Artificial intelligence (AI), robotics, automation – as well as less technologically-driven disruptions such as pandemics – mean the way our children and grandchildren are working will look very different to how we work today. We don't even have to look that far ahead to see change on a dramatic scale. It's been predicted that 85% of the jobs that will be available in 2030 don't yet exist! Factors such as the widespread shift to remote working, the emergence of the gig economy, and employees' increasing expectations of flexibility in their relationship with their employers will also play their part. Adding seismic shifts such as the great resignation into the mix means companies are frantically searching for new strategies when it comes to hiring and retaining talent.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner What's the difference between a knowledge based system and an expert system? KBS/ES Knowledge Bases Automated reasoning engines (Inference engines, theorem provers, classifiers), - "expert system" refers to the type of task the system is trying to assist with – to replace or aid a human expert in a complex task requiring expert knowledge; - "knowledge-based system" refers to the architecture of the system – that it represents knowledge explicitly, rather than as procedural code; While the earliest knowledge-based systems were almost all expert systems, the same tools and architectures can and have since been used for a whole host of other types of systems. Virtually all expert systems are knowledge-based systems, but many knowledge-based systems are not expert systems. Expert systems is going as a computer system emulating the decision-making ability of a human expert, solve complex problems by reasoning through bodies of knowledge, represented mainly as if-then rules rather than through conventional procedural code. The knowledge base represents facts and rules about the world, via KRR formalisms, as ontologies, frames, conceptual graphs or logical assertions.

A common way to learn and analyze statistical models is to consider operations in the model parameter space. But what happens if we optimize in the parameter space and there is no one-to-one mapping between the parameter space and the underlying statistical model space? Such cases frequently occur for hierarchical models which include statistical mixtures or stochastic neural networks, and these models are said to be singular. Singular models reveal several important and well-studied problems in machine learning like the decrease in convergence speed of learning trajectories due to attractor behaviors. In this work, we propose a relative reparameterization technique of the parameter space, which yields a general method for extracting regular submodels from singular models. Our method enforces model identifiability during training and we study the learning dynamics for gradient descent and expectation maximization for Gaussian Mixture Models (GMMs) under relative parameterization, showing faster experimental convergence and a improved manifold shape of the dynamics around the singularity. Extending the analysis beyond GMMs, we furthermore analyze the Fisher information matrix under relative reparameterization and its influence on the generalization error, and show how the method can be applied to more complex models like deep neural networks.

Despite recent advances in algorithmic fairness, To address these issues there has recently been a significant methodologies for achieving fairness with generalized body of work in the machine learning community on linear models (GLMs) have yet to be algorithmic fairness in the context of predictive modeling, explored in general, despite GLMs being widely including (i) data preprocessing methods that try to reduce used in practice. In this paper we introduce two disparities, (ii) in-process approaches which enforce fairness fairness criteria for GLMs based on equalizing during model training, and (iii) post-process approaches expected outcomes or log-likelihoods. We prove which adjust a model's predictions to achieve fairness after that for GLMs both criteria can be achieved via training is completed. However, the majority of this work a convex penalty term based solely on the linear has focused on classification problems with binary outcome components of the GLM, thus permitting efficient variables, and to a lesser extent on regression.

It is possible to design and deploy advanced machine learning algorithms that are essentially math-free and stats-free. People working on that are typically professional mathematicians. These algorithms are not necessarily simpler. See for instance a math-free regression technique with prediction intervals, here. Or supervised classification and alternative to t-SNE, here. Interestingly, this latter math-free machine

With exponential growth over the past years, the data science field has become very popular in the IT sector. Many businesses have started adopting data science techniques in order to derive meaningful information to make precise business decisions. Because of this data science has become an in-demand skill and one of the most highly paid careers in the tech industry. In order to be a successful business data scientist, it is crucial to understand and know how to use complex algorithms to build models, manipulate different datasets found from various sources, and be able to analyze and present findings to non-technical audiences. With so many resources available one can use them to learn more about data science but nothing beats reading data science books.

Data analysing, irrespective of its form, can be extremely chaotic and challenging. This is where feature engineering steps in. A method to ease data analysis, feature engineering simplifies data reading for machine learning models. A feature or variable is nothing but the numerical representation of all kinds of data– structured and unstructured. Feature engineering is a vital part of the process of predictive modelling.

Duke University researchers have discovered that machine learning algorithms can gain new degrees of transparency and insight into the properties of materials after teaching them known physics. According to researchers at Duke University, incorporating known physics into machine learning algorithms can help the enigmatic black boxes attain new levels of transparency and insight into the characteristics of materials. Researchers used a sophisticated machine learning algorithm in one of the first efforts of its type to identify the characteristics of a class of engineered materials known as metamaterials and to predict how they interact with electromagnetic fields. The algorithm was essentially forced to show its work since it first had to take into account the known physical restrictions of the metamaterial. The method not only enabled the algorithm to predict the properties of the metamaterial with high accuracy, but it also did it more quickly and with additional insights than earlier approaches.

Andrew Ng's DeepLearning.AI, in partnership with Stanford Online, recently announced a new Machine Learning Specialisation course on Coursera. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. The 3-course program is a new version of Ng's pioneering machine learning course, taken by over 4.8 million learners since 2012. The program provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation. The new Machine Learning Specialization by @DeepLearningAI_ & @StanfordOnline is now available on @Coursera!

How AI Is Used in Education Artificial intelligence can optimize and improve any process it touches, and education is no exception. AI's decision-making capabilities introduce new possibilities to every aspect of studying. Here are some examples: Personalized learning and smart scheduling. AI makes it possible to develop a truly individualized approach. It can quickly analyze every student's learning style and preferences and then create a detailed, personalized academic plan.