Meet Our Speakers

Conference Chair

Advanced Technical / Research

Click Speaker to Read Abstract

Business Strategy

Click Speaker to Read Abstract

Case Studies

Click Speaker to Read Abstract

Workshops

Click Speaker to Read Abstract

Virtual Workshops

Click Speaker to Read Abstract

Lightning Talks

Click Speaker to Read Abstract

Tickets

Agenda

This agenda is still subject to changes

Talk: Large Neural Nets for Amortized Probabilistic Inference for Highly Multimodal Distributions and Model-Based ML

Presenter:
Yoshua Bengio, Scientific Director, Mila / Full Professor, University of Montreal

About the Speaker:
Recognized worldwide as one of the leading experts in artificial intelligence, Yoshua Bengio is most known for his pioneering work in deep learning, earning him the 2018 A.M. Turing Award, “the Nobel Prize of Computing,” with Geoffrey Hinton and Yann LeCun.

He is a Full Professor at Université de Montréal, and the Founder and Scientific Director of Mila – Quebec AI Institute. He co-directs the CIFAR Learning in Machines & Brains program as Senior Fellow and acts as Scientific Director of IVADO.

In 2019, he was awarded the prestigious Killam Prize and in 2022, became the computer scientist with the highest h-index in the world. He is a Fellow of both the Royal Society of London and Canada, Knight of the Legion of Honor of France and Officer of the Order of Canada.

Concerned about the social impact of AI and the objective that AI benefits all, he actively contributed to the Montreal Declaration for the Responsible Development of Artificial Intelligence.

Talk Track: Technical / Research

Talk Technical Level: 7/7

Abstract:
Current large language models and other large-scale neural nets directly fit data, thus learning to imitate its distribution. Is it possible to do better? Consider the possibility, which we claim is actually the typical case, where a world model that captures causal structure in the world would require substantially fewer bits than the code that performs the kinds of inferences that we may desire from that knowledge. For example, the rules of the game of Go or the rules of logical reasoning are fairly compact, whereas inference (e.g., playing Go at Champion level, or being able to discover or prove theorems) may require a lot more computation (i.e., huge neural nets).

In fact, exact inference for many problems (in science, computing, or in coming up with Bayesian posteriors) is often intractable and so we resort to approximate solutions. With amortized probabilistic inference, we expend computation upfront to train a neural net that learns to answer questions (for example sample latent variables or parameters) in a way that is as consistent as possible with the world model, making run-time inference quick.

This also makes it possible to exploit the generalization ability of the approximate inference generative learner to guess where good answers (e.g., modes of a highly multimodal posterior distribution) might be. It also allows to decouple the capacity needed for inference from the capacity needed to describe the world model. This is unlike current state-of-the-art in deep learning, where inference and world model are confounded, yielding overfitting of the world knowledge and underfitting of the inference machine.

We have recently introduced a novel framework for achieving this kind of model-based ML, with generative flow networks (or GFlowNets), which have relations to reinforcement learning, variational inference and generative models. We’ll highlight some of the advances achieved with GFlowNets and close with our research programme to exploit such probabilistic inference machinery to incorporate in ML inductive biases inspired by high-level human cognition and build AI systems that focus on understanding the world in a Bayesian and causal way and generating probabilistically truthful statements.

Talk: Bloomberg GPT: How We Built A 50 Billion Parameter Financial Language Model

Presenter:
David Rosenberg, Head of ML Strategy, Office of the CTO Bloomberg

About the Speaker:
David Rosenberg leads the Machine Learning Strategy team in the Office of the CTO at Bloomberg. He is also an adjunct associate professor at the Center for Data Science at New York University, where he has repeatedly received NYU’s Center for Data Science “Professor of the Year” award. Previously, he was Chief Scientist at Sense Networks, a location data analytics and mobile advertising company, and served as scientific adviser to Discovereads, a book recommendation company first acquired by Goodreads and later Amazon. He received his Ph.D. in statistics from UC Berkeley, where he worked on statistical learning theory and natural language processing. David received a Master of Science in applied mathematics, with a focus on computer science, from Harvard University, and a Bachelor of Science in mathematics from Yale University. He is currently based in Toronto.

Talk Track: Technical / Research

Talk Technical Level: 5/7

Talk Abstract:
We will present BloombergGPT, a 50 billion parameter language model, purpose-built for finance and trained on a uniquely balanced mix of standard general-purpose datasets and a diverse array of financial documents from the Bloomberg archives. Building a large language model (LLM) is a costly and time-intensive endeavor. To reduce risk, we adhered closely to model designs and training strategies from recent successful models, such as OPT and BLOOM. Nevertheless, we faced numerous challenges during the training process, including loss spikes, unexpected parameter drifts, and performance plateaus.

In this talk, we will discuss these hurdles and our responses, which included a complete training restart after weeks of effort. Our persistence paid off: BloombergGPT ultimately outperformed existing models on financial tasks by significant margins, while maintaining competitive performance on general LLM benchmarks. We will also provide several examples illustrating how BloombergGPT stands apart from general-purpose models.

Our goal is to provide valuable insights into the specific challenges encountered when building LLMs and to offer guidance for those debating whether to embark on their own LLM journey, as well as for those who are already determined to do so.

What you’ll learn:
You’ll learn about the challenges that are often faced when designing and training LLMs, as well as some approaches to address these challenges. You’ll also see how domain-specific datasets can benefit LLMs.

Talk: Can Grammar-Based Ground-Up Models be Relevant in the Age of ChatGPT?

Presenter:
Suhas Sreehari, Staff Research Scientist, Oak Ridge National Lab

About the Speaker:
Dr. Suhas Sreehari is a Staff Research Scientist at the Oak Ridge National Lab in the US. His research interests are in explainable AI models, signal processing, and Bayesian estimation and optimization. He is the recipient of several important research awards such as the SIAM Imaging Sciences Best Paper Prize, the IEEE Young Author Award, and the Presidential Scholar Award. Dr. Sreehari holds a PhD in electrical engineering from Purdue University, and is a Senior Member of the IEEE.

Talk Track: Technical

Talk Technical Level: 5/7

Talk Abstract:
With large language models such as GPT-4, LaMBDA, and LLaMA, research and commercial interest in natural language understanding (NLU), analysis, and generation has soared. However, these models typically require hundreds of billions of parameters and enormous volume of data to perform self-supervised learning. Furthermore, there is a lack of control and explainability in these models.

Although not to directly compete with GPT-level performance, in this work, we propose a ground-up approach to NLU that is based on graphs. The motivation for this research is two-fold:

To evaluate the efficacy of using grammar and “mild conditioning” in the
performance of NLU algorithms.
To keep the algorithm simple enough to be able to explain its outputs.

