TORONTO, Thursday, Oct. 10, 2019 - Docebo Inc. (TSX:DCBO) ("Docebo" or the "Company"), has successfully launched its IPO on the Toronto Stock Exchange, marking a milestone moment and a significant achievement for the SaaS e-Learning platform. Docebo, developer of a leading AI-powered learning platform, has seen significant growth thanks to its dedication to its customers' success and consistent string of innovation, from launching its social learning functionality in 2016 to the implementation of in-house build learning specific artificial intelligence algorithms in 2018. Docebo has since become a truly international company with offices in Toronto, Milan, London, Atlanta, and Dubai. With over 2/3s of its revenue based in North America, Docebo's headquarters in Canada has been the hub for the company's international expansion and growth. "Completing this IPO is an exciting achievement for the organization and comes as a result of the talent and dedication of our team and support from our global base of customers and partners," said Claudio Erba, CEO of Docebo.

Data Science is such a broad field that includes several subdivisions like data preparation and exploration; data representation and transformation; data visualization and presentation; predictive analytics; machine learning, etc. For beginners, learning the fundamentals of data science can be a very daunting task especially if you don't have proper guidance as to the necessary training required, or what courses to take, and in what order. Before discussing the steps necessary to become a data scientist, let's discuss the skills that every data scientist should have in his skills set toolbox. I started learning data science about a year ago. It was quite challenging from the beginning, but let me share with you the approach that worked for me.

The government has unveiled plans to invest £13m in postgraduate conversion courses in data science and artificial intelligence. The initiative will see universities and higher education providers partner with industry to develop new courses that train graduates, who may have studied a non-STEM degree, in the skills required to take up jobs in the field. The funding forms part of a wider £400m investment in maths, digital and technical education through the government's AI sector deal, which was launched last year amid criticism that ministers were failing to protect the UK's tech scene ahead of Brexit. Under the new initiative, the Office for Students and the Department for Digital, Culture, Media and Sport have allocated £3m to course development and £10m to scholarships for candidates from underrepresented backgrounds, including female, disabled and black students. It is hoped that 2,500 students will have enrolled in one of the new courses by 2023.

Despite becoming an increasingly common phrase, there is still some confusion around machine learning (ML) and how it relates to artificial intelligence (AI). Key to machine learning is data; algorithms are designed to learn from this data and then make a determination or prediction about the subject. In machine learning, computers don't have to be programmed to complete tasks, it's about getting them to actually acquire knowledge. Machine learning is a subset of the much broader world of artificial intelligence, however, AI is more focused on developing a machine that can do something that only a human would normally be able to do. In the field of education, there are many opportunities for machine learning to make an impact. However, there are also concerns that need to be addressed, not least the vast amounts of data that have to be stored and analysed in order to create effective machine learning algorithms.

Meet DigiDoug, the first digital human to give a TED talk in real time. DigiDoug is the virtual version of Dr. Doug Roble, senior director of Software R&D at Digital Domain, the award-winning visual effects studio behind the characters and visual effects for movies like The Curious Case of Benjamin Button, Maleficent, Disney's The Beauty and the Beast and Avengers: Endgame. Roble and Digital Domain's Digital Human Group have presented DigiDoug at multiple events, showcasing their state-of-the-art digital human technology that's driven by an inertial motion-capture suit and a single camera capture for facial animation. But to capture and recreate emotions and actions in real time, the Los Angeles-based studio turned to more powerful and advanced technology: machine learning and real-time rendering. With NVIDIA RTX technology and Unreal Engine from Epic Games, Digital Domain is bringing photorealistic digital humans to life -- all in real time.

Artificial Intelligence is still a nascent technology; much of the groundbreaking work moving the industry forward is done inside AI research labs. It's often from those labs that open source projects are started. Institutes like Open AI, NASA's JPL, Google Deepmind, MIT CSAIL, BAIR, The Turing Institute, and Max Planck -- to name just a handful -- are presenting at ODSC in 2019, helping us bring our community to the leading edge of AI. To learn more about the labs' role at ODSC, visit ODSC West. Since our next conference is in San Francisco, we're looking west at a few exciting research labs in the area that are participating in ODSC this year.

