The findings can be generalized to many other settings, to assess and monitor the performance of existing ML pipelines even in the absence of A/B testing. We will discuss what it means to build equity into data practices and what dismantling systemic racism can look like in technology (and the pitfalls to avoid). Yes, there will be spaces for company displays. Specific techniques have been developed to help reduce bias at each stage of an ML system. Virtual Toronto Tech Summit 2020 . BIG DATA & AI TORONTO 2020: VIRTUAL CONFERENCE & EXPO. This generally takes the form of large call center and repair technician workforces that are waiting for an issue to happen, in order to help solve it. Wednesday 9 December 2020. Abstract: With the widespread use of machine learning, there have been serious societal consequences from using black-box models for high-stakes decisions, including flawed bail and parole decisions in criminal justice. CSC 311 Fall 2020: Introduction to Machine Learning Overview. Business leaders will learn from the experience of those who have successfully implemented ML/AI and actively manage data teams. The results are pervasive across technology subcategories within the field of natural language: parsing, natural language understanding, sentiment detection, entity linking, speech recognition, abstractive summarization, and so on. Times Higher Education’s World Academic Summit 2020 will be held in partnership with the University of Toronto next September.. The Virtual Higher Education Summit 2020 (#HES2020) took place from 31 August – 2 September 2020. You will learn about various machine learning methods that can be used to address this problem. 1) The risk of using machine learning in healthcare when you can't understand what the model is learned. Mon, Jun 15, 9:00 AM EDT. This talk will delve into Banorte's transformation journey into an AI-enhanced organization with data science projects yielding a net revenue that exceeds 3 billion USD during the past five years and avoiding transformational fatigue. However, this aspect is often overlooked in practice. Mon, Jun 15, 9:00 AM EDT. Given that the world and its data are ever more varied and dynamic, to take advantage of this power models need to be highly adaptable to represent the local diversity of events, people, markets, and operations. #machine_learning Abstract: AI-driven, including ML models, provide the capability to process a greater volume and variety of data to power new global platforms and products and to optimize global business operations. In this talk, I will give a high-level overview of the key ideas that make this possible. The Big Data & Analytics Summit Canada is designed to provide data executives with current trends, strategic insights, and best practices trending in technology, data, AI, machine learning, risk management, and retaining talent.. #data_analytics_training. What You Will Learn: Attendees will gain an understanding of the principles of knowledge translation in applied machine learning in healthcare and understand issues related to privacy and ethics as well as legal considerations. Why You Should Attend: Another conference focusing on deep learning, a subfield of machine learning. Deep Learning Summit. Deep Learning Summit. *CONFERENCE BREAKOUTS (ON HOPIN.TO PLATFORM) ARE 18TH AND 19TH*Taken from the real-life experiences of our community, the Steering Committee has selected the top applications, achievements and knowledge-areas to highlight.Data November 18th-19th750-950 attendeesWhere? Aim: The main objective is to design a pricing product that can help to : 1) Identify groups of elastic and inelastic customers. For each customer segmentation, the company found the demand curve function and formulate the nonlinear optimization problem that maximizes the sale or revenue using PYOMO and IPOPT. While the tech unicorns and their proxies have conducted almost an "arms race" since early 2018, sometimes publishing papers twice monthly to outdo their competitors' most recently published benchmarks -- how are these advances diffusing into practical use cases, and becoming adopted by mainstream businesses for their needs? Accelerate 2021: Data Analytics Summit . 3. Alejandro Perdomo-Ortiz, Lead Quantum Applications at Zapata Computing Inc. Abstract: With quantum computing technologies nearing the era of commercialization and quantum advantage, machine learning (ML) has been proposed as one of the promising killer applications. We create and organise globally renowned summits, workshops and dinners, bringing together the brightest minds in AI from both industry and academia. Abstract: The talk will give an overview of China's AI/ML ecosystem, and a deep dive into its capabilities when it comes to leading-edge research in neural networks. The participants established 11 working groups, each of which developed and performed experiments relevant to existing industry needs. Description: Learn how to use AI to solve common business problems. Today, AI researchers & practitioners increasingly use deep neural networks for many applications across different modalities and areas such as NLP, Vision, Speech, Conversational, and Multimodal AI. Your email will only be seen by the event organizer. ( See abstract at the bottom). Speakers this year include Mastercard, Google, Facebook, Uber, LG, Haliburton, Telus, Sunlife, Uber, KFC, and more!. Whether you are developing your