How AI Happens

Xiaoyang Yang, Head of Data AI Security and IT over at Second Dinner Studios, explains how Second Dinner navigates the issue of excess data with intention and discover the metrics that go deeper than the surface to measure the quality of competition, balance, and fairness within gaming. Xiaoyang also describes the difference between AI and gaming AI and shows us how each can be used to enhance the other. Listen to today’s episode for a careful look at how AI can be used to improve player experience and how gaming can act as a testing ground to improve AI in everyday life. 

Key Points From This Episode:

• Introducing Xiaoyang Yang, head of Data AI Security and IT at Second Dinner Studios.
• His recently launched video game, MARVEL SNAP.
• How he uses data as a tool to listen to players before translating it into insights.
• The role of scale and how it changes the parameters around which players you attract.
• The discrepancy between how different players experience the same feature.
• Xiaoyang’s background in theoretical physics, machine learning, and gaming.
• How an internship at Blizzard helped him enter a new industry.
• His time working on World of Warcraft and with Riot Games.
• Second Dinner’s partnership with Marvel to create MARVEL SNAP.
• Xiaoyang’s aim to use data to make the game accessible to a wider audience who hasn’t tried collectible card games before. 
• The issue of excess data and how Second Dinner combats this with careful intention.
• Data metrics that go deeper to enhance design and balance.
• Competition, fairness, and balance as indicators for how fun a game will be for players.
• How AI can be used to test fairness and balance in gaming.
• How game AI differs from AI in general and how each can be used to inform the other. 
• The competitive experience you can have with gaming AI due to different skill levels.
• The new experience you can offer users today that has been facilitated by AI. 

Tweetables:

“We try to really listen to what our players are saying. One way to do that is through data. We use data as a tool.” — Xiaoyang Yang [0:02:28]

“When you see the scale, you begin to really understand that different players have different desires. Sometimes, different players see the same feature or the same experience in a very different type of way.” — Xiaoyang Yang [0:04:46]

“We see a lot of opportunities to use technology data AI to make MARVEL SNAP approachable to a wide audience of players and, hopefully, some players who have never tried the genre of collectible card games.” — Xiaoyang Yang [0:11:25]

“We want to make sure that there are different sets of cards you can use to have fun and still be competitive in the game. That's not an easy task.” — Xiaoyang Yang [0:19:25]

Links Mentioned in Today’s Episode:

Xiaoyang Yang on LinkedIn

Second Dinner Studios

MARVEL SNAP

Blizzard

Riot Games

How AI Happens

Sama

Tune in to hear more about Becks’ role as a lead full stack AI engineer at Rogo, how they determine what should and should not be added into the product tier for deep learning, the types of questions you should be asking along the investigation-to-product roadmap for AI and machine learning products, and so much more!

Key Points From This Episode:

• An introduction to today’s guest, Lead Full Stack AI Engineer, Becks Simpson. 
• Becks’ cover band Des Confitures made up of machine learning engineers and other academics. 
• Becks’ career background and how she ended up in her role at Rogo. 
• How Rogo enables people to unlock or make sense of unstructured or unorganized data.
• Why Becks’ role could be compared to that of an AI Swiss Army Knife. 
• How they determine what should and should not be added to the product tier for deep learning. 
• Becks’ experience of having to give someone higher up a reality check about the technical needs of their product.  
• Why Becks believes there are so many nontechnical hats you need to wear as an AI or ML expert. 
• Thoughts on the trend of product managers being taught how to do AI but not AI people being taught to do product management.
• The importance of bringing data about data into the conversation. 
• The types of questions you should be asking and where the answers to understanding your dataset will then take you. 
• How the investigation-to-product roadmap is not something you would learn in academia for AI machine learning and why it should be.  
• Thoughts as to why it is so common for someone to have one foot in the industry and one foot in academia.  
• An area of AI machine learning that Becks is truly excited about: off the shelf models. 

