By Siemens Digital Industry Software
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The podcast currently has 40 episodes available.
Chatbots and digital assistants aren’t anything new, but their
abilities and perceived intelligence were often extremely limited, giving them
no place in the complex world of industrial design and manufacturing. Now,
thanks to advances in industrial grade generative AI, that’s all beginning to
change. The Industrial Copilot is the first step in that change, offering
human-like assistance and intelligence to users at every level of the
industrial value chain.
In this episode, host Spencer Acain is joined by Michi
Lebacher and Alessia Bortolotti to discuss the applications of AI in the Industrial
Copilot, a generative AI-based tool that assists users across a broad range of
tasks and with intuitive natural language abilities.
In this episode you will learn:
What are the key areas the Industrial Copilot is
applying AI? (6:08
Implementing AI into a complex and often mission-critical
application is rarely an easy task even though it is often highly worthwhile.
Even as AI experts work to bring AI into the applications where it can provide
the greatest benefits, their efforts also have a democratizing effect on both the
tools its being added to and the AI models themselves. This ensures that
everyone will have full access to the tools they need to capitalize on their
own domain knowledge without needing to become an expert in the tool itself.
Join host Spencer Acain in a conversation with Subba Rao,
Director of Manufacturing Industries Cloud for Mendix, a part of Siemens
Xcelerator as he discusses the challenges, benefits and future of AI within
Mendix and the industry at large.
In the episodes you will learn:
·
Challenges of bringing AI to Mendix (0:00)
·
Mendix democratizes industrial AI (4:16)
·
What the future holds (8:08)
Going forward, AI will be an important part of many
industrial processes, from data analytics to development to manufacturing, there
are many places where AI could step in to boost productivity. However, it is
equally important to make sure that AI is suitable for the roles it takes –
that of an assistant, not a replacement for human expertise.
Join host Spencer Acain along with Subba Rao, Director of
Manufacturing Industries Cloud for Mendix, a part of Siemens Xcelerator as he examines
the application of AI within Mendix, their limitations, and why they chose the
AI integration path they did.
In the episodes you will learn:
·
AI augmented vs. AI assisted (0:48)
·
Applications of AI in industry (8:02)
When it comes to developing industrial software and
workflows it’s not just expert domain knowledge that is a limiting factor, but
also the ability to transfer that expertise into the required software and
programming languages. Low- and no-code solutions combat this by helping anyone
with an idea translate it, with little to no coding knowledge, into a
full-fledged application and generative AI is at the heart of this process.
Join host Spencer Acain in a conversation with Subba Rao, Director
of Manufacturing Industries Cloud for Mendix, a part of Siemens Xcelerator as
he discusses the ways Mendix leverages AI in low-code application development
and how it is supporting the integration of AI withing industrial apps.
In the episodes you will learn:
·
What is Mendix? (1:19)
·
Key applications of AI within Mendix (4:27)
·
How AI helps build AI apps (7:11)
When designing a product, there are countless parameters
that must be considered and balanced to arrive at a final, optimal result. In a
traditional design cycle, this is a highly manual process that seeks to reduce
the number of variables as much as possible to simplify the process. Now thanks
to advances in AI, it’s possible to not only handle a greater number of
variables but extract additional information from each one – allowing for
further design refinement.
In this episode, host Spencer Acain is joined once again by
Dr. Gabriel Amine-Eddine, Technical Product Manager for the HEEDS Design
Exploration Team, to examine the ways AI can be used to aid in design space
exploration and what that will mean for the future.
In this episode you will learn:
·
Using AI to handle high dimensionality models
(1:09)
·
Reuse of AI models (9:37)
·
How AI will change the design process (12:24)
Bringing AI into the fold isn’t always easy. Sometimes, even
knowing when and where it makes sense to apply it can prove challenging and
once potential applications are identified, building trust in the model is also
a critical factor. These are common challenges faced by AI applications in
every industry and while the solutions each one reaches will be unique, they
all share some commonalities.
In this episode, host Spencer Acain is joined once again by
Dr. Gabriel Amine-Eddine, Technical Product Manager for the HEEDS Design
Exploration Team, to continue discussing the creation of HEEDS AI Boost and how
such a complex tool can find its place in industry.
In this episode you will learn:
·
What prompted the creation of HEEDS AI Simulation
Predictor? (0:43)
·
How uncertainty-aware AI can build trust (6:24)
Design space exploration is a critical step in any product
design lifecycle but just as it’s important, so too does it present numerous
challenges. Designing a product requires balancing a multitude of, often
contradicting, requirements to arrive at as close to an optimal solution as
time constraints allow. Now, thanks to advances in AI, it’s possible to reach
those optimal designs faster and more efficiently than ever.
In this episode, host Spencer Acain is joined by Dr. Gabriel
Amine-Eddine, Technical Product Manager for the HEEDS Design Exploration Team, to
explore the ways HEEDS AI Simulation Predictor is leveraging AI to speed up the
design space exploration process, and what impact that will have on the product
design process.
In this episode you will learn:
·
What is HEEDS? (2:04)
·
How AI is accelerating design space exploration
(5:03)
·
Balancing simulation vs. inference (9:34)
Predictive maintenance has long been a topic of interest in
industry but implementing and scaling theoretical models into the real world has
proven to be fraught with challenges. However, by approaching the problem from
a different angle, Senseye seeks to develop a scalable, general-purpose solution
that can easily apply to the often less than ideal real-world data coming from
factories. With intelligent use of AI models, predictive maintenance can be achieved
without the use of the costly and difficult to scale bespoke models that have
dominated the field for many years.
In this final episode on predictive maintenance, host
Spencer Acain is joined by Dr. James Loach, Head of Research for Senseye
Predictive Maintenance, to discuss Senseye’s unique approach, the struggles of
adopting predictive maintenance and AI in the real world, and what the future
for AI holds.
In this episode you will learn:
·
General purpose decision support (1:06)
·
Challenges of adoption (6:20)
·
A rapidly changing world (10:02)
Decision making is a key part of any business, but it can take years to build up the knowledge and experience required to make quick, accurate judgements within a domain of expertise. This is just as true when it comes to deciding the course for a massive company as it is for deciding when a single machine needs to be taken down for maintenance. With the rise of conversational AI, the process can be made easier with smart systems that bring key information to the forefront.
In this episode, host Spencer Acain is joined once again by Dr. James Loach, Head of Research for Senseye Predictive Maintenance to discuss the ways Senseye is using AI to build intelligent decision support systems. James explains the importance of these systems, as well as their limitations and how Senseye is working to build trust in them.
In this episode you will learn:
·
Why AI decision support systems are important (1:24)
·
How Senseye is building trust in the system
(6:58)
·
The value of where AI and humans meet (12:00)
When operating a factory, one of the major goals is to
minimize issues, downtime, or anything else outside the status quo and ensure
smooth operation. However, this is easier said than done, as all machines require
maintenance and must contend with unforeseen failures. Predictive maintenance is
emerging as a powerful tool that leverages AI and machine learning to better
understand when and where maintenance is required to minimize downtime and preemptively
handle issues before they become catastrophic.
In this episode, host Spencer Acain is joined by Dr. James
Loche, Head of Research for Senseye Predictive Maintenance, to explore the unique
approach Senseye is taking to the problem of keeping factories running as
smoothly as possible.
In this episode you will learn:
·
What is Senseye (2:40)
·
Senseye as a decision support system (4:30)
·
How AI brings flexibility and scalability to
predictive maintenance (11:04)
·
Monitoring operations vs. looking for failures
(13:13)
The podcast currently has 40 episodes available.