"AI systems–like people–must often act despite partial and uncertain information. First, the information received may be unreliable (e.g., a patient may mis-remember when a disease started, or may not have noticed a symptom that is important to a diagnosis). In addition, rules connecting real-world events can never include all the factors that might determine whether their conclusions really apply (e.g., the correctness of basing a diagnosis on a lab test depends whether there were conditions that might have caused a false positive, on the test being done correctly, on the results being associated with the right patient, etc.) Thus in order to draw useful conclusions, AI systems must be able to reason about the probability of events, given their current knowledge."
– from David Leake, Reasoning Under Uncertainty
Computers are embedded in almost all of our devices, and most of them are digital. Information at the low levels is stored as binary. Biology, in contrast, often makes use of analog systems. Take fuzzy logic for example. Fuzzy logic techniques typically involve the concept of intermediate values between true and false. But you don't need a special computer for fuzzy logic -- it's just a program running on the digital computer like any other program.
Interpretable, highly accurate segmentation models have the potential to provide substantial benefit for automated clinical workflows. Estimating the uncertainty in a model's prediction (predictive uncertainty) can help clinicians quantify, visualize, and communicate model performance. Variational inference, Monte Carlo dropout, and ensembles are reliable methods to estimate predictive uncertainty. Interpretable artificial intelligence is key for clinical translation of this technology. Artificial intelligence (AI) has seen a resurgence in popularity since the development of deep learning (DL), a method to learn representations within data with multiple levels of abstraction (1). DL frameworks have been widely successful for a variety of applications, including image object recognition and detection tasks where there is a particular interest in applying this technology to interpret complex medical images (2). As modern DL frameworks are structured through multiple hidden layers of network weights, these networks are coined as black box models.
Ingenious e-Brain Solutions forecasts that artificial intelligence will transform the cars in the near future as many companies such as Hyundai, Lear Corporation, Yamaha, Volkswagen, and others are working around different AI algorithms and have developed their solutions at various stages (ideation or concept, prototype, pre-commercialized, and commercial). In the automotive industry, AI provides solutions to drivers or passengers to relieve stress, discomfort, anxiety, drowsiness, maintaining temperature, humidity, weather, climate, and improving visualizations. The AI technologies used are machine learning, deep learning, neural network, facial recognition, bayesian network, fuzzy logic, and classification algorithm. In this report, the use of artificial intelligence or any other computational algorithm for the wellbeing or comfort of passengers and drivers is highlighted along with some of their technology development partners, solutions from other industries such as healthcare, aerospace, entertainment, and others which can be implemented in the automotive industry are listed, along with some other sections which are listed in the table of content of the report. The key players profiled in the report are Tesla, Toyota, Volkswagen, Nio, Daimler, General Motors, BMW, Stellantis, Honda, and Hyundai.
Learning from human feedback has shown to be a useful approach in acquiring robot reward functions. However, expert feedback is often assumed to be drawn from an underlying unimodal reward function. This assumption does not always hold including in settings where multiple experts provide data or when a single expert provides data for different tasks -- we thus go beyond learning a unimodal reward and focus on learning a multimodal reward function. We formulate the multimodal reward learning as a mixture learning problem and develop a novel ranking-based learning approach, where the experts are only required to rank a given set of trajectories. Furthermore, as access to interaction data is often expensive in robotics, we develop an active querying approach to accelerate the learning process. We conduct experiments and user studies using a multi-task variant of OpenAI's LunarLander and a real Fetch robot, where we collect data from multiple users with different preferences. The results suggest that our approach can efficiently learn multimodal reward functions, and improve data-efficiency over benchmark methods that we adapt to our learning problem.
In this work, we propose Random Walk-steered Majority Undersampling (RWMaU), which undersamples the majority points of a class imbalanced dataset, in order to balance the classes. Rather than marking the majority points which belong to the neighborhood of a few minority points, we are interested to perceive the closeness of the majority points to the minority class. Random walk, a powerful tool for perceiving the proximities of connected points in a graph, is used to identify the majority points which lie close to the minority class of a class-imbalanced dataset. The visit frequencies and the order of visits of the majority points in the walks enable us to perceive an overall closeness of the majority points to the minority class. The ones lying close to the minority class are subsequently undersampled. Empirical evaluation on 21 datasets and 3 classifiers demonstrate substantial improvement in performance of RWMaU over the competing methods.
There are 4 main types of Machine Learning Algorithm, the choice of the algorithm depends on the data type in the use case. It is an equation which describes a line, which represents relationship between input (x) and output (y) variables. By finding specific weightage for input variables called coefficients (b). Predictive modeling is primarily concerned when minimizing system errors or making the most accurate predictions possible at the expense of expansibility. It is a graphical representation of all possible solutions to a decision based on few conditions, it uses predictive models to achieve results, it is drawn upside down with its root at the top and it splits into branches based on a condition or internal node The end of the branch that doesn't not split, is the decision leaf.
This paper introduces Logical Credal Networks, an expressive probabilistic logic that generalizes many prior models that combine logic and probability. Given imprecise information represented by probability bounds and conditional probability bounds of logic formulas, this logic specifies a set of probability distributions over all interpretations. On the one hand, our approach allows propositional and first-order logic formulas with few restrictions, e.g., without requiring acyclicity. On the other hand, it has a Markov condition similar to Bayesian networks and Markov random fields that is critical in real-world applications. Having both these properties makes this logic unique, and we investigate its performance on maximum a posteriori inference tasks, including solving Mastermind games with uncertainty and detecting credit card fraud. The results show that the proposed method outperforms existing approaches, and its advantage lies in aggregating multiple sources of imprecise information.