Big Data & AI Toronto 2020
as the organisers put it
Big Data & AI Toronto 2020 will take place on September 29 - 30, 2020 online. Find out more about Big Data & AI Toronto 2020 and discover the best artificial intelligence and machine learning event alternatives.
| title | author | length |
|---|---|---|
| Leveraging existing technologies in a digital transformation | Christopher Janca, Roberto Gennaro, Sidita Zhabjaku | 42m |
Panelists report concrete moves: shifting print/TV budgets into Google Ads and analytics doubled measurable response, ecommerce cuts stores from 11 to 3, and… | ||
| Prioritizing well-being in the age of the algorithm | John C. Havens | 31m |
Argues that AI success metrics must shift from GDP/profit to triple bottom line outcomes, citing New Zealand’s 2019 well‑being budget and the Stiglitz… | ||
| Fairness and bias in AI | Stephen Thomas | 21m |
AI models inherit problems from training data: historical bias, representation bias, measurement bias. Examples include COMPAS showing twice the false positive… | ||
| A more human-centered approach to AI | Jess Holbrook | 31m |
Argues that AI must be designed around people not just metrics and shows concrete tools and fixes: People + AI Research (PAIR) publishes a six‑chapter… | ||
| AI predictions | Maciej Hryniewicki | 24m |
PwC projects $15.7 trillion by 2030, split roughly into 6.6T productivity and 9.1T consumption effects, and finds only 4% of firms plan enterprise AI in 2020… | ||
| Data governance | Mitu Mann, Julien Daniel, Nazia Shahrin, Mark Treveil | 47m |
Panelists report concrete practices: banks keep sensitive sets on-prem while translating policies for cloud migration, and teams avoid big‑bang moves by doing… | ||
| Harnessing the power of storytelling in sports | Renata Policicio | 34m |
A six-month study uses 56 fan diaries, eight focus groups and a 1,200-fan quantitative test of 60 short videos to turn 18 audience-derived attributes into five… | ||
| Securing the future of work: How cyber AI learns on the job | Marcus Fowler | 23m |
Darktrace describes an unsupervised learning system that models each environment’s “normal” without external retraining, claims near-seamless adaptation during… | ||
| Unintended consequences of technology: Enabling NextGen discrimination | Sarah Saska | 29m |
Under concrete examples, big companies’ systems reproduce and amplify bias: Amazon trains an AI resume screener on decade-old male-dominated CVs and scrapped… | ||
| Healthcare and IoT cybersecurity in the new age | Logan Wolfe | 28m |
Healthcare shifts to cloud during COVID drives a 5-fold increase in attacks on WHO staff and Google flags groups using COVID lures (including Charming Kitten)… | ||
| Accelerating ML impact | Stephen Chen | 22m |
Transforms an underperforming RBC ML team into a high-output engine by pushing 60 models in production in 6 months, cutting end-to-end model delivery from… | ||
| Deep learning for self-driving vehicle technology | Inmar Givoni | 31m |
Uber ATG describes moving from a modular stack to a single trainable system: a joint detection+prediction network that outputs multimodal future trajectories… | ||
| Re-thinking the journey to the cloud | Phil Keast | 33m |
Frames the cloud move as six concrete priorities: six pillars to plan for, with IT cost bump — double-running legacy and cloud causes higher bills up front —… | ||
| Demystifying the emerging legal issues of AI | Myron Mallia-Dare | 28m |
Rapid innovation creates concrete legal gaps: an autonomous trucking prototype (AutoTruck) learns from customer, sensor and third‑party feeds and produces an… | ||
| Predicting content virality to drive social traffic | Sriram Subramanian | 20m |
Conde Nast builds an AI system called Falcon that scores every article using models trained on past editor-selected posts and live site signals to predict… | ||
| The IoT storm... Will we survive the coming deluge? | Robert Falzon | 27m |
Claims that IoT scales from ~16 billion in 2016 to ~85 billion devices and ~127 devices per second, with 75% of new cars connected by 2023 and healthcare IoT… | ||
| Thinking Ahead About AI Security and Privacy | Olivera Zatezalo | 27m |
Forecasts rapid 5G-driven sensor growth and says AI must be governed with concrete controls: a five-category framework (policy, org/personnel, process/design… | ||
