Global Artificial Intelligence Conference 2020
2020-10-20..2020-10-22
-- online --
[Global Artificial Intelligence Conference 2020, Global Artificial Intelligence Conference 2024]
who it's for
data scientists, machine learning engineers, data engineers, mlops engineers, data platform architects, analytics translators, healthcare data scientists
topics
trustworthy AI explainability and robustness; productionizing ML with MLflow; cloud AI services and pipelines; SageMaker Debugger and Model Monitor; hyperparameter search with RAPIDS and SageMaker; quantum machine learning with TensorFlow Quantum; deepfakes and AI generated synthetic media; natural language processing for healthcare; satellite imagery damage assessment; platform oriented data architecture; data labeling cleaning and enrichment; conversational AI for COVID-19
| title | author | length |
|---|---|---|
| The Truth About Data: Why It’s Really Valuable.... | William Thomson | 19m |
Argues that data is inherently a non-rival good but becomes valuable when processes make it rival through labeling, cleaning, context enrichment and… | ||
| How Blockchain is disrupting the Finance Industry | Anurag Yadav | 01h 07m |
Claims that blockchain replaces intermediaries with a distributed ledger and smart contracts are illustrated with concrete numbers and products: global… | ||
| Data Science and ML with an emphasis on Banking... | Deepti Gupta | 27m |
Banking use case predicts loan default from a dataset with 88% non-defaulters, using support vector machines, random forest, and extreme gradient boosting and… | ||
| Future of Forecasting the Future in... | Arun Verma | 39m |
Bloomberg aggregates crowd forecasts via Whisper and cleans them with a three-pass waterfall that uses mean and median absolute deviation plus regime-detection… | ||
| Waking the Data Scientist at 2am: Detect... | Chris Fregly | 27m |
Explains how SageMaker Debugger captures tensors to detect vanishing gradients and “loss not decreasing” and can automatically stop a training job or send an… | ||
| Toward Trustworthy AI: Explainability and Robustness | Agus Sudjianto | 24m |
Regulated banking use cases focus on explainability and robustness for credit, fraud, market hedging and trader surveillance; models create financial and legal… | ||
| Making Happy Modelers: Build and Maintain... | Stephanie Kirmer | 32m |
Practical recipe for running ML pipelines: use COPY from S3 into Redshift with distribution and sort keys, run ANALYZE COMPRESSION and EXPLAIN to avoid… | ||
| Productionizing ML with ML Ops and Cloud AI | Kaz Sato | 31m |
Explains a concrete MLops stack using Google Cloud: AI Platform Notebooks, Deep Learning VM and Containers, AI Platform Training and Prediction, GKE, BigQuery… | ||
| The Essential Role of the Analytics Translator | Diedre Downing | 28m |
Overview of a practical translator role that shows specific tactics rather than high-level platitudes: uses an issue tree to narrow a real decision (ordering… | ||
| Semantic SentenceRank to Facilitate Reading.... | Jie Wang | 28m |
A tool ranks sentences into hierarchical reading layers for faster comprehension, handling English and Chinese and turning a 14‑page HBS case into a 10%… | ||
| Simplify and accelerate AI for the entire data science team | Erez Barak | 27m |
Claims that enterprises accelerate ML by broadening who builds models beyond data scientists to data engineers and BI analysts, by mixing local and multi cloud… | ||
| Cloud AI Services: What they are and how to use them | Karl Weinmeister | 36m |
Overview of concrete tools, limits and examples: Google Cloud offers prebuilt APIs for text, image and video (Natural Language, Vision, Video Intelligence)… | ||
| Platform Oriented Data Architecture for Acceler... | Vladimir Bacvanski | 30m |
Argues that platforms must own both operational and analytical data and publish high-quality data product interfaces, not just APIs, to eliminate the “mickey… | ||
| Operationalizing Model Lifecycle with MLFlow | Anindita Mahapatra | 33m |
MLflow is an open source lifecycle platform that records code data env params metrics and stores artifacts so runs are reproducible; it packages models for… | ||
| Workflow Integration in AI in Healthcare | Matthew DiDonato | 34m |
GE builds Edison AI to embed models into imaging workflows and claims concrete gains: a smart scheduling model that predicts no-shows per patient… | ||
| AI for Provider Fraud & compliance for Insurance & Healthcare Industry | Srini Madala, Samuel King | 38m |
