Everywhere we look today, people are talking about artificial intelligence and machine learning, and you probably hear a lot of buzzwords around this topic. You might be curious on the resources needed to train a machine or what exactly the process entails. Well, simply put, think of it like this: the specialists in the AI & ML industry aim at mimicking the amazing human brain. That’s not really an easy task, but huge advancements have been made in the past decade. In today’s episode, Mike Fingeroff – Senior Member of Consulting Staff at Calypto Design Systems - and his guest, Ellie Burns – Director of Marketing at Siemens EDA - share the basics of artificial intelligence and machine learning and help us understand how neural networks work. Tune in, to learn more! In this episode, you will learn: Then and now – the changes through AI & ML history. (01:07) The catalyst for the boom of the AI industry. (05:32) What a deep neural network is & how it works. (06:34) The different types of neural networks. (08:35) Connect with Mike Fingeroff: LinkedIn Connect with Ellie Burns: LinkedIn Resources: Catapult High-Level Synthesis Siemens EDA AI in industry Read the transcript here:

In the world of AI, a key concept is how to train a neural network to perform a particular task efficiently and accurately, then a hardware solution is created that uses the results from that training - and this is called inferencing. The difference between these two concepts - training and inferencing - often creates confusion among people, and that's why, in today's episode, we are diving deep into explaining these two terms and how exactly they differ.  We are also painting a clear picture of the industries that use artificial intelligence and machine learning and what they're working on, so tune in, and find out more!    In this episode, you will learn: The difference between training versus inferencing a neural network. (00:46) Examples of frameworks that help with the training process of a neural network. (01:24) The stage AI & ML is at, currently, in terms of safety-critical applications. (04:42) The industries that are currently using AI & ML, and the types of applications they’re focusing on. (06:52) Connect with Mike Fingeroff: LinkedIn Connect with Ellie Burns: LinkedIn Resources: Catapult High-Level Synthesis Siemens EDA

The field of artificial intelligence and machine learning - just like any other industry where innovation happens - faces lots of challenges, and specialists are relentlessly looking for ways to overcome them. In this episode, Mike and Ellie tackle some of these challenges and discuss the different compute platforms, their limitations, and the surge of new platform development, as well as the many challenges that hardware designers face as they try to move AI to IoT edge devices. Tune in, and learn some of the challenges of implementing the latest cutting-edge neural network algorithms on today's compute platforms.   In this episode, you will learn: The amount of energy neural networks use. (00:54) Why analog starts to be in the spotlight again. (04:30) How applications moving to the Edge impacts training and inferencing. (05:39) Data movement requires most of the energy consumption. (07:50) Connect with Mike Fingeroff: LinkedIn Connect with Ellie Burns: LinkedIn Resources: Catapult High-Level Synthesis Siemens EDA

The gap between what the best AI applications can perform today versus the human brain is vast. Among many other differences, power efficiency and learning speed are two of the most challenging factors the AI & ML industry is dealing with when trying to design brain-like neural networks.   Today, in the final episode of the series, Mike and Ellie discuss that gap and the challenges that hardware designers have in their design flow. They also touch on the clashing requirements of coming up with a generic AI application that can perform many tasks versus applications that perform one task really well.   Tune in, to find out what the AI industry is doing to narrow the gap between the brain and artificial intelligence.   In this episode, you will learn: The gaps between AI applications and the human brain. (00:45) The Holy Grail of AI: one-shot learning. (01:48) The energy consumption of the human brain versus deep neural networks. (02:50) The industry’s struggle of creating specific networks versus generic ones. (03:56) The resources required by one of the most complex neural networks. (06:08) The industry’s challenge of keeping up with the rapid changes in AI architectures. (06:57) Connect with Mike Fingeroff: LinkedIn Connect with Ellie Burns: LinkedIn Resources: Catapult High-Level Synthesis Siemens EDA

Artificial intelligence is becoming increasingly more common in the workplace. To really understand how it works and the benefits that it can bring about, talking to people with first-hand experience is key. To learn more about how AI technology is being used, we turn to our very own experts here at Siemens.   In today’s episode, I’m talking to Roberto D'Ippolito, Senior Technical Product Manager of the HEEDS team at Siemens Digital Industries Software based in Belgium. We’ll discuss the range of possibilities within AI, where all that data comes from, and how to create value from it. AI has the potential to offer big advantages over the competition, and machine learning puts all of the information into focus.  You’ll also learn where HEEDS fits into the simulation equation, the key benefits of using the technology, and the process of designing automated vehicles so that unpredictable situations are accounted for. We’ll wrap up by touching on a few misconceptions about AI, and where it might lead us in the future.   In this episode, you will learn: How we can utilize AI industrially and in general (1:48) The role of HEEDS (2:57) The key benefit of AI and machine learning technology (6:51) How the adaptive sampling strategy is being used (9:06) How machine learning meets the challenge of designing autonomous vehicles (11:02) The AV design process (14:13) Where all of the data is coming from (18:16) Challenging beliefs and misconceptions about AI (23:21) The future of AI in engineering (25:00) Connect with Roberto D'Ippolito: LinkedIn Connect with Thomas Dewey: LinkedIn