first machine learning application, creating an enterprise ML infrastructure startup, or creating new Machine/Deep Learning tools, this hands-on session is designed to share practical strategies, growth hacks, and specific techniques to use that will win you your first customers and scale. By contrast, while we’ve seen explosive growth in the adoption of the machine and deep learning (ML/DL) across industries, putting ML/DL models into production isn’t as well supported. We will share results demonstrating generalizability towards existing emotion benchmarks from other domains. Jaya Kawale, Director of Machine Learning at Tubi. Source: Re-Work. Sedef Akinli Kocak, Project Manager at Applied AI Project, Vector Institute. Each ticket includes:- Access 80+ hours of live-streamed content (incl. INTELLIGENT ROBOTIC PROCESS AUTOMATION SUMMIT. However, it’s not very common to come across companies that have both. This talk provides a brief overview of Indigenous language technology projects at the National Research Council of Canada, before focusing on one project in particular: the development of neural machine translation systems to translate between Inuktitut and English. Abstract: Building recommendation systems in production that can serve millions of customers goes way beyond just having a great algorithm. Tue, Sep 15 – 16, 2020. Abstract: The data scientist’s job does not finish when the model is shipped. 25 people interested. Abstract: 'Race' is a concept, a tool, and a structure that defines a set of relationships between people. Our, Director of Data Science, Ashwin Swarup, will be a part of this event as a Speaker. BACKGROUND. What You Will Learn: The current state of quantum computation. In this talk, I will present some of the challenges in understanding the data and present our platform for content understanding. Kan Deng, Ph.D., Founder and CEO at Beijing Rxthinking Inc. Abstract: This talk will discuss the practice of applied machine learning technology in healthcare, particularly covering its relevance with the ongoing coronavirus pandemic. We anticipate that FTL will enable the machine learning community to benefit from large datasets with uncertain labels in fields such as biology and medicine. Last November, we had the opportunity to attend the Toronto Machine Learning Summit (TMLS) one of the most respected Machine Learning Conference & Exhibitions. For Futher Information Visit The AI Summit Contact Us Page. Machine learning, deep learning, and AI are some of the fastest-growing and most exciting areas for knowledge workers - simultaneously, they are the key to untapped revenue sources and strategic insights for businesses. How to measure AI contribution to the bottom line, 2. Machine Learning Developers Summit 2020 (#MLDS2020) brings together the India’s leading Machine Learning innovators and practitioners to share their ideas and experience about machine learning tools. *Content is non-commercial and speaking spots cannot be purchased. Abstract: There are high expectations about AI initiatives across different industries in North America. At Microsoft Research, a learning method has been developed that is as accurate as full complexity models such as boosted trees and random forests, but even more intelligible than linear models. Data Summit Connect 2020 is a new free webinar series taking place June 9 - 11, 2020 and will focus on analytics, machine learning, AI, data lakes, and much more. Q: Are there ID or minimum age requirements to enter the event? Yes, we will have talks that cover Finance, Healthcare, Retail, Transportation, and other key industries where applied ML has made an impact. Lots of HR and recruiting conferences include a session or two on AI, but this TAtech Leadership Summit is different. Xunyu Zhou, Professor, Department of IEOR at Columbia University. 2020 edition of AI & Machine Learning Strategies Summit will be held at Old Mill Toronto, Toronto starting on 15th September. The talk is designed so that those managing projects (e.g., data science directors/managers) and those executing the work (e.g., data scientists/analysts) can walk away with tips to help their ML projects start and close off successfully. Researchers will have the opportunity to share with their peer's cutting-edge advancements in the field. The benefits of scaling global models through regional data strategies will be illustrated with examples from fraud detection, credit decisioning, economic modeling, and understanding consumer preferences. Other examples with differences in data point label confidence include radiological or histopathological images or image segment labels, and measured resistance to cancer drugs. Explore how deep learning will impact healthcare, manufacturing, search & transportation. How emotions can be detected from textual content for business use cases & research purposes, 2. Toronto Machine Learning Summit Visit the Innodata virtual event booth November 19th for the presentation “Bogged Down by Annotation, Why SMEs Should Do the Heavy Lifting” with Innodata’s Chief Product & Marketing Officer. Firms are using AI to create unprecedented business advantages that are reshaping the global - but more specifically Canadian - economic landscape. Our, Director of Data Science, Ashwin Swarup, will be a part of this event as a Speaker. DATE:July 24,2020. The huge swathes of data owned by industrial organisations is providing