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) …
Random Forest is a supervised machine learning algorithm that is composed of individual decision trees. This type of model is called an ensemble model because an "ensemble" of independent models is used to compute a result. The basis for the Random Forest is formed by many individual decision trees, the so-called Decision Trees. A tree consists of different decision levels and branches, which are used to classify data. The Decision Tree algorithm tries to divide the training data into different classes so that the objects within a class are as similar as possible and the objects of different classes are as different as possible. This tree helps to decide whether to do sports outside or not, depending on the weather variables "weather", "humidity" and "wind force".
Abstract: The spread of COVID-19 has coincided with the rise of Graph Neural Networks (GNNs), leading to several studies proposing their use to better forecast the evolution of the pandemic. Many such models also include Long Short TermMemory (LSTM) networks, a common tool for time series forecasting. In this work, we further investigate the integration of these two methods by implementing GNNs within the gates of an LSTM and exploiting spatial information. In addition, we introduce a skip connection which proves critical to jointly capture the spatial and temporal patterns in the data. We validate our daily COVID-19 new cases forecast model on data of 37 European nations for the last 472 days and show superior performance compared to state-of-the-art graph time series models based on mean absolute scaled error (MASE).
New research in which machine-learning models were trained to verify more than one million companies has demonstrated that artificial intelligence (AI) can precisely quantify the success and failure aspects of a startup. The outcome is a tool that allows investors to identify the next opportunities. A known fact is that about 90% of startups are unsuccessful - about 10% to 20% fail within their first year. This shows the notable risk to Venture Capitalists and other investors in early-stage companies. In an attempt to identify which companies are most likely to succeed, researchers have developed a machine-learning model trained on the historical performance of more than one million companies.
As I continue working on exciting research in Artificial Intelligence, I like making parallels with other areas of science and life in general. I think there is a perfect metaphor from music that explains the AI market. Since helping people fight their health problems is my passion, I'll focus on AI in healthcare. The AI market is currently overwhelmingly at the two opposite extremes -- a high school band and a 7,500-person orchestra. Both extremes are perfectly acceptable and have their audiences. However, there is not much in the middle.
These 101 algorithms are equipped with cheat sheets, tutorials, and explanations. Think of this as the one-stop shop/dictionary/directory for machine learning algorithms. The algorithms have been sorted into 9 groups: Anomaly Detection, Association Rule Learning, Classification, Clustering, Dimensional Reduction, Ensemble, Neural Networks, Regression, Regularization. In this post, you'll find 101 machine learning algorithms with useful Python tutorials, R tutorials, and cheat sheets from Microsoft Azure ML, SAS, and Scikit-Learn to help you know when to use each one (if available). At Data Science Dojo, our mission is to make data science (machine learning in this case) available to everyone.
Ensemble methods are well established as an algorithmic cornerstone in machine learning (ML). Just as in real life, in ML a committee of experts will often perform better than an individual provided appropriate care is taken in constituting the committee. Since the earliest days of ML research, a variety of ensemble strategies have been developed with random forests and gradient boosting emerging as leading-edge methods in classification today. It has been recognised since the early days of ML research that ensembles of classifiers can be more accurate than individual models. In ML, ensembles are effectively committees that aggregate the predictions of individual classifiers. They are effective for very much the same reasons a committee of experts works in human decision making, they can bring different expertise to bear and the averaging effect can reduce errors. This article presents a tutorial on the main ensemble methods in use in ML with links to Python notebooks and datasets illustrating these methods in action. The objective is to help practitioners get started with ML ensembles and to provide an insight into when and why ensembles are effective. There have been a lot of developments since then and the ensemble idea is still to the forefront in ML applications. For example, random forests  and gradient boosting  would be considered among the most powerful methods available to ML practitioners today. The generic ensemble idea is presented in Figure 1. All ensembles are made up of a collection of base classifiers, also known as members or estimators.
Imagine training a deep network twice with two different random seeds on the same data, and then measuring the rate at which they disagree on unlabeled test points. Naively, they can disagree with one another with probability anywhere between zero and twice the error rate. But surprisingly, in practice, we observe that the disagreement and test error of deep neural network are remarkably close to each other. The variable refers to the average generalization error of the two models and the variable refers to the disagreement of the two models. Estimating the generalization error of a model -- how well the model performs on unseen data -- is a fundamental component in any machine learning system. Generalization performance is traditionally estimated in a supervised manner, by dividing the labeled data into a training set and test set.
Despite the extensive application of topic models in natural language processing tasks in recent years, the Chinese texts of short comments characterised by large scale, high noise and small information points have put forward higher requirements for the accuracy and stability of the results, which fails to be satisfied by existing topic models. In this paper, a product typicality attribute mining method based on a topic clustering ensemble was proposed. By introducing multiple topic models into ensemble learning, the problems of semantic representation loss, clustering inefficiency and lack of interpretability in the mining of product typicality attributes of short comment texts should be solved. By an effective combination of the topic clustering algorithm based on the diversity of speech, the topic clustering ensemble algorithm based on the Non-negative matrix factorization, and the interpretation method of product typicality attributes based on the mean-shift algorithm, an unsupervised model of product typicality attribute mining for short comment texts is constructed. As shown by the experimental results, the modelling method assumes favourable performance in topic clustering and feature selection, suggesting its advantages in product typicality attribute identification and interpretability compared with common methods.
The almost paradoxical concept of deterministic chaos describes systems which are so sensitive to initial conditions that long term forecasting becomes impossible. Therefore, despite the fact that there is no randomness in the dynamical equations, even the slightest error in calculation -- for instance numerical precision errors in a computer -- will cause future predictions to be wildly off. Applications of chaotic systems range from weather prediction, turbulent flows in fluids, plasma dynamics, chemical reactions, population dynamics, the motion of celestial bodies, the stock market, and many others. Therefore it is important to be able to use data driven methods such as machine learning (ML) to forecast such systems. The present era of understanding and insight into chaotic dynamics was initiated by Edward Lorenz in 1963.
Knowledge distillation (KD), also known as model distillation (MD), is an impressive neural network training method proposed by the God Father of deep learning, Geoffrey Hinton, to gain neural network's performances. If you have never heard about KD, you can reach my post via this link. Shortly, the core idea of KD is to distill knowledge from a large model (teacher) or an ensemble of neural network models and use that knowledge as soft labels to guide (train) a smaller neural network (student) so that the student can learn more efficiently thereby improving its performance, which can not be achieved by training the student from scratch. Despite the promising potency of KD, it has a limitation in the training phase because it needs a large resource of hardware and a long time for training a large teacher model or a cumbersome ensemble of models to achieve the goal of generating good pseudo labels (soft labels) for guiding the student model. To this end, Ilija Radosavovic, Kaiming He et al. from Facebook AI Research (FAIR) have proposed Data Distillation which applies semi-supervised learning to improve the performance of CNNs in object detection by utilizing a limited amount of labeled data and the internet-scale amount of unlabeled data.