Estelle PlatiniMay 30, 2024

'#AI is a marketing term, not a technical term of art. The term “artificial intelligence” was coined in 1956 by cognitive and computer scientist John McCarthy – about a decade after the first proto-neural network architectures were created. In subsequent interviews McCarthy is very clear about why he invented the term. First, he didn’t want to include the mathematician and philosopher Norbert Wiener in a workshop he was hosting that summer. You see, Wiener had already coined the term “cybernetics,” under whose umbrella the field was then organized. McCarthy wanted to create his own field, not to contribute to Norbert’s – which is how you become the “father” instead of a dutiful disciple. […] Secondly, McCarthy wanted grant money. And he thought the phrase “artificial intelligence” was catchy enough to attract such funding from the US government'.

Meredith Whittaker acceptance speech: https://www.helmut-schmidt.de/en/news-1/detail/the-prizewinners-speech

#metrics #probabilities #ModelCalibration #MachineLearning #ML #ethics

The Prizewinner's Speech

In her speech, Meredith Whittaker warns against the power of the tech industry and explains why it is worth thinking positively right now.

Show threadEstelle PlatiniApr 6, 2024

The sources said that the approval to automatically adopt #Lavender’s kill lists, which had previously been used only as an auxiliary tool, was granted about two weeks into the war, after intelligence personnel “manually” checked the accuracy of a random sample of several hundred targets selected by the #AI system. When that sample found that Lavender’s results had reached 90 percent accuracy in identifying an individual’s affiliation with Hamas, the army authorized the sweeping use of the system. From that moment, if Lavender decided an individual was a militant in Hamas, the sources were essentially asked to treat that as an order.

“Still, I found them more ethical than the targets that we bombed just for ‘deterrence’ — highrises that are evacuated and toppled just to cause destruction.”

https://www.972mag.com/lavender-ai-israeli-army-gaza/ @israel @data

#metrics #probabilities #usability #ModelCalibration #MachineLearning #ML #OutputAudit #FoundationalModels

‘Lavender’: The AI machine directing Israel’s bombing spree in Gaza

The Israeli army has marked tens of thousands of Gazans as suspects for assassination, using an AI targeting system with little human oversight and a permissive policy for casualties, +972 and Local Call reveal.

+972 Magazine
Dominic BoutetJan 6, 2023

#arxivfeed :

"Dynamic Bayesian Learning and Calibration of Spatiotemporal Mechanistic Systems"
https://arxiv.org/abs/2208.06528

#DynamicalSystems #ModelCalibration #Bayesian #ParameterEstimation #GaussianProcess

Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models

We develop an approach for Bayesian learning of spatiotemporal dynamical mechanistic models. Such learning consists of statistical emulation of the mechanistic system that can efficiently interpolate the output of the system from arbitrary inputs. The emulated learner can then be used to train the system from noisy data achieved by melding information from observed data with the emulated mechanistic system. This joint melding of mechanistic systems employ hierarchical state-space models with Gaussian process regression. Assuming the dynamical system is controlled by a finite collection of inputs, Gaussian process regression learns the effect of these parameters through a number of training runs, driving the stochastic innovations of the spatiotemporal state-space component. This enables efficient modeling of the dynamics over space and time. This article details exact inference with analytically accessible posterior distributions in hierarchical matrix-variate Normal and Wishart models in designing the emulator. This step obviates expensive iterative algorithms such as Markov chain Monte Carlo or variational approximations. We also show how emulation is applicable to large-scale emulation by designing a dynamic Bayesian transfer learning framework. Inference on $\bm η$ proceeds using Markov chain Monte Carlo as a post-emulation step using the emulator as a regression component. We demonstrate this framework through solving inverse problems arising in the analysis of ordinary and partial nonlinear differential equations and, in addition, to a black-box computer model generating spatiotemporal dynamics across a graphical model.

arXiv.org