- Offline First is the New Mobile First — Luke Wroblewski’s notes from John Allsopp’s talk about “Breaking Development” in Nashville. Offline technologies don’t just give us sites that work offline, they improve performance, and security by minimizing the need for cookies, http, and file uploads. It also opens up new possibilities for better user experiences.
- Winograd Schemas as Alternative to Turing Test (IEEE) — specially constructed sentences that are surface ambiguous and require deeper knowledge of the world to disambiguate, e.g. “Jim comforted Kevin because he was so upset. Who was upset?”. Our WS [Winograd schemas] challenge does not allow a subject to hide behind a smokescreen of verbal tricks, playfulness, or canned responses. Assuming a subject is willing to take a WS test at all, much will be learned quite unambiguously about the subject in a few minutes. (that last from the paper on the subject)
- Reclaiming Your Nest (Forbes) — Like so many connected devices, Nest devices regularly report back to the Nest mothership with usage data. Over a month-long period, the researchers’ device sent 32 MB worth of information to Nest, including temperature data, at-rest settings, and self-entered information about the home, such as how big it is and the year it was built. “The Nest doesn’t give us an option to turn that off or on. They say they’re not going to use that data or share it with Google, but why don’t they give the option to turn it off?” says Jin. Jailbreak your Nest (technique to be discussed at Black Hat), and install less chatty software. Loose Lips Sink Thermostats.
- SyncNet — decentralised browser: don’t just pull pages from the source, but also fetch from distributed cache (implemented with BitTorrent Sync).
ENTRIES TAGGED "ai"
Some of AI's viable approaches lie outside the organizational boundaries of Google and other large Internet companies.
Editor’s note: this post is part of an ongoing series exploring developments in artificial intelligence.
Here’s a simple recipe for solving crazy-hard problems with machine intelligence. First, collect huge amounts of training data — probably more than anyone thought sensible or even possible a decade ago. Second, massage and preprocess that data so the key relationships it contains are easily accessible (the jargon here is “feature engineering”). Finally, feed the result into ludicrously high-performance, parallelized implementations of pretty standard machine-learning methods like logistic regression, deep neural networks, and k-means clustering (don’t worry if those names don’t mean anything to you — the point is that they’re widely available in high-quality open source packages).
Google pioneered this formula, applying it to ad placement, machine translation, spam filtering, YouTube recommendations, and even the self-driving car — creating billions of dollars of value in the process. The surprising thing is that Google isn’t made of magic. Instead, mirroring Bruce Scheneier’s surprised conclusion about the NSA in the wake of the Snowden revelations, “its tools are no different from what we have in our world; it’s just better funded.” Read more…