs (though many of them are already paid for with public funds). If this was source code I’d just say it should be a project on GitHub. I think that's what we need.

We need a GitHub for data. A place we can all share and fork common data resources, beholden to no one and computable by everyone.

Building and populating a GitHub for data, at least for these smaller and well defined data sets, doesn't seem like a huge technical problem. Why doesn’t it exist yet? What am I missing?

During SXSW this year I had the great fortune to see the keynote given by Stephen Wolfram. If you’ve not heard of him before, he’s the guy who created Mathematica, and more recently Wolfram Alpha, an online cloud brain. He’s an insanely smart guy with the huge ambition to change how we think.

When Stephen started, back in the early 1980s, he was interested in physics but wasn’t very good at integral calculus. Being an awesome nerd he wrote a program to do the integration for him. This eventually became Mathematica. He has felt for decades that with better tools we can think better, and think better thoughts. He didn’t write Mathematica because he loves math. He wrote it to get beyond math. To let the human specify the goals and have the computer figure out how to do it.

After a decade of building and selling Mathematica he spent the next decade doing science again. Among other things this resulted in his massive tome: A New Kind Of Science, and the creation of Wolfram Alpha, a program that systematizes knowledge to let you ask ask questions about anything.

In 1983 he invented/discovered a one dimensional cellular autonomy called Rule 30, (which he still has the code printed on his business cards). Rule 30 creates lots of complexity from a very simple equation. Even if one runs just a tiny program it can end up making interesting complexity from very little. He feels there is no distinction between emergent complexity and brain like intelligence. IE: we don't need a brain like AI, the typical Strong AI claim. Rather, with emergent complexity we can augment human cognition to answer ever more difficult questions.

The end result of all of this is the Wolfram Language, which they are just started to release now in SDK form. By combining this language with the tools in Mathematica and the power of a data collecting cloud; they have created something qualitatively different. Essentially a super-brain in the cloud.

The Wolfram Language is a 'knowledge based language’ as he calls it. Most programming languages stay close to the operation of the machine. Most features are pushed into libraries or other programs. The Wolfram Language takes the opposite approach. It has as much as possible built in; that is the language itself does as much as possible. It automates as much as possible for the programmer.

After explaining the philosophy Stephen did a few demos. He was using the Wolfram tool, which is a desktop app that constantly communicates with the cloud servers. In a few keystrokes he created 60k random numbers, then applied a much of statistical tests like mean, numerical value, and skewness. Essentially Mathematica. Then he drew his live Facebook friend network as a nicely laid out node graph. Next he captured a live camera image from his laptop, partitioned it blocks of size 50, applies some filters, compressed the result to a single final image and tweeted the result. He did all of this through the interactive tool with just a few commands. It really is a union of textual, visual, and the network.

For his next trick, Mr. Wolfram asked the cloud for a time series of air temperatures from Austin for the past year then drew it as a graph. Again, he used only a few commands and all data was pulled from the Wolfram Cloud brain. Next he asked for the countries which border Ukraine, calculated the lengths of the borders, and made a chart. Next he asked the system for a list of all Former Soviet Republics, grabbed the flag image for each, then used a ‘nearest’ function to see which flag is closest to the French flag. This ‘nearest’ function is interesting because it isn’t a single function. Rather the computer will automatically select the best algorithm from an exhaustive collection. It seems almost magical. He did a similar demo using images of hand written numbers and the ‘classify’ function to create a machine learning classifier for new hand drawn numbers.

He’s right. The Wolfram Language really does have everything built in. The cloud has factual data for almost everything. The contents of wikipedia, many other public databases, and Wolfram’s own scientific databases are built in. The natural language parser makes it easier to work with. It knows that NYC probably means New York City, and can ask the human for clarification if needed. His overall goal is maximum automation. You define what you want the language to do and then it’s up the language to figure out how to do it. It’s taken 25 years to make this language possible, and easy to learn and guess. He claims they’ve invented new algorithms that are only possible because of this system.

Since all of the Wolfram Language is backed by the cloud they can do some interesting things. You can write a function and then publish it to their cloud service. The function becomes a JSON or XML web service, instantly live, with a unique URL. All data conversion and hosting is transparently handled for you. All symbolic computation is backed by their cloud. You can also publish a function as a web form. Function parameters become form input elements. As an example he created a simple function which takes the names of two cities and returns a map containing them. Published as a form shows the user two text fields to ask for the city names. Type in two cities and press enter, an image of a map is returned. These aren’t just plain text fields, though. They contain are backed by the full natural language understanding of the cloud. You get auto-completion and validation automatically. And it works perfectly on mobile devices.

Everything I saw was sort of mind blowing if we consider what this system will do after a few more iterations. The challenge, at least in my mind, is how to sell it. It’s tricky to sell a general purpose super-brain. Telling people "It can do anything" doesn't usually drive sales. They seem to be aware of this, however, as they now have a bunch of products specific to different industry verticals like physical sciences and healthcare. They don’t sell the super-brain itself, but specific tools backed by the brain. They also announced an SDK that will let developers write web and mobile apps that use the NLP parser and cloud brain as services. They want it to be as easy to put into an app as a Google Maps. What will developers make with the SDK? They don’t know yet, but it sure will be exciting.

The upshot of all this? The future looks bright. It’s also inspired me to write a new version of my Amino Shell with improved features. Stay tuned.