machine learning

Lightning Talk: Can Blockchain and Machine Learning Cooperate?

Lightning Talk: Can Blockchain and Machine Learning Cooperate?

January 3, 2020

The role of collaborative technologies in training AI systems: How to adopt a blockchain approach to contribute to Visual Recognition Framework increasing accuracy in detection of plant diseases. The …
Best practices for scaling modeling platforms

Best practices for scaling modeling platforms

January 2, 2020

Companies are increasingly building modeling platforms to empower their researchers to efficiently scale the development and productionalization of their models. Scott Clark and Matt Greenwood share a case study from a leading algorithmic trading firm to illustrate best practices for building these types of platforms in any industry.
Better Together Diversity Networking Lunch

Better Together Diversity Networking Lunch

January 2, 2020

If youd like to make new professional connections and hear ideas for supporting diversity in the tech community, come to the diversity and inclusion networking lunch on Wednesday.
Bringing your machine learning to production with ML Ops (sponsored by Microsoft)

Bringing your machine learning to production with ML Ops (sponsored by Microsoft)

January 2, 2020

Sarah Bird offers an overview of ML Ops (DevOps for machine learning), sharing solutions and best practices for an end-to-end pipeline for data preparation, model training, and model deployment while maintaining a comprehensive audit trail. Join in to learn how to build a cohesive and friction-free ecosystem for data scientists and app developers to collaborate together and maximize impact.
Deep learning for recommender systems, Or How to compare pears with apples

Deep learning for recommender systems, Or How to compare pears with apples

January 1, 2020

Recommender systems support decision making with personalized suggestions and have proven useful in ecommerce, entertainment, and social networks. Sparse data and linear models are a burden, but the application of deep learning sets new boundaries and offers remarkable results. Join Marcel Kurovski to explore a use case for vehicle recommendations at Germany's biggest online vehicle market.
Executive Briefing: 5 key questions to kick off your AI implementation

Executive Briefing: 5 key questions to kick off your AI implementation

January 1, 2020

The significant hype bubble building up around AI has convinced many executives that if theyre not already tech savvy, they might not be ready for AIs transformative power. However, the reality is that AI is just another tool that can help your business, and youre probably not that far behind. Vinay Seth Mohta explains how to evaluate AI as you would any other strategic investment.
Executive Briefing: From cutting-edge AI research to business impact

Executive Briefing: From cutting-edge AI research to business impact

January 1, 2020

Larry Carin, one of the worlds most published machine learning researchers, discusses the state of the art in machine learning and how it translates to business impact. Along the way, Larry shares examples of how modern machine learning is transforming business in several sectors, including healthcare delivery, security, and back-office business processing.
Executive Briefing: Overview of data governance

Executive Briefing: Overview of data governance

December 31, 2019

Effective data governance is foundational for AI adoption in enterprise, but it's an almost overwhelming topic. Paco Nathan offers an overview of its history, themes, tools, process, standards, and more. Join in to learn what impact machine learning has on data governance and vice versa.
GAIA: The Global AI Allocator

GAIA: The Global AI Allocator

December 31, 2019

Aric Whitewood details WilmotML's research on the application of AI to investment management and offers an overview of the company's prediction engine, GAIA (the Global AI Allocator), which has been running in production since January 2018.
Game engines and machine learning

Game engines and machine learning

December 31, 2019

Games are wonderful contained problem spaces, making them great places to explore AIeven if you're not a game developer. Paris Buttfield-Addison, Mars Geldard, and Tim Nugent teach you how to use Unity to train, explore, and manipulate intelligent agents that learn. You'll train a quadruped to walk, then train it to explore, fetch, and manipulate the world.
Getting to ROI: Case studies in operationalizing machine learning (sponsored by Dataiku)

Getting to ROI: Case studies in operationalizing machine learning (sponsored by Dataiku)

December 31, 2019

AI and machine learning are top priorities for nearly every company. Despite this, "productionalizing" machine learning processes is an underappreciated problem, and as a result, businesses often find themselves failing to maximize ROI from their data initiatives. Will Nowak identifies best practices and common pitfalls in bringing machine learning and AI models to production.
How to build privacy and security into deep learning models

How to build privacy and security into deep learning models

December 31, 2019

In recent years, we've seen tremendous improvements in artificial intelligence, due to the advances of neural-based models. However, the more popular these algorithms and techniques get, the more serious the consequences of data and user privacy. Yishay Carmiel reviews these issues and explains how they impact the future of deep learning development.
Is AI human-ready?

Is AI human-ready?

December 30, 2019

Aleksander Madry discusses major roadblocks that prevent current AI frameworks from having a broad impact and outlines approaches to addressing these issues and making AI frameworks truly human-ready.
Machine learning for personalization

Machine learning for personalization

December 30, 2019

For many years, the main goal of the Netflix recommendation system has been to get the right titles in front of each member at the right time. Tony Jebara details the approaches Netflix uses to recommend titles to users and discusses how the company is working on integrating causality and fairness into many of its machine learning and personalization systems.
Manipulating and measuring model interpretability

Manipulating and measuring model interpretability

December 29, 2019

Forough Poursabzi-Sangdeh argues that to understand interpretability, we need to bring humans in the loop and run human-subject experiments. She describes a set of controlled user experiments in which researchers manipulated various design factors in models that are commonly thought to make them more or less interpretable and measured their influence on users behavior.
ML at Twitter: A deep dive into Twitter's timeline