Aquila is a deployed provider-fraud system inside a NASDAQ-listed carrier that turns millions of billing lines plus external feeds (medical board, licensing… | ||
| Natural Disaster Damage Assessment from Satellite Imagery | Zulfiqar Ahmed, Farshad Saberi Movahed | 24m |
A pipeline uses NOAA high‑resolution hurricane imagery cropped to 300×300 and COCO roof‑damage annotations; semantic segmentation with U‑Net + ResNet50 (dice… | ||
| AI in healthcare: Data driven transformation | Shahidul Mannan | 31m |
Healthcare generates vast, growing datasets and most of it is unusable until cleaned, with 80% unstructured data and data teams spending 80% of their time on… | ||
| How to build a successful AI practice in Insurance and Government organization? | Hudson Mahboubi | 29m |
Workplace Safety and Insurance Board runs a small 20-person team that supports 320,000 businesses and handles about 210k claims/year for 4.8 million workers… | ||
| Computer vision, Data Analytics, Third Party data | Jaap - de Vries | 30m |
Insurance performance is framed as the combined ratio (expenses+losses)/premium and AI improves it by three levers: cut expenses, enable pricing, or reduce… | ||
| Python + MPP Database = Large Scale AI/ML Projects in Production Faster | Paige Roberts | 26m |
Shows that you can take Python ML into an MPP analytical database and move from prototype to production in minutes not months, using Vertica and the open… | ||
| AI-Generated Synthetic Media, aka Deepfakes and its impact | Ashish Jaiman | 30m |
Deepfakes are already practical: face swaps, lip‑sync, puppeteering, full image and voice cloning plus GPT‑style text generation create hyperreal… | ||
| Keynote Panel AI in Quantum Computing | Doug Finke, Shahar Keinan, Mehdi Bozzo-Rey, Christopher Bishop | 59m |
Panel outlines concrete claims and tools: a drug‑discovery stack called Tachyon narrows a 10^60 chemical space down to 10–20 lead molecules, running today on a… | ||
| Keynote Panel AI in Healthcare | Sanji Fernando, Matthew DiDonato, Nataraj Dasgupta, Slava Akmaev, Shahidul Mannan | 59m |
Panelists describe deployed systems and concrete problems not vague promises: a PrismRA diagnostic using a random forest predicts anti TNF response and runs in… | ||
| Keynote Panel State of AI in the Enterprise | Srini Chadalavada, Sreekar Bhaviripudi, Sourabh Bajaj, Vikas S. Shah, Helen Yu, Kim Naess, Shailesh Basani | 39m |
Discussion centers on practical enterprise hurdles: poor data quality and missing datasets, 16,000 lost records from a UK COVID Excel example, and the fact… | ||
| Natural Language Processing for Healthcare: Current Trends... | Amir Tahmasebi | 26m |
Overview of concrete claims, tools and failures: clinical NLP uses BERT-family models with many domain variants like BioBERT and ClinicalBERT, and the big leap… | ||
| AI and ML for Computational Advertising | Abraham Bagherjeiran | 29m |
Computational advertising frames each impression as an optimization: choose ad a to maximize value b, solving roughly 100 billion auctions/day in under 100 ms… | ||
| Future of Data Platforms: making use-case driven... | Ketan Umare | 28m |
Lyft runs an open source platform (Flyte) that treats ML as a superset of data, offering serverless orchestration across Kubernetes clusters, Java/Python SDKs… | ||
| Responsible AI – Principles & Practice | Tirthankar Barari | 30m |
Defines Microsoft’s six Responsible AI principles and three ML pillars and cites a Capgemini finding that 9 of 10 executives report ethical AI issues. Covers… | ||
| Learning Market Dynamics for Optimal Pricing | Sharan Srinivasan | 34m |
Airbnb clusters millions of unique listings into demand-driven groups using listing embeddings from view sessions and a recursive partitioning tree, then… | ||
| Value Engineering: How to Get Your Big Data Investment..... | Josh Siegel | 30m |
Argues that enterprises fund analytics only after near-term returns are proven, citing trillions lost to bad data, 40% of the Fortune 100 claiming data-driven… | ||
| Quantum Machine Learning With TensorFlow Quantum | Nixon Patel | 27m |
Overview of practical setup and claims: current devices sit around 50–72 qubits (Google 53 cited) with a roadmap claiming 1000 qubits by 2023, and frameworks… | ||
| Improving customer experience through journey... | Srinivas Chilukuri | 26m |
A deployed pipeline converts event sequences into LSTM embeddings that predict the next interaction and a realtime NPS proxy so the system flags customers… | ||
| Productizing Machine Learning over Big Data with AWS tools | Maximo Gurmendez | 31m |
