Data Science has gained a lot of popularity in the last few years. This field's primary focus is to convert meaningful data into marketing and business strategies which helps a company grow. The data is stored and researched to get in a logical solution. Previously only the top IT companies were involved in this field but today businesses from various sector and fields such as e-commerce, health care, finance, and others are using data analytics. There are various tools available for data analytics such as Hadoop, R programming, SAS, SQL and many more. However the most popular and easy to use tools for data analytics is Python.

Artificial Intelligence (alternate unit) was written and developed by Beverly Clarke. She is author of the book "Computer Science Teacher – insight into the computing classroom." Additionally, she is an Education consultant and former teacher. In writing this unit the following are acknowledged for their contributions in proof reading, checking for technical accuracy, testing activities in the classroom, filming, being sound boards and committed to seeing an AI curriculum available for high school students – Mike Mendelson (NVIDIA), James McClung (formerly of NVIDIA), Joanna Goode (University of Oregon), Alison Lowndes (NVIDIA), Rosie Lane (South Wilts Grammar School for Girls), Peter McOwan (Queen Mary University of London), Paul Curzon (Queen Mary University of London), Liz Austin (NVIDIA), Gemma Bond (Screen Boo Productions) and Neil Rickus (University of Hertfordshire). Morals and Ethics supporting cards were sampled from material by Andrew Csizmadia (Newman University).

Campuses are using artificial intelligence technology to respond and advise, on-demand, to student, faculty, and staff via texting or auditory. These tools extend the accessibility of student support services 24/7 and engage students in a manner most conducive to them.

Wide adoption of complex RNN based models is hindered by their inference performance, cost and memory requirements. To address this issue, we develop AntMan, combining structured sparsity with low-rank decomposition synergistically, to reduce model computation, size and execution time of RNNs while attaining desired accuracy. AntMan extends knowledge distillation based training to learn the compressed models efficiently. Our evaluation shows that AntMan offers up to 100x computation reduction with less than 1pt accuracy drop for language and machine reading comprehension models. Our evaluation also shows that for a given accuracy target, AntMan produces 5x smaller models than the state-of-art. Lastly, we show that AntMan offers super-linear speed gains compared to theoretical speedup, demonstrating its practical value on commodity hardware.

Distillation is a method to transfer knowledge from one model to another and often achieves higher accuracy with the same capacity. In this paper, we aim to provide a theoretical understanding on what mainly helps with the distillation. Our answer is "early stopping". Assuming that the teacher network is overparameterized, we argue that the teacher network is essentially harvesting dark knowledge from the data via early stopping. This can be justified by a new concept, {Anisotropic Information Retrieval (AIR)}, which means that the neural network tends to fit the informative information first and the non-informative information (including noise) later. Motivated by the recent development on theoretically analyzing overparameterized neural networks, we can characterize AIR by the eigenspace of the Neural Tangent Kernel(NTK). AIR facilities a new understanding of distillation. With that, we further utilize distillation to refine noisy labels. We propose a self-distillation algorithm to sequentially distill knowledge from the network in the previous training epoch to avoid memorizing the wrong labels. We also demonstrate, both theoretically and empirically, that self-distillation can benefit from more than just early stopping. Theoretically, we prove convergence of the proposed algorithm to the ground truth labels for randomly initialized overparameterized neural networks in terms of $\ell_2$ distance, while the previous result was on convergence in $0$-$1$ loss. The theoretical result ensures the learned neural network enjoy a margin on the training data which leads to better generalization. Empirically, we achieve better testing accuracy and entirely avoid early stopping which makes the algorithm more user-friendly.

Spiking neural networks (SNNs) are distributed trainable systems whose computing elements, or neurons, are characterized by internal analog dynamics and by digital and sparse synaptic communications. The sparsity of the synaptic spiking inputs and the corresponding event-driven nature of neural processing can be leveraged by energy-efficient hardware implementations, which can offer significant energy reductions as compared to conventional artificial neural networks (ANNs). The design of training algorithms lags behind the hardware implementations. Most existing training algorithms for SNNs have been designed either for biological plausibility or through conversion from pretrained ANNs via rate encoding. This article provides an introduction to SNNs by focusing on a probabilistic signal processing methodology that enables the direct derivation of learning rules by leveraging the unique time-encoding capabilities of SNNs. We adopt discrete-time probabilistic models for networked spiking neurons and derive supervised and unsupervised learning rules from first principles via variational inference. Examples and open research problems are also provided.

Classifiers are among the most widely used supervised machine learning algorithms. Many classification models exist, and choosing the right one for a given task is difficult. During model selection and debugging, data scientists need to asses classifier performance, evaluate the training behavior over time, and compare different models. Typically, this analysis is based on single-number performance measures such as accuracy. A more detailed evaluation of classifiers is possible by inspecting class errors. The confusion matrix is an established way for visualizing these class errors, but it was not designed with temporal or comparative analysis in mind. More generally, established performance analysis systems do not allow a combined temporal and comparative analysis of class-level information. To address this issue, we propose ConfusionFlow, an interactive, comparative visualization tool that combines the benefits of class confusion matrices with the visualization of performance characteristics over time. ConfusionFlow is model-agnostic and can be used to compare performances for different model types, model architectures, and/or training and test datasets. We demonstrate the usefulness of ConfusionFlow in the context of two practical problems: an analysis of the influence of network pruning on model errors, and a case study on instance selection strategies in active learning.

Inverse reinforcement learning (IRL) is used to infer the reward function from the actions of an expert running a Markov Decision Process (MDP). A novel approach using variational inference for learning the reward function is proposed in this research. Using this technique, the intractable posterior distribution of the continuous latent variable (the reward function in this case) is analytically approximated to appear to be as close to the prior belief while trying to reconstruct the future state conditioned on the current state and action. The reward function is derived using a well-known deep generative model known as Conditional Variational Auto-encoder (CVAE) with Wasserstein loss function, thus referred to as Conditional Wasserstein Auto-encoder-IRL (CWAE-IRL), which can be analyzed as a combination of the backward and forward inference. This can then form an efficient alternative to the previous approaches to IRL while having no knowledge of the system dynamics of the agent. Experimental results on standard benchmarks such as objectworld and pendulum show that the proposed algorithm can effectively learn the latent reward function in complex, high-dimensional environments.

The talk is Wednesday afternoon, Jan. 30. Here is the paper: Envisioning AI for K-12: What should every child know about AI?

When I say Artificial Intelligence (AI), what comes to mind? Chances are that it's something about robots taking over the world. That's what I thought of the first time I started learning about it just a few years ago. But as I dove deeper into how AI really works, on both a programming and even somewhat of a mathematical level, I realized that AI is nothing like that, and yet so much more. First, let's clear something up.