Bryan DeBois, Director of Industrial AI at RoviSys, discusses the concept of industrial AI, deep reinforcement learning, machine teaching, and the future of generative AI. They explore the potential applications of AI in the industrial sector, the challenges of replicating human expertise with machines, and the importance of reliable systems. The conversation also delves into the current state of AI in the industrial landscape, the differences between monolithic deep learning and standard deep learning, and the significance of predictability in AI decision-making.

2023 ML Pulse Report Joining us today are our panelists, Duncan Curtis, SVP of AI products and technology at Sama, and Jason Corso, a professor of robotics, electrical engineering, and computer science at the University of Michigan. Jason is also the chief science officer at Voxel51, an AI software company specializing in developer tools for machine learning. We use today’s conversation to discuss the findings of the latest Machine Learning (ML) Pulse report, published each year by our friends at Sama. This year’s  report focused on the role of generative AI by surveying thousands of practitioners in this space. Its findings include feedback on how respondents are measuring their model’s effectiveness, how confident they feel that their models will survive production, and whether they believe generative AI is worth the hype. Tuning in you’ll hear our panelists’ thoughts on key questions in the report and its findings, along with their suggested solutions for some of the biggest challenges faced by professionals in the AI space today. We also get into a bunch of fascinating topics like the opportunities presented by synthetic data, the latent space in language processing approaches, the iterative nature of model development, and much more. Be sure to tune in for all the latest insights on the ML Pulse Report!Key Points From This Episode:Introducing today’s panelists, Duncan Curtis and Jason Corso.An overview of what the Machine Learning (ML) Pulse report focuses on.Breaking down what the term generative means in AI.Our thoughts on key findings from the ML Pulse Report.What respondents, and our panelists, think of hype around generative AI.Unpacking one of the biggest advances in generative AI: accessibility.Insights on cloud versus local in an AI context.Generative AI use cases in the field of computer vision.The powerful opportunities presented by synthetic data.Why the role of human feedback in synthetic data is so important.Finding a middle ground between human language and machine understanding.Unpacking the notion of latent space in language processing approaches.How confident respondents feel that their models will survive production.The challenges of predicting how well a model will perform.An overview of the biggest challenges reported by respondents.Suggested solutions from panelists on key challenges from the report.How respondents are measuring the effectiveness of their models.What Duncan and Jason focus on to measure success.Career advice from our panelists on making meaningful contributions to this space.Quotes:“It's really hard to know how well your model is going to do.” — Jason Corso [0:27:10]“With debugging and detecting errors in your data, I would definitely say look at some of the tooling that can enable you to move more quickly and understand your data better.” — Duncan Curtis [0:33:55]“Work with experts – there's no replacement for good experience when it comes to actually boxing in a problem, especially in AI.” — Jason Corso [0:35:37]“It's not just about how your model performs. It's how your model performs when it's interacting with the end user.” — Duncan Curtis [0:41:11]“Remember, what we do in this field, and in all fields really, is by humans, for humans, and with humans. And I think if you miss that idea [then] you will not achieve – either your own potential, the group you're working with, or the tool.” — Jason Corso [0:48:20]Links Mentioned in Today’s Episode:Duncan Curtis on LinkedInJason CorsoJason Corso on LinkedInVoxel512023 ML Pulse ReportChatGPTBardDALL·E 3How AI HappensSama