Tweetables:

“People think that [AI] can do more than what it can and it has only been the last few years where we realized that actually, there’s a lot of work to put it in production successfully, there’s a lot of catastrophic ways it can fail, there are a lot of considerations that need to be put in.” — Becks Simpson [0:11:39]

“Make sure that if you ever want to put any kind of machine learning or AI or something into a product, have people who can look at a road map for doing that and who can evaluate whether it even makes sense from an ROI business standpoint, and then work with the teams.” — Becks Simpson [0:12:55]

“I think for the people who are in academia, a lot of them are doing it to push the needle, and to push the state of the art, and to build things that we didn’t have before and to see if they can answer questions that we couldn’t answer before. Having said that, there’s not always a link back to a practical use case.” — Becks Simpson [0:20:25]

“Academia will always produce really interesting things and then it’s industry that will look at whether or not they can be used for practical problems.” — Becks Simpson [0:21:59]

Links Mentioned in Today’s Episode:

Becks Simpson 

Rogo

Des Confitures  

Montreal Institute of Learning Algorithms

Sama

 Dr. Seymour aims to take cutting-edge technology and apply it to the special effects industry, such as with the new AI platform, PLATO. He is also a lecturer at the University of Sydney and works as a consultant within the special effects industry. He is an internationally respected researcher and expert in Digital Humans and virtual production, and his experience in both visual effects and pure maths makes him perfect for AI-based visual effects. In our conversation we find out more about Dr. Seymour’s professional career journey, and what he enjoys the most about working as both a researcher and practitioner. We then get into all the details about AI in special effects as we learn about Digital Humans, the new PLATO platform, why AI dubbing is better, the biggest challenges facing the application of AI in special effects.

Key Points From This Episode:

• Dr. Seymour explains his background and professional career journey. 
• Why he enjoys bridging the gap between researcher and practitioner.
• An outline of the different topics that Dr. Seymour lectures in and what he is currently working on.
• He explains what he means by the term ‘digital humans’ and provides examples.
• The special effects platform, PLATO, he is currently working on and what it will be used for.
• An explanation of how PLATO was used in the Polish movie, The Champion.
• He explains the future goals and aims for auto-dubbing using AI and visual effects.
• Why auto-dubbing procedure will not add or encumber existing processes of making a movie. 
• Reasons why AI auto-dubbing is better than traditional dubbing.
• Whether this is a natural language processing challenge or more of a creative filmmaking challenge.
• A discussion about why new technologies take long to be applied to real-world scenarios.
• How the underlying process of PLATO are different from what is required to make a deepfake video. 
• His approach to overcoming challenges facing the PLATO platform. 
• Other areas of the entertainment industry Dr. Seymour expects AI to be disruptive.

Tweetables:

“In the film, half the actors are the original actors come back to just re-voice themselves, half aren’t. In the film hopefully, when you watch it, it’s indistinguishable that it wasn’t actually filmed in English. — @mikeseymour [0:10:15]

“In our process, it doesn’t apply because if you were saying in four words what I’d said in three, it would just match. We don’t have to match the timing, we don’t have to match the lip movement or jaw movement, it all gets fixed.” — @mikeseymour [0:15:15]

“My attitude is, it’s all very well for us to get this working in the lab, but it has to work in the real world.” — @mikeseymour [0:19:56]

Links Mentioned in Today’s Episode:

Dr. Mike Seymour on LinkedIn

Dr. Mike Seymour on Twitter

Dr. Mike Seymour on Google Scholar 

University of Sydney

fxguide

Dr. Paul Debevec

Pixar

Darryl Marks on LinkedIn

Adapt Entertainment

PLATO Demonstration Link

The Champion

Pinscreen

Respeecher

Rob Stevenson on LinkedIn

Rob Stevenson on Twitter

Sama

 Ethics in AI is considered vital to the healthy development of all AI technologies, but this is easier said than done. In this episode of How AI Happens, we speak to Maria Luciana Axente to help us unpack this essential topic. Maria is a seasoned AI policy expert, public speaker, and executive and has a respected track record of working with companies whose foundation is in technology. She combines her love for technology with her passion for creating positive change to help companies build and deploy responsible AI. Maria works at PwC, where her work focuses on the operationalization of AI, and data across the firm. She also plays a vital role in advising government, regulators, policymakers, civil society, and research institutions on ethically aligned AI public policy. In our conversation, we talk about the importance of building responsible and ethical AI, while leveraging technology to build a better society. We learn why companies need to create a culture of ethics for building AI, what type of values encompasses responsible technology, the role of diversity and inclusion, the challenges that companies face, and whose responsibility it is. We also learn about some basic steps your organization can take and hear about helpful resources available to guide companies and developers through the process.

