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AI is a constantly changing field, sometimes with a
staggering pace of innovation so when considering the development and
deployment of AI solutions it’s important to also understand where the
technology will go in the future. Adapting to the challenges of today while
preparing for the advancements of tomorrow must be key considerations when
developing any AI technology, especially broad-reaching ones like the
Industrial Copilot.
In this episode, join host Spencer Acain and guests Michi
Lebacher and Alessia Bortolotti as they examine the challenges of bringing an
ambitions project like the Industrial Copilot to life, how that project will
evolve in the future, and where AI is leading the industry as a whole.
In this episode you will learn:
The
future of the Industrial Copilot (0:31)
What
it takes to bring AI to industry (5:20) -
The Industrial Copilot is already beginning to prove its
value across industries but ensuring such a powerful AI tool is industrial
grade and ready to deploy not in months or years but in days, isn’t without its
challenges. Addressing these challenges requires a smarter approach to data and
training, as well as extensive cooperation both between new and existing
software tools and with partners seeking to deploy these AI solutions.
In this episode, host Spencer Acain is joined by Michi
Lebacher and Alessia Bortolotti to examine the approaches to data and
deployments, trade-offs and customer benefits of the Industrial Copilot.
In this episode you will learn:
How
the Industrial Copilot integrates across the Siemens ecosystem (0:28)
Training
AI across different disciplines (9:20)
RAG
vs. fine tuning for industrial grade AI (13:55) -
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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
is the Industrial Copilot? (2:48)
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) -
In this engaging two-part podcast, join the conversation between Justin Hodges and Dale Tutt, a seasoned aerospace and defense professional, as they delve into the intertwined realms of digital transformation and artificial intelligence (AI) in the realm of computer-aided design.
For part two, the discussion shifts into the manufacturing landscape (emissions, batteries, among others). A nod is given to the tech giants that make such data pipelines possible (for example Meta), as a conversation is had on what the possibilities are for other industries (like ours) with such a wealth of data available for digital and machine learning models. Welcome to the era of data-driven models -
In this engaging two-part podcast, join the conversation between Justin Hodges and Dale Tutt, a seasoned aerospace and defense professional, as they delve into the intertwined realms of digital transformation and artificial intelligence (AI) in the realm of computer-aided design.
Initially in part one, Dale unfolds the essence of digital transformation, paving the way for an enlightening discussion on how AI significantly propels optimization cycles, with mention of generative design. The discussion then navigates towards real-world applications of AI, with Dale shedding light on mundane yet significant use cases where AI can be instrumental. This segment of the conversation sets a solid foundation, wrapping up with an appreciation for the enlightening discussion thus far, and a teaser for the upcoming exploration of digitalization across various industries in the second part of the podcast. -
In many fields, ranging from design to manufacturing to operation, time is often a limiting factor when it comes to exploring new ideas. Thanks to recent advances in generative AI and what that will mean in the future, many be possible to cut down on many of these time limiting factors, offering a new level of flexibility across countless domains.
In this episode, host Spencer Acain is joined by Dr. Justin Hodges, an AI/ML Technical Specialist and Product Manager for Simcenter to look ahead at the many ways generative AI is poised to change the industrial world.
In this episode you will learn:
- The future of AI-generated designs (0:46)
- How to rely on generative AI? (6:41)
- The need for human education (11:21) -
Generative AI is a powerful tool, offering a powerful new way to interact with information and technology, as explored in part one of this series. Moving beyond the role of a helper, generative AI also offers great potential to expand the design space, enabling new methods such as inverse design while expanding new capabilities atop existing systems.
In this episode, host Spencer Acain is joined by Dr. Justin Hodges, an AI/ML Technical Specialist and Product Manager for Simcenter to consider the applications of generative AI in expanding the design space and building new functionality in the world of design and simulation.
In this episode you will learn:
- Generative AI for inverse design (4:21)
- AI in requirement driven design (9:00)
- The value of a connected tool chain (12:20) -
Generative AI has become a global phenomenon since the public release of AI chatbots such as ChatGPT however it’s not just consumers interested in exploring what generative AI has to offer. Many industries are investigating the ways generative AI can redefine existing processes and enable new, previously impractical ideas to breakdown the barriers between people, information, and technology.
In this episode, host Spencer Acain is joined by Dr. Justin Hodges, an AI/ML Technical Specialist and Product Manager for Simcenter to discuss the many applications of generative AI to the CAE process and what that means for the future of product design.
In this episode you will learn:
- What is Generative AI? (2:56)
- Applications of Generative AI in simulation and design (7:44)
- How Generative AI eases the burden on users (9:25)
- How Generative AI makes data easier to access (11:34) -
When it comes to AI, data is everything. Everything from training models to leveraging them after deployment relies on having access to large quantities of high-quality data to work with. When examining the global electronics value chain in the way Supplyframe does it is easy to see why AI is a valuable tool there thanks to a staggering volume of available data. However, gaining insight and actionable information from all that noise is no easy feat.
In this episode, host Spencer Acain is joined one again by Richard Barnett, Chief Marketing Officer for Supplyframe, to explore the ways AI leveraging the massive data available in the global electronics market and where he sees AI going in the future.
In the episode you will learn:
· How Supplyframe gets training data (1:03)
· Expanding into the world of mechanical design (8:03)
· Impact of generative AI (12:08) -
As global supply chains become increasingly interconnected and products more complex it also becomes increasingly important to understand how perturbations to this system will effect everything from supply availability to consumer demand. Understanding the complex interplay between various factors is vital for success in the global market and a field where AI can provide invaluable assistance.
In this episode, host Spencer Acain is joined by Richard Barnett, Chief Marketing Officer of Supplyframe to examine the ways AI is helping to find patterns and correlations in the vast web of data generated by the global supply chain. -
For any marketplace, understanding buyer intent is vital. In the consumer space, many companies have invested in developing systems – both with and without AI – capable of doing just that. However, for B2B marketplaces gauging buying intent becomes far more difficult. This is exactly the challenge Supplyframe seeks to address with AI by understanding not just the engineering intent of companies buying electronic components but what impact events in the larger world will have as well.
In this episode, host Spencer Acain is joined by Richard Barnett, CMO of Supplyframe to discuss the ways their using AI to predict market in the electronics industry and what those predictions allow them to do. - Daha fazla göster
