Stephen Wolfram's accomplishments and contributions to science and computing are numerous. He earned a PhD in particle physics from Caltech at 20, and has been cited by over 30,000 research publications. Wolfram is the the author of A New Kind of Science, creator of Mathematica, the creator of Wolfram Alpha, and the founder and CEO of Wolfram Research. He developed Wolfram Language, a general multi-paradigm programming language, in 2014. Stephen has graciously agreed to answer any questions you may have for him. As usual, ask as many as you'd like, but please, one per post.
theodp writes "The devil will be in the details, but if you were stoked about last November's announcement of the Wolfram programming language, you'll be pleased to know that a just-released dry-but-insanely-great demo delivered by Stephen Wolfram does not disappoint. Even if you're not in love with the syntax or are a FOSS devotee, you'll find it hard not to be impressed by Wolfram's 4-line solution to a traveling salesman tour of the capitals of Western Europe, 6-line camera-capture-to-image-manipulation demo, or 2-line web crawling and data visualization example. And that's just for starters. So, start your Raspberry Pi engines, kids!"
First time accepted submitter sixoh1 writes "Scientific American has an excellent summary of a new book 'The Improbabilty Principle: Why Coincidences, Miracles, and Rare Events Happen Every Day' by David J. Hand. The summary offers a quick way to relate statistical math (something that's really hard to intuit) to our daily experiences with unlikely events. The simple equations here make it easier to understand that improbable things really are not so improbable, which Hand call the 'Improbability Principle:' 'How can a huge number of opportunities occur without people realizing they are there? The law of combinations, a related strand of the Improbability Principle, points the way. It says: the number of combinations of interacting elements increases exponentially with the number of elements. The 'birthday problem' is a well-known example. Now if only we could harness this to make an infinite improbability drive!"
Nerval's Lobster writes "Which football team will win the Super Bowl this weekend? That's a multi-million-dollar question, given the amount of cash people will bet on either the Seattle Seahawks or the Denver Broncos to win. Fortunately, Wolfram Alpha (the self-billed "computational knowledge engine") can analyze the historical statistics for both teams and throw out some potentially useful numbers. Developed by Stephen Wolfram and based his Wolfram Research's Mathematica analytical platform, Wolfram Alpha is an altogether different search engine from Bing or Google, which generally return pages of blue hyperlinks in response to queries. Instead of multiple results leading to still other Webpages, Wolfram Alpha usually returns set of definitive, numerical answers. (A lengthy rundown of the engine's capabilities is found on its 'About' page.) So how does Wolfram's engine, which features sophisticated algorithms chewing through trillions of pieces of data, break down the potentials for Sunday's game? Out of the 38 times the two teams have met on the field, the Broncos have triumphed 25 times (versus 12 wins for the Seahawks), scoring 98 total touchdowns to the Seahawks' 84. It's definitely advantage Broncos, in that sense. But the teams' percentages are fairly close with regard to total yardage, penalties, penalty yards, and other metrics, although the Seahawks have managed to nab more interceptions (47, versus the Broncos' 37). But while Wolfram Alpha can crunch all the historical data it wants, and that data can suggest one team will likely triumph over another, there's always the likelihood that something random—a freak injury, or a tweak to the player lineup—can change the course of the game in ways that nobody can anticipate. Also, given how player and coaching rosters vary from year to year, the teams taking the field can change radically between meetings." EA has correctly predicted eight of the last ten Super Bowl winners using the latest Madden game.
theodp writes "Weighing in for the WSJ on Spike Jonze's Oscar-nominated, futuristic love story Her (parodies), Stephen Wolfram — whose Wolfram Alpha drives the AI-like component of Siri — thinks that an operating system like Samantha as depicted in the film isn't that far off. In Her, OS Samantha and BeautifulHandwrittenLetters.com employee Theodore Twombly have a relationship that appears to exhibit all the elements of a typical romance, despite the OS's lack of a physical body. They talk late into the night, relax on the beach, and even double date with friends. Both Wolfram and Google director of research Peter Norvig (who hadn't yet seen the film) believe this type of emotional attachment isn't a big hurdle to clear. 'People are only too keen, I think, to anthropomorphize things around them,' explained Wolfram. 'Whether they're stuffed animals, Tamagotchi, things in videogames, whatever else.' By the way, why no supporting actor nomination for Jonze's portrayal of foul-mouthed animated video game character Alien Child?"
An anonymous reader writes "Working with the Raspberry Pi Foundation, effective immediately, there's a pilot release of the Wolfram Language — as well as Mathematica—that will soon be bundled as part of the standard system software for every Raspberry Pi computer. Quite soon the Wolfram Language is going to start showing up in lots of places, notably on the web and in the cloud."