| The cloud has changed cyber security forever | Mark Ostrowski | 22m |
A supervised-then-unsupervised ML pipeline named Maggie trains on labeled endpoint and network features, uses feature extraction plus cloud-scale compute and… | ||
| Future of AI | Shyana Srikanthalingam, Masha Kandybin, Samer Geissah, Yevgeniy Vahlis | 44m |
Panelists claim firms can apply provable bias mitigation and set enterprise governance via a "trustworthy AI council," describe using a feature store to expose… | ||
| Cultivating an AI-driven enterprise | Lindsay Kulkin | 19m |
Large enterprises report 55% of Fortune 500 firms investing over $50M in AI and spending grows ~44% year over year, yet 90% of AI projects hit operational… | ||
| How organizations can put their data to work and build 10,000 responsible, human-centric AI models | Kurt Muehmel | 30m |
Argues companies must internalize AI capability rather than outsource, combining a Dataiku-style platform, contextualized training, certification, a branded… | ||
| The tale of data science | Ella Hilal | 32m |
Argues data science starts over 20,000 years ago with Shango bones (≈18,000 BC) and Sumerian tallies leading to written language, traces the abacus… | ||
| Developing AI that enhances human creativity | Domhnaill Hernon | 30m |
Built an AI digital twin called Second Self by training ML algorithms on Reepsone the beatboxer’s vocal techniques over a relatively short period, and the… | ||
| AI-ML and space exploration at NASA | Victoria Da Poian | 27m |
A machine‑learning pipeline aims to give the MOMA mass spectrometer on ExoMars on‑board science autonomy by turning raw LDMS traces into 1,000–to‑2000‑bin 1D… | ||
| Defending our reality in the era of deepfakes | Subbarao Kambhampati | 28m |
Describes concrete defenses and limits: AI Foundation builds the Reality Defender (rd2020.org) platform that aggregates detectors and returns confidence… | ||
| Enabling the next generation in voice technology | Thomas Henson | 26m |
Voice moves from novelty to infrastructure: combining Speech-to-Text and NLP for call-center triage and remote-support automation, with a cited jump from 3%… | ||
| Using big data and AI to boost geospatial insights for hazard prediction | Vik Pant | 28m |
Natural Resources Canada runs public projects that turn satellite and aerial archives into hazard models: GeoDeep Learning on GitHub publishes code for U‑Net… | ||
| Getting to yes - What investors look for in big data & AI startups | Ivy Nguyen, Jamie Rosenblatt, John Cowgill | 35m |
Investors prioritize concrete advantages: a clear day-one ROI that pays back within a year, a founding team mixing deep ML chops and domain expertise, and a… | ||
| The indigenous languages technology project at NRC: An empowerment approach to language | Roland Kuhn | 40m |
Three‑year, $6M NRC project builds practical tooling for Indigenous language work rather than abstract research: a usable read-along studio that aligns… | ||
| Canadian Landscape | CG Chen, Arsene Toumani, Ryan Khurana | 46m |
Two Canadian startups describe concrete wins and constraints: one builds a conversational AI chatbot that anonymizes triage for people facing homelessness and… | ||
| Data virtualization - Unlocking data - From silos to success? | Jean-Marc Boivin, Vincent L'Archeveque | 22m |
Desjardins deploys Denodo as a logical data fabric to stop data duplication, centralize access and avoid building another physical lake or warehouse. A six… | ||
| Bridging the Skills Gap | Maryam Haghighi, Kerry Joel, Christian Menkens, Michael Baraban | 45m |
Panelists describe concrete moves: Intact launches a Future of Work program with yearly action plans, monthly engagement tracking and new parallel technical… | ||
| AI in healthcare - Deep learning for precision psychiatry | Caitrin Armstrong | 28m |
Company develops a deep learning pipeline called Vulkan framework that ingests pooled clinical trial data and compresses 100,221 candidate predictors into… | ||
| Cloud repatriation - When does it make sense for you? | Hubert Chan | 29m |
IDC finds 50% considering repatriation and only two thirds of cloud projects stay on or under budget. Examples include a Windows Server 2008 VM moved to Azure… | ||
| Delivering business success with AI | Alina Rivilis | 23m |
