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Subject: Call for Participation - SEMLA 2021 (free Webinars, multiple dates, June 2021)
From: SEWORLD Moderator <[log in to unmask]>
Reply-To:SEWORLD Moderator <[log in to unmask]>
Date:Tue, 25 May 2021 04:38:51 -0000
Content-Type:text/plain

The Software Engineering for Machine Learning Applications (SEMLA) 
international symposium aims at bringing together leading researchers 
and practitioners in software engineering and machine learning to 
reflect on and discuss the challenges and implications of engineering 
complex data-intensive software systems.

Given the current pandemic situation, SEMLA 2021 will be in the form of 
a series of free Webinars.

Speakers include Ben Shneiderman, Nisha Talagala, Q. Vera Liao, Danilo 
Sato, Arif Wider, Yingnong Dang, Grace Lewis. 
(https://semla.polymtl.ca/2021speakers/)

We invite everyone to join. Registration to the Webinars is free but 
required. For the full program and the registration links, please visit: 
https://semla.polymtl.ca

To register to all SEMLA 2021 Webinars at once, please visit: 
https://www.eventbrite.ca/e/semla-2021-full-registration-tickets-154740638415


SEMLA 2021 Program
==================

Tuesday, June 8, 2021

Speaker: Ben Shneiderman, Professor of Computer Science, University of 
Maryland

Title: Human-Centered AI: What it is and how software engineering can 
contribute to its success.

Date/time: June 8, 2021, 12:00pm-1:00pm EDT

Registration link: 
https://www.eventbrite.ca/e/semla-2021-tickets-154239627879


Abstract:

A new synthesis is emerging that integrates AI technologies with HCI 
approaches to produce Human-Centered AI (HCAI). Advocates of this new 
synthesis seek to amplify, augment, and enhance human abilities, so as 
to empower people, build their self-efficacy, support creativity, 
recognize responsibility, and promote social connections. These 
passionate advocates of HCAI are devoted to furthering human values, 
rights, justice, and dignity, by building reliable, safe, and 
trustworthy systems. The talk offers three ideas:
•    HCAI framework, which shows how it is possible to have both high 
levels of human control AND high levels of automation.
•    Design metaphors emphasizing powerful supertools, active 
appliances, tele-operated devices, and information abundant displays.
•    Governance structures to guide software engineering teams, safety 
culture lessons for managers, independent oversight to build trust, and 
government regulation to accelerate innovation.
The talk will emphasize the software engineering practices that will 
make machine learning more reliable, by increasing audit trails, 
reducing bias, and supporting explainability. These ideas are drawn from 
Ben Shneiderman’s forthcoming book (Oxford University Press, January
2022). Further information at: https://hcil.umd.edu/human-centered-ai  
Join the Human-Centered AI Google Group at:  
https://groups.google.com/g/human-centered-ai and follow 
@HumanCenteredAI on Twitter.

----------------------------------------------------------

Tuesday, June 15, 2021

Speaker: Nisha Talagala, CTO/VP of Engineering at ParallelM

Title: AI Literacy: Its Role in the Workforce of the Future

Date/time: June 15, 2021, 12:00pm-1:00pm EDT

Registration link: 
https://www.eventbrite.ca/e/semla-2021-tickets-154319145719

Abstract:

The AI market is projected to grow to $190 Billion by 2025. AI is being 
used in every industry and is projected to be a core skill for the 
future. While lack of production machine learning (MLOps) was a limiter 
in the last few years, these limits are starting to be overcome, with 
MLOps practices now standard in many organizations. More businesses are 
starting to see positive returns from their AI initiatives. We are 
shifting to a new phase of AI development, where broad segments of the 
non-technical workforce are encountering AI in their job roles. This is 
both exciting and fraught with peril. Instances of AI failures, legal 
issues, and ethical issues are rising. There is pressure on AI 
development to accommodate not just data scientists but people from all 
walks of life. To achieve the next levels of returns from AI require 
technologies and solutions that combine AI and human intelligence.

In this talk, we discuss recent AI and Machine Learning technology 
trends and the role of MLOps in driving AI commercial success. We then 
discuss what it takes to bring AI knowledge out of the technical domain 
and into the broader workforce, and technology trends like low-code that 
enable broad adoption. We will then discuss a framework for AI Literacy 
– the Four Cs – and experiences of bringing AI literacy to individuals 
worldwide.