Demo shows how to go from notebook prototype to a deployable ML product using SageMaker notebooks and training jobs, scikit‑learn locally and EMR Spark… | ||
| Better Understand Your Documents with AI | Elliott Ning | 29m |
Shows a concrete pipeline that turns varied files into queryable tables using OCR, AutoML Natural Language and BigQuery, with prebuilt processors created in… | ||
| Accelerate Model Hyperparameter Search with RAPIDS and SageMaker | Miro Enev | 01h 38m |
Shows a reproducible workflow that containers RAPIDS (cuDF, cuML), Dask and XGBoost into an AWS SageMaker estimator, runs hyperparameter search over an airline… | ||
| Challenges & future trends of Deep Learning | Mohammad Shokoohi-Yekta | 28m |
Autonomous cars generate data equal to 2,600 average internet users and that scale drives both opportunity and cost; a real-time mask detector and an AOI… | ||
| Fairness in health AI: ensuring equity in healthcare cost. | Miguel Martínez | 41m |
Walks through a fairness audit on a 2.4 million‑member claims set using Equitas to predict top 2% high‑cost patients, with demographics 90% White and 9.3%… | ||
| Augmenting machine learning with experimental data for drug... | Slava Akmaev | 24m |
Scipher Medicine deploys a predictive blood test called prismaray: a 23-biomarker panel combining 10 SNPs, transcript measures and five clinical features into… | ||
| AI in Drug Discovery | Nataraj Dasgupta | 30m |
Argues that AI is reducing the biggest bottlenecks in drug R&D: target selection, screening and design. Gives concrete results: predicted 1,000 gene targets… | ||
| Translating AI Capabilities Into Better Business Results | Dan Becker | 25m |
Claims that moving from predictions to decisions raises revenue by optimizing action thresholds and modeling downstream effects using profit curves and… | ||
| Use of AI to quantify the "safety of autonomous vehicles" | Edward Schwalb | 24m |
Autonomous vehicle safety requires demonstrating roughly 200–400 million miles between fatal crashes so physical testing alone is infeasible; the claim is that… | ||
| AI Human Impact and Ethical Investing | James Brusseau | 36m |
Proposes an investment-ready scoring system that rates AI-intensive firms on specific human-impact criteria — autonomy, dignity, privacy, safety, performance… | ||
| What Leaders Need to Know About the Role of AI... | Helen Yu | 17m |
Describes AI as a four-role system — sense think act learn — and gives concrete wins: a conversational AI rollout that scales from 2 to 36 customers and… | ||
| Using Conversational AI to combat the impact of COVID-19 | Mahi de Silva | 26m |
Company repurposes a customer-engagement platform into a citizen-facing conversational AI that government and NGO channels plug into via Facebook, Instagram… | ||
| Responsible and Ethical Governance of AI Models | Robert Bernard | 30m |
PwC argues responsible AI must combine ethics and governance with performance checks, citing a market of roughly 13% of $2.6B in consulting now rising to ~17%… | ||
| Secure Large-Scale Data Analysis and AI with Polyglot Data Sources | Tushar Katarki, Buck Woody | 25m |
Concrete setup showing SQL Server Big Data Clusters running on Red Hat OpenShift to provide a secure HTAP data hub that serves OLTP, OLAP and Spark/HDFS… | ||
| Keynote Panel AI from Investment perspective | Sudip Chakrabarti, Ian Foley, Anupam Rastogi, Vik Sasi, Dennis Liu, Cyril Philip | 01h 00m |
Investors name concrete wins and tools: a company uses GPT-3 customer support to replace human responders and reports human‑like interaction in pilots… | ||
| Modern Data Platform, IoT & the Cloud -how... | Prakriteswar Santikary | 34m |
Claims a cloud native, lambda architecture data platform that ingests any data type at streaming scale and centralizes 1500 active studies, 5 million patients… | ||
| From Black Scholes to Black Holes | Revant Nayar | 34m |
Uses symmetry groups (Schrödinger, 2D conformal, Lifshitz) to derive explicit two‑point option pricing kernels and a nonlinear Black‑Scholes family determined… | ||
| Using OKRs to Manage AI and Data Science Teams | Stephen Smith | 29m |
Argues that Objectives and Key Results make AI and data science work align with company goals by adding measurable outcomes to creative, iterative projects… | ||
| Hitachi applying AI in Industry 4.0 digital transformation projects | Kim Naess | 30m |
Hitachi applies sensor to cloud pipelines to predict equipment failures and cut costs, fitting trains with 3,000–3,500 sensors that stream over 3G/4G into… | ||
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