The first point is useful to understand the sparsity of language data that are currently used in LLMs. Although we do not directly answer this, what we are discovering is whether the LLMs can learn effectively from much smaller data if the data are projected to lower-dimensional manifolds that are related to fundamental language concepts such as grammar and social constructs. The second point is of paramount importance in light of ethical and factual concerns currently associated with LLMs.

Here is a succinct outline. We formulate an algorithm that is able to read through text sequentially, updating the core graph. The graph nodes represent characters, objects, ideas, and events. The edges represent their relationships. Unlike traditional graphs, edges are time-stamped to account for dynamic behavior and relationships that could be time-varying and/or causal. We then learn collectively-exhaustive contexts that span the entire text. We also discuss loss functions for the context learning. We then use our proposed “context mixture models” (CMMs) and to perform robust Bayesian inference. We finally demonstrate the promise of such an approach through text summarization, question answering, and explainability analysis.

What You’ll Learn:
This work, to the best of our knowledge, is new. We are proposing “context mixture models”, which is similar to Gaussian mixture models, but is more flexible because of the looser parameterization of the probabilistic models defining context in language. Further, we approach a well-explored field from a different angle. Instead of having large language models train on huge datasets, we aim to find a lower dimensional subspace governed by grammar rules, context, and social conditioning — to make these models way lighter, but still effective.

Talk: Paving the Road to Trustworthy AI: Bias in Large Language Models

Presenter:
Jekaterina Novikova, Director of Machine Learning Research, ARVA / Cambridge Cognition

About the Speaker:
Jekaterina is leading Machine Learning research efforts at AI Risks & Vulnerability Aliance and at Cambridge Cognition. Over the last decade Jekaterina worked in the intersection of Language Technology and Machine Learning, for interdisciplinary applications including Natural Language Generation, Machine Learning for Healthcare, Spoken Dialogue Systems, and Human-Robot Interaction.

Now, her research focus is on trustworthy machine learning: she works on ensuring qualities such as fairness, transparency, privacy, and robustness, and implementing them in real-world use cases. Jekaterina has been recognized as one of the “Top 25 Women in AI in Canada”, “30 Influential Women Advancing AI in Canada”, and received the “Industry Icon” award from the Applied Research in Action program at the University of Toronto. She has several best research paper nominations at international academic conferences. More information on her work can be found at jeknov.github.io

Talk Track: Technical

Talk Technical Level: 4/7

Talk Abstract:
Language models have become essential components in various NLP applications, such as machine translation, sentiment analysis, and question-answering systems. However, there is a growing concern that these models may perpetuate and amplify bias present in the data on which they were trained. Detecting bias in large language models is crucial because biased language models can amplify existing societal biases, such as racism and sexism, or perpetuate harmful stereotypes. When not properly controlled, the use of biased language models in decision-making processes, such as those used in loan applications, healthcare, hiring, and criminal justice, can result in unfair outcomes.

Bias in NLP models can arise from historical patterns, biases, and stereotypes encoded in the training data. For example, a study of classifiers trained to predict the occupation of a person based on their biography was more likely to predict “surgeon” for men’s biographies compared to women’s. In our work, we focus here on masked language models such as BERT that are fine tuned to perform specific tasks. We want to understand how pretraining and fine tuning each contribute to the observed biases of these models.

Such an understanding not only provides practitioners actionable guidance on how to responsibly use and finetune pretrained language models, it also highlights ‘infeasibility conditions’, i.e. situations where it is impossible to fully mitigate fairness-specific risks of downstream applications of a model.

What You’ll Learn:
This is and extremely hot topic right now (think of the “Pause Giant AI Experiments” open letter and similar high-scale concerns of AI advancements) and it was not properly presented and discussed at TMLS previosly

Talk: Safely Managing Generative AI with Humans-in-the-Loop: Lessons Learned from Risky Real-World Applications

Presenters:
Richard Zuroff, Senior Vice President Growth, BlueDot | Alexei Nordell-Markovits Director AI Delivery, Moov AI

About the Speakers:
“Richard is the Senior Vice President of Growth at BlueDot, a global infectious disease intelligence company that uses human and artificial intelligence to detect and assess the risks of emerging and endemic disease threats. Richard began his career at McKinsey where he advised clients on advanced analytics, before founding the strategy and governance practice at Element AI, and continuing to work on democratizing the use of AI at DataRobot. Richard has spoken about the social and economic impact of AI/ML at the World Economic Forum, Partnership on AI, and Brookfield Institute. He is the author of a recent book chapter on Explainable AI for Financial Services, and has published in the Canadian Journal of Law and Technology. Richard holds an MBA, two Law degrees, and a BSc in Cognitive Science from McGill University.

Alexei has been building systems and teams within the context of modern AI for the better part of a decade. He has worked in diverse subfields, including process optimization, Mlops and Responsible AI. He is an AI director at Moov AI where he works with clients helping them discover and then implement AI solutions within business processes. In his spare time, he enjoys reading junky science-fiction.”

Talk Track: Case Study

Talk Technical Level: 3/7

Talk Abstract:
Rapid progress in large language models (LLMs) has ignited interest in a wide array of exciting generative use cases for both consumer- and enterprise-facing applications. As adoption accelerates, it is critical to ensure these systems remain safe and trustworthy. Researchers are exploring technical solutions (such as constitutional AI) but in this talk we discuss two design patterns that involve putting humans-in-the-loop (HITL) of production-ready generative AI systems. HITL approaches to responsible generative AI are well aligned with regulators’ push for human oversight of “high risk” systems and are more likely to help companies create a strategic moat (compared to just using foundational models).

We present two real-world case studies. BlueDot’s global disease surveillance team uses LLMs to identify concerning outbreaks of infectious diseases around the world. This is challenging because each outbreak is unique and has potentially conflicting media reports, but it is essential that an LLM-based summary be factual and specific – not hallucinated based on past events. This has implications for how internal experts (epidemiologists) use the system to create value and potential IP, and what can be exposed externally to users. Moov.AI needed to develop two question and answer services incorporating a knowledge base and decided to use a LLM to produce the content for these systems. The challenge was to avoid hand-crafting every aspect of the question and answer style while managing the reputational risk that inappropriate or off-style answers would create. The solution involved different HITL at different steps for fine-tuning and data enhancement.

We conclude by summarizing the lessons learned from these two use cases and the implications for other organizations that are considering generative AI use cases.

What you’ll learn:
Real-world case studies; insights from practitioners

Talk: Demystifying Large Language Models: ChatGPT, GPT-4, and the Future of AI Communication

Somayeh Sadat, Assistant Director, CARTE, University of Toronto | Alex Olsen, Senior Research Associate, CARTE, University of Toronto

About the Speakers:
At CARTE, Alex collaborates with faculty, industry partners, and students to bring the latest advances in AI to a broad audience. He has contributed to machine learning-based research in a wide variety of fields, and runs training for learners at every level.