Key Points From This Episode:

• Maria’s professional career journey and her involvement in various AI organizations. 
• The motivation which drives AI and machine learning professionals in their careers.
• How to create and foster a system that instills people with positivity. 
• Examples of companies that have successfully fostered a positive and ethical culture.
• What are good values for building responsible and ethical technology. 
• We learn about the values the responsible AI toolkit prescribes.
• Some of the challenges faced when building responsible and ethical technology.
• An outline of the questions a practitioner can ask to ensure operation by the universal ethics.
• She shares some helpful resources concerning ethical guidelines for AI. 
• Why diversity and inclusion are essential to building technology. 
• Whose responsibility it should be to ensure the ethical and inclusive development of AI.
• We wrap up the episode with a takeaway message that Maria has for listeners. 

Tweetables:

“How we have proceeded so far, via Silicon Valley, 'move fast and break things.' It has to stop because we are in a time when if we continue in the same way, we're going to generate more negative impacts than positive impacts.” — @maria_axente [0:10:19]

“You need to build a culture that goes above and beyond technology itself.” — @maria_axente [0:12:05]

“Values are contextual driven. So, each organization will have their own set of values. When I say organization, I mean both those who build AI and those who use AI.” — @maria_axente [0:16:39]

“You have to be able to create a culture of a dialogue where every opinion is being listened to, and not just being listened to, but is being considered.” — @maria_axente [0:29:34]

“AI doesn't have a technical problem. AI has a human problem.” — @maria_axente [0:32:34]

Links Mentioned in Today’s Episode:

Maria Luciana Axente on LinkedIn

Maria Luciana Axente on Twitter

PwC UK

PwC responsible AI toolkit

Sama

 The gap between those creating AI systems and those using the systems is growing. After 27 years on the other side of technology, Mieke decided that it was time to do something about the issues that she was seeing in the AI space. Today she is an Adjunct Professor for Sustainable Ethical and Trustworthy AI at Vlerick Business School, and during this episode, Mieke shares her thoughts on how we can go about building responsible AI systems so that the world can experience the full range of benefits of AI.

Key Points From This Episode:

• An overview of Mieke’s educational and career background.
• Elements of the AI space that have and haven’t changed since Mieke studied robotics AI in 1992.
• What drew Mieke back into the AI space five years ago.
• The importance of understanding the limitations of AI.
• Mieke shares her thoughts on how to build responsible AI systems.
• The challenges of building responsible AI systems.
• Why the European AI Act isn’t able to address the complexities of the AI sector.
• The missing link between the people creating AI systems and the people using them.
• Exploring the issue of deep fakes.
• The role of AI Translators, and an overview of the AI Translator course available in Belgium.

Tweetables:

“The compute power had changed, and the volumes of data had changed, but the [AI] principles hadn't changed that much. Only some really important points never made the translation.” — @miekedk [0:02:03]

“[AI systems] don't automatically adapt themselves. You need to have your processes in place in order to make sure that the systems adapt to the changing context.” — @miekedk [0:04:06]

“AI systems are starting to be included into operational processes in companies, but only from the profit side, not understanding that they might have a negative impact on people especially when they start to make automated decisions.” — @miekedk [0:04:52]

“Let's move out of our silos and sit together in a multidisciplinary debate to discuss the systems we're going to create.” — @miekedk [0:07:52]

Links Mentioned in Today’s Episode:

Mieke de Ketelaere

Mieke's Books

The European AI Act

Sama

Today, on How AI Happens, we are joined by the Chief Digital Officer at Allied Digital, Utpal Chakraborty, to talk all things AI at Allied Digital. You’ll hear about Utpal’s AI background, how he defines Allied Digital’s mission, and what Smart Cities are and how the company captures data to achieve them, as well as why AI learning is the right approach for Smart Cities. We also discuss what success looks like to Utpal and the importance of designing something seamless for the end-user. To find out why customer success is Allied Digital’s success, tune in today! 

