The technology of AI has been improving every year for the past 20 years, and today it is a very mature technology. Many companies and organizations are actively employing AI in different ways. AI is also developing into the next generation of computing, where big ideas can come from and many people can become an expert on a new discipline. Today, there are many companies working on various AI projects that are shaping the future of the technology. While AI is generally synonymous with artificial intelligence, more technically sophisticated systems can be described as "artificial general intelligence".

CHENNAI: Two startups incubated at Indian Institute of Technology Madras have joined hands with a mission to create one Lakh experts in Artificial Intelligence (AI) and Deep Learning by the year 2020. GUVI, which offers a platform for students in Tier 2,3 cities to learn in vernacular languages, is now collaborating with One Fourth Labs, a startup founded by IIT Madras Faculty which offers advanced AI courses. AI is one of the dominant technologies of this generation, which has helped machines reach human-level performance on specific tasks such as identifying faces, classifying images, playing complex strategy games, detecting anomalies in medical images and so on. There is a huge demand for AI talent in India, but the supply is limited due to a shortage of affordable courses which take students from basics to advanced topics. GUVI will be the platform partner and One Fourth Labs will be the content partner for this joint initiative.

In fact, the business plans of the next 10,000 startups are easy to forecast: Take X and add AI. Find something that can be made better by adding online smartness to it Over the past few years, artificial intelligence continues to be one of the hottest topics. The best minds participate in AI research, the largest corporations allocate astronomical sums for the development of competencies in this area, and AI startups collect multibillion-dollar investments annually. If you are engaged in business processes improvement or are looking for new ideas for your business, then you will most likely come across AI. And in order to work effectively with it, you need to understand its constituent parts. Let's find out what artificial intelligence is all about.

Abstract: Attempts from different disciplines to provide a fundamental understanding of deep learning have advanced rapidly in recent years, yet a unified framework remains relatively limited. In this article, we provide one possible way to align existing branches of deep learning theory through the lens of dynamical system and optimal control. By viewing deep neural networks as discrete-time nonlinear dynamical systems, we can analyze how information propagates through layers using mean field theory. When optimization algorithms are further recast as controllers, the ultimate goal of training processes can be formulated as an optimal control problem. In addition, we can reveal convergence and generalization properties by studying the stochastic dynamics of optimization algorithms.

Researchers seek to understand our Universe by making model predictions to match observations. Historically, they have been able to model simple or highly simplified physical systems, jokingly dubbed the "spherical cows," with pencils and paper. Later, the arrival of computers enabled them to model complex phenomena with numerical simulations. For example, researchers have programmed supercomputers to simulate the motion of billions of particles through billions of years of cosmic time, a procedure known as the N-body simulations, in order to study how the Universe evolved to what we observe today. "Now with machine learning, we have developed the first neural network model of the Universe, and demonstrated there's a third route to making predictions, one that combines the merits of both analytic calculation and numerical simulation," said Yin Li, a Postdoctoral Researcher at the Kavli Institute for the Physics and Mathematics of the Universe, University of Tokyo, and jointly the University of California, Berkeley.

A semantic equivalence assessment is defined as a task that assesses semantic equivalence in a sentence pair by binary judgment (i.e., paraphrase identification) or grading (i.e., semantic textual similarity measurement). It constitutes a set of tasks crucial for research on natural language understanding. Recently, BERT realized a breakthrough in sentence representation learning (Devlin et al., 2019), which is broadly transferable to various NLP tasks. While BERT's performance improves by increasing its model size, the required computational power is an obstacle preventing practical applications from adopting the technology. Herein, we propose to inject phrasal paraphrase relations into BERT in order to generate suitable representations for semantic equivalence assessment instead of increasing the model size. Experiments on standard natural language understanding tasks confirm that our method effectively improves a smaller BERT model while maintaining the model size. The generated model exhibits superior performance compared to a larger BERT model on semantic equivalence assessment tasks. Furthermore, it achieves larger performance gains on tasks with limited training datasets for fine-tuning, which is a property desirable for transfer learning.

With the advents of deep learning, improved image classification with complex discriminative models has been made possible. However, such deep models with increased complexity require a huge set of labeled samples to generalize the training. Such classification models can easily overfit when applied for medical images because of limited training data, which is a common problem in the field of medical image analysis. This paper proposes and investigates a reinforced classifier for improving the generalization under a few available training data. Partially following the idea of reinforcement learning, the proposed classifier uses a generalization-feedback from a subset of the training data to update its parameter instead of only using the conventional cross-entropy loss about the training data. We evaluate the improvement of the proposed classifier by applying it on three different classification problems against the standard deep classifiers equipped with existing overfitting-prevention techniques. Besides an overall improvement in classification performance, the proposed classifier showed remarkable characteristics of generalized learning, which can have great potential in medical classification tasks.

The most successful examples of such representations, those learned by convolutional neural networks, are structured by the scale and translational symmetries of the underlying space (e.g. a two-dimensional Euclidean space for images). It has been suggested that in humans the ability to make rich inferences based on abstract reasoning is rooted in the same neural mechanisms underlying relational reasoning in space [16, 19, 6, 7] and more specifically that abstract reasoning is facilitated by the learning of structural representations which serve to organise other learned representations in the same way that space organises the representations that enable spatial navigation [68, 41]. This raises a natural question: are there any ideas from mathematics that might be useful in designing general inductive biases for learning such structural representations? As a motivating example we take the recent progress on natural language tasks based on the Transformer architecture [66] which simultaneously learns to represent both entities (typically words) and relations between entities (for instance the relation between "cat" and "he" in the sentence "There was a cat and he liked to sleep"). These representations of relations take the form of query and key vectors governing the passing of messages between entities; messages update entity representations over several rounds of computation until the final representations reflect not just the meaning of words but also their context in a sentence. There is some evidence that the geometry of these final representations serve 2 to organise word representations in a syntax tree, which could be seen as the appropriate analogue to two-dimensional space in the context of language [33].

This is because ML is effective to handle and interpret big data sets for the purpose of finding certain patterns from the data. In particular, Deep Neural Network (DNN), which is based on an Artificial Neural Network (ANN) with multiple hidden layers between input and output layers allows to handle complex shapes with nonlinear functions with multidimensional input data. DNN has been successfully used in a large number of practical applications. Well trained neural network then provides precise pattern recognition based on data sets in real time. These features, big data recognition and real time estimation of nonlinear functions, of ML approaches are attractive to dynamics and control engineers who are handling nonlinear system dynamics with real world data. There have been several previous studies on applying ML, DNN, or other big-data handling techniques to rigid multibody system problems.

Bias is known to be an impediment to fair decisions in many domains such as human resources, the public sector, health care etc. Recently, hope has been expressed that the use of machine learning methods for taking such decisions would diminish or even resolve the problem. At the same time, machine learning experts warn that machine learning models can be biased as well. In this article, our goal is to explain the issue of bias in machine learning from a technical perspective and to illustrate the impact that biased data can have on a machine learning model. To reach such a goal, we develop interactive plots to visualizing the bias learned from synthetic data.