KHARAGPUR: Researchers at IIT Kharagpur have evolved an Artificial Intelligence-aided method to automate the reading of legal case judgments, the premier institute said in a statement on Friday. The researchers from IIT Kharagpur's Computer Science and Engineering department have developed two deep neural models to understand the rhetorical roles of sentences in a legal case judgment, which could prove phenomenal in India where AI is yet to sufficiently penetrate the legal field. The country uses a Common Law system that prioritises the doctrine of legal precedent over statutory law, and where legal documents are often written in an unstructured way. "Taking 50 judgments from the Supreme Court of India, we segmented these by first labelling sentences with the help of three senior law students from IIT Kharagpur's Rajiv Gandhi School of Intellectual Property Law, then performing extensive analysis of the human-assigned labels and developing a high quality gold standard corpus to train the machine to carry out the task," explained research lead Professor Saptarshi Ghosh. Unlike earlier attempts which required substantial human intervention, the neural methods used by Ghosh's team enables automatic learning of the features, given sufficient amount of data, and can be used across multiple legal domains.

Finnish technology firm Reaktor and the University of Helsinki joined forces to educate people on AI for free. The institutions combined to develop an online course to teach the basics of AI to anyone interested in the technology. Reaktor and the University also challenged organizations to train their staff in AI, so far over 200 organisations have pledged to do so – including banks, telecoms, and healthcare organizations. Almost 90 000 students have signed up for the course since it began in May. While popular with Finns, the course is already seeing strong demand globally, attracting students from over 80 different countries.

Python has massive applications in Artificial Intelligence (AI) applications, data science, Machine Learning (ML) and data analytics, US-based online education company according to Coursera. The top 10 list of courses, such as "Programming for Everybody," Python Data Structures," Python for Data Science and AI," has been dominated by python. Python has a lot of advantages. One of them is that it is extremely easy getting started with. It offers a lot of flexibility.

As AI has permeated our lives most innovatively, the interest and investment are expected to grow further to drive the innovation engine across all sectors of society. Such large scale investments by government or private firms will create a long-appraised impact on society and its citizens. AI researches powered by such investment will help root-out the societally relevant problems to engage people in creating a more diverse workforce to better tackle the problems. The report "A 20-Year Community Roadmap for Artificial Intelligence Research in the US" highlights six significant areas where AI research is likely to impact in the next 20 years. In the near term, chronic health conditions like diabetes, cancer, and heart and neurological diseases are likely to benefit most from new applications of AI, according to a survey of healthcare professionals.

At Mawson, we partner with exceptionally talented founders to build AI startups that redefine industries. We are always on the lookout for outstanding technical talent and entrepreneurs, who like us at Mawson, believe building companies is not a job, but a way of life. If you're curious about what the journey of a Machine Learning Engineer at Mawson is like, we invite you to step into the shoes of one as you continue to read on… Congratulations, welcome to the Mawson family, where you've joined the team along with several other new Machine Learning Engineers. The fact that you've made it this far already is an achievement, as we've specifically selected individuals who are exceptional in Math and Programming. Our journey starts by advancing your Deep Learning skills.

Suppose that you have a very long list of string sequences, such as a list of amino acid structures ('PHE-SER-CYS', 'GLN-ARG-SER',…), product serial numbers ('AB121E', 'AB323', 'DN176'…), or users UIDs, and you are required to create a validation process of some kind that will detect anomalies in this sequence. An anomaly might be a string that follows a slightly different or unusual format than the others (whether it was created by mistake or on purpose) or just one that is extremely rare. To make things even more interesting, suppose that you don't know what is the correct format or structure that sequences suppose to follow. This is a relatively common problem (though with an uncommon twist) that many data scientists usually approach using one of the popular unsupervised ML algorithms, such as DBScan, Isolation Forest, etc. Many of these algorithms typically do a good job in finding anomalies or outliers by singling out data points that are relatively far from the others or from areas in which most data points lie.

Any high school student would guess there is a cosine involved when they see an integral of a sine. Regardless of whether the person understands the thought process behind these functions, it does the job for them. This intuition behind calculus is rarely explored. Though Newton and Leibnitz developed advanced mathematics to solve real-world problems, today most of the schools teach differential equations through semantics. The linguistic appeal of mathematics might get grades in high school, but in the world of research, this is hysterical.

We explore trust in a relatively new area of data science: Automated Machine Learning (AutoML). In AutoML, AI methods are used to generate and optimize machine learning models by automatically engineering features, selecting models, and optimizing hyperparameters. In this paper, we seek to understand what kinds of information influence data scientists' trust in the models produced by AutoML? We operationalize trust as a willingness to deploy a model produced using automated methods. We report results from three studies -- qualitative interviews, a controlled experiment, and a card-sorting task -- to understand the information needs of data scientists for establishing trust in AutoML systems. We find that including transparency features in an AutoML tool increased user trust and understandability in the tool; and out of all proposed features, model performance metrics and visualizations are the most important information to data scientists when establishing their trust with an AutoML tool.

We consider gradient descent with `momentum', a widely used method for loss function minimization in machine learning. This method is often used with `Nesterov acceleration', meaning that the gradient is evaluated not at the current position in parameter space, but at the estimated position after one step. In this work, we show that the algorithm can be improved by extending this `acceleration' --- by using the gradient at an estimated position several steps ahead rather than just one step ahead. How far one looks ahead in this `super-acceleration' algorithm is determined by a new hyperparameter. Considering a one-parameter quadratic loss function, the optimal value of the super-acceleration can be exactly calculated and analytically estimated. We show explicitly that super-accelerating the momentum algorithm is beneficial, not only for this idealized problem, but also for several synthetic loss landscapes and for the MNIST classification task with neural networks. Super-acceleration is also easy to incorporate into adaptive algorithms like RMSProp or Adam, and is shown to improve these algorithms.

As the field of Spoken Dialogue Systems and Conversational AI grows, so does the need for tools and environments that abstract away implementation details in order to expedite the development process, lower the barrier of entry to the field, and offer a common test-bed for new ideas. In this paper, we present Plato, a flexible Conversational AI platform written in Python that supports any kind of conversational agent architecture, from standard architectures to architectures with jointly-trained components, single- or multi-party interactions, and offline or online training of any conversational agent component. Plato has been designed to be easy to understand and debug and is agnostic to the underlying learning frameworks that train each component.