Somayeh Sadat is the assistant director at the Centre for Analytics and Artificial Intelligence Engineering (CARTE), University of Toronto. In this role, she facilitates and oversees partnerships with industry and government to collaboratively conduct state-of-the-art research and translate and commercialize effective solutions in analytics and artificial intelligence, where applicable with leveraged funds. She also helps nurture the next generation of engineering talent through facilitating experiential learning opportunities with external partners. Somayeh holds a Ph.D. from the University of Toronto, and has over fifteen years of experience in research and education, business development, and consulting.

Talk Track: Case Study

Talk Technical Level: 1/7

Talk Abstract:
The rapid advancements in artificial intelligence (AI) have given rise to a new era of large language models (LLMs) such as ChatGPT and GPT-4. These sophisticated AI systems are capable of understanding and generating human-like text, making them invaluable tools for communication and problem-solving. However, the complexity of these models often leaves the general public feeling overwhelmed and unsure of their implications. This talk aims to demystify LLMs, explaining their inner workings in an accessible manner, while highlighting their potential impact on our society.

The presentation will begin by offering an introduction to the concept of LLMs, touching on their history and development. We will explore the key principles behind these AI systems, such as neural networks and natural language processing, in a way that is easy to grasp for a non-technical audience. The primary focus will be on ChatGPT and GPT-4, two of the most advanced and widely-discussed models currently available.

Next, we will delve into the practical applications of these LLMs, demonstrating their versatility in tasks ranging from content creation to customer support. By showcasing real-world examples, attendees will gain a better understanding of how these AI models can transform various industries and streamline everyday tasks.

Finally, we will address some of the ethical and societal implications brought about by the increasing adoption of LLMs. Topics such as data privacy, algorithmic bias, and the potential for AI-generated misinformation will be discussed, providing a balanced perspective on the benefits and challenges of these revolutionary technologies.

By the end of this talk, attendees will have a clearer understanding of large language models and their impact on our lives. Join us for an enlightening journey into the world of AI communication, where we will explore the potential of ChatGPT, GPT-4, and other LLMs to shape our future interactions with technology.

What You’ll Learn:
I have experience giving this talk to a completely non-technical audience, for which I’ve had great feedback. The aim of this talk is for people to walk away feeling like they actually understand the functionality of LLMs, without needing a technical background.

Talk: How Companies can Navigate and Approach the New Advancements in Generative AI

Michel Dubois, Principal Director, Experimental Development, Mila | Cameron Schuler Chief Commercialization Officer & VP, Industry Innovation, Vector Institute | Kirk Rockwel, Chief Operating Officer, Alberta Machine Intelligence Institute | Alex LaPlante, Interim Head, Borealis AI, Royal Bank of Canada (RBC)

About the Speakers:
As Director, AI Activation at Mila, the Quebec Artificial Intelligence Institute, Michel Dubois actively participates in the development of AI for the benefit of all. He holds a master’s degree in mathematics and is currently a PhD candidate in engineering (machine learning). He is also the author of a patent on the mathematical optimization of high-bandwidth signal switching. Over the past 28 years, Michel Dubois has consolidated his experience in several aspects of machine learning and artificial intelligence. Before joining Mila, Michel Dubois held the role of Associate Partner at IBM, and, in his previous role, he was Vice President of Artificial Intelligence at Newtrax, a Montreal start-up that quickly experienced international success, reaching an average annual revenue growth of 80% and opening offices in Santiago, Perth, Moscow and London.

Cameron Schuler is the Chief Commercialization Officer & Vice President, Industry Innovation at the Vector Institute. He is the former Executive Director of Amii, where, for 8 years, he led one of the top-ranked Machine Learning and AI groups in the world. Cameron’s multifaceted career has covered finance, business & product development, consumer products, IT and general management from start-ups to mature companies. His industry experience includes Alternative Energy, Banking, Consumer Products, Information Technology (Consumer and Enterprise), Investment Sales and Trading, Life Sciences, Manufacturing, Medical Devices, Oil & Gas, and Oil & Gas Services. Roles have included COO, CFO, President and CEO and he was COO & CFO of a food manufacturer whose products lead to sales of over 250 million units.

Kirk Rockwell is a project and operations manager with more than 20 years of experience managing technology and innovation initiatives, across a broad range of sectors in partnership with academia, all levels of government, and some of Canada’s largest companies.

Within an arm’s length government agency, he had responsibility for investment of public funds into research and innovation projects across multiple sectors including energy, clean tech, agriculture and health, utilizing technologies like AI and Machine Learning, Nanotechnology and Genomics.

He has experience with numerous public/private partnerships (P3s) including membership on advisory and governance boards where the stakeholders include large multinational corporations, the governments of Alberta and Canada, universities, and other small or medium-sized companies.

He has a Diploma in Environmental Technologies, a Bachelor of Science Degree in Environmental Sciences, and a Master’s of Business Administration Degree with a specialization in Innovation and Entrepreneurship.

Kirk is currently the VP, Public Strategy and Gants at the Alberta Machine Intelligence Institute (Amii) in Edmonton, Alberta, Canada. He resides in Edmonton with his wife and two daughters.

Alex LaPlante is currently the interim Head of Borealis AI, RBC’s R&D lab for artificial intelligence, where she and her team build and deploy leading-edge AI solutions to complex business problems found across the enterprise. Previously, Alex led Borealis’ business development and product management teams, which saw her design the company’s long-term product vision and build strategic partnerships with RBC stakeholders. Before joining Borealis, Alex held a number of leadership roles at the intersection of technology and finance. She holds a PhD in operations research from the University of Toronto.

Talk Track: Panel Discussion

Talk Technical Level: TBA

Talk Abstract:
TBA

What You’ll Learn:
TBA<

Talk: Building A Measurement Platform for Personalization: A Bayesian Approach

Presenter:
Vanessa Pizante, Data Scientist, Sobeys

About the Speaker:
I am a data scientist at Sobeys working on the personalization team to help deliver personalized offers to customers through our Scene+ rewards program. My academic background is more mathematics focused, as I have an undergraduate degree in Applied Mathematics from the University of Calgary and a Masters in Statistics from the University of Toronto. During my career, I’ve also learned a lot about software development, design and MLOps; and enjoy combining these newer skills with my math-focused education to build effective and long-lasting machine learning and measurement frameworks.

