Training magazine's TechLearn 2019 Conference is designed for learning and development professionals who want to leverage the latest in learning research and training technologies to improve workplace performance. Topics and content include: Design, Implementation, Evaluation, Assessment and Testing, eLearning, Adaptive Learning, Virtual Classrooms, Games & Simulations, Social Learning, Augmented and Virtual Reality, Artificial Intelligence, and more.

Disruptive technologies and Artificial Intelligence (AI) are making their way into classrooms as humanoid robots to teach students and interact with them as teachers do, at a school in Bengaluru. Though the 5 foot 7 inch robots, dressed in formal female attire, do not replace real teachers, they complement them in teaching lessons in the subjects and reply to FAQs (frequently asked questions) from students. "We have programmed the interactive robots to answer questions students frequently ask on the subjects and related to them. With AI in play, the robots are able to respond to questions and doubts of our wards after a lesson is taught," said Mr Rao. The private international day-cum-boarding school has 25 co-ed students in each of the four sections for Classes 7-9.

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.

AI can now flag people based on their clothing, behaviour or race, log an individual's emotions, understand their actions and predict their next move. It can detect when luggage is left unattended, or if someone is loitering; it can even recognise when an individual is acting'unusual' based on others around them. AI is everywhere and getting more advanced every day. Facial recognition technology, in particular, has made leaps and bounds, partially thanks to tagged photographs on Facebook and Instagram as well as government-collected images such as drivers licenses and ID cards. The quality of cameras has also drastically improved, so much so that they no longer just record, they can'see' in real-time.

The electronic calendar is a valuable resource nowadays for managing our daily life appointments or schedules, also known as events, ranging from professional to highly personal. Researchers have studied various types of calendar events to predict smartphone user behavior for incoming mobile communications. However, these studies typically do not take into account behavioral variations between individuals. In the real world, smartphone users can differ widely from each other in how they respond to incoming communications during their scheduled events. Moreover, an individual user may respond the incoming communications differently in different contexts subject to what type of event is scheduled in her personal calendar. Thus, a static calendar-based behavioral model for individual smartphone users does not necessarily reflect their behavior to the incoming communications. In this paper, we present a machine learning based context-aware model that is personalized and dynamically identifies individual's dominant behavior for their scheduled events using logged time-series smartphone data, and shortly name as ``CalBehav''. The experimental results based on real datasets from calendar and phone logs, show that this data-driven personalized model is more effective for intelligently managing the incoming mobile communications compared to existing calendar-based approaches.

Avaya Conversational Intelligence (ACI) is an end-to-end, cloud-based solution for real-time Spoken Language Understanding for call centers. It combines large vocabulary, real-time speech recognition, transcript refinement, and entity and intent recognition in order to convert live audio into a rich, actionable stream of structured events. These events can be further leveraged with a business rules engine, thus serving as a foundation for real-time supervision and assistance applications. After the ingestion, calls are enriched with unsupervised keyword extraction, abstractive summarization, and business-defined attributes, enabling offline use cases, such as business intelligence, topic mining, full-text search, quality assurance, and agent training. ACI comes with a pretrained, configurable library of hundreds of intents and a robust intent training environment that allows for efficient, cost-effective creation and customization of customer-specific intents.

The repaired code example in Figure 3b deletes assignment operator " ", and inserts an equality operator " ". Hence its set of repair tokens are {, - }. D. Error Repair Class Given a buggy source program that suffers from compilation errors ( E s) which require a set of repair tokens ( R s) to fix, its error-repair class ( C) is defined as the merged set of errors and repairs {E s R s}. For example, the erroneous-repaired code pair in Figure 3 belongs to C 8 {E 10 - }, the 8 th most frequently occurring error-repair class. We determine the error-repair class of the 23, 275 erroneous-repaired code pairs in our dataset. Table III lists the error-repair classes ( C s) sorted in decreasing order of frequency, along with the number of buggy programs belonging to each class.

Anyone can take this course. If you already have experience using PyCharm and running Python files and programs on the interface, you can simply skip ahead to whatever section best suits your needs. Or, you can follow the progression of this meticulously curated course especially designed to take any absolute beginner off the street and make them a data modeler. This course is divided into days, but of course you can learn at your own pace. In Day 2 we teach you all the fundamentals of the Python programming language.

On the day I met Sebastian Thrun in Palo Alto, the State of California legalized self-driving cars. Gov. Jerry Brown arrived at the Google campus in one of the company's computer-controlled Priuses to sign the bill into law. "California is a big deal," said Thrun, the founder of Google's autonomous-car program, "because it tends to be hard to legislate here." He said it with typical understatement. An idea that was in its technological infancy a decade ago, when Thrun and his colleagues were racing to develop a vehicle that could drive itself more than a few miles on a desert test course, was now being officially sanctioned by the country's most populous state.

The main goal of this specialization is to provide the knowledge and practical skills necessary to develop a strong foundation on core paradigms and algorithms of machine learning (ML), with a particular focus on applications of ML to various practical problems in Finance. The specialization aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) mapping the problem on a general landscape of available ML methods, (2) choosing particular ML approach(es) that would be most appropriate for resolving the problem, and (3) successfully implementing a solution, and assessing its performance. The specialization is designed for three categories of students: · Practitioners working at financial institutions such as banks, asset management firms or hedge funds · Individuals interested in applications of ML for personal day trading · Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance. The modules can also be taken individually to improve relevant skills in a particular area of applications of ML to finance.