AI & Smart Automation Conference 2023

This revolution goes by many names: Industry 4.0, The Factory of the Future, Smart Manufacturing, the Industrial Internet of Things (IIoT). No matter the label, this transformation will touch every aspect of the automation ecosystem, from product design to manufacturing processes to the delivery at a customer’s door. The AI & Smart Automation Conference will help start your journey to unlock the power of AI by featuring discussions on data strategy, advances in AI robotics and machine vision, and AI-powered optimization and prediction.

Intelligent Autonomous Agents

Intelligent Autonomous Agents

Kence Anderson - CEO & Co-Founder, Composabl, Inc.

Autonomous driving and AutoGPT have captured our imagination about automated AI systems that make real-time decisions. While intelligent automation has been around for a long time, modern artificial intelligence promises more human-like decision-making as well as success in more diverse situations. Intelligent autonomous agents digitize high-value skills that can be used to train and upskill operators, analysts, and engineers. They can operate machines and processes autonomously and continue learning on the job.

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AI and Augmented Reality Within Manufacturing

AI and Augmented Reality Within Manufacturing

Ted Rozier - Director of Digital, Advanced Technology and Robotics, Festo

We will explore the integration of artificial intelligence (AI) and augmented reality (AR) in critical sectors of the manufacturing and production environment as ways to empower the frontline worker and the CEO through a tangible, rich data experience

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Multi-modal AI Foundation Model

Multi-modal AI Foundation Model

Rajat Gupta - Sr. Director, Business Development - AI & Emerging Technologies, Microsoft

Generative AI Foundation models and their deployments have opened up a plethora of opportunities for enterprises to automate processes as well as customer and employee interaction. This session focuses on how multi-modal (text, images, video, 3D, etc.) AI Foundation models could unlock new Industrial Automation scenarios. 

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Human-Centric Manufacturing Process Analytics

Human-Centric Manufacturing Process Analytics

Itay Cnaan-On - Head of AI, Industrial Next

As automation and digitization of manufacturing processes continues to expand rapidly for the foreseeable future, many tasks will remain manual or only semi-automated (including human-in-the-loop operations) due to the scale of economics for complex tasks and the cost of robotic platform and total integration costs. Using recent advantages in applied artificial intelligence, this work demonstrates a real-world framework that allows a complete station contextual understanding using object detection, object classification, scene understating and temporal and spatial context of actions. Results from successful deployments will be included, where a station process is fully decomposed, analytics extracted, and optimization markers are learned and highlighted

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Manufacturing Process+Supply Chain Transformation

Manufacturing Process+Supply Chain Transformation

Larry Sweet - Director, Engineering, ARM Institute

AI and Machine Learning (ML) are accelerating gains in advanced manufacturing, enabling levels of productivity accessible to the diverse eco-system of large to small-sized manufacturers. Combined with advanced robotics, “point of need” manufacturing has the potential to transform the entire logistics supply chain, reduce the size of vast inventories, and prioritize transportation and skilled field technicians for critical items that cannot be manufactured in the field. In recent years severe shortages of critical components occurred due to Covid19, offshore semiconductor production constraints, and shipping blockages. These blockages starved manufacturing process flows downstream and blocked upstream logistics with overflowing inventories.  Advanced robotic manufacturing will provide capabilities for on-site production, repair, and refurbishment of parts and consumables while inspecting for defects to capture potential failures before they occur. 

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AI and Machine Vision Powering Automation

AI and Machine Vision Powering Automation

Arye Barnehama - CEO, Elementary

Ed Goffin - Vice President, Product Marketing, Pleora Technologies

Rajesh Iyengar - Co-Founder and CEO, Lincode Labs Inc.

