This is a Podcast by WiselyWise, from our series " Impact of Education and Knowledge podcast series". This series will cover the Impact of education and knowledge in day-to-day life. Our 1st Podcast Episode is on the Importance of Python in Artificial Intelligence with our guest Sathya Dayanithi. This is a Podcast by WiselyWise, from our series " Impact of Education and Knowledge podcast series". This series will cover the Impact of education and knowledge in day-to-day life.

Remembering and forgetting mechanisms are two sides of the same coin in a human learning-memory system. Inspired by human brain memory mechanisms, modern machine learning systems have been working to endow machine with lifelong learning capability through better remembering while pushing the forgetting as the antagonist to overcome. Nevertheless, this idea might only see the half picture. Up until very recently, increasing researchers argue that a brain is born to forget, i.e., forgetting is a natural and active process for abstract, rich, and flexible representations. This paper presents a learning model by active forgetting mechanism with artificial neural networks. The active forgetting mechanism (AFM) is introduced to a neural network via a "plug-and-play" forgetting layer (P\&PF), consisting of groups of inhibitory neurons with Internal Regulation Strategy (IRS) to adjust the extinction rate of themselves via lateral inhibition mechanism and External Regulation Strategy (ERS) to adjust the extinction rate of excitatory neurons via inhibition mechanism. Experimental studies have shown that the P\&PF offers surprising benefits: self-adaptive structure, strong generalization, long-term learning and memory, and robustness to data and parameter perturbation. This work sheds light on the importance of forgetting in the learning process and offers new perspectives to understand the underlying mechanisms of neural networks.

Artificial intelligence (AI) and machine learning (ML) have become more popular in Vietnam with a large proportion of young people having dabbled in these fields after realizing their potential. CoderSchool, a startup in virtual programming and education in Vietnam, has recently received a U$$2.6 million investment in the pre-Series A fund rounds to expand their scope. In response to the Industrial Revolution 4.0, the needs for workers in technology have tremendously increased. A lot of young people have left their comfort zone and entered the AI and ML fields. Nguyen The Chinh, 35, is a former manager in the technical department of a multinational corporation. He switched to AI and ML and signed up for a three-month bootcamp course.

Chennai (Tamil Nadu) [India], November 19 (ANI/NewsVoir): DeepSphere.AI, an EdTech company with its presence in Palo Alto, USA and Chennai, India offering a wide range of foundation, intermediate, and advanced courses on Artificial Intelligence for students and professionals, achieves 100% personalization of learning by providing personalized study materials, lab projects, and assessments on its on-cloud intelligent Learning Management System (iLMS). It also achieves 100% student engagement, besides boosting the learning potential of students by 96%. Available on a subscription model for schools, colleges & universities, organizations and self-employed, iLMS offers recommendations and feedback on the learning speed, ability, and participation of students, as they learn. DeepSphere.AI maps and reviews the goals of learners and takes appropriate corrective action to help students realize their goals. Founded in September 2018, DeepSphere.AI's team comprises board members of the University of California, lead instructors, MIT learning facilitators, Harvard PhDs, Stanford alumni, industry leaders, and entrepreneurs.

While past information technology (IT) advances have transformed society, future advances hold great additional promise. For example, we have only just begun to reap the changes from artificial intelligence--especially machine learning--with profound advances expected in medicine, science, education, commerce, and government. All too often forgotten, underlying the IT impact are the dramatic improvements in the programmable hardware. Hardware improvements deliver performance that unlocks new capabilities. However, unlike in the 1990s and early 2000s, tomorrow's performance aspirations must be achieved with much less technological advancement (Moore's Law and Den-nard Scaling).

Computational thinking, a K–12 education movement begun in 2006, has defined a curriculum to teach basic computing in pre-college schools. It has been dramatically more successful than prior computer literacy or fluency movements at convincing K–12 school teachers and boards to adopt a computer curriculum. Learning problem-solving with algorithms is seen widely as valuable for students. Hundreds of CT initiatives have blossomed around the world. By 2010, the movement settled on a definition of CT that can be paraphrased as "Designing computations that get computers to do jobs for us."

It is designed to give you a complete understanding of Text Processing and Mining with the use of State-of-the-Art NLP algorithms in Python. We will learn Spacy in detail and we will also explore the uses of NLP in real-life. This course covers the basics of NLP to advance topics like word2vec, GloVe, Deep Learning for NLP like CNN, ANN, and LSTM. I will also show you how you can optimize your ML code by using various tools of sklean in python. At the end part of this course, you will learn how to generate poetry by using LSTM.

Constructivism is a knowledge and learning theory that can be applied to artificial intelligence. It argues that learning, knowledge, and understanding are constructive processes that build on prior knowledge. For example, rather than forming a single conception of the world, pieces of information are layered on top of our existing knowledge. When it comes to constructivism in AI, there is the belief that learning or knowledge is created by constructing internal models of the world that are constantly adjusted to fit with new experiences. Constructivism in AI affirms that machine intelligence is best realized by programming machine intelligence systems to behave like infants, with instinctive reflexes, and then gradually learning how to interact with their surroundings.

We present a novel task that measures how people generalize objects' causal powers based on observing a single (Experiment 1) or a few (Experiment 2) causal interactions between object pairs. We propose a computational modeling framework that can synthesize human-like generalization patterns in our task setting, and sheds light on how people may navigate the compositional space of possible causal functions and categories efficiently. Our modeling framework combines a causal function generator that makes use of agent and recipient objects' features and relations, and a Bayesian non-parametric inference process to govern the degree of similarity-based generalization. Our model has a natural "resource-rational" variant that outperforms a naive Bayesian account in describing participants, in particular reproducing a generalization-order effect and causal asymmetry observed in our behavioral experiments. We argue that this modeling framework provides a computationally plausible mechanism for real world causal generalization.

In this paper, we propose a deterministic variational inference approach and generate low-discrepancy points by minimizing the kernel discrepancy, also known as the Maximum Mean Discrepancy or MMD. Based on the general energetic variational inference framework by Wang et. al. (2021), minimizing the kernel discrepancy is transformed to solving a dynamic ODE system via the explicit Euler scheme. We name the resulting algorithm EVI-MMD and demonstrate it through examples in which the target distribution is fully specified, partially specified up to the normalizing constant, and empirically known in the form of training data. Its performances are satisfactory compared to alternative methods in the applications of distribution approximation, numerical integration, and generative learning. The EVI-MMD algorithm overcomes the bottleneck of the existing MMD-descent algorithms, which are mostly applicable to two-sample problems. Algorithms with more sophisticated structures and potential advantages can be developed under the EVI framework.