Artificial intelligence is everywhere – it selects the next video on YouTube and your route on Google Maps. But soon, AI will make decisions that significantly impact your life – diagnosing illnesses, performing surgery, and driving your kids’ school bus. The stakes are high. Bradley Hayes argues that we can only trust AI if we understand how it makes decisions and why. We need explainable AI. Bradley Hayes directs the Collaborative AI and Robotics Research Lab at the University of Colorado Boulder, where he is an Assistant Professor. As CTO of Circadence Corporation, he develops revolutionary educational programming to expand the cybersecurity workforce. He completed a Ph.D. in Computer Science at Yale University’s Social Robotics Lab and was a postdoctoral associate in MIT’s Interactive Robotics Group. Currently, he’s developing novel explainable AI techniques for safe human-robot collaboration. He will never say no to sushi. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
“AI isn’t magic, it’s just math under the covers.” We should all be responsible and ethical while using data and Jennifer Marsman gives some good reasons for that.
See Jennifer’s presentation from WROC# 2019: https://youtu.be/JXNO_d0c0pk
With the increasing popularity of AI, new frontiers are emerging in predictive maintenance and manufacturing decision science. However, there are many complexities associated with modeling plant assets, training predictive models for them, and deploying these models at scale for near real-time decision support. This talk will discuss these complexities in the context of building an example system.
First, you must have failure data to train a good model, but equipment failures can be expensive to introduce for the sake of building a data set! Instead, physical simulations can be used to create large, synthetic data sets to train a model with a variety of failure conditions.
These systems also involve high-frequency data from many sensors, reporting at different times. The data must be time-aligned to apply calculations, which makes it difficult to design a streaming architecture. These challenges can be addressed through a stream processing framework that incorporates time-windowing and manages out-of-order data with Apache Kafka. The sensor data must then be synchronized for further signal processing before being passed to a machine learning model.
As these architectures and software stacks mature in areas like manufacturing, it is increasingly important to enable engineers and domain experts in this workflow to build and deploy the machine learning models and work with system architects on the system integration. This talk also highlights the benefit of using apps and exposing the functionality through API layers to help make these systems more accessible and extensible across the workflow.
This session will focus on building a system to address these challenges using MATLAB, Simulink. We will start with a physical model of an engineering asset and walk through the process of developing and deploying a machine learning model for that asset as a scalable and reliable cloud service.
More details: https://confengine.com/odsc-india-2019/proposal/10105
Conference Link: https://india.odsc.com
Oded: “Decision-making is an important role in most businesses in the last decade. More and more tools based on artificial intelligence and machine learning are introduced to support these decisions.
The artificial intelligence and machine learning one-day program is designed for senior executives and for corporate decision makers who already invested or consider to invest in artificial intelligence and machine learning software.”
Fabrizio: “For the professional success of managers and competitive advantage of companies the interaction between human decision-making and machine learning is going to be crucial in the future and this day will keep you with all the knowledge required to prosper and benefit.”
Oded: “The format of a day AI and machine learning one day program is a mix of lectures, introduction for theories, discussion of applications and discussion of case studies. It will be done by experts from academia and by practitioners who are coming from array of industries with huge experience.
The benefits of managers for completing their artificial intelligence and machine learning program is their better understanding of their artificial intelligence and machine learning analytical tools. These tools are designed to support decisions.”
Fabrizio: “This program is designed for both multinational global corporates as well as smaller fast growing disruptors in multiple industries. As a result of the cost of these technologies dramatically reducing it is now economical feasible for big and smaller companies to leverage them for their own benefit.”
Oded: “There is sometimes a gap between what managers think they can generate from it from what actually is generated from these tools.”
Fabrizio: “Senior executives after attending this program will be able to manage digital transformations in a much more effective way because they will be able to tell the difference between just collecting data a few hundred pounds month of cost to managing large digital transformation projects at the few hundred thousand pounds of millions of pounds a month and this program will enable them to understand what to do, how and when.”
Are humans really at risk from artificial intelligence? Will there be a rise of the machines, and if so, is that a good thing? UNSW’s Professor Toby Walsh, an expert in AI and one of Australia’s rock stars of the digital revolution, dispels some myths about AI and tells us what we should really be worried about. From UNSW’s UNSOMNIA event.
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