AI and machine learning adopters with limitless opportunities to deliver new products, new business models, and greater insights into their business. This video is unavailable. Alba Cervera-Lierta, Postdoctoral Researcher at the University of Toronto. What You Will Learn: We will describe sources of bias in ML technology, why addressing bias matters, and techniques to mitigate bias, with examples from our work on inclusive AI at Pinterest. #machinelearning Abstract: This talk will discuss CheckList, a task-agnostic methodology, and tool for testing NLP models inspired by principles of behavioral testing in software engineering, showing a lot of fun bugs that were discovered with CheckList, both in commercial models (Microsoft, Amazon, Google) and research models (BERT, RoBERTA for sentiment analysis, QQP, SQuAD). Thu, Nov 2, 2017, 9:00 AM: Hello Folks!Based on the needs of our community we've created a 2 day event for you, bringing together 1. practitioners 2. enthusiasts and 3. businessesThe content will be p On Friday, September 20, 2019, the Library of Congress hosted the Machine Learning + Libraries Summit. We create and organise globally renowned summits, workshops and dinners, bringing together the brightest minds in AI from both industry and academia. Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering and Statistical Science at Duke University. The speaker will give few examples of how ideas are scaled into products across the whole organization and will also talk about how the culture changes within organizations as they start to benefit more from progressive data solutions – what are the future skills that every organization should have and how to get started with the change. Q: Can I speak at the event? The goal of TMLS is to empower data practitioners, academics, engineers, and business leaders with direct contact to the people that matter most, and the practical information to help advance your projects. While these are questions universal to any industry, they are particularly challenging to answer in the insurance industry because of its highly regulated and risk-averse nature. I will share some of the technical challenges that we encountered throughout the project and how we overcome them. Q: Can my company have a display? A "dramatic shift" would be an understatement: since 2018, the field of natural language has undergone a sea change. This makes it easy to understand what a model has learned and to edit the model when it learns inappropriate things, making it possible for medical experts to understand and repair a model as most clinical data have unexpected problems that is quite critical. Abstract: This talk tackles the process of building scalable deep learning pipelines for hundreds of model training on giant time-series datasets and on how it helped saved 80% of the cost along the way. Eventbrite - Toronto Machine Learning Society (TMLS) presents Toronto Machine Learning 'Micro-Summit' Series ( TMLS) - Finance Special Focus - Wednesday, 15 April 2020 - … Methodology: Scotiabank proposes to use model-based recursive partitioning (MOB) which uses product characteristics and customer attributes as input and customer willingness to pay as output to segment customers. Attendees will learn about the Bank’s customer segmentation approach, highlighting the flexibility of the model's given data availability. services are deployed to produce improvements to important business metrics, e.g. Finally, I will explain the state of development of experimental quantum computers and future prospects. How do you convince your stakeholders to put your ML models into production? Breakthroughs in the usage of deep learning, as well as the availability of more sophisticated hardware and cloud resources, led to sudden advances in natural language. Data Summit Connect 2020 is a new free webinar series taking place June 9 - 11, 2020 and will focus on analytics, machine learning, AI, data lakes, and much more. Dave S. Ali; Alice R. 28 attendees; MLOps, Production & Engineering World 2020. This will also cover some lessons learned from the space industry that can be applied to industrial applications here on Earth. All sessions will be recorded during the event (provided speaker permissions) and will be made available to attendees approximately 2-4 weeks after the event and be available for 12 months after release. Lastly, we will share how organizations could use this dataset to train custom models for their use cases. The deep neural network is fine-tuned in a hierarchical process by iteratively removing (filtering) data points with lower label confidence and retraining. What You Will Learn: In this talk, we will share lessons we learned in answering three questions and the metrics stakeholders care about. Abstract: While most existing reinforcement learning (RL) research is in the framework of Markov Decision Processes (MDPs), it is important and indeed necessary, both theoretically and practically, to consider RL in continuous time with continuous feature and action spaces, for which stochastic control theory offers a natural underpinning. Tickets are refundable up to 30 days before the event. Run your chat groups and virtual gatherings! 