Provides a practical framework for turning models into value: business lead, business opportunity, data, AI capability. Shows concrete wins — product… | ||
| The architecture behind modern cloud data warehouses | Shane Johnson | 28m |
Compares first‑gen cloud warehouses like Redshift (Postgres 8 derivative running on EC2 with attached disks) to newer designs that use separation of compute… | ||
| Scaling data science in the enterprise | Ajinkya Kulkarni, Jed Dougherty | 31m |
RBC standardizes on Dataiku as a single hybrid platform so 200+ data scientists share code, models and pipelines; a concrete project ingests PDFs and… | ||
| AI, big data, digital and future of agriculture | Rozita Dara | 26m |
Research shows farm sensors and IoT already produce half a million data points per day on some research sites, and pilots combine cloud, AI and image… | ||
| "Cybertelligence": How AI is changing the face of cybersecurity | Hisham Qaddoumi | 27m |
Overview of concrete claims and tools: maps AI onto NIST and the OODA loop, uses supervised models for malware, spam and IDS and unsupervised for DNS… | ||
| ETL vs ELT: Why the T has shifted | Sean Spediacci | 16m |
Argues that the industry moves from ETL to ELT because cloud warehouses like Snowflake BigQuery Redshift and Delta Lake separate storage and compute so teams… | ||
| Data saves lives: The power of crowdsourcing in a pandemic | Shrey Jain | 32m |
A web-based syndromic surveillance survey is built and launched on March 23 launch to feed public health heat maps; tech stack and workflow use Slack, Zoom… | ||
| An architecture for big data: Supporting journalism through transparency in advertising | Catherine Strong, Raman Sarvaria | 30m |
Index Exchange runs about 140 billion auctions/day producing ~100 TB of telemetry and ~20 GB of compressed reporting, and uses Vertica COPY from HDFS to ingest… | ||
| Workshop: Product development for identifying and leveraging ML opportunities | Amir Feizpour | 01h 09m |
Argues that most AI effort should be product-first not model-first, backing it with time breakdowns (40–50% data, 30–40% modeling + deployment, 10–30% problem… | ||
| Workshop: Intro to topic modeling using Python | Russell Martin | 01h 01m |
Uses Python with spaCy preprocessing, scikit-learn vectorizers and K‑Means, and gensim LDA on a 3% Yelp sample (~31,500 reviews). K‑Means on 1,000-term TF‑IDF… | ||
| Challenges of machine learning: The new ML lifecycle | Diego Oppenheimer | 30m |
Claims that only 10% deployed of organizations and that 50% never see the light frame the scale of the problem, with lab-to-prod delays often hitting 18… | ||
| Deploying distributed AI and machine learning in financial services | Dmitry Baev | 27m |
Deploys H2O stack on Hadoop Spark and Kubernetes to train distributed models and export portable binaries, claiming H2O Core algorithms scale to petabytes and… | ||
| Combing RPA with cognitive technologies | Penka Bocheva | 25m |
Data from a bank show 245 bots in production but maintenance drags results down: ~1 incident per bot per month and ownership runs about $5,000 per bot… | ||
| Machine learning and financial time series analysis | Keyvan Kasiri | 24m |
Compares market vs transactional series, claims stationarity, seasonality, asynchronous timestamps and inter-channel correlation cause most failures, and shows… | ||
| Knowledge graphs for financial institutions | Mike Carey, Paolo Délano Alonso | 25m |
BMO builds a Knowledge Graph with Neo4j to join 12 million customers, 880 billion in assets and federated relational data with unstructured docs and social… | ||
| Collaborative intelligence: How the emergence of data fabric technology unlocks the next big shift | Dan DeMers | 31m |
Cinchy demonstrates a working data fabric that turns siloed app stores into a shared network, showing a live demo connecting Salesforce to Cinchy and enriching… | ||
| Workshop: Intro to GPU data science with RAPIDS | Rhys Williams | 01h 03m |
Shows RAPIDS running on a Tesla V100 processing a 7 GB Twitter CSV with cuDF DataFrame and cuML random forest: preprocessing converts float64 to float32 to get… | ||
| Leveraging real time data & actionable insights for the financial sector | Vinnie Saini | 36m |
Banks need to act in micro‑moments using streaming pipelines, ML and cloud+edge so decisions happen in seconds not days; IDC predicts 30% of the global data… | ||