Nielsen (NLSN) and the University of Oxford today announced a two-year collaboration to advance the use of artificial intelligence (AI) to identify and classify consumer packaged goods (CPG) products on shelves in retail stores. Facilitated between Nielsen's Image Recognition group and the Visual Geometry Group (VGG) at the University of Oxford, this partnership brings together the world's largest pool of product reference data with industry-leading brainpower around AI technology to yield greater accuracy in product identification and discovery. Through this partnership, Nielsen is working directly with University of Oxford Professors Andrew Zisserman and Andrea Vedaldi (Department of Engineering Science), world-renowned computer scientists and pioneers in image recognition and AI research. Zisserman, Vedaldi and their team of research scientists will work together with Nielsen to more precisely and quickly identify and classify in-store products based on product images captured through Nielsen's eCollection solution. The Oxford researchers will focus on building and enhancing the eCollection algorithms with increasingly advanced deep learning capabilities, enabling a more automatic detection of store products, promotions and prices without the need for manual intervention.

To predict the impacts of human emissions, researchers need a complete picture of the atmosphere's methane cycle. They need to know the size of the inputs -- both natural and human -- as well as the outputs. They also need to know how long methane resides in the atmosphere. To help develop this understanding, Tom Weber, an assistant professor of earth and environmental sciences at the University of Rochester; undergraduate researcher Nicola Wiseman '18, now a graduate student at the University of California, Irvine; and their colleague Annette Kock at the GEOMAR Helmholtz Centre for Ocean Research in Germany, used data science to determine how much methane is emitted from the ocean into the atmosphere each year. Their results, published in the journal Nature Communications, fill a longstanding gap in methane cycle research and will help climate scientists better assess the extent of human perturbations.

Dr. Ganapathi Pulipaka was a recipient of the Top 50 Technology Leader awards for recognition of his contribution to artificial intelligence, machine learning, and data science; for the past five years on Twitter as a machine learning and data science influencer; as a contributor to thought leadership and of project implementation articles on Medium, Data Driven Investor, LinkedIn, GitHub; as a best-selling author of two books on Amazon - "The Future of Data Science and Parallel Computing: A Road to Technological Singularity," published on June 29, 2018, and "Big Data Appliances for In-Memory Computing: A Real-World Research Guide for Corporations to Tame and Wrangle Their Data," published Dec. 8, 2015 - and other eBooks that have reached all-time high rankings from the world's largest book ratings authority (featured on Forbes), BookAuthority; and also for writing another 400 research papers as part of academic research programs for PostDoc and PhD. He is an American data scientist and AI luminary who has been featured in top-tier magazines and news and industry publications and was a speaker for multiple media distribution networks and some of the top media station affiliates, including ABC, FoxNews, NBC, Yahoo Finance, MarketWatch, The CW, VentureBeat, MirrorReview, CIOReview, SAP, Erie News Now, USA Today, Double T 97.3 Lubbock's Radio station, 100.7 KFM BFM San Diego, KITV, Telemundo Lubbock 46, AZCentral, Insights Success, NewsOk, Pittsburgh Post-Gazette, MarketWatch, and Ask.

This paper presents BiPaR, a bilingual parallel novel-style machine reading comprehension (MRC) dataset, developed to support multilingual and cross-lingual reading comprehension. The biggest difference between BiPaR and existing reading comprehension datasets is that each triple (Passage, Question, Answer) in BiPaR is written parallelly in two languages. We collect 3,667 bilingual parallel paragraphs from Chinese and English novels, from which we construct 14,668 parallel question-answer pairs via crowdsourced workers following a strict quality control procedure. We analyze BiPaR in depth and find that BiPaR offers good diversification in prefixes of questions, answer types and relationships between questions and passages. We also observe that answering questions of novels requires reading comprehension skills of coreference resolution, multi-sentence reasoning, and understanding of implicit causality, etc. With BiPaR, we build monolingual, multilingual, and cross-lingual MRC baseline models. Even for the relatively simple monolingual MRC on this dataset, experiments show that a strong BERT baseline is over 30 points behind human in terms of both EM and F1 score, indicating that BiPaR provides a challenging testbed for monolingual, multilingual and cross-lingual MRC on novels. The dataset is available at https://multinlp.github.io/BiPaR/.