- Neglected Machine Learning Ideas — Perhaps my list is a “send me review articles and book suggestions” cry for help, but perhaps it is useful to others as an overview of neat things.
- First Crowdfunded Book on Booker Shortlist — Booker excludes self-published works, but “The Wake” was through Unbound, a Threadless-style “if we hit this limit, the book is printed and you have bought a copy” site.
- Watson Can Debate Its Opponents (io9) — Speaking in nearly perfect English, Watson/The Debater replied: “Scanned approximately 4 million Wikipedia articles, returning ten most relevant articles. Scanned all 3,000 sentences in top ten articles. Detected sentences which contain candidate claims. Identified borders of candidate claims. Assessed pro and con polarity of candidate claims. Constructed demo speech with top claim predictions. Ready to deliver.”
- ipfs — a global, versioned, peer-to-peer file system. It combines good ideas from Git, BitTorrent, Kademlia, and SFS. You can think of it like a single BitTorrent swarm, exchanging Git objects, making up the web. IPFS provides an interface much simpler than HTTP, but has permanence built in.. (via Sourcegraph)
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…