Key Points From This Episode:

• A brief overview of Utpal’s background and how he ended up in his current role at Allied. 
• How Utpal would characterize Allied Digital’s mission. 
• The definition of Smart Cities.
• How Allied Digital is able to capture the data needed to make a city a Smart City. 
• What made it clear to Utpal that AI machine learning was the right approach for the Smart City services.
• Insight into what success and an end goal looks like for Utpal. 
• Why it is everyone’s job to design something that is seamless for the end-user. 
• A look at what Utpal thinks has been truly disruptive in the AI space. 

Tweetables:

“I looked at how we can move this [Smart City] tool ahead and that’s where the AI machine learning came into the picture.” — @utpal_bob [0:11:11]

“[Allied Digital] wants to bring that wow factor into each and every service product and solution that we provide to our customers and, in turn, that they provide to the industry.” — @utpal_bob [0:16:27]

Links Mentioned in Today’s Episode:

Utpal Chakraborty on LinkedIn

Utpal Chakraborty on Twitter

Allied Digital Services

Sama

 In today’s conversation, we learn about Jason and Kevin’s career backgrounds, the potential that the deep technology sector has, what ideas excite them the most, the challenges when investing in AI-based companies, what kind of technology is easily understood by the consumer, what makes a technological innovation successful, and much more. 

Key Points From This Episode:

• Background and professional paths that led Jason and Kevin to their current roles.
• Reasons behind starting Calibrate Ventures and originally entering the sector.
• How the deep-technology sector can solve current problems.
• What kind of new technology and innovation they get most excited about.
• The most essential quality of innovative technology: what people want.
• Rundown of the diverse, experienced, and talented team they work with.
• Jason shares an example of a technological innovation that solved a real-world problem.
• How they differentiate the approach when investing in AI companies.
• What influences the longer sale cycles in the AI technology sector.
• An example of the challenges when integrating AI technology with the business side.
• The benefits that Jason and Kevin’s experience adds to the company.
• Some examples of the kind of technology that translates well to the consumer.
• We find out what their opinion is about automation and augmentation.
• We wrap up the show with some advice from Jason and Kevin for AI entrepreneurs. 

Tweetables:

“I think for me personally, the cycle-time was very long. You work on projects for a very long time. As an investor, I get to see new ideas and new concepts every day. From an intellectual curiosity standpoint, there couldn’t be a better job.” — Kevin Dunlap [0:05:17]

“So that lights me up. When I hear somebody talk about a problem that they are looking to solve and how their technology can do it uniquely with some type of competitive or differentiated advantage we think is sustainable.” — Jason Schoettler [0:08:14]

“The things that really excite us are not, where can we do better than humans but first, where are there not humans work right now where we need humans doing work.” — Jason Schoettler [0:20:44]

“Anytime that someone is doing a job that is dangerous, that is able to be solved with technology, I think we owe it to ourselves to do that.” — Kevin Dunlap [0:22:39]

Links Mentioned in Today’s Episode:

Jason Schoettler on LinkedIn

Kevin Dunlap on LinkedIn

Calibrate Ventures

Calibrate Ventures on LinkedIn

GrayMatter Robotics

GrayMatter Robotics on LinkedIn

Whether you’re building AI for self-driving cars or for scheduling meetings, it’s all about prediction! In this episode, we’re going to explore the complexity of teaching the human power of prediction to machines.

Key Points From This Episode:

• Dennis shares an overview of what he has spent his career focusing on.
• How Dennis defines an intelligent agent.
• The role of prediction in the AI space.
• Dennis explains the mission that drove his most recent entrepreneurial venture, x.ai (acquired by Bizzabo).
• Challenges of transferring humans’ capacity for prediction and empathy to machines.
• Some of Dennis’s key learnings from his time working on the technology for x.ai.
• Unrealistic expectations that humans have of machines.
• How we can teach humans to have empathy for machines. 
• Dennis’s hope for the next generation in terms of their approach to AI.
• A lesson Dennis learned from his daughter about AI and about human nature.
• What Dennis is most excited about in the AI space.