Talk Track: Case Study

Talk Technical Level: 3/7

Talk Abstract:
Personalization programs can have a significant impact on a business, but measuring their effectiveness can be challenging. Our team at Sobeys faced the task of building a system that could communicate the value of our personalized offers to external stakeholders and inform our internal team about what works and what does not. This required us to develop an experimentation and measurement platform that could measure the effects of a hierarchy of treatment variants, compute these effects across different customer segments, and aggregate the results consistently. In this talk, we will share our experience in creating a configurable, automated process that can handle repeated experimentation and measurement.

Simple frequentist A/B measurement approaches proved to be insufficient when measuring the effectiveness of our complex hierarchy of treatment variants, across an independent hierarchy of customer segments. The issue is that contradictory results can regularly arise when slices of hierarchical data are tested independently. For instance, two segments can show a positive lift individually, but the overall lift across both segments could be negative (this phenomenon is known as Simpson’s Paradox). Multilevel models can be leveraged in both the frequentist and Bayesian paradigm to help address this. However, in our use case the Bayesian paradigm offered several useful advantages including the capability to explicitly define the structure of the hierarchy via prior specification and better handling of smaller customer segments.

In general, attendees of this talk can expect to learn:

How to effectively assign customers to treatment variants to optimize measurement – regardless of the statistical paradigm in use.
The basics of Bayesian statistics and the advantages it can offer in measurement of hierarchical experiments.
How we used this Bayesian model to attribute lift to different components of the program, compared model variants, and measured results across various customer segments.
How we adopted an MLOps-like approach in designing our measurement platform in order to support this Bayesian model.

What you’ll learn:
Bayesian statistics at scale and taking an automated approach to complex Bayesian modelling is a topic I struggled to find much about. Also dealing with nested experiments in a robust manner, at scale, within this automated framework.

Talk: Automated Inspection for Rail Cars Using Computer Vision and Machine Learning

Presenter:
Ashley Varghese, Data Scientist, Canadian National Railways (CN)

About the Speaker:
Ashley is a data scientist at Canadian National Railway. She works in the automated inspection program overseeing the development and retraining aspects for the inspection of rail cars. She has over 12 years of research experience in computer vision and deep learning. Her research papers were published in multiple international conferences and journals. She has previously worked as an AI Scientist with Qii.AI and as a researcher with TCS Innovation Lab. She holds an MTech in Computer Science from the International Institute of Information Technology, Bangalore.

Talk Track: Case Study

Talk Technical Level: 4/7

Talk Abstract:
CN is one of the organizations which realized the importance of AI very early and adopted some of the latest technologies in railroad operations. We at CN have been evaluating and adopting some of the latest approaches to carry out automated inspections of railcars that improve overall operational efficiency. The automated inspection of the railcars is achieved by leveraging computer vision and machine learning techniques. For automated inspection of railcars, the train passes through the portals which have equipped with two generations of cameras and are positioned to capture all sides of the railcar including the undercarriage. Once these images are captured, they are sent to the inference engine to detect the defects. However, developing machine learning pipelines and training robust models comes with its own challenges.

In this talk, I would be covering how computer vision and machine learning techniques are used for developing some of the use cases, and various challenges associated with developing these machine learning pipelines. One of the key challenges is selecting the right data for training which must be representative of the actual data from the portal. Self-supervised learning-based techniques are adopted to identify the unique samples from the pool of unlabeled datasets.

Another challenge associated with the model performance is image quality. The quality of the image captured at the portals is affected by different weather and lighting conditions. In addition to these, there are several other challenges such as subjectivity in defect classification, lack of samples for the defective class and imbalanced datasets, etc. I will pick one of the use cases we developed and then go over discussing some challenges faced and how we tackled these challenges to improve the performance of the use cases. By choosing the right data and computer vision approaches, it is possible to develop effective solutions for the automated inspection of cars.

What you’ll Learn:
This talk provides insight into how computer vision and machine learning are applied in the railroad domain for the inspection of rail cars. It includes the challenges, strategies, and practical considerations that are associated with developing an ML-based automated inspection pipeline for a real-world, safety-critical application.

Talk: Looking into AI/ML from A Venture Capital Lens

Presenters:
Yatong Li, Managing Director, Sixty Degree Capital | Ryan Shannon, Investor, Radical Ventures | Vik Pant, PhD, Founder, Synthetic Intelligence Forum | Michelle Yu, Investor, Georgian

About the Speakers:
With over 10 years of experience in technology, finance and investment banking, Yatong leads Sixty Degree Capital’s investment in the technology space. He joined Sixty Degree Capital as an Associate in 2017 and quickly rose through the ranks to become a Managing Director on the technology investment team where he focuses on enterprise software opportunities. During his six year tenure at the firm, he managed to build the firm’s portfolio in the tech space with over 12 portfolio companies — including GrubMarket, MioVision, Pragma, DataGrail, Arctic Wolf, MacroMeta, Tact.AI, Lime, and Schrödinger.

Ryan Shannon is a member of the investment team at Radical Ventures.

Prior to joining Radical, Ryan was a Private Equity Investor at TPG Capital in San Francisco, where he focused on Leveraged Buyouts, Corporate Carve-outs, and Take-privates of North American businesses. Previously, Ryan worked as an Investment Banker in the Financial Sponsors group at Barclays in Los Angeles.

Ryan received an HBA from the Ivey Business School at Western University, where he graduated as an Ivey Scholar, and an MBA from Harvard Business School.

Vik is the Founder of the Synthetic Intelligence Forum, a global community of practice focused on Data Science and applied AI. SIF is a professional networking, knowledge sharing, and collaborative learning hub for professional data scientists, AI researchers, and ML engineers.
He is an Adjunct Professor in the Faculty of Information (iSchool) at the University of Toronto and the Department of Geography, Environment, and Geomatics at the University of Ottawa.
Vik earned a doctorate from the Faculty of Information (iSchool) in the University of Toronto where his thesis was unanimously accepted by the examination committee As-Is and without any changes. His scholarship is focused on game-theoretic optimization of strategic coopetition in complex multi-agent systems. His research has been published in numerous peer-reviewed scholarly journals and he has also presented his academic research at refereed scholarly conferences and juried workshops.

Michelle is an Investor at Georgian, a growth-stage firm investing in high growth technology companies that harness the power of data in a trustworthy way. Michelle focuses on sourcing investment opportunities, investment due diligence, and advising companies post-investment on go-to-market, market analysis and product strategy. Prior to joining Georgian, Michelle worked on the investment team at Vectr Ventures in Hong Kong and for BDC Capital in Montreal. Michelle has a Bachelor of Commerce with a Double Major in Finance & International Management, with a Minor in Political Science, from McGill University.

