Teaching AI models what they don’t know - MIT News
Jun 3, 2025 · A team of MIT researchers founded Themis AI to quantify artificial intelligence model uncertainty and address knowledge gaps.
Security Constrained Optimal Power Flow with Distributionally Robust Chance Constraints
The growing amount of fluctuating renewable infeeds and market liberalization increases uncertainty in power system operation. To capture the influence of fluctuations in operational planning, we model the forecast errors of the uncertain in-feeds as...
Pachinko Prediction: A Bayesian method for event prediction from social media data
The combination of large open data sources with machine learning approaches presents a potentially powerful way to predict events such as protest or social unrest. However, accounting for uncertainty in such models, particularly when using diverse, u...
Diffusion-Inversion-Net (DIN): An End-to-End Direct Probabilistic Framework for Characteri…
We propose the Diffusion-Inversion-Net (DIN) framework for inverse modeling of groundwater flow and solute transport processes. DIN utilizes an offline-trained Denoising Diffusion Probabilistic Model (DDPM) as a powerful prior leaner, which flexibly...
A Cost Analysis of Generative Language Models and Influence Operations
Despite speculation that recent large language models (LLMs) are likely to be used maliciously to improve the quality or scale of influence operations, uncertainty persists regarding the economic value that LLMs offer propagandists. This research con...
Axiomatic Foundations for a Class of Generalized Expected Utility: Algebraic Expected Util…
Expected Utility: Algebraic Expected Utility In this paper, we provide two axiomatizations of algebraic expected utility, which is a particular generalized expected utility, in a von Neumann-Morgenstern setting, i.e. uncertainty representation is sup...
Uncertainty principles with error term in Gelfand-Shilov spaces
In this note, an alternative approach to establish observability for semigroups based on their smoothing properties is presented. The results discussed here are closely related to those recently obtained in [arXiv:2112.01788], but the current proof a...
Why does English spelling use silent letters?
Jan 16, 2011 · Why have a letter in a word when itâs silent in pronunciation, like the b in debt? Can anyone please clarify my uncertainty here?
Political elections and uncertainty -Are BRICS markets equally exposed to Trump's agenda?
There certainly is little or no doubt that politicians, sometimes consciously and sometimes not, exert a significant impact on stock markets. The evolving volatility over the Republican Donald Trump's surprise victory in the US presidential election...
A Distributionally Robust Optimization Approach for Unit Commitment in Microgrids
This paper proposes a distributionally robust unit commitment approach for microgrids under net load and electricity market price uncertainty. The key thrust of the proposed approach is to leverage the Kullback-Leibler divergence to construct an ambi...
Deep Hype in Artificial General Intelligence: Uncertainty, Sociotechnical Fictions and the…
Artificial General Intelligence (AGI) is promoted by technology leaders and investors as a system capable of performing all human intellectual tasks, and potentially surpassing them. Despite its vague definition and uncertain feasibility, AGI has att...
Conservative & Aggressive NaNs Accelerate U-Nets for Neuroimaging
Deep learning models for neuroimaging increasingly rely on large architectures, making efficiency a persistent concern despite advances in hardware. Through an analysis of numerical uncertainty of convolutional neural networks (CNNs), we observe that...
FoSS: Modeling Long Range Dependencies and Multimodal Uncertainty in Trajectory Prediction…
Accurate trajectory prediction is vital for safe autonomous driving, yet existing approaches struggle to balance modeling power and computational efficiency. Attention-based architectures incur quadratic complexity with increasing agents, while recur...
Uncertainty-Aware Temporal Self-Learning (UATS): Semi-Supervised Learning for Segmentation…
Various convolutional neural network (CNN) based concepts have been introduced for the prostate's automatic segmentation and its coarse subdivision into transition zone (TZ) and peripheral zone (PZ). However, when targeting a fine-grained segmentatio...
Regulation or Competition:Major-Minor Optimal Liquidation across Dark and Lit Pools
We study the optimal liquidation problem in both lit and dark pools for investors facing execution uncertainty in a continuous-time setting with market impact. First, we design an optimal make--take fee policy for a large investor liquidating her pos...
LIT: Light-field Inference of Transparency for Refractive Object Localization
Translucency is prevalent in everyday scenes. As such, perception of transparent objects is essential for robots to perform manipulation. Compared with texture-rich or texture-less Lambertian objects, transparency induces significant uncertainty on o...
Weighted-Scenario Optimisation for the Chance Constrained Travelling Thief Problem
The chance constrained travelling thief problem (chance constrained TTP) has been introduced as a stochastic variation of the classical travelling thief problem (TTP) in an attempt to embody the effect of uncertainty in the problem definition. In thi...
Forecasting elections using compartmental models of infection
Forecasting elections -- a challenging, high-stakes problem -- is the subject of much uncertainty, subjectivity, and media scrutiny. To shed light on this process, we develop a method for forecasting elections from the perspective of dynamical system...
Identifying Candidate Atmospheres on Rocky M dwarf Planets via Eclipse Photometry
Most rocky planets in the galaxy orbit a cool host star, and there is large uncertainty among theoretical models whether these planets can retain an atmosphere. The James Webb Space Telescope (JWST) might be able to settle this question empirically,...
Cold feet - Wikipedia
Cold feet is a phrase that refers to a person not going through with an action, particularly one which requires long-term commitment, due to fear, uncertainty