21 , May , 2020 - Online (EDT) Read more. 10:10 – 10:45 Opening Keynote: #data_engineering Abstract: Tubi is an advertiser based video-on-demand service that allows its users to watch content online. Ashish Bansal, Director, Recommendations Systems at Twitch. Start Date: January 30th, 2020. #deep_learning Causal assessments are usually done through A/B tests, which however are not always feasible. Although it is a fundamental step for many data science tasks, an efficient and standard framework is absent. The Old Mill, Toronto, ON ... Suite 401 Toronto, Ontario M5V 3A8 Ai & Machine Learning Strategies Summit 2020. Many product companies have an established team of data science experts; many have an established team of UX experts. Business Leaders, including C-level executives and non-tech leaders, will explore immediate opportunities, and define clear next steps for building their business advantage around their data. Abstract: A.I. Abstract: In this talk, I will overview the basic concepts of quantum computing and its applications. The special focus will be on artificial intelligence and machine learning for COVID-19. It starts with the gap between ML in research and ML in production. What You Will Learn: ML infrastructure and tool stacks are endlessly interesting and convoluted. #data_analytics Event cost: From C$1,395. Jacopo Tagliabue, Lead A.I. Ari Kalfayan, Senior Business Development Manager - AI/ML & VC at Amazon Web Services. Read more. Online Conferences Q: How can I contact the organizer with any questions? Presenters will speak to optimization realized through the approach and provide insights into how the business was considered throughout the data and analytics journey. Join the AI and Machine Learning Strategies Summit to learn about the latest technologies and imple... 3 °C | Thursday, February 4, 2021 toronto.com Abstract: In machine learning, often a tradeoff must be made between accuracy and intelligibility: the most accurate models usually are not very intelligible, and the most intelligible models usually are less accurate. 3) Two real-world use cases demonstrating why using synthetic data generation can significantly improve model performances. What You Will Learn: Practical advice and mistakes from having launched two top tier ML tools companies, Joe Greenwood, Vice President Data Strategy - North America at Mastercard. Despite the vast opportunities that lie within our data, there are also explicit challenges to revealing their potential. - The modeling process, identifying, using, and cleaning data from many sources, - The planning and operationalizing of findings in a quick efficient manner, - Key decision points faced (cost of being wrong, false positives, etc. Q: What are the technical requirements to be able to participate? What You Will Learn: How to maximize the value of geospatial data using machine learning and artificial intelligence techniques, business problems that can be tackled in a variety of industries using this type of data, and how to utilize algorithms specific to spatial data. Biases may arise at different stages in machine learning systems, from existing societal biases in the data to biases introduced by the data collection or modeling processes. Deep Learning Summit, Toronto 2020 has 6 exhibitors including Alegion, Algorithmia, and Neurosoph. Abstract: In 1955, John McCarthy and colleagues proposed an AI summer research project with the following aim: “An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.” More than six decades later, all of these research topics remain open and actively investigated in the AI community. #data_science Models developed only with a global perspective can result in missing valuable insights, and potential harms from models that are biased in their results, or inadvertently exclude groups in society. Online. TIME: 14:00 IST / 09:30BST / 18:30 AEST / 16:30 ACT(3HOURS) REGISTER HERE. Abstract: In recent years, fuelled by the advances in supervised machine learning, we have seen astonishing leaps in the application of deep neural networks. Some cognitive scientists have proposed that analogy-making is a central mechanism for conceptual abstraction and understanding in humans. For Futher Information Visit The AI Summit Contact Us Page. These hurdles limit the accessibility many organizations have to NLP capabilities, putting the significant benefits advanced NLP can provide out of reach. This performance can be impacted by biases, which can lead to a subpar experience for subsets of users, content providers, applications, or use cases. Results: This pricing product has been used in three different countries: Peru, Columbia, and Mexico in various products such as a mortgage, SPL, and term deposit with great feedback that has helped Scotiabank to capture international banking customer behavior and their price sensitivity more promptly. Matt Sheehan, Fellow at The Paulson Institute. Toronto, Canada. Abstract: The Vector Institute’s project, Recreation of Large Scale Pre-Trained Language Models (the NLP Project), is an industry-academia collaboration that explores how state-of-the-art natural language processing (NLP) models could be applied in business and industry settings at scale. Q: Will you focus on any industries in particular? Details about data for training own models, Emeli Dral, CTO and Co-founder at Evidently AI. Abstract: Recent advances in machine translation has resulted in systems of very high quality, but only for a very limited set of the world’s more than 7000 languages. This often limits the accuracy of models that can safely be deployed in mission-critical applications