Talk Track: Panel Discussion

Talk Technical Level: 3/7

Talk Abstract:
As a Toronto based Canadian VC focusing on global investments, Sixty Degree Capital invests in software that’s transforming industries, and the digital infrastructure that supports it. We have invested in lots of exciting portfolio companies including Arctic Wolf in the Cybersecurity space, DataGrail innovating on Data Privacy, Paperspace providing MLOps platform, MacroMeta solving Edge Computing bottlenecks, Pragma work in the Gaming Infrastructure space, Tact.ai and Radius Agent as Vertical SaaS products etc.). We believe AI/ML is a fundamental building block for next generation software, so Sixty Degree has spent lots of time to research, source, invest in the sub sectors. While we’re a direct VC fund, we’ve allocated capital to become LPs of notable venture funds such as Lightspeed, a16z, NEA, Tiger, NfX, Initialized, Foundation Capital and Upfront Ventures, which gives us a special information advantage on what are the top tier Silicon Valley VCs are investing.

What You’ll Learn:
Insider information on how investors are allocating capital in AI/ML now

Talk: A Machine Learning-Driven Phenotyping Platform for Rapid in Vivo Target Validation and Precision Medicine Development

Presenter:
Rokshana Stephny Geread, Data Scientist – Computer Vision, Biosymetrics

About the Speaker:
Steph joined BioSymetrics in January 2021, with a background in biomedical ECE engineering (MASc) and electrical engineering (BEng). She has diverse work experience, starting with terminal web development for Bell Canada before building, validating, and delivering a production-ready machine learning model for breast cancer imaging diagnosis. This last project was a collaboration with University Health Network and St Michael’s Hospital, Toronto, and resulted in three journal articles.

“I transitioned from the telecommunications industry to healthcare because I wanted to make a positive impact on patients’ quality of life. I wanted to contribute to bridging the gap between technology and healthcare, which has the potential to enhance the overall healthcare system.

Talk Track: Case Study

Talk Technical Level: 4/7

Talk Abstract:
Ebrafish are a popular model organism in developmental biology, molecular genetics, and toxicology studies due to their small size, low breeding costs, transparent embryos, morphological identification, and similarity of their genome to the human genome. They are particularly well suited for large scale phenotypic screening in drug discovery studies, where observable characteristics (phenotypes) can be monitored due to biological effects from a small molecule or gene modification (via CRISPR gene editing). Using imaging and computer vision analysis, some zebrafish phenotypes that can be monitored include morphology, heart rate, and ejection fraction (the amount of blood the heart pumps). To scale up these experiments, some logistical challenges need to be addressed. First, standardizing the positioning of live zebrafish embryos is necessary to produce consistent imaging data and maximizing the value of experiments. Second, up to thousands of images need to be labelled for specific organs to analyze specific phenotypes, a time-consuming task if performed manually. To address these challenges, we have developed a comprehensive zebrafish phenotyping assay that uses custom 3D-printed mounting platforms to affix embryos in a standardized orientation for consistent image and video acquisition. We then developed a computer vision analysis pipeline that couples a fine-tuned instance segmentation model with downstream phenotyping analysis, providing quantitative. The model, based on the Detectron2 segmentation model architecture, is trained to automatically detect individual zebrafish embryos and its organs from images generated by our screening assay. We applied our approach to study several genes associated with cardiovascular disease using CRISPR injections, identifying subtle cardiac abnormalities. Our results demonstrate highly accurate segmentation on validation data, achieving average precision scores of 60% for the ventricle, and over 80% for other major organs. Our phenotyping platform is designed to close the gap between gene and phenotypes, thereby empowering and accelerating drug discovery programs with a higher probability of clinical translation.

What you’ll Learn:
Using AI driven technology and image processing to analyze cardiac phenotypes in zebrafish images and videos.

Talk: Leveraging ML in Multi-Family Real Estate Industry: From Predicting Rentals to Streamlining Operations

Presenter:
Zahra Kharal, Data Scientist (Principal) Venterra Realty & ML Engineer

About the Speaker:
Published 12 journal papers and more than 20 conference papers. I was a women in engineering rising star: here. Lastly, I was responsible for building the AI/ML department at Venterra, from it’s conception to where it is today; in less than 3 years, we have implemented many models in production and developed a propriety in-house app using AI that allows us to invest considerably better.

Talk Track: Case Study

Talk Technical Level: 3/7

Talk Abstract:
The multifamily real estate industry has witnessed an increased adoption of machine learning (ML) in recent years; allowing it to recover from its initial relatively slow adoption of ML technology and catch up to other industries in the field. The use of ML started to move into the multifamily real estate industry, offering new and innovative ways to analyze and operate properties, providing greater efficiency and optimization to investors, owners, and property managers. This presentation will showcase a collection of innovative ML case studies and application projects in multi-family real estate at Venterra Realty.

One case study at Venterra where ML has shown its value was in predicting renter trends and tenure tendencies. At Venterra, we started 20 years ago, capturing and structuring data, that we thought would eventually be useful for this purpose. By utilizing the 20 years of data that Venterra had, including 70+ features, Venterra built ML algorithms and pipelines that can more accurately help in identifying tenant behavior patterns, such as rent-paying habits and the likelihood of renewing a lease. This accumulative data was then used to make occupancy forecasts months in advance. The results of these models were used to understand tenant behavior better, improve retention rates and help planning and budgeting. An interesting question arose from this model: If we give the model results to the property managers, how will it influence their behavior in regard to tenant dealings.

Another interesting application of ML at Venterra was identifying maintenance needs and developing models for preventative maintenance. By analyzing data from millions of maintenance requests and several other sources, over many years, Venterra used NLP and classification techniques to predict request categories and thus assign these requests to the correct department in almost real time. Additionally, using deep-learning time-series models Venterra was able to predict when repairs will be necessary and schedule them proactively, while also predicting the budget required for these repairs.

Through these case studies and application projects, attendees will gain an understanding of how ML is influencing operations at Venterra. They will see to succeed in ML endeavours, data strategies and proper data collection methodologies needs to be made decades in advance, and learn how pipelines need to be developed to successfully put the models in production. The benefits that Venterra has received since then will also be briefly discussed. Ultimately, this presentation will inspire traditionally non-tech industries to explore the potential of AI and ML in their own operations, and will prepare them for what’s needed in order to succeed.

A bit about Venterra: Venterra specializes in the identification, development, finance, acquisition, and management of multi-family residential communities in the United States. We manage a portfolio of multi-family real estate assets totaling over $4.7 billion in value and have completed more than $8.7 billion of real estate transactions. Venterra has differentiated itself from its competitors by deploying industry leading technology to the benefit of both residents and investors. This technology includes a comprehensive suite of entirely web-enabled applications covering rent management, pricing optimization, purchase order management, utility billing, work order and capital expenditure management; an entire suite of back-end financial applications and customized workflows; and reporting and more recently the adoption of ML.