Tweetables:

“The whole umbrella of AI is really just one big prediction engine.” — @DennisMortensen [0:03:38]

“Language is not a solved science.” — @DennisMortensen [0:06:32]

“The expectation of a machine response is different to that of a human response to the same question.” — @DennisMortensen [0:11:36]

Links Mentioned in Today’s Episode:

Dennis Mortensen on LinkedIn

Bizzabo [Formerly x.ai]

Leading AI companies are adopting simulation, synthetic data and other aspects of the metaverse at an incredibly fast rate, and the opportunities for AI/machine learning practitioners are endless. Tune in today for a fascinating conversation about how the real world and the virtual world can be blended in what Danny refers to as “the real metaverse.”

Key Points From This Episode:

• The career path that led Danny to his current role as Senior Vice President of AI at Unity.
• Data; the machine learning challenge that Danny has dealt with in many different forms throughout his career. 
• An explanation of how Unity uses data to make game recommendations to players. 
• How deep learning embedding works.
• What drew Danny to Unity.
• The benefits of using synthetic data.
• How Unity ensures that the synthetic data they create is as unbiased as possible. 
• The importance of anchoring your synthetic data to a real-world counterpart.
• Danny’s thoughts on the potential of the Metaverse.
• Examples of the career opportunities that the Metaverse has opened up for AI/machine learning practitioners.

Tweetables:

“When you play a game, I don’t need to know your name, your age. I don’t need to know where you live, or how much you earn. All that really matters is that my system needs to learn the way you play and what you are interested in in your gameplay, to make excellent recommendations for other games. That’s what drives the gaming ecosystem.” — @danny_lange [0:03:16]

“Deep learning embedding is something that is really driving a lot of progress right now in the machine learning AI space.” — @danny_lange [0:06:04]

“The world is built on uncertainty and we are looking at simulation in an uncertain world, rather than in a Newtonian, deterministic world.” — @danny_lange [0:23:23]

Links Mentioned in Today’s Episode:

Danny Lange on LinkedIn

Unity

In today’s episode, Archy De Berker, Head of Data and Machine learning at CarbonChain, explains how he and his team calculate carbon footprints, some of the challenges that they face in this line of work, the most valuable use of machine learning in their business (and for climate change solutions as a whole), and some important lessons that he has learned throughout his diverse career so far! 

Key Points From This Episode:

• An overview of Archy’s career trajectory, from academic neuroscientist to Head of Data and Machine learning at CarbonChain.
• The foundational mission of CarbonChain.
• Archy explains how machine learning can be applied in the context of energy storage as a climate change solution. 
• Industries that CarbonChain focuses on.
• How Archy and his team calculate carbon footprints.
• A key challenge for carbon footprinting.
• Where machine learning provides the most value for CarbonChain.
• The importance of the field of document understanding. 
• A story from Archy’s time at Element AI that highlights the value of having technical people working as close to the design and data generation as possible. 
• Why Archy chose to move into the product management realm.
• Additional ways that machine learning can help solve climate change issues. 

Tweetables:

“We build automated carbon footprinting for the world’s most polluting industries. We’re really trying to help people who are buying things from carbon-intense industries figure out where they can get lower carbon versions of the same kind of products.” — @ArchydeB [0:02:14]

“A key challenge for carbon footprinting is that you need to be able to understand somebody’s business in order to tell them what the carbon footprint of their activities is.” — @ArchydeB [0:13:01]

“Probably the most valuable place for machine learning in our business is taking all this heterogeneous customer data from all these different systems and being able to map it onto a very rigid format that we can then retrieve information from our databases for.” — @ArchydeB [0:13:24]

Links Mentioned in Today’s Episode:

Archy de Berker on LinkedIn

Carbon Chain