Avsnitt
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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) -
Saknas det avsnitt?
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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:
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General purpose decision support (1:06)
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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. -
Microchips are among the most, if not the most, complex devices ever created by humankind, packing billions of transistors into a package the size of a thumbnail. For these chips to function properly every one of those transistors must function perfectly and are rigorously verified so any problems can be corrected in future revisions. However, test data can only narrow down the cause of a problem to a certain degree and narrowing it down further is a key area where machine learning is coming into play.
In this episode of AI Spectrum, Spencer Acain is joined by an expert from Siemens EDA with more than 20 years experience to discuss the ways machine learning is playing an important role in the verification of these complex chips.
In this episode you will learn:
· The use of AI/ML in the chip verification process (0:44)
· Difficulties in identifying root cause (6:58)
· Challenges of analyzing large chips (09:38)
· Gathering ML training data (11:37)
· The push for industry standardization (15:10) -
As AI grows increasingly integrated with modern products, finding a way to quickly and efficiently design purpose-built AI accelerators for a wide range of applications is vitally important. Designing chips using High-Level Synthesis (HLS) not only allows for aggressive optimization of power usage and performance but the possibility of integrating AI itself into the design process for even greater gains. As the design process becomes increasingly interdisciplinary, HLS also offers a path to integrating electronics into MBSE workflows.
In this episode of AI Spectrum, Spencer Acain is once again joined by Russell Klein, program director at Siemens EDA and a member of the Catapult HLS team to discuss the benefits of HLS and the ways it will integrate with AI in the future.
In this episode you will learn:
· HLS-designed accelerators vs. general purpose accelerators (00:30)
· How HLS compares to manual optimization (03:18)
· How AI improves on optimization heuristics (07:27)
· Integrating chips into the MBSE process (11:39)
Connect with Russell Klein:
· LinkedIn
Connect with Spencer Acain:
· LinkedIn -
Designing microchips is a daunting task which is growing increasingly challenging as new algorithms and software push the demand for efficient, specialized chips capable of running AI algorithms on everything from self-driving cars to edge IIoT sensors. To meet these demands in a timely manner, High-Level Synthesis (HLS) tools, like Siemens EDAs Catapult are proving themselves to be a vital tool in designing chips for the fast-passed world of AI technology.
In this episode of AI Spectrum, Spencer Acain is joined by Russell Klein, program director at Siemens EDA and a member of the Catapult HLS team to discuss the benefits of HLS and why it is playing a key role in developing the AI accelerators of tomorrow.
In this episode you will learn:
· How Catapult can support AI (00:32)
· AI accelerators vs. GPUs (02:32)
· What is HLS? (04:25)
· How HLS verifies algorithms instead of transistors (10:27)
· Usage of HLS designed chips (11:32)
Connect with Russell Klein:
· LinkedIn
Connect with Spencer Acain:
· LinkedIn -
Artificial intelligence has been a hot topic for the last few years as it starts to disrupt the status quo of countless industries but for EDA tools such as Solido, AI and ML have already become an indispensable proven technology. Solido leverages powerful machine learning abilities to provide answers that would normally require millions of simulations to acquire down to just a few thousand, offering a glimpse of where the AI industry may be going.
In this episode, Spencer Acain is joined by Amit Gupta, VP & GM of the Analog/Mixed-Signal Division at Siemens EDA and serial entrepreneur and founder of Solido Design Automation before its acquisition by Siemens EDA in 2017. Amit discusses why he and his team were such early adopters of AI/ML technology and the benefits of using it in the EDA space.
In this episode you will learn:
· The role of AI in Solido (1:58)
· The benefits of AI in EDA (4:00)
· Validating multi-billion transistor chip designs using ML (8:32)
· Why Solido was at the forefront of AI/ML adoption (15:16)
· AI collaboration across industries (21:04)
Connect with Amit Gupta:
LinkedIn
Connect with Spencer Acain:
LinkedIn -
Even as AI drives a new level of interconnectedness between tools, it also offers the potential to reinvent the way complex physics-based simulations are run. When it comes to the use of physics informed neural networks, or PINNs for short, a number of challenges are still left to overcome, however while the road ahead for PINNs is a long one, they offer the potential for great reward at the end as well.
In this episode, Spencer Acain is joined once again by Dr. Justin Hodges, an AI/ML Technical Specialist and Product Manager for Simcenter. Justin discusses not only the ways AI is enabling connections between tools but also the challenges and benefits of PINNs and AI in physics going forward.
In this episode you will learn:
· How AI is driving connections between tools (00:32)
· How AI is changing physics-based simulation (4:40)
· The challenges of using PINNs (6:36)
· The benefits of PINNs (8:50)
· Where AI is going in the future (12:08)
Connect with Justin Hodges:
LinkedIn
Siemens Simcenter
Connect with Spencer Acain:
LinkedIn -
The products being designed and manufactured today must surpass the capabilities of what came before and then deliver them with fewer resources for various reasons, from environmental regulations to increased market competition. Making that happen is a challenging task, and even with some of the best tools, it can be difficult when relying only on the abilities of a few engineers and designers. As a result, computational resources in a digital business are becoming the differentiator for many companies looking to capture their market.
To discuss this shift and what it means for the companies adopting these new techniques, one of our guest hosts – Nicholas Finberg, a writer for the Thought Leadership team at Siemens Digital Industries Software – sat down with one of the NX product managers – Tod Parrella. Together they talk through the concept of generative design, why it’s different from topology optimization, and how it can be applied to the other challenges businesses are trying to solve.
If you are interested in learning more about the how of this process and what Siemens is doing with AI and machine learning to improve the capabilities, check out the sister podcast on the AI Spectrum series from Siemens Software – hosted by Spencer Acain.
Connect with Tod Parrella:
LinkedIn
Connect with Nick Finberg:
LinkedIn -
AI is not only empowering tools to function with greater efficiency and usability, but it’s also helping spearhead the next generation of interconnected technologies which will help drive further innovation through a more holistic design approach. This in turn will help parallelize the traditionally serial design process, enabling a faster design cycle and exploration of a broader design space.
In this episode, Spencer Acain is once again joined by Dr. Justin Hodges, an AI/ML Technical Specialist and Product Manager for Simcenter. Justin highlights some of the ways AI is helping build connections between different tools, and where that will lead in the future.
In this episode you will learn:
- How AI enables interconnected technology (2:18)
- How AI is evolving through cross pollination between fields (5:57)
- The ways AI facilitates the transfer of simulations and data between tools (10:47)
- How AI will help parallelize the design process (14:33)
- Knowledge capture through AI (16:55)
Connect with Justin Hodges:
LinkedIn
Siemens Simcenter
Connect with Spencer Acain:
LinkedIn - Visa fler