What You’ll Learn:
Through the case studies and application projects at Venterra Realty, attendees will gain an understanding of how ML is transforming the operations at Venterra and the multi-family real estate industry at large. They will learn specifically what data problems are encountered in the real estate domain, how the models were developed and what pipelines needed to be developed to successfully put the models in production. Issues encountered, unique to this industry, will also be discussed.

Talk: Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors’ Reasoning with Deep Reinforcement Learning

Presenter:
Arsene Fansi Tchango, Senior Applied Research Scientist, Mila – Quebec Institute in Artificial Intelligence

About the Speaker:
Arsene Fansi Tchango is a senior applied AI scientist at Mila, the Quebec AI Institute. His topics of interest are reinforcement learning, natural languange processing, and the application of deep learning techniques on graphs. Before joining Mila, Arsene obtained his PhD at INRIA, the French National Institute for Research in Digital Science and Technology and spent more than 5 years working in the industry.

Talk Track: Workshop

Talk Technical Level: 4/7

Talk Abstract:
The automation of the medical evidence acquisition and diagnosis process has recently attracted increasing attention in order to reduce the workload of doctors and democratize access to medical care.

However, most works proposed in the machine learning literature focus solely on improving the prediction accuracy of a patient’s pathology. We argue that this objective is insufficient to ensure doctors’ acceptability of such systems.

In their initial interaction with patients, doctors do not only focus on identifying the pathology a patient is suffering from; they instead generate a differential diagnosis (in the form of a short list of plausible diseases) because the medical evidence collected from patients is often insufficient to establish a final diagnosis. Moreover, doctors explicitly explore severe pathologies before potentially ruling them out from the differential, especially in acute care settings.

Finally, for doctors to trust a system’s recommendations, they need to understand how the gathered evidences led to the predicted diseases. In particular, interactions between a system and a patient need to emulate the reasoning of doctors.

In this talk, we propose to model the evidence acquisition and automatic diagnosis tasks using a deep reinforcement learning based approach that considers three essential aspects of a doctor’s reasoning, namely generating a differential diagnosis using an exploration-confirmation approach while prioritizing severe pathologies. We discuss metrics for evaluating interaction quality based on these three aspects, and demonstrate that the proposed solution performs better than existing models while maintaining competitive pathology prediction accuracy.

What you’ll learn:
Integrating doctor’s reasoning in a Machine Learning system

Talk: Building A Customized, High-Performance Chat Engine with GPT-4

Presenters:
Ethan C. Jackson, Co-Founder & Research Lead, ChainML | Ron Bodkin Co-Founder & CEO, ChainML | Daniel Kur Machine Learning Scientist, ChainML

About the Speakers:
Ethan is co-founder and research lead at ChainML, a startup focused on building scalable, composable AI systems and applications. He is also a founding member of the Social AI Research Group at the University of Toronto. Ethan was previously an Applied ML Scientist with Vector Institute’s AI Engineering Team, where he served as technical lead for several applied AI projects in collaboration with many industry and public sector partners. Ethan holds a PhD in Computer Science from Western University and trained as a postdoc under the supervision of Graham Taylor at the University of Guelph.

Ron is co-founder and CEO of ChainML, a startup that is delivering Generative AI supercharged by Web3. Ron was previously VP of AI Engineering and CIO at the Vector Institute and before that was responsible for Applied AI in the Google Cloud CTO office. Ron also was co-founder and CEO of enterprise AI startup Think Big Analytics that was acquired by Teradata and VP Engineering at AI pioneer Quantcast. Ron was also co-founder and CTO of C-Bridge Internet Solutions that IPO’d. Ron has a Master’s in Computer Science from MIT and an honors B.S. in Math and Computer Science from McGill University.

Daniel is a Machine Learning Scientist at ChainML, a startup that is delivering Generative AI supercharged by Web3. Prior to that, Daniel worked in the machine learning team at ServiceNow, joining as part of the acquisition of Element AI. Daniel also has experience in finance, starting his career in investment banking working on mergers and acquisitions at TD Securities. Daniel has a Master of Business Analytics and Bachelor of Business Administration from the Schulich School of Business at York University.

Talk Track: Workshop

Talk Technical Level: 5/7

Talk Abstract:
As business interest in generative AI continues to grow, it’s crucial for developers to understand the technical aspects of integrating cutting-edge language models like GPT-4 into business applications with strong requirements for customization, performance, and reliability. In this hands-on workshop, we will present our reusable process for successful integration of GPT-4 into business applications. We will give details about our strategies for prompt engineering, context building, and response evaluation – all with the goal of helping LLMs to give their best possible answers, according to a diverse set of evaluation criteria. As a point of reference, we will share details about our experience developing a highly customized chat experience to the Space and Time Web3 Data Warehouse that includes special features like AI-assisted database querying, data visualization, and time series forecasting.

We will also discuss our process for ongoing development and adaptation of chat applications, with a focus on how we are anticipating the rapid emergence of new tools and frameworks, particularly in the open-source domain. By the end of this workshop, participants will be able to simplify their approach to business chat application development by adopting or extending our process, and they will understand how development processes are likely to evolve with rapidly changing AI and LLM ecosystems.

What You’ll Learn:
You’ll learn how to apply an end-to-end experimental approach to building robust chat systems with GPT-4 at the core. We will go into details about the strenghts and limitations of both proprietary APIs and open-source frameworks, giving a pragmatic view on how to be productive with these tools today.

Talk: Deploying an OpenAI Chatbot as A Personalized Digital Tutor for Undergraduate Students in Higher Education, A Workshop

Presenter:
Dr. Sean Wise, Professor, Toronto Metropolitan University

About the Speaker:
Dr. Sean Wise BA LLB MBA PhD is an expert on startups & venture capital. He uses this expertise in his various roles as: tenured Toronto Metropolitan University, formerly Ryerson University, Professor, bestselling author, international business speaker, and partner at Ryerson Futures, a seed stage venture capital fund and technology accelerator.

Dr. Wise has been an entrepreneur since age 13. His latest venture is the edtech startup ProfBot.ai, which is in stealth mode. He’s already sold one of his startups for a staggering $30 million dollars, demonstrating his keen eye for successful business opportunities.

He has been in the Venture Capital industry for more than 15 years and has mentored hundreds of innovation focused startups; collectively, these companies have raised more than $2.1 Billion in capital. Over the last seven years, Dr. Wise has invested in over 26 startups, making him a seasoned veteran in the world of entrepreneurship.

Dr. Wise spent five seasons as an advisor for CBC on the mega hit venture reality show Dragons’ Den (aka Shark Tank, in America) before moving in front of the camera as the host of the Naked Entrepreneur, which airs on the Oprah Winfrey Network. Dr. Wise writes a national column for the Huffington Post (titled: “Dear Professor Investor”) in which he answers some of the toughest startup questions posted.

