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Conclusion (~1,700 words).
All backed up by over 200 references (~6,500 words).
We must stop crediting the wrong people for inventions made by others.
Instead let s heed the recent call in the journal
Nature: Let 2020 be the year in which we value those who ensure that
science is self-correcting [SV20].
Like those who know me can testify, finding and citing original sources of scientific and technological innovations is important to me, whether they are mine or other people s [DL1][DL2][HIN][NASC1-9]. The present page is offered as a resource for computer scientists who share this inclination.
By grounding research in its true intellectual foundations and crediting the original inventors,
Benchmarking Building Control Systems Thanks to Calibrated Models
Sponsored by CSEMJan 19 2021
Buildings play an important role in total energy consumption and worldwide greenhouse gas (GHG) emissions. Smart control strategies are key technological enablers for reducing the GHG footprints of buildings.
3,4 but their simple nature, combined with possible tuning errors, can lead to sub-optimal control behavior.
5 Automatized, efficient building control can significantly reduce energy consumption and emissions.
Research has been done into model predictive control (MPC),
6,7,8,9 adaptive or learning-based MPC
10 and reinforcement learning (RL)
11,12,13 but many of these studies have suffered from the non-standardized evaluation of their control performance.
Energym’s Contribution
Supporting Responsible Use Of AI And Equitable Outcomes In Financial Services â Federal Reserve Governor Lael Brainard At The AI Academic Symposium Hosted By The Board Of Governors Of The Federal Reserve System, Washington, D.C. (Virtual Event) Date
12/01/2021
Today s symposium on the use of artificial intelligence (AI) in financial services is part of the Federal Reserve s broader effort to understand AI s application to financial services, assess methods for managing risks arising from this technology, and determine where banking regulators can support responsible use of AI and equitable outcomes by improving supervisory clarity.1
The potential scope of AI applications is wide ranging. For instance, researchers are turning to AI to help analyze climate change, one of the central challenges of our time. With nonlinearities and tipping points, climate change is highly complex, and quantification for risk assessments requires the analysis of vast amounts of data, a task
Incremental Risk Minimization Algorithm Incremental Regression with Polynomials ↑ Incremental (or on-line) learning regression is the process of adapting a model one example at a time without accumulating a batch of data. It has the advantages of allowing continuous adaptation to non-stationary environments, easily handling big data through stream processing, and a fixed low computation and memory demand. The easiest solution is to perform a gradient descent on a squared error metric with each new training example. But this solution does not work well for complex model structures. Especially, the influence of a non-linear transformation of the inputs through a fixed model structure has long been an open problem. During my PhD I worked on an approach which is able to deal with a broad class of non-linear model structures. Its emphasis is on minimizing the effect of local training examples on changes of the global model. Thus, it yields a robust behavior by prevent