such as healthcare where being able to understand, validate, edit, and ultimately trust a model is important. Toronto Machine Learning Summit Visit the Innodata virtual event booth November 19th for the presentation “Bogged Down by Annotation, Why SMEs Should Do the Heavy Lifting” with Innodata’s Chief Product & Marketing Officer. Gonzalo Corrales, Sr. Director - Robotics and Machine Learning and Shahid Amlani, Director of Machine Learning at Rogers Communications. The Big Data & Analytics Summit Canada is designed to provide data executives with current trends, strategic insights, and best practices trending in technology, data, AI, machine learning, risk management, and retaining talent.. We will discuss recent experimental implementations of these quantum generative models, in both, superconducting-qubit and ion-trap quantum computers. In this presentation, we study a specific synthetic data generation task called downscaling, a procedure to infer high-resolution information (e.g., individual-level records) from low-resolution variables (e.g., an average of many individual records), and propose a multi-stage framework. Online. Here are the top 22 machine learning conferences in 2020: 1. You may also find my experience helpful, which is that we have never needed a black box model for a high stakes decision because we have always been able to construct an interpretable model that is at the same level of predictive performance as the best black box we could find. These event series bring together the latest technological advancements as well as practical examples to apply AI to solve challenges in business and society. What You Will Learn: Real-world learnings from putting deep learning models rapidly from research to production through solid Ops and orchestration. See who else is going to Toronto Machine Learning Summit 2020, and keep up-to-date with conversations about the event. I will present what are the state-of-the-art quantum algorithms, its advantages, and limitations. ... I Studied 365 Data Visualizations in 2020. Recently the AI community has witnessed an increasing trend for training larger and larger neural models (e.g., GPT-3, T5, BERT) that achieve state-of-the-art results but require enormous computation, memory, and energy resources on the Cloud. ... Reinforcement Learning Summit Toronto, Canada: Oct 19 - Oct 20, 2021: NA: Discount: AI for Good Summit Toronto, Canada: Nov 11 - Nov 12, 2021: NA: Discount: ODSC West 2021 If we use interpretable machine learning models, they come with their own explanations, which are faithful to what the model actually computes. At each RE•WORK event, we combine the latest technological innovation with real-world applications and practical case studies. Go to Main Content. We will unpack the idea of race as relationships and race as data in its historical and current contexts. Validating your business model (1-10 customers). Ali Madani, Leader of Machine Learning at Cyclica Incorporation. What You Will Learn: How to set out an enterprise approach to the responsible use of data and AI, how to translate that into global data strategy elements and frameworks, and then how to use regional or country-specific data and model building strategies, Marco Túlio Ribeiro, Senior Researcher at Microsoft Research. recordings)- Talks for beginners/intermediate & advanced- Network and connect through our event app- Q+A with speakers- Channels to share your work with the community- Run your chat groups and virtual gatherings!- Hands-on Workshops*PLEASE NOTE BONUS WORKSHOPS ARE ON THE 16TH AND 17TH OF NOVEMBER. Abstract: Working with and analyzing geospatial data requires a different and often nuanced approach from most data types, especially to derive spatial predictions and detect patterns using machine learning applications. Learn how they built a machine learning system for automatically moderating comments from millions of readers. Shreyansh Daftry, AI Research Scientist at NASA Jet Propulsion Laboratory. There’s a record amount of exciting Machine Learning (ML) and Deep Learning conferences worldwide and keeping track of them may prove to be a challenge. 5-6 Apr, Middle East Banking AI & Analytics Summit. This phenomenon—known as the cold start problem—is a pain point for almost any AI company that wants to scale. Practical applications will be discussed, including personalized medicine, humanoid robotics, and grammar learning. recordings), Talks for beginners/intermediate & advanced, Network and connect through our event app, Channels to share your work with the community. These biases may impact the performance of various components of ML systems, from offline training to evaluation and online serving in production systems. It covers the differences between DataOps, ML Engineering, MLOps, and data science, and where each fits into the framework. What You Will Learn: Case studies from the media sector, How to drive the change in your organization, and what do you actually need to make the change. Alegion Alegion’s platform blends human and machine intelligence to provide accurate labeled data used to train or validate machine learning models. What You Will Learn: Neural machine translation, applications of machine learning to Indigenous languages, challenges of domain adaptation in low-resource settings, Jaakko Lempinen, Head Of Customer Experience at Yle.
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