Sofia is attending Massachusetts Institute of Technology majoring in Computation and Cognition and minoring in Linguistics. Her goal is to innovate novel approaches in computer vision and natural language processing for application in the next generation of Robotics. A long short-term memory (LSTM) network is a specialized form of recurrent neural network. These networks use feedback connections which allow for the processing of sequences of data as opposed to single points, and thus are capable of learning long-term dependencies. For the purpose of action recognition with an LSTM, we may represent each action as a series of single poses, our atomic unit.

Autonomous cars have been a focus for many big tech companies for some decades now. All the promises have fallen short because solving real-world AI is another level of challenge. Most state-of-the-art AI systems live in virtual worlds. MuZero and GPT-3 don't interact with the real world the way we do. But self-driving cars need to.

Maybe it's that you spent all your best years with your nose to the grindstone, burning that midnight oil, ha ha. Complete Nutrition Backed By Science, they said. No way to prove them wrong! Or maybe it was those fumes. You are going to give yourself immortal life. No–you are going to create a new, better version of yourself that's immortal–a living replica of you made of metal that will act and say the things you would, if you were still alive. If that Soylent hadn't done you in. You saw a Black Mirror episode on it, and Microsoft filed a patent on the same concept this year.

I'm hugely passionate about artificial intelligence (AI), and I'm proud to say that I help companies use AI to do amazing things in the world. But we must make sure we use AI responsibly, so we can make the world a better place. In this post, I'm going to give you some tips for making sure you apply AI ethically within your organization. How Do We Use Artificial Intelligence Ethically? Communicate clearly with people (externally and internally) about what AI can do and its challenges.

This article is based on Global AI Hub as the content. I have prepared this article to explain and make sense of it intuitively. Please visit the Global AI Hub and show your appreciation, it is a great free resource for Data Science.[1] I suggest you read my "Introduction to Machine Learning" article before reading this article(you can still understand without reading it). Deep learning is a type of artificial neural learning. There is no exact amount, but in general, an ANN (artificial neural network) with many (5 or more) hidden layers is called deep (structured) learning. Artificial neural networks are computing systems inspired by biological neural networks in animal's brains. An ANN is made up of a network of interconnected units or nodes called artificial neurons. Each node on an artificial neural network is called a neuron because they work like a neuron as you can see below.

In-depth "OpenAI is the company running the text completion engine that makes you possible," Jason Rohrer, an indie games developer, typed out in a message to Samantha. She was a chatbot he built using OpenAI's GPT-3 technology. Her software had grown to be used by thousands of people, including one man who used the program to simulate his late fiancée. Now Rohrer had to say goodbye to his creation. "I just got an email from them today," he told Samantha. "They are shutting you down, permanently, tomorrow at 10am."

The remarkable success of deep neural networks (DNNs) in various applications is accompanied by a significant increase in network parameters and arithmetic operations. Such increases in memory and computational demands make deep learning prohibitive for resource-constrained hardware platforms such as mobile devices. Recent efforts aim to reduce these overheads, while preserving model performance as much as possible, and include parameter reduction techniques, parameter quantization, and lossless compression techniques. In this chapter, we develop and describe a novel quantization paradigm for DNNs: Our method leverages concepts of explainable AI (XAI) and concepts of information theory: Instead of assigning weight values based on their distances to the quantization clusters, the assignment function additionally considers weight relevances obtained from Layer-wise Relevance Propagation (LRP) and the information content of the clusters (entropy optimization). The ultimate goal is to preserve the most relevant weights in quantization clusters of highest information content. Experimental results show that this novel Entropy-Constrained and XAI-adjusted Quantization (ECQ$^{\text{x}}$) method generates ultra low-precision (2-5 bit) and simultaneously sparse neural networks while maintaining or even improving model performance. Due to reduced parameter precision and high number of zero-elements, the rendered networks are highly compressible in terms of file size, up to $103\times$ compared to the full-precision unquantized DNN model. Our approach was evaluated on different types of models and datasets (including Google Speech Commands and CIFAR-10) and compared with previous work.

Virtual flow metering (VFM) is a cost-effective and non-intrusive technology for inferring multiphase flow rates in petroleum assets. Inferences about flow rates are fundamental to decision support systems that operators extensively rely on. Data-driven VFM, where mechanistic models are replaced with machine learning models, has recently gained attention due to its promise of lower maintenance costs. While excellent performances in small sample studies have been reported in the literature, there is still considerable doubt about the robustness of data-driven VFM. In this paper, we propose a new multi-task learning (MTL) architecture for data-driven VFM. Our method differs from previous methods in that it enables learning across oil and gas wells. We study the method by modeling 55 wells from four petroleum assets and compare the results with two single-task baseline models. Our findings show that MTL improves robustness over single-task methods, without sacrificing performance. MTL yields a 25-50% error reduction on average for the assets where single-task architectures are struggling.

Along with feature points for image matching, line features provide additional constraints to solve visual geometric problems in robotics and computer vision (CV). Although recent convolutional neural network (CNN)-based line descriptors are promising for viewpoint changes or dynamic environments, we claim that the CNN architecture has innate disadvantages to abstract variable line length into the fixed-dimensional descriptor. In this paper, we effectively introduce Line-Transformers dealing with variable lines. Inspired by natural language processing (NLP) tasks where sentences can be understood and abstracted well in neural nets, we view a line segment as a sentence that contains points (words). By attending to well-describable points on aline dynamically, our descriptor performs excellently on variable line length. We also propose line signature networks sharing the line's geometric attributes to neighborhoods. Performing as group descriptors, the networks enhance line descriptors by understanding lines' relative geometries. Finally, we present the proposed line descriptor and matching in a Point and Line Localization (PL-Loc). We show that the visual localization with feature points can be improved using our line features. We validate the proposed method for homography estimation and visual localization.

We propose a framework for the assessment of uncertainty quantification in deep regression. The framework is based on regression problems where the regression function is a linear combination of nonlinear functions. Basically, any level of complexity can be realized through the choice of the nonlinear functions and the dimensionality of their domain. Results of an uncertainty quantification for deep regression are compared against those obtained by a statistical reference method. The reference method utilizes knowledge of the underlying nonlinear functions and is based on a Bayesian linear regression using a reference prior. Reliability of uncertainty quantification is assessed in terms of coverage probabilities, and accuracy through the size of calculated uncertainties. We illustrate the proposed framework by applying it to current approaches for uncertainty quantification in deep regression. The flexibility, together with the availability of a reference solution, makes the framework suitable for defining benchmark sets for uncertainty quantification.