Curren is a curious, driven, and creative leader with vast experience in data science and AI. Her original background was in neuroscience and cognitive neuroscience but entered the industry when she realized how much she enjoyed programming, maths, and statistics. Additionally, her biology background gave her an advantage, making her a perfect fit for managing the neuroscience portfolio for Johnson & Johnson. In our conversation with Curren, we learn about her professional background, how her biology background is an advantage, and what she enjoys most about data science, as well as the important work she does at Johnson & Johnson. We then talk about AI in the pharmaceutical industry, how it is used, what it is used for, the benefits of AI both to the company and patients, and her approach to tackling data science problems. She also tells us what it was like moving into a leadership role and shares some advice for people wanting to take the plunge into leadership.  Key Points From This Episode:Curren’s professional background and how she ended up in her role at Johnson & Johnson.The connection between traditional neuroscience and neural networks in AI.Ways in which traditional scientific education in neurology informs AI.How much we currently understand about human learning.Curren explains her role and responsibilities in her position at Johnson & Johnson.What the term ‘precision’ means in her line of work and examples.Outline of Curren’s approach to data science and her role at Johnson & Johnson.We find out what Curren’s definition of success is.The significant benefits of optimizing processes and procedures.Curren outlines the various ways AI is deployed at Johnson & Johnson.Her experience moving from an individual contributor role into a leadership role.Advice Curren has for people who are considering entering a leadership role.The importance of trusting your team as a leader.Tweetables:“Finding new ways to use data to drive diagnosis is a big focus for us.” — @CurrenKatz [0:11:56]“In data science, it can be challenging to define success. But choosing the right problem to solve can make that a lot easier.” — @CurrenKatz [0:15:27]“I want the best data scientists in the world and to have those people on my team or the best managers in the world. I just need to give them the space to be successful.” — @CurrenKatz [0:23:55]Links Mentioned in Today’s Episode:Curren Katz on LinkedInCurren Katz on TwitterJohnson & JohnsonJohnson & Johnson on LinkedInSama

Dr. Kruft unpacks how she went from earning a Ph.D. focused on quantum chemistry, to working in AI and machine learning. She shares how she first discovered her love of data science, and how her Ph.D. equipped her with the skills she needed to transition into this new and exciting field. We also discuss the data science approach to problem-solving, deep learning emulators, and the impact that machine learning could have on the natural sciences.  Key Points From This Episode:Introducing today's guest, Bonnie Kruft, Senior Director at Microsoft’s AI for Science.A quick look at Bonnie’s background and the research she is currently doing.The work that Bonnie did on quantum chemistry for her Ph.D. dissertation.How quantum chemistry led to her working in the field of AI.An overview of the transferable skills that Bonnie picked up during her Ph.D.Learn about Bonnie’s work with pharmaceutical companies.How Bonnie became interested in data science and machine learning.The data science approach to problem-solving.The concept of falling faster and how to facilitate it.What the word ‘quantum’ means and how it applies to computing.How Bonnie’s Ph.D. prepared her for a career in machine learning.The impact that machine learning could have on the natural sciences.A breakdown of the four paradigms through which science has evolved.The emulator approach and how it can apply to anywhere data science is being done.Learn about Microsoft's AI for science and what they are doing with machine learning.What Bonnie’s typical day looks like.Tweetables:“Although I wasn't really working on machine learning, or data science during my Ph.D., there's a lot of transferable skills that I picked up along the way while I was working on quantum chemistry.” — Bonnie Kruft [0:03:00]“We believe that deep learning could have a really transformational impact on the natural sciences.” — Bonnie Kruft [0:13:02]“The idea is that deep learning emulators will be used for the things that are going to make the most impact on the world. Solving healthcare challenges, combating disease, combating climate change, and sustainability. Things like that.” — Bonnie Kruft [0:21:29]Links Mentioned in Today’s Episode:Bonnie Kruft on LinkedInMicrosoftHow AI HappensSama