Computer vision is one of the fastest-growing AI fields. This has been fuelled by the progress made in data models training and its widespread adoption. Automation that results from this increases the quality of products and lowers the cost of production. In today’s episode, I’m talking to Shahar Zuler, a data scientist and machine learning engineer at Siemens. We'll discuss object recognition in factories and the unique challenges being faced in its deployment.  Tune in and learn more about computer vision in machine learning as well as the use of synthetic data in model training. Some Questions I Ask: How do you see AI impacting the industrial industry? (3:06) What are the unique challenges of employing AI/ML in the industrial environment? (10:59) What are you doing at Siemens to help solve the industrial environment’s AI/ML challenges? (19:33) What do you do to validate the correctness of synthetic data? (23:15) Can you predict what you think will happen with machine learning in the next 10 years? (26:57) In this episode, you will learn: Different tasks of computer vision machine learning (11:30) How to train an object detection model (16:34) How synthetic images are used in ML model training (20:56) How to validate synthetic data (23:38) The benefits of partnerships between Siemens and their customers (25:08) Connect with Shahar Zuler:  LinkedIn Connect with Thomas Dewey:  LinkedIn

Artificial intelligence has come a long way in the last few years and it is making a significant impact in many industries. However, there is still notable reluctance to hand over more operations to AI-based systems because they are still not seen as being robust enough to be fully relied upon. In today’s episode, I am talking to Michael Berger, the head of Munich Reinsurance’s AI Insurance Unit, and Boris Scharinger, a senior innovation manager at Siemens Digital Industries. We’ll discuss AI performance risk insurance and the progress of industrial-grade AI. Tune in and learn more about what’s happening in the field of AI, the challenges it’s facing, and what the future holds for it. In this episode, you will learn: How AI performance risk contributes to the adoption of technology (2:32) What industrial-grade AI concept entails (7:09) Ingredients of industrial-grade AI (8:03) Challenges facing industrial-grade AI development (10:20) Importance of AI models’ robustness (11:06) How AI risk is assessed by an insurer (13:32) Qualities of a good AI solution (14:36) Experts' thoughts on where AI will be in 3-4 years (17:35) Connect with Michael Berger:  LinkedIn Munich Reinsurance Connect with Boris Scharinger:  LinkedIn Connect with Thomas Dewey:  LinkedIn

Integration of AI into engineering solutions has changed how engineers approach product selection and development. One of the areas that have benefited the most from this integration is performance simulation. It has led to accurate decisions being made much faster as engineers use accurate insights that AI makes available to them. In today’s episode, I am talking to Krishna Veeraraghavan - Project Manager at Siemens Digital Industries Software. We’ll discuss the role AI is playing in computational fluid dynamics (CFD), the benefits and the challenges in implementing AI into CFD simulations, as well as the different techniques that are deployed in CFD. Tune in and learn more about the process of integrating AI into CFD, and the impact it’s having on the users. In this episode, you will learn: What is computational fluid dynamics (CFD)? (3:11) How the CFD journey looks like for the customer (4:40) How AI is used in interpreting CFD simulation results (14:02) What AI techniques are deployed in CFD (15:43) The AI model training process in CFD (17:12) How customers are using AI in CFD (19:24) Where AI/ML will be in the future (21:53) The benefits of bringing AI into CFD simulation (24:48) The challenges faced by customers in AI-powered CFD adoption (25:44) Connect with Krishna Veeraraghavan:  LinkedIn Connect with Thomas Dewey:  LinkedIn

Creating an accurate AI model requires millions of images and data points to be fed into the computing system. This is a difficult task that can slow down the speed to market or lower the accuracy of the model that is created. Synthetic data helps in solving this problem by reducing the amount of real data that needs to be collected. That results in reduced time to market and increased model accuracy. In today’s episode, I’m talking to Zachi Mann. He leads a new initiative that is focused on advanced robotics simulation capabilities at Siemens. He’ll help us understand AI model training for factory robots. He’ll also share with us how Siemens solutions such as CAD and NX help in model development. In this episode, you’ll learn about the use of synthetic data in training AI-reliant factory robots. You’ll also learn about the challenges and the benefits that come with combining synthetic data with real data. Additionally, you’ll learn about Synth AI, a new synthetic data generating solution from Siemens. In this episode, you will learn: The meaning of synthetic data (03:03) The challenges that come with the use of synthetic data (04:19) The benefits of using synthetic data (08:05) Why the use of synthetic data has been on the rise (11:06) Other uses of synthetic data besides AI model training (17:25) Connect with Zachi Mann: LinkedIn Connect with Spencer Acain: LinkedIn