| Building large scale, transactional data lakes with Apache Hudi | Nishith Agarwal | 31m |
Apache Hudi implements ACID transactional lakes on DFS using Avro/Parquet, Spark/Hive/Presto clients and Kafka ingestion, and runs in production at Uber at… | ||
| Generating targeted AI revenue at mass scales | Jane Moran | 27m |
Air Miles builds an in‑house personalization system called Smokey platform that uses an ensemble of collaborative filtering, deep learning, reinforcement… | ||
| Blockchain in energy sector | Abhinav Tiwari | 28m |
Alectra Utilities builds a permissioned blockchain platform (based on Hyperledger Fabric) that records procurement, delivery and settlement for distributed… | ||
| Optimizing the future: The 5G & IoT revolution | Ferris Adi | 51m |
Claims that 5G delivers 1 millisecond latency, supports up to 1 million devices per km, and pushes speeds toward “10 gigabytes” while enabling 25 billion IoT… | ||
| AI risk governance | Mihaela Capra | 31m |
Argues that existing model-risk frameworks scale to AI by adding concrete practices: require independent review plus enhanced monitoring for production models… | ||
| How AI can help saving time, money, and your customers information? | Sahar Rahmani | 25m |
RBC builds a joint security operations analytics team that grows from ~3 to 13 data scientists and deploys a production pipeline: a cleaned data lake plus… | ||
| How Snowflake powers marketing analytics | Tim Fletcher | 23m |
Marketing still leaves value on the table: 87% say data is under‑utilized and only 20% of marketing spend is data‑driven, so the concrete goal is moving toward… | ||
| Data science for real problems | Xiaoming Zhang | 19m |
A data science team builds personalization as narrow microservices deployed to Google Cloud Platform using containers, Kubernetes, Helm and GitHub CI, training… | ||
| Fireside chat: Scaling on demand | Abbas Jaffery, Patrick Lopez | 30m |
Claims a sudden 40–45% transaction growth in e‑commerce and P2P payments during the pandemic, cites Amazon up ~63% and Netflix adding ~10M subscribers, and… | ||
| How Plan International embraced their digital journey with Talend data fabric | Dion Maynes, Altaz Hemani, Ivan Barsby | 26m |
Plan International Canada has 300,000 active supporters and faces integration across over 90 interfaces so the team chooses Talend for data prep, quality… | ||
| Use cases that drive the need for NoSQL databases | Jeff Morris | 21m |
A NoSQL platform that combines in‑memory key/value speed with JSON documents and N1QL (SQL for JSON) claims real customers and measurable scale: LinkedIn… | ||
| Deliver trusted data faster than the speed of business | Meena Ram, Dion Maynes | 28m |
Claims that poor data quality costs firms $12.9M a year and that the fix is not more hand coding but putting data lineage and metadata-driven controls in… | ||
| Cloud Migration | David Polsky, Karthik Ram, Veronica Arreaza | 45m |
A midmarket firm survives a ransomware attack by lifting about 40 servers and a week‑old ERP backup into public cloud and restores operations in 4–5 business… | ||
| Moving into the cloud | Amar Narain | 21m |
Retail chain migrates to Google Cloud with a phased hybrid design, rewrote code in Python using microservices/PaaS while keeping an on‑prem data lake and ETL… | ||
| Using tech for good can power greater representation | Eric Merkow | 14m |
Cites concrete initiatives: Ads for Equality (ad tools and best practices to reduce bias), Whirlpool’s Care Counts (installing washers/dryers in schools), and… | ||
| Putting big data self-service analytics in the hands of business people | Peter Kacandes | 21m |
Argues that a real‑time analytics stack built on Druid with streaming ingestion (Kafka) plus an intuitive UI (Imply Pivot) delivers subsecond queries on fresh… | ||
| MLOps, governance and trust | André Balleyguier | 32m |
Cites 90% McKinsey and survey numbers: 84% of data scientists distrust their models, 70% of executives see no AI value. Describes DataRobot MLOps: agents… | ||
| Architecting for plasticity - Demystifying the data fabric | Karanjot Jaswal | 33m |
Cinchy’s data fabric claims to eliminate point‑to‑point integration (integration “burns half of IT budget”) and to effectively double IT capacity by managing… | ||
| nothing matches | ||