In our conversation, we discuss Brandon's approach to problem-solving, the use of synthetic data, challenges facing the use of AI in drug development, why the diversity of both data and scientists is important, the three qualities required for innovation, and much more.Key Points From This Episode:We hear about Brandon’s unconventional background and professional career journey. Why he has a passion for combining AI and machine learning with biology.An outline of the Opal platform and how it is used for drug discovery.Brandon’s approach to innovating and improving various stages of pharmaceutical development.Whether or not he thinks his approach can be applied outside of pharmaceutical development.How data science is used in traditional companies and how this differs at Valo.What signs people should look out for to ensure they are at a data-driven organization. A brief discussion about the benefits of using non-traditional approaches. Ways in which Brandon sees synthetic data being used in the future.The biggest challenge currently limiting the use of synthetic data. A breakdown of the three competing qualities that are required to innovate.Reasons why Brandon thinks current algorithms and the underlying datasets need to be improved. Brandon shares his approach to ensuring fairness and rooting out bias in datasets.Another problem the industry faces with scientists: a lack of diversity.The value of re-weighting a training set.Innovations in AI and machine learning that keeps Brandon motivated and inspired.Tweetables:“Instead of improving the legacy, is there a way to really innovate and break things? And that’s the way we think about it here at Valo.” — @allg00d [0:08:46]“Here at Valo, if data scientists have good ideas, we let them run with them, you know? We let them commission experiments. That’s not generally the way that a traditional organization would work.” — @allg00d [0:11:31]“While you might be able to get synthetic data that represents the bulk, you are not going to get the resolution within those patients, within those subgroups, within the patient set.” — @allg00d [0:15:15]“We suffer right now from a lack of diversity of data, but then, on the other side, we also suffer as a field from lack of diversity in our scientists.” — @allg00d [0:19:42]Links Mentioned in Today’s Episode:Brandon AllgoodBrandon Allgood on LinkedInValoValo on LinkedInOpal platformDALI AllianceLogicaBrandon Allgood on TwitterRob Stevenson on LinkedInSama

In this episode, Heather shares her background in both farming and commerce, and explains how her in-field experience and insights aid both her and the AI team in the development cycle. We learn about the advantages of drone-based precision spraying, the function of the herbicides that Precision AI’s drones spray onto crops, and the various challenges of creating AI models that can recognize plant variations. Key Points From This Episode:Introducing Heather Clair, Product Manager at precision.ai.Heather’s background in farming and commerce; and what led her to precision.ai.precision.ai’s dramatically different approach to agriculture.The advantages of drone-based precision spraying, as opposed to land-based high-clearance spraying.The function of the herbicides that precision.ai’s drones spray onto crops.precision.ai’s use of AI to teach their drones to identify crops and distribute herbicides with precision.  The relationship between Heather, as product manager, and the AI experts at precision.ai.Heather’s involvement in the development cycle.Sama’s reliable accuracy rate.The challenge of creating AI models that recognize and can work with plant variations.How the varying colors of soil impact the AI models.The phenomenon of phenoplasticity and the challenge it presents to the AI team. The advantage Heather has of having in-field experience.Heather’s closing tip: how to have happier, healthier houseplants.Tweetables:“Up until now, everybody just went, ‘How do we get more efficient [with] fewer passes?’ But nobody questioned, ‘Are we doing the passes with the right equipment?’” — Heather Clair [0:07:07]“[precision.ai is] moving from land-based high-clearance sprayers to drone-based precision spraying.” — Heather Clair [0:07:24]“I never thought when I was a little farm kid that I would be playing with drones, but it is one of my favorite things to do.” — Heather Clair [0:07:45]“Trying to create these AI models that can work on any stage of plant can be a challenge.” — Heather Clair [0:21:15]“It's incredible how working with my AI team has opened up my eyes to being able to look at these plants from a very logical standpoint.” — Heather Clair [0:25:34]Links Mentioned in Today’s Episode:Heather Clair on LinkedInprecision.aiSama

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 LinkedInSecond Dinner StudiosMARVEL SNAPBlizzardRiot GamesHow AI HappensSama

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 RogoDes Confitures  Montreal Institute of Learning AlgorithmsSama

 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 LinkedInDr. Mike Seymour on TwitterDr. Mike Seymour on Google Scholar University of SydneyfxguideDr. Paul DebevecPixarDarryl Marks on LinkedInAdapt EntertainmentPLATO Demonstration LinkThe ChampionPinscreenRespeecherRob Stevenson on LinkedInRob Stevenson on TwitterSama

 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 LinkedInMaria Luciana Axente on TwitterPwC UKPwC responsible AI toolkitSama

 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 KetelaereMieke's BooksThe European AI ActSama

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 LinkedInUtpal Chakraborty on TwitterAllied Digital ServicesSama