Imagine an engineering software that anticipates the commands you want to execute by studying your usage patterns. Such a feature would definitely decrease the design time as well as increase your overall user experience. That’s exactly what Siemens NX is doing thanks to its machine learning capabilities. In this episode, the first part of two, Spencer Acain interviews Shirish More, Product Manager at Siemens Digital Industries Software, responsible for driving innovations inside Siemens NX. He’ll share with us how they are using AI to personalize NX users’ experience and improve productivity. Tune in and learn more about how AI is transforming the world of mechanical engineering software. In this episode, you will learn: The role played by AI in mechanical engineering software (01:34) The direct benefits of using AI (02:12) The meaning of personalization in the context of AI (05:41) Areas where AI is being implemented to improve Siemens NX users' experience (12:14) Connect with Shirish More:  LinkedIn Connect with Spencer Acain:  LinkedIn

Automation helps in accelerating repetitive but critical tasks that consume too much time. However, determining the tasks to automate requires deep knowledge of the actual steps, in chronological order, necessary to complete a process. Siemens NX is accelerating the design process by using knowledge gathered from past models to automate predictable tasks. In this episode, the second part of two, Spencer Acain interviews Shirish More, Product Manager at Siemens Digital Industries Software, responsible for driving innovations inside Siemens NX. He’ll share with us how Siemens NX is using AI to accelerate the design process. Tune in and learn more about how AI is transforming the world of mechanical engineering software. In this episode, you will learn: How AI in NX will help with virtual reality (00:48) How AI is helping NX takes prediction to the next level (02:07) How NX is speeding the design process using ML (05:49) The future of AI in NX (09:36) Connect with Shirish More:  LinkedIn Connect with Spencer Acain:  LinkedIn

Companies look for every advantage over their competitors that can help them bring new products to the market faster and at a lower cost. One of the ways to increase the speed to market is by improving the design speed. That is why Siemens NX has developed a new Sketch Solver that makes design work easier, more accurate, and faster. I’m your host, Spencer Acain, and today I’m joined by Scott Felber, Product Marketing Manager Siemens NX Design. And, Mike Yoder, who works with the Product Management group focused on the NX design tools. They’ll help us understand the new Sketch Solver and the impact it is having on the market. In this episode, you’ll learn about how Siemens NX’s new Sketch Solver works and how it compares to the traditional Solver. You’ll also learn about the new features that have been introduced and how they impact design speed and accuracy. Additionally, you’ll learn about how the market is reacting to this new product. What You’ll Learn in this Episode: How the new Sketch Solver works (01:52) Why the NX team decided to update the Sketch Solver (04:07) The difference between the new Sketch Solver and traditional sketchers (07:11) Questions that customers are asking about the new Sketch Solver (12:38) How the new Sketch Solver helps you attain 30% savings in time (20:05) Connect with Scott Felber: LinkedIn Twitter Connect with Mike Yoder: Email: michael.yoder@siemens.com Connect with Spencer Acain:  LinkedIn

Simulation of digital models has completely transformed the product development process. However, it can be a time-consuming and expensive venture if the product being developed has many components. AI technology simplifies this process by creating thousands of models and test scenarios within a few hours. In this episode, the first part of two, Spencer Acain interviews Justin Hodges, AI/ML specialist and Product Manager for Siemens Simcenter. He’ll help us understand how the different ways in which Simcenter is using AI technology. He’ll also share with us the benefits of using AI in simulation. Tune in and learn more about how AI is transforming the world of product modeling and testing. In this episode, you will learn: How Simcenter is currently using AI technology (02:22) How Simcenter is using AI to improve the user’s experience (04:20) How AI is used in classifying different parts of a car (06:54) How AI helps in optimization simulation scenarios (08:19) Connect with Justin Hodges: LinkedIn Siemens Simcenter Connect with Spencer Acain:  LinkedIn

Virtual testing of products has significantly been improved by the use of AI. We can now use models based on data from previous development cycles to create feasible designs faster. This translates to a shorter time to market and a lower cost of simulation because optimal cases are identified in advance. In this episode, the second part of two, Spencer Acain interviews Justin Hodges, AI/ML specialist and Product Manager for Siemens Simcenter. He’ll help us understand how AI’s predictive capabilities help in simulation. He’ll also share with us how Siemens is approaching the field of AI/ML. Tune in and learn more about how AI/ML is being applied in the world of product modeling and testing. In this episode, you will learn: Predictive capabilities of AI in simulation and design (00:35) How product testing benefits from AI/ML technology (03:55) Simcenter’s competitive advantage in the field of AI/ML (06:02) How to build trust in AI solutions (10:04) Connect with Justin Hodges: LinkedIn Siemens Simcenter Connect with Spencer Acain:  LinkedIn

As AI gets smarter, it will play an increasing role in designing new products. This change will take many forms, with Generative Design and Engineering being key new technologies to enable faster development in a broader design space. This, in turn, will redefine the role engineers have in the product design process from one of intensive manual work to that of orchestrators. In this episode, Spencer Acain interviews Tod Parrella, Senior Product Manager for NX Design solutions. Tod explains the benefits of Generative Design and the challenges it faces in building trust as a new technology.   In this episode, you will learn: The importance of Generative Design (00:51) How the role of engineers and designers is changing (06:35) Building trust in AI (11:54) Early adopters of generative practices (15:17) AI beyond Generative Design (19:06)

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

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

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

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

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