Dr. Wise studied economics and engineering at Carleton University, earned his MBA and Law degrees from the University of Ottawa, and received his PhD in Business from the Adam Smith Business School at the University of Glasgow. He has published five books and more than two dozen peer-reviewed research papers and case studies of high-growth startups. His research on social network analysis for collective intelligence and innovation has been featured at MIT’s COINs conference and the Academy of Management. Dr. Wise is also a talented moderator having spent five years as the host of the Naked Entrepreneur, which aired on the Oprah Winfrey Network.

As faculty at Toronto Metropolitan University, Dr. Wise has been exploring the ethical and social impacts of AI use cases for campus. As an investor with Ryerson Futures, he has supported dozens of startups, including one of Canada’s leading AI companies: Ada.

Talk Track: Workshop

Talk Technical Level: 3/7

Talk Abstract:
Join us for a cutting-edge workshop at the AI conference that will explore how to deploy OpenAI chatbots as personalized digital tutors for undergraduate students in higher education. This workshop will delve into the use of natural language processing and machine leDeploying an OpenAI Chatbot as a Personalized Digital Tutor for Undergraduate Students in Higher Education, A workshoparning algorithms to create chatbots that adapt to the pace, style, and knowledge level of individual students.

Participants will learn how to design and implement chatbots that provide a 24/7 accessible and tailored educational experience, improving student engagement and satisfaction. Don’t miss this opportunity to discover how AI can revolutionize education and enhance learning outcomes for the next generation of students.

Please bring a short exam you have given recently, along with the correct answers and the grading rubric for that test to the workshop and YOU WILL LEAVE WITH YOUR OWN WORKING CHATBOT.

What you’ll Learn:
At this workshop, everyone will be building hands-on. They’re very own digital tutor to take back to their office.

Workshop: Fine-Tuning Language Models with Declarative ML Orchestration

Presenter:
Niels Bantilan, Chief Machine Learning Engineer, Union.ai

About the Speaker:
Niels is the Chief Machine Learning Engineer at Union.ai, and core maintainer of Flyte, an open source workflow orchestration tool, author of UnionML, an MLOps framework for machine learning microservices, and creator of Pandera, a statistical typing and data testing tool for scientific data containers. His mission is to help data science and machine learning practitioners be more productive.

He has a Masters in Public Health with a specialization in sociomedical science and public health informatics, and prior to that a background in developmental biology and immunology. His research interests include reinforcement learning, AutoML, creative machine learning, and fairness, accountability, and transparency in automated systems.

Track: Virtual Workshop

Technical Level: 4/7

Abstract:
The use of Language Models (LMs) has become more widespread in recent years, thanks in part to the broader accessibility of datasets and the ML frameworks needed to facilitate the training of these models. Many of these models are large – hence the terminology of Large Language Models (LLMs) – and serve as so-called foundation models, which are trained by organizations with the compute resources to train them. These foundation models, in turn, can be fine-tuned by the broader machine learning community for specific use cases, perhaps on proprietary data. One of the barriers that make fine-tuning these models is infrastructure: even with cloud tools like Google Colab and the wider availability of consumer-grade GPUs, putting together a runtime environment to fine-tune these models is still a major challenge. This workshop will give attendees hands-on experience on how to use Flyte to declaratively specify infrastructure so that they can configure training jobs to run on the required compute resources to fine-tune LMs on their own data.

What You’ll Learn:
This workshop has two main learning goals. First, attendees will learn the main concepts behind Flyte, a workflow orchestrator for data and machine learning. Many of these concepts are orchestrator-agnostic, such as containerization for reproducibility, declarative infrastructure, and type-safety. Secondly, they will also learn how to leverage the latest deep learning frameworks that optimize memory and compute resources required to fine-tune language models in the most economical way.

Prerequisite Knowledge:
Intermediate Python, working knowledge of Docker, and intermediate knowledge of machine learning.

Workshop: Making GenAI Safe, Trustworthy and Fit-for-purpose with Auto Alignment

Presenters:
Rahm Hafiz, CTO & Co-Founder, Armilla AI | Dan Adamson, CEO & Co-Founder, Armilla AI

About the Speakers:
Rahm Hafiz is the co-founder and CTO of Armilla AI, a company helping institutions testing and building more fair, safe, trustworthy, and useful AI. In the past, Rahm headed AI initiatives at Outside IQ and Exiger where he worked with global financial institutions, government and regulatory bodies to bring innovative AI into reality. Rahm’s PhD work offers an efficient and modular framework for syntactic and semantic analyses and understanding of natural language by addressing some long standing NLP problems including correct processing of ambiguity. Rahm’s research on NLP has been published in over 10 reputable journals and conferences.

Dan Adamson is the Co-Founder and CEO of Armilla.AI, a company helping institutions testing and building more fair, safe, trustworthy, and useful AI. Previously, he founded and served as OutsideIQ’s CEO, since its inception in 2010 until its acquisition in 2017 by Exiger, where he remained as their President overseeing product and cognitive computing research. Dan also previously served as Chief Architect at Medstory, a vertical search start-up acquired by Microsoft. He is an expert on vertical search, investigative use of big data and cognitive computing with more than 15 years in the industry. He holds several search algorithm and cognitive computing patents, has been named among the most influential “must-see” thought leaders in AI and FinTech, in addition to being a recipient of numerous academic awards and holding a Master of Science degree from U.C. Berkeley.

Track: Virtual Workshop

Technical Level: 4/7

Abstract:
We discuss a new alignment templating technology that can be used to enhance the fairness, robustness and safety of the generative AI by understanding the expected behaviour of the model, measuring where the model is underperforming with synthetic test data, and iteratively improving the model with minimal humans in the loop fine-tuning approach.

This alignment platform can be used for use case specific tasks including coercing a generative model to be more fair towards under represented groups, less toxic and less misogynistic, more leaning towards a desired political viewpoints, to use specific tone while generating answers for customer service applications, to preserve PII, to guard models from generating potentially harmful responses etc.

Our alignment technology can interact with users as needed to adjudicate critical decision points to guide its intention-understanding, data generation, testing and tuning capabilities to be more contextual.

What You’ll Learn:
By the end of the workshop, participants will understand common shortcomings of Gen AI, how to use a self-governing alignment technology to overcome those shortcomings and to make GenAI useful for user specific tasks.

Prerequisite Knowledge:
Concepts of large language models, text 2 image models.