----------------------------------------------------------

Thursday, June 17, 2021

Speaker: Q. Vera Liao, Research AI at IBM T.J Watson Research Center

Title: Questioning the AI: Towards Human-Centered Explainable AI (XAI)

Date/time: June 17, 2021, 12:00pm-1:00pm EDT

Registration link: 
https://www.eventbrite.ca/e/semla-2021-tickets-154319603087

Abstract:

Artificial Intelligence technologies are increasingly used to make 
decisions and perform autonomous tasks in critical domains such as 
healthcare, finance, and criminal justice. The needs to understand AI in 
order to improve, contest, develop appropriate trust and better interact 
with AI systems have spurred great academic and public interest in 
Explainable AI (XAI). Recently, open-source toolkits, including IBM 
Research’s AI Explainability 360, are making a growing collection of XAI 
techniques into practitioners’ toolbox. My colleagues and I at IBM 
Research conduct human-computer interaction (HCI) research that aims to 
empower AI practitioners to make effective and responsible use of such a 
toolbox to create good XAI user experiences. Meanwhile, our work 
provides insights into real-world user needs for AI explainability to 
inform gaps and opportunities for XAI algorithmic research. Our work 
follows two complementary paths. First, we conduct HCI research by 
designing and studying XAI systems of various use cases in the AI 
lifecycle. Second, we study AI design practices of product teams and 
engage with the design community to develop and advocate for 
user-centered design processes for XAI. I will conclude the talk with 
lessons learned for bridging the process of creating responsible AI 
systems and empowering people in the process.

----------------------------------------------------------

Tuesday, June 22, 2021

Speaker: Danilo Sato and Arif Wider, Head of Data & AI Services at 
ThoughtWorks UK and professor at HTW Berlin

Title: Continuous Delivery for Machine Learning

Date/time: June 22, 2021, 12:00pm-1:00pm EDT

Registration link: 
https://www.eventbrite.ca/e/semla-2021-tickets-154319976203

Abstract:

Machine Learning applications are becoming popular in our industry, 
however the process for developing, deploying, and continuously 
improving them is more complex compared to more traditional software, 
such as a web service or a mobile application. Continuous Delivery for 
Machine Learning (CD4ML) is the discipline of bringing Continuous 
Delivery principles and practices to Machine Learning applications. In 
this talk we will share our industry experiences implementing CD4ML, 
introduce its technical components, and explore what future challenges 
need to be solved.

----------------------------------------------------------

Thursday, June 24, 2021

Speaker: Yingnong Dang, Principal Data Scientist Manager in Microsoft Azure

Title: AIOps: From Research Innovations to Industrial Adoptions

Date/time: June 24, 2021, 12:00pm-1:00pm EDT

Registration link: 
https://www.eventbrite.ca/e/semla-2021-tickets-154320267073

Abstract:

The scale and complexity of cloud computing has been ever-increasing. 
This brings challenges on effectively building and managing cloud 
computing systems that are highly efficient and reliable, enable high 
customer satisfaction, and achieve high engineering productivity. In 
this talk, I will first share an AIOps vision of infusing AI into the 
cloud computing platform and DevOps process. I will then share a few 
AIOps efforts in Microsoft Azure to demonstrate how an AIOps solution 
can be built and adopted in industrial settings. Specifically, I will 
share how Azure uses intelligent anomaly detection and correlation for 
safeguarding the rollouts of hundreds of component payloads to millions 
of machines spreading in 60+ Azure regions across five continents 
(project Gandalf safe deployment). I will also share how we built a 
resilient mechanism for Azure against failures by employing ML-based 
prediction and an online learning mechanism (project Narya). I will then 
talk about a few open challenges on cloud computing that need more AIOps 
research and innovations.

----------------------------------------------------------

Thursday, July 1, 2021

Speaker: Grace Lewis, Principal Researcher at CMU Software Engineering 
Institute

Title: Architecting ML-Enabled Systems

Date/time: July 1, 2021, 12:00pm-1:00pm EDT

Registration link: 
https://www.eventbrite.ca/e/semla-2021-tickets-154320509799

Abstract:

Developing software systems that contain machine learning (ML) 
components requires an end-to-end perspective that considers the unique 
life cycle of these components — from data acquisition to model training 
to model deployment and evolution. While there is an understanding that 
ML components in the end are software components, there are some 
characteristics of ML components that bring challenges to software 
architecture and design activities, such as data-dependent behavior, 
drift over time, and timely capture of ground truth to inform 
retraining. The goal of this talk is to highlight some of these 
challenges, along with proposed practices and remaining gaps for 
successfully architecting ML-enabled systems.


=================================
Organizers: https://semla.polymtl.ca/organizers/

Follow SEMLA 2020 on:
- Twitter: @SEMLA_sym
- Facebook: @SEMLA_Symposium




-- 
Foutse Khomh, ing., Ph.D.
Professeur titulaire / Full Professor of Software Engineering
Titulaire de la Chaire de recherche FRQ-IVADO en assurance qualité des logiciels d'apprentissage automatique
FRQ-IVADO Research Chair on Software Quality Assurance for Machine Learning Applications
SWAT Lab., Mila - Quebec AI Institute, Polytechnique Montréal 1-514-340-4711 #4233 (Tél. / Phone)
C.P. 6079, succ. Centre-Ville     1-514-340-4657       (Téléc. / Fax)
Montréal, QC, H3C 3A7, Canada     www.khomh.net


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