Nerval's Lobster writes "Stephen Wolfram, the chief designer of the Mathematica software platform and the Wolfram Alpha 'computation knowledge engine,' has another massive project in the works—although he's remaining somewhat vague about details for the time being. In simplest terms, the project is a new programming language—which he's dubbing the 'Wolfram Language'—which will allow developers and software engineers to program a wide variety of complex functions in a streamlined fashion, for pretty much every single type of hardware from PCs and smartphones all the way up to datacenters and embedded systems. The Language will leverage automation to cut out much of the nitpicking complexity that dominates current programming. 'The Wolfram Language does things automatically whenever you want it to,' he wrote in a recent blog posting. 'Whether it's selecting an optimal algorithm for something. Or picking the most aesthetic layout. Or parallelizing a computation efficiently. Or figuring out the semantic meaning of a piece of data. Or, for that matter, predicting what you might want to do next. Or understanding input you've given in natural language.' In other words, he's proposing a general-purpose programming language with a mind-boggling amount of functions built right in. At this year's SXSW, Wolfram alluded to his decades of work coming together in 'a very nice way,' and this is clearly what he meant. And while it's tempting to dismiss anyone who makes sweeping statements about radically changing the existing paradigm, he does have a record of launching very big projects (Wolfram Alpha contains more than 10 trillion pieces of data cultivated from primary sources, along with tens of thousands of algorithms and equations) that function reliably. At many points over the past few years, he's also expressed a belief that simple equations and programming can converge to create and support enormously complicated systems. Combine all those factors together, and it's clear that Wolfram's pronouncements—no matter how grandiose—can't simply be dismissed. But it remains to be seen how much of an impact he actually has on programming as an art and science."
Gary Marcus writes in the New Yorker about the state of artificial intelligence, and how we take it for granted that AI involves a very particular, very narrow definition of intelligence. A computer's ability to answer questions is still largely dependent on whether the computer has seen that question before. Quoting: "Siri and Google’s voice searches may be able to understand canned sentences like 'What movies are showing near me at seven o’clock?,' but what about questions—'Can an alligator run the hundred-metre hurdles?'—that nobody has heard before? Any ordinary adult can figure that one out. (No. Alligators can’t hurdle.) But if you type the question into Google, you get information about Florida Gators track and field. Other search engines, like Wolfram Alpha, can’t answer the question, either. Watson, the computer system that won “Jeopardy!,” likely wouldn’t do much better. In a terrific paper just presented at the premier international conference on artificial intelligence (PDF), Levesque, a University of Toronto computer scientist who studies these questions, has taken just about everyone in the field of A.I. to task. ...Levesque argues that the Turing test is almost meaningless, because it is far too easy to game. ... To try and get the field back on track, Levesque is encouraging artificial-intelligence researchers to consider a different test that is much harder to game ..."
Nerval's Lobster writes "Back in January, when Wolfram Alpha launched an updated version of its Personal Analytics for Facebook module, the self-billed 'computational knowledge engine' asked users to contribute their detailed Facebook data for research purposes. The researchers at Wolfram Alpha, having crunched all that information, are now offering some data on how users interact with Facebook. For starters, the median number of 'friends' is 342, with the average number of friends peaking for those in their late teens before declining at a steady rate. Younger people also have a tendency to largely add Facebook friends around their own age — for example, someone who's 20 might have lots of friends in the twenty-something range, and comparatively few in other decades of life—while middle-aged people tend to have friends across the age spectrum. Beyond that, the Wolfram Alpha blog offers up some interesting information about friend counts (and 'friend of friend' counts), how friends' networks tend to 'cluster' around life events such as school and sports teams, and even how peoples' postings tend to evolve as they get older — as people age, for example, they tend to talk less about video games and more about politics. 'It feels like we're starting to be able to train a serious "computational telescope" on the "social universe,"' the blog concluded. 'And it's letting us discover all sorts of phenomena.'"
Nerval's Lobster writes "At this year's SXSW conference, Stephen Wolfram—most famous in tech circles as the chief designer of the Mathematica software platform, as well as the Wolfram Alpha 'computation knowledge engine'—demonstrated his upcoming Programming Cloud, and indicated he was developing a mobile platform for engineering and mathematical applications based on the Wolfram programming language built for Mathematica. He also talked more broadly about the future of Wolfram Alpha, which he said will become more anticipatory of peoples' queries. 'People generally don't understand all the things that Wolfram Alpha can do,' Wolfram told the audience. His researchers are also working on a system modeler tool, which will allow researchers to simulate complex devices with tens of thousands of components; in theory, you could even use such a platform for 3D printing. Wolfram also wants to set Wolfram Alpha loose on documents, with the ability to apply complex calculations to, say, company spreadsheets. 'A whole bunch of things that I've been working on for 30 years are converging in a very nice way,' he said."