Workshop: Building an End-to-End Web Application Integrated with Microsoft's Semantic Kernel and a Large Language Model

Presenter:
Chinmay Sheth, Senior Machine Learning Engineer, Royal Bank of Canada (RBC) | Colleen Gilhuly, PhD, Data Scientist, Royal Bank of Canada (RBC) | Haleh Shahzad, Director, Data Science, Royal Bank of Canada (RBC)

About the Speaker:
Chinmay Sheth is a Senior Machine Learning Engineer at the Royal Bank of Canada and is completing his MSc in Computer Science at McMaster University. His responsibilities include providing support for MLOps tooling, promoting ML models to production, and developing production-grade data pipelines.

Colleen completed her PhD in Astronomy & Astrophysics at the University of Toronto. She then turned to data science in order to combine her favourite aspects of research with a non-academic career. She joined the Chief Data Office at RBC in July 2022, working primarily on projects in NLP.

Haleh is a Director, Data Science at RBC with 9+ years of experience in AI (Deep learning, machine learning), software development and advanced analytics.

She is currently leading AI initiatives for cutting-edge solutions to maximize the impact of data across organization at RBC. She is also an instructor in the school of continuing studies at York University.

Haleh has a Ph.D. degree in Electrical and Computer Engineering from McMaster university where she was also part of the sessional faculty in the Electrical and computer engineering department.

Track: Virtual Workshop

Technical Level: 5/7

Abstract:
Large language models (LLMs) such as ChatGPT are able to interpret text input and generate human-like responses. Many individuals and companies are excited to use this technology, but integration remains a question mark. Applications using LLMs are also limited by their tendency to invent information and give unpredictable answers.

Microsoft’s Semantic Kernel is an open source, lightweight SDK which enables fast integration of LLMs into a wide range of applications. It also enhances the power of LLM with a structured approach to responses and the ability to refer to external sources of truth.

In this workshop, we will give a crash-course on Microsoft Semantic Kernel and demonstrate how to create a simple web app that harnesses the power of LLMs from OpenAI. This workshop will be hands on so please come prepared with an OpenAI API key.

What You’ll Learn:
– Learn the basics of LLMs, and their applications/impacts in industry
– Hands-on experience with Semantic Kernel’s skills, planner, memories, and chains
– You will be able to build your own web app with an LLM integrated into the backend

Toronto Machine Learning Summit

Meet Our Speakers

Conference Chair

Advanced Technical / Research

Business Strategy

Case Studies

Workshops

Virtual Workshops

Lightning Talks

Tickets

Agenda

TMLS

Talk: Large Neural Nets for Amortized Probabilistic Inference for Highly Multimodal Distributions and Model-Based ML

Talk: Bloomberg GPT: How We Built A 50 Billion Parameter Financial Language Model

Talk: Quantifying the Uncertainty in Model Predictions

Talk: Efficient Inference of Extremely Large Transformer Models

Talk: OpenFL: A Federated Learning Project to Power (and Secure) Your Projects

Talk: Can Grammar-Based Ground-Up Models be Relevant in the Age of ChatGPT?

Talk: Paving the Road to Trustworthy AI: Bias in Large Language Models

Talk: Prompt Optimization and Parameter Efficient Fine-Tuning

Talk: Explainable AI in Finance: Present and Future Challenges

Talk: Explainable Machine Learning in Finance – What If Machines Beat the Human Intelligence?

Talk: Do We Still Need Clinical Large Language Models?

Talk: Privacy & Security of Large Language Models, Risks and Mitigation

Talk: Data Storytelling for Beginners

Talk: The Natural Language Processing Revolution in Investment Management: Advice and Responsible Investing

Talk: Understanding Where Generative AI Fits into Business

Talk: AI @ Scale to Understand Impact of Weather on Businesses

Talk: Safely Managing Generative AI with Humans-in-the-Loop: Lessons Learned from Risky Real-World Applications

Talk: Demystifying Large Language Models: ChatGPT, GPT-4, and the Future of AI Communication

Talk: How Companies can Navigate and Approach the New Advancements in Generative AI

Talk: Building A Measurement Platform for Personalization: A Bayesian Approach

Talk: Automated Inspection for Rail Cars Using Computer Vision and Machine Learning

Talk: Looking into AI/ML from A Venture Capital Lens

Talk: A Machine Learning-Driven Phenotyping Platform for Rapid in Vivo Target Validation and Precision Medicine Development

Talk: Leveraging ML in Multi-Family Real Estate Industry: From Predicting Rentals to Streamlining Operations

Talk: ML in Production: Implementation, Tooling & Engineering, Data/MLOps

Talk: Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors’ Reasoning with Deep Reinforcement Learning

Talk: Behind the Scenes - PyTorch 2.0

Talk: Unlocking the Power of Generative AI with Hugging Face Transformers

Talk: A Technical Deep Dive into Integrating Large Language Models into your Product

Talk: Accelerate Machine Learning with Pycaret

Talk: Profiling ML Workloads for Energy Footprint

Talk: Building A Customized, High-Performance Chat Engine with GPT-4

Talk: HoloViz: Visualization and Interactive Dashboards in Python

Talk: Build A Serverless ML System in Python in Less Than 1 Hour

Talk: Troubleshooting Unstructured Data with Embeddings

Talk: Deploying an OpenAI Chatbot as A Personalized Digital Tutor for Undergraduate Students in Higher Education, A Workshop

Talk: Accelerating Large Models in Production: A Practical Guide

Talk: Training Machines for Autonomous Interactive Artworks

Talk: Prompt Engineering with Large Language Models

Talk: Training Machines for Autonomous Interactive Artworks

Workshop: Deploying generative AI Models: Best Practices and An Interactive Example

Workshop: Fine-Tuning Language Models with Declarative ML Orchestration

Workshop: An Open-Source Approach to Build Guardrails for Deploying ML Models in Healthcare

Workshop: Making GenAI Safe, Trustworthy and Fit-for-purpose with Auto Alignment

Workshop: Challenges and Learnings of Machine Learning at Scale

Workshop: Building an End-to-End Web Application Integrated with Microsoft's Semantic Kernel and a Large Language Model

Workshop: Creating an ML Model as A Business User

Workshop: Unleashing the Full Potential of Your Recommender System in Production

Talk: How Do You Make GenAI Safe, Trustworthy and Useful for Enterprises?

Talk: RISC-y Business: How Open Chip Architectures are Changing the AI Ecosystem for the Better

Talk: Expert Insight - The Value of Solving Business Weather Challenges

Talk: Monitoring LLMs: Where to Begin? What's Important?

Talk: Giving Everyone Super Powers To Build the Next GenAI Apps

Talk: New Performance Frontier - Deep Learning Compilers

Sign Up for TMLS 2023 News Updates

Who Attends

TMLS 2021 Event Demographics

TMLS 2021 Technical Background

TMLS 2021 Attendees & Thought Leadership