ML at Twitter: A deep dive into Twitter's timeline

December 29, 2019

Twitter is a company with massive amounts of data, so it's no wonder that the company applies machine learning in myriad of ways. Cibele Montez Halasz and Satanjeev Banerjee describe one of those use cases: timeline ranking. They share some of the optimizations that the team has madefrom modeling to infrastructurein order to have models that are both expressive and efficient.
Open source tools for machine learning model and dataset versioning

Open source tools for machine learning model and dataset versioning

December 29, 2019

ML model and dataset versioning is an essential first step in the direction of establishing a good process. Dmitry Petrov and Ivan Shcheklein explore open source tools for ML models and datasets versioning, from traditional Git to tools like Git-LFS and Git-annex and the ML project-specific tool Data Version Control or DVC.org.
Privacy-preserving machine learning in TensorFlow with TF Encrypted

Privacy-preserving machine learning in TensorFlow with TF Encrypted

December 29, 2019

Morten Dahl reviews modern cryptographic techniques such as homomorphic encryption and multiparty computation, sharing concrete examples in TensorFlow using the open source library TF Encrypted. Join in to learn how to get started with privacy-preserving techniques today, without needing to master the cryptography.
Synergize your tech stack to realize AIs full potential (sponsored by SAS)

Synergize your tech stack to realize AIs full potential (sponsored by SAS)

December 28, 2019

The whole of AI is greater than the sum of its parts, but achieving the best analytics edge often requires a mixture of technologieschaining together AI technologies to build smart end-to-end processes. Katie Taylor explores use cases within key industries to uncover how companies are succeeding with AI through a layered technology stack.
The intersection between human learning and machine learning. How AI will fundamentally change teaching and learning. (sponsored by Squirrel AI Learning)

The intersection between human learning and machine learning. How AI will fundamentally change teaching and learning. (sponsored by Squirrel AI Learning)

December 28, 2019

One of the most critical issues of traditional education is the lack of high-quality teachers for the personalized attention of individual student need. AI technology, especially the AI adaptive technology can enable the new generation of teachers to teach student much more effectively and improve the efficiency of the education industry.
Turn devices into data scientistsat the edge

Turn devices into data scientistsat the edge

December 28, 2019

Todays approach to processing streaming data is based on legacy big-data centric architectures, the cloud, and the assumption that organizations have access to data scientists to make sense of it allleaving organizations increasingly overwhelmed. Simon Crosby shares a new architecture for edge intelligence that turns this thinking on its head.
Using AI to create interactive digital actors

Using AI to create interactive digital actors

December 28, 2019

Digital character interaction is hard to fake, whether its between two characters, between users and characters, or between a character and its environment. Nevertheless, interaction is central to building immersive XR experiences, robotic simulation, and user-driven entertainment. Kevin He explains how to use physical simulation and machine learning to create interactive character technology.
Applications of mixed effects random forests

Applications of mixed effects random forests

December 27, 2019

Clustered data is all around us. The best way to attack it? Mixed effect models. Sourav Dey explains how the mixed effects random forests (MERF) model and Python package marries the world of classical mixed effect modeling with modern machine learning algorithms and shows how it can be extended to be used with other advanced modeling techniques like gradient boosting machines and deep learning.
Applied machine learning in finance

Applied machine learning in finance

December 27, 2019

Quantitative finance is a rich field in finance where advanced mathematical and statistical techniques are employed by both sell-side and buy-side institutions. Chakri Cherukuri explains how machine learning and deep learning techniques are being used in quantitative finance and details how these models work under the hood.
Augmented OLAP for big data from on-premises to multicloud (sponsored by Kyligence)

Augmented OLAP for big data from on-premises to multicloud (sponsored by Kyligence)

December 27, 2019

Augmenting data management and analytics platforms with artificial intelligence and machine learning is game changing for analysts, engineers, and other users. It enables companies to optimize their storage, speed, and spending. Yang Li details the Kyligence platform, which is evolving to the next level with augmented capabilities such as intelligent modeling, smart pushdowns, and more.
Using machine learning to automate car damage assessment and document workflows

Using machine learning to automate car damage assessment and document workflows

December 27, 2019

Vladimir Starostenkov and Siarhei Sukhadolski discuss two ML solutions from Altoros: one was developed to facilitate the process of assessing car damage right at the accident scene, while the second helps to automate recognition, extraction, and analysis. Join in to see how to integrate both solutions into the existing workflows of insurance, car rental, and maintenance services.
What you must know to build AI systems that understand natural language

What you must know to build AI systems that understand natural language

December 27, 2019

New AI solutions in question answering, chatbots, structured data extraction, text generation, and inference all require deep understanding of the nuances of human language. David Talby shares challenges, risks, and best practices for building NLU-based systems, drawing on examples and case studies from products and services built by Fortune 500 companies and startups over the past seven years.
Building high-performance text classifiers on a limited labeling budget

Building high-performance text classifiers on a limited labeling budget

December 26, 2019

Robert Horton, Mario Inchiosa, and Ali Zaidi demonstrate how to use three cutting-edge machine learning techniquestransfer learning from pretrained language models, active learning to make more effective use of a limited labeling budget, and hyperparameter tuning to maximize model performanceto up your modeling game.