Nerval's Lobster writes "Whatever your actual feelings about football and this weekend's Super Bowl, you have to admire Wolfram Alpha's willingness to crunch any dataset and see what it can find. The self-billed 'computational knowledge engine' has analyzed the historical data for both teams involved in this Sunday's Super Bowl XLVII. Its conclusion? The San Francisco 49ers and Baltimore Ravens are 'annoyingly similar' when it comes to numbers, although some players stand out as potential game changers — if the math plays out right."
Nerval's Lobster writes "Wolfram Alpha has upgraded its Personal Analytics for Facebook module, giving users the ability to dissect their own social-networking data in new ways. Wolfram Alpha's creators first launched its Facebook data-mining module in August 2012. Users could leverage the platform's computational abilities to analyze and visualize their weekly distribution of Facebook posts, types of posts (photos, links, status updates), weekly app activity, frequency of particular words in posts, and more. This latest update isn't radical, but it does offer some interesting new features, including added color coding for 'interesting' friend properties, including relationship status, age, sex, and so on; users can also slice their network data by metrics such as location and age." Wolfram users could also use some of that new site-specific searching power to come up with some unsavory results.
TsukiKage writes "A 50-card M:tG combo for four players is demonstrated that is used to construct a simple Turing machine, performing arbitrary computations just by following the rules of Magic and card text thereafter."
Nerval's Lobster writes "Ever wanted to mine your own Facebook data? Wolfram Alpha is offering you the chance. Wolfram Alpha bills itself as a 'computational knowledge engine.' In contrast to other search engines such as Google and Bing, which return pages of blue hyperlinks in response to queries, Wolfram Alpha offers up objective data: type in the name of a person, for example, and you might receive their dates of birth and death, a timeline, and a graph of Wikipedia page hits. Now Wolfram Alpha's offering a new feature that can spit back years of your personal Facebook data sliced, diced, visualized and analyzed."
New submitter submeta writes "Katsunobu Imai at Hiroshima University has figured out a way to construct a universal Turing machine using cellular automata in a Penrose tile universe. 'Tiles in the first state act as wires that transmit signals between the logic gates, with the signal itself consisting of either a 'front' or 'back' state. Four other states manage the redirecting of the signal within the logic gates, while the final state is simply an unused background to keep the various states separate.' He was not aware of the recent development of the Penrose glider, so he developed this alternative approach."
judgecorp writes "Apple has changed the answer Siri gives to the question 'What is the best smartphone ever?' to prevent the voice-driven assistant from promoting the Nokia Lumia 900. Originally Siri trawled online reviews on the web, using the Wolfram Alpha search engine, to come up with the Lumia, much to Apple's embarrassment. Now, Apple has intervened, replacing that answer with a joke: 'Wait there are other phones?'"
An anonymous reader tips news of a new feature announced by Google today: Account Activity. Writing on their official blog, Google's Andreas Tuerk said, "If you sign up, each month we’ll send you a link to a password-protected report with insights into your signed-in use of Google services. For example, my most recent Account Activity report told me that I sent 5 percent more email than the previous month and received 3 percent more. An Italian hotel was my top Gmail contact for the month. I conducted 12 percent more Google searches than in the previous month, and my top queries reflected the vacation I was planning: [rome] and [hotel]." You may remember from earlier this month that Stephen Wolfram began showing some of the extensive personal analytics data he has collected over the past 20 years.
New submitter Manzanita writes "The domain of personal analytics, or 'Quantified Self,' is rich with interesting things to measure and many hackers have started projects. But they will only take off if it is sufficiently easy to gather and use the data. Stephen Wolfram has collected and analyzed a lot of his personal data over the last 20 years, but that is far beyond what most of us have the time for. What do you find worth tracking? What is ripe for developing into a business?"
porsche911 points out a recent post by Stephen Wolfram in which he plots out data on his communication habits collected over a period of years — or in some cases, decades. He presents visualizations of the times and frequency of a third of a million emails since 1989, 100 million keystrokes since 2002, phone calls, meetings, modification times on his personal files, and even the number of footsteps he takes in a day. It provides some interesting correlations and insights into the structure of a person's life, and how that structure shifts over the years. He says, "What is the future for personal analytics? There is so much that can be done. Some of it will focus on large-scale trends, some of it on identifying specific events or anomalies, and some of it on extracting 'stories' from personal data. And in time I'm looking forward to being able to ask Wolfram|Alpha all sorts of things about my life and times—and have it immediately generate reports about them. Not only being able to act as an adjunct to my personal memory, but also to be able to do automatic computational history—explaining how and why things happened—and then making projections and predictions. As personal analytics develops, it’s going to give us a whole new dimension to experiencing our lives."
kodiaktau writes "This week The Lifeboat Foundation announced that Stephen Wolfram would be joining its organization. The purpose of the group is to think through scientific solutions to existential problems that might be used to save humanity from such risks as asteroids hitting the earth or some other diabolical disaster. Wolfram brings computational science to the table and has posited that the earth and universe can be understood as a computer program that can be significantly altered as we continue to advance in technology."