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tsuomela : machine-learning   46

[1703.06207v1] Cooperating with Machines
"Since Alan Turing envisioned Artificial Intelligence (AI) [1], a major driving force behind technical progress has been competition with human cognition. Historical milestones have been frequently associated with computers matching or outperforming humans in difficult cognitive tasks (e.g. face recognition [2], personality classification [3], driving cars [4], or playing video games [5]), or defeating humans in strategic zero-sum encounters (e.g. Chess [6], Checkers [7], Jeopardy! [8], Poker [9], or Go [10]). In contrast, less attention has been given to developing autonomous machines that establish mutually cooperative relationships with people who may not share the machine's preferences. A main challenge has been that human cooperation does not require sheer computational power, but rather relies on intuition [11], cultural norms [12], emotions and signals [13, 14, 15, 16], and pre-evolved dispositions toward cooperation [17], common-sense mechanisms that are difficult to encode in machines for arbitrary contexts. Here, we combine a state-of-the-art machine-learning algorithm with novel mechanisms for generating and acting on signals to produce a new learning algorithm that cooperates with people and other machines at levels that rival human cooperation in a variety of two-player repeated stochastic games. This is the first general-purpose algorithm that is capable, given a description of a previously unseen game environment, of learning to cooperate with people within short timescales in scenarios previously unanticipated by algorithm designers. This is achieved without complex opponent modeling or higher-order theories of mind, thus showing that flexible, fast, and general human-machine cooperation is computationally achievable using a non-trivial, but ultimately simple, set of algorithmic mechanisms. "
paper  cooperation  machine  artificial-intelligence  machine-learning 
april 2017 by tsuomela
Machine Learning Project at the University of Waikato in New Zealand
"An exciting and potentially far-reaching development in computer science is the invention and application of methods of machine learning (ML). These enable a computer program to automatically analyse a large body of data and decide what information is most relevant. This crystallised information can then be used to automatically make predictions or to help people make decisions faster and more accurately. "
machine-learning  software  project  tool 
october 2016 by tsuomela
Facebook says its new AI can understand text with 'near-human accuracy'
"Facebook is using its latest AI project to get a lot smarter at understanding text. In fact, the social network says DeepText, its new "text understanding engine," is so good, it can interpret "several thousands posts a second" with "near-human accuracy." Introduced Wednesday, DeepText offers an intriguing look into how Facebook is using artificial intelligence to make its platform better at parsing the billions of lines of text that pass through it each day. "
text-analysis  deep-learning  machine-learning  facebook  digital-humanities 
june 2016 by tsuomela
BioCaster Global Health Monitor
Based on a combination of text mining algoithms, BioCaster aims to provide an early warning monitoring station for epidemic and environmental diseases (human, animal and plant). It does this by aggregating online news reports, processing them automatically using human language technology and trying to spot unusual trends. For example, the trend spotting algorithm we use on the top page is CDC's Early Aberration Reporting System (EARS) C2 algorithm. Being able to spot unusual health events still requires skilled human analysts for risk assessment and verification. Automated methods like BioCaster try to make human tasks easier by providing intelligently filtered news.

BioCaster started in 2006 and provides a demonstration portal for public health workers, clinicians and researchers. The portal is currently under development at the National Institute of Informatics, Japan
diseases  machine-learning  data-mining  pandemic  health  monitor  global  natural-language-processing 
april 2010 by tsuomela

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