If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
An important feature of human intelligence is the ability to learn. The amazing learning abilities of the human brain enable babbling babies to grow into learned and easy-to-talk adults. For human beings, learning is an innate ability. The universal existence of this ability makes us ignore its strangeness and preciousness. As far as artificial intelligence research is concerned, how to make machines possess the most universal capabilities in the human world is a very challenging research direction. In different research paths, the subjects, contents and methods of learning are quite different.
Frangula californica (California coffeeberry): Matriculating undergraduates, now, for 1/1/21, Realistic Virtual Earth for Machine Learning - WUaS News, Livestream, Q&A - i) Seeking to matriculate our 2nd undergraduate class Jan 1, 2021, and potentially with students taking WUaS Open edX courses, ii) How WUaS or edX could provide a letter to prospective employers that a student is matriculated officially at WUaS / edX and similar?, iii) Creating a single #RealisticVirtualEarth beginning w #GoogleResearchFootball for learning machine learning / AI, and with Lego Robotics too, iv) WUaS Monthly Business Meeting Minutes for 8/15 * * How to BEGIN 1 #RealisticVirtualEarth in #GoogleStreetView w #TimeSlider for learning #MachineLearning & w #LegoRobotics? Add Lego Robotics with similar Film-To-3D App, such as - 6.270 MIT Lego Robot Competition 1999 - https://youtu.be/SXH-bBw3uxg And with Lego weDo 2.0 too - Special WeDo 2.0 Scratch Project BOXER from Roboriseit! https://youtu.be/HjD1zAWToYU It is unlikely that I will get to contribute to this work. So please take me off all your mailing lists.
Hello and welcome to this course on Machine learning .My name is Aakash Singh i am instructor of this course .This course is structured in way so that anyone can easily grasp the concept of programming,fundamentals,concepts of the Machine learning .No prior knowledge is required through this course .we
Real-time motion prediction of a vessel or a floating platform can help to improve the performance of motion compensation systems. It can also provide useful early-warning information for offshore operations that are critical with regard to motion. In this study, a long short-term memory (LSTM) -based machine learning model was developed to predict heave and surge motions of a semi-submersible. The training and test data came from a model test carried out in the deep-water ocean basin, at Shanghai Jiao Tong University, China. The motion and measured waves were fed into LSTM cells and then went through serval fully connected (FC) layers to obtain the prediction. With the help of measured waves, the prediction extended 46.5 s into future with an average accuracy close to 90%. Using a noise-extended dataset, the trained model effectively worked with a noise level up to 0.8. As a further step, the model could predict motions only based on the motion itself. Based on sensitive studies on the architectures of the model, guidelines for the construction of the machine learning model are proposed. The proposed LSTM model shows a strong ability to predict vessel wave-excited motions.
Some believe that the Extreme Learning Machine is one of the smartest neural network inventions ever created. Some believe that the Extreme Learning Machine is one of the smartest neural network inventions ever created -- so much so that there's even a conference dedicated exclusively to the study of ELM neural network architectures. Proponents of ELMs argue that it can perform standard tasks at exponentially faster training times, with few training examples. On the other hand, besides from the fact that it's not big in the machine learning community, it's got plenty of criticism from experts in deep learning, including Yann LeCun, who argue that it's gotten far more publicity and credibility than it deserves. Mostly, people seem to think it's an interesting concept.
Recent advances in sensing technologies require the design and development of pattern recognition models capable of processing spatiotemporal data efficiently. In this work, we propose a spatially and temporally aware tensor-based neural network for human pose recognition using three-dimensional skeleton data. Our model employs three novel components. First, an input layer capable of constructing highly discriminative spatiotemporal features. Second, a tensor fusion operation that produces compact yet rich representations of the data, and third, a tensor-based neural network that processes data representations in their original tensor form. Our model is end-to-end trainable and characterized by a small number of trainable parameters making it suitable for problems where the annotated data is limited. Experimental validation of the proposed model indicates that it can achieve state-of-the-art performance. Although in this study, we consider the problem of human pose recognition, our methodology is general enough to be applied to any pattern recognition problem spatiotemporal data from sensor networks.
We design a ReLU-based multilayer neural network to generate a rich high-dimensional feature vector. The feature guarantees a monotonically decreasing training cost as the number of layers increases. We design the weight matrix in each layer to extend the feature vectors to a higher dimensional space while providing a richer representation in the sense of training cost. Linear projection to the target in the higher dimensional space leads to a lower training cost if a convex cost is minimized. An $\ell_2$-norm convex constraint is used in the minimization to improve the generalization error and avoid overfitting. The regularization hyperparameters of the network are derived analytically to guarantee a monotonic decrement of the training cost and therefore, it eliminates the need for cross-validation to find the regularization hyperparameter in each layer.
This question begs one to define the words "machine" and "think". Instead of defining them -- which is seemingly easy, let's replace the question with one that is very similar. Before that, we introduce the imitation game. The game is played by three. The interrogator is isolated from the other two and can ask each one of them questions, with a goal of identifying who the man and who the woman is.
You immigrate to a new country that speaks a different language, and start work with some of the brightest engineers in the world. Now, you're leading teams of people who are 10 or 20 years older than you, working on one of the fastest growing internet companies of the last decade. You have two options: sink or swim. That's the position Simon Eskildsen found himself in early in his career. He left his home in Denmark after high school, and moved to Canada alone to take a pre-college gap year working at Shopify. When he started, Shopify had 150 employees supporting tens of thousands of merchants. Now, it has 5,000 employees and over a million merchants.
Most edge AI focuses on prediction tasks on resource-limited edge devices, while the training is done at server machines, so retraining a model on the edge devices to reflect environmental changes is a complicated task. To follow such a concept drift, a neural-network based on-device learning approach is recently proposed, so that edge devices train incoming data at runtime to update their model. In this case, since a training is done at distributed edge devices, the issue is that only a limited amount of training data can be used for each edge device. To address this issue, one approach is a cooperative learning or federated learning, where edge devices exchange their trained results and update their model by using those collected from the other devices. In this paper, as an on-device learning algorithm, we focus on OS-ELM (Online Sequential Extreme Learning Machine) and combine it with Autoencoder for anomaly detection. We extend it for an on-device federated learning so that edge devices exchange their trained results and update their model by using those collected from the other edge devices. Experimental results using a driving dataset of cars demonstrate that the proposed on-device federated learning can produce more accurate model by combining trained results from multiple edge devices compared to a single model.