Centaurs Not Butlers (Matt Jones) — In competitive chess, teams of human and non-human intelligences are referred to as ‘Centaurs’ How might we create teams of human and non-human intelligences in the service of better designed systems, products, environments?
Casino-Funded In-Game War — this was just the opening round of what could be the largest military mobilization in that game’s history. Digging deeper into the subject, we’ve been able to chart the rise of a new in-game faction, called the Moneybadger Coalition, a group of thousands of players being bankrolled by an online casino. (via BoingBoing)
More Chinese Mobile UI Trends — This year, Microsoft China released an AI chatbot called 小冰 (xiǎobīng) that has been popular. She’s accessible via the web, via a standalone app, via WeChat, via Cortana, and through a dedicated button in Xiaomi’s own seldom-used messaging app. It’s fun to toss annoying questions at her and see how she responds. Some people even confide in her. She’s kind of the love child of Siri, ELIZA, and Cleverbot.
Crossword-Solving Neural Networks — Hill describes recent progress in learning-based AI systems in terms of behaviourism and cognitivism: two movements in psychology that effect how one views learning and education. Behaviourism, as the name implies, looks at behaviour without looking at what the brain and neurons are doing, while cognitivism looks at the mental processes that underlie behaviour. Deep learning systems like the one built by Hill and his colleagues reflect a cognitivist approach, but for a system to have something approaching human intelligence, it would have to have a little of both. “Our system can’t go too far beyond the dictionary data on which it was trained, but the ways in which it can are interesting, and make it a surprisingly robust question and answer system – and quite good at solving crossword puzzles,” said Hill. While it was not built with the purpose of solving crossword puzzles, the researchers found that it actually performed better than commercially-available products that are specifically engineered for the task.
Mathematical Foundations for Social Computing (PDF) — collection of pointers to existing research in social computing and some open challenges for work to be done. Consider situations where a highly structured decision must be made. Some examples are making budgets, assigning water resources, and setting tax rates. […] One promising candidate is “Knapsack Voting.” […] This captures most budgeting processes — the set of chosen budget items must fit under a spending limit, while maximizing societal value. Goel et al. prove that asking users to compare projects in terms of “value for money” or asking them to choose an entire budget results in provably better properties than using the more traditional approaches of approval or rank-choice voting.
Intelligence-Augmented Rat Cyborgs in Maze Solving (PLoS) — We compare the performance of maze solving by computer, by individual rats, and by computer-aided rats (i.e. rat cyborgs). They were asked to find their way from a constant entrance to a constant exit in 14 diverse mazes. Performance of maze solving was measured by steps, coverage rates, and time spent. The experimental results with six rats and their intelligence-augmented rat cyborgs show that rat cyborgs have the best performance in escaping from mazes. These results provide a proof-of-principle demonstration for cyborg intelligence. In addition, our novel cyborg intelligent system (rat cyborg) has great potential in various applications, such as search and rescue in complex terrains.
Paul Ford on Racter — But don’t get too ahead of things. Using Racter is not as different from using Siri as you might expect. It’s just that Siri has petabytes of stuff in her brain, whereas Racter has a floppy’s worth. Computers have changed a ton in the last 30 years, humans barely at all. Don’t mistake their progress for ours. We’ve learned how to talk to computers, and they’ve learned how to pretend to understand us. Useful when driving. People love chatting with their Amazon Echo. But the conversation still doesn’t really mean anything.
Not Quite So Broken TLS (Adrian Colyer) — instead of ad-hoc codery, A precise and testable specification (in this case for TLS) that unambiguously determines the set of behaviours it allows (and hence also what it does not). The specification should also be executable as a test oracle, to determine whether or not a given implementation is compliant. The paper outlines this for TLS, but I see formal methods growing in importance in coming years. We can’t build an airport with cardboard on a swamp. In this metaphor, cardboard represents our ad hoc dev practices and the swamp is our platform of crap code. The airport is … look, never mind, I’ll work on the metaphor. Read the paper.
Chimera (Paper a Day) — the authors summarise six main lessons learned while building Chimera: (1) Things break down at large scale; (2) Both learning and hand-crafted rules are critical; (3) Crowdsourcing is critical, but must be closely monitored; (4) Crowdsourcing must be coupled with in-house analysts and developers; (5) Outsourcing does not work at a very large scale; (6) Hybrid human-machine systems are here to stay.
Microsoft Embedding Research — To break down the walls between its research group and the rest of the company, Microsoft reassigned about half of its more than 1,000 research staff in September 2014 to a new group called MSR NExT. Its focus is on projects with greater impact to the company rather than pure research. Meanwhile, the other half of Microsoft Research is getting pushed to find more significant ways it can contribute to the company’s products. The challenge is how to avoid short-term thinking from your research team. For instance, Facebook assigns some staff to focus on long-term research, and Google’s DeepMind group in London conducts pure AI research without immediate commercial considerations.
Google’s Go-Playing AI — The key to AlphaGo is reducing the enormous search space to something more manageable. To do this, it combines a state-of-the-art tree search with two deep neural networks, each of which contains many layers with millions of neuron-like connections. One neural network, the “policy network,” predicts the next move, and is used to narrow the search to consider only the moves most likely to lead to a win. The other neural network, the “value network,” is then used to reduce the depth of the search tree — estimating the winner in each position in place of searching all the way to the end of the game.