AI Spectrum

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)