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Usability, User Experience and Autonomy

Usability, User Experience and Autonomy

Kel Guerin - Co-Founder and Chief Innovation Officer, READY Robotics

In this talk, we will delve into the critical need for enhancing and fine-tuning human-robot interactions, particularly in light of emerging technologies such as Large Language Models and advanced Conversational AI. As robots are becoming increasingly sophisticated and our interactions with them more natural and dialogue-driven, the quality and nature of these interactions gain paramount importance. The discussion will underscore the challenges and nuances of transitioning from explicit, hard-coded programs to nuanced language-based instructions for robots. A central theme will be the human-robot-interaction mechanisms necessary for such a transition and the importance of building these mechanisms on reliable data, resulting in desired behaviors. We are at the beginning of a rapid expansion in the capability of robots, through simulated training and LLM-based behavior generation, and this means it is doubly important to understand how we will interact with these newly empowered devices.

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Large Language Models Embody Physical Intelligence

Large Language Models Embody Physical Intelligence

Damion Shelton - CEO and Co-Founder, Agility Robotics

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High-Fidelity Simulation for Robotics + Automation

High-Fidelity Simulation for Robotics + Automation

Buck Babich - Senior Robotics R&D Manager, NVIDIA

The era of simulation in robotics is only just beginning. This session will explore the many roles played by simulation in the deployment of production systems today as well as some exciting directions for the near future.

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Artificial Intelligence and Manufacturing Agility

Artificial Intelligence and Manufacturing Agility

Steffen Klawitter - Digital Enterprise Lead Architect, Siemens Digital Industries

Artificial intelligence makes control logic more agile and manufacturing processes more flexible and precise. During this session, we will show you how AI can simplify challenging applications where there is high variance, small batch runs, or frequent changes due to short product life cycles. Using AI in combination with computer vision, industrial automation, and standard robotics, machines no longer require special programming to perform specific tasks, as they can now learn from their own experiences.

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Why Aren't Robots More Widespread?

Why Aren't Robots More Widespread?

Juan Luis Aparicio - CEO and Co-founder, Stealth Startup

The vast majority of manufacturers today are coping with shortage of a machine operators. Despite a recent surge of consumer demand for US-produced goods, manufacturers are falling behind due to a workforce deficit of more than 800,000 open positions. 60% of manufacturers are unable to take new business opportunities due to inadequate staffing levels. Robots seem the answer, but 98% of manufacturers still don’t have a single robot arm in production. In this session, we will deep dive into the reasons for this gap and answer the question: "Is AI the silver bullet for higher robot adoption?

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Introduction to AI for Industrial Automation

Introduction to AI for Industrial Automation

Kence Anderson - CEO & Co-Founder, Composabl, Inc.

Artificial Intelligence (AI) promises better, more human-like decision-making and more autonomous operation, yet 87% of machine learning models never make it to production. The disconnect between AI technologists (IT) and industrial stakeholders (OT, from executive to the plant floor) significantly drives this failure. This disconnect shows itself as a lack of common understanding of AI capabilities, lack of common terminology to discuss AI solutions, and no framework for discussing AI Automation at various organizational altitudes.

The audience for this course is industrial stakeholders that own and operate industrial processes. This includes business executives, innovation teams, plant / line managers, process engineers, controls engineers, line supervisors, and industrial data scientists. We will provide a 101-level introduction that helps industrial stakeholders like you navigate the confusing AI landscape. The course arms will arm you with context that helps you select technologies, service providers, and vendors that will help you improve your manufacturing and logistics process control. The result is the foundation of building AI-powered automation that passes the ethics, explainability, and trust criteria needed to reach production and significant return on investment.

The course has been designed and will be presented by Kence Anderson, AI author and start-up founder, formerly with Microsoft Bonsai. 

COURSE OUTLINE:

  • How Machines Make Decisions
  • How AI Can Improve Industrial Automation
  • AI for Perception: How Machine Learning Helps You Understand What's Happening
  • AI for Taking Action: Reinforcement Learning Decides What to Do
  • Deploying AI-Powered Automation
  • Capstone Exercise

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