Sama 2023 ML Pulse ReportML Pulse Report: How AI Happens Live WebinarAMD's Advancing AI EventOur guest today is Ian Ferreira,  the Chief Product Officer for Artificial Intelligence over at Core Scientific until they were purchased by his current employer Advanced Micro Devices, AMD, where he is now the Senior Director of AI Software. In our conversation, we talk about when in his career he shifted his focus to AI, his thoughts on the nobility of ChatGPT and applications beyond advertising for AI, and he touches on the scary aspect of Large Language Models (LLMs). We explore the possibility of replacing our standard conceptions of search, how he conceptualizes his role at AMD, and Ian shares his insights and thoughts on the “Arms Race for GPUs”. Be sure not to miss out on this episode as Ian shares valuable insights from his perspective as the Senior Director of AI Software at AMD. Key Points From This Episode:An introduction to our guest on today’s episode: Ian Ferreira.The point in his career when AI became the main focus. His thoughts on the idea that ChatGPT is noble. The scary aspect of Large Language Models (LLMs).The possibilities of replacing our standard conceptions of search.Ian shares how he conceptualizes his role as Senior Director of AI Software at AMD, and the projects they’re currently working on. His thoughts on the “Arms Race” for GPUs. Ian underlines their partnership with research companies like the Allen Institute.Attempting to make a powerful GPU model easily available to the general public.He explains what he means by a sovereign model. Ian talks about AMD’s upcoming events and announcements. Quotes:“It’s just remarkable, the potential of AI —and now I’m fully in it and I think it’s a game-changer.” — @Ianfe [0:03:41]“There are significantly more noble applications than advertising for AI and ChatGPT was great in that it put a face on AI for a lot of people who couldn’t really get their heads wrapped around [AI].” — @Ianfe [0:04:25]“An LLM allows you to have a natural conversation with the search agent, so to speak.” — @Ianfe [0:09:21]“All our stuff is open-sourced. AMD has a strong ethos, both in open-source and in partnerships. We don’t compete with our customers, and so being open allows you to go and look at all our code and make sure that whatever you are going to deploy is something you’ve looked at.” — @Ianfe [0:12:15]Links Mentioned in Today’s Episode:Advancing AI EventIan Ferreira on LinkedInIan Ferreira on XAMDAMD Software StackHugging FaceAllen InstituteOpen AIHow AI HappensSama

Generative AI is becoming more common in our lives as the technology grows and evolves. There are now AI companions to help other AI models execute their tasks more efficiently, and Amazon CodeWhisperer (ACW) is among the best in the game. We are joined today by the General Manager of Amazon CodeWhisperer and Director of Software Development at Amazon Web Services (AWS), Doug Seven. We discuss how Doug and his team are able to remain agile in such a huge organization like Amazon before getting a crash course on the two-pizza-team philosophy and everything you need to know about ACW and how it works. Then, we dive into the characteristics that make up a generative AI model, why Amazon felt it necessary to create its own AI companion, why AI is not here to take our jobs, how Doug and his team ensure that ACW is safe and responsible, and how generative AI will become common in most households much sooner than we may think.   Key Points From This Episode:Introducing the Director of Software Development and General Manager of Amazon CodeWhisperer at Amazon Web Services, Doug Seven. A day in the life of Doug in his role at Amazon. What his team currently looks like.Whether he and his team retain their agility in a massive organization like Amazon. A crash course on the two-pizza-team philosophy. How Doug ended up at Amazon Web Services (AWS) and leading ACW. What ACW is, how it works, and why you need it for you and your business. Assessing if generative AI models need to produce new code to be considered generative. Why Amazon felt it pertinent to create its own AI companion in ACW. How to use ACW to its full potential. The way recommendations change and improve once ACW has access to your code base. Examples that reiterate how AI is not here to take your job but to do the jobs you hate.Guardrails that ACW is putting up to ensure that it remains safe, secure, and responsible. How generative AI will become more accessible to the masses as it evolves. 

In today’s episode, we are joined by Dalia Shanshal, Senior Data Scientist at Bell, Canada's largest communications company that offers advanced broadband wireless, Internet, TV, media, and business communications services. With over five years of experience working on hands-on projects, Dalia has a diverse background in data science and AI. We start our conversation by talking about the recent GeekFest Conference, what it is about, and key takeaways from the event. We then delve into her professional career journey and how a fascinating article inspired her to become a data scientist. During our conversation, Dalia reflects on the evolving nature of data science, discussing the skills and qualities that are now more crucial than ever for excelling in the field. We also explore why creativity is essential for problem-solving, the value of starting simple, and how to stand out as a data scientist before she explains her unique root cause analysis framework.Key Points From This Episode:Highlights of the recent Bell GeekFest Conference.AI-related topics focused on at the event.Why Bell’s GeekFest is only an internal conference.Details about Bell and Dalia’s role at the company.Her background and professional career journey.How the role of a data scientist has changed over time.The importance of creativity in problem-solving.Overview of why quality data is fundamental.Qualities of a good data scientist.The research side of data science.Dalia reveals her root cause analysis framework.Exciting projects she is currently working on.Tweetables:“What I do is to try leverage AI and machine learning to speed up and fastrack investigative processes.” — Dalia Shanshal [0:06:52]“Data scientists today are key in business decisions. We always need business decisions based on facts and data, so the ability to mine that data is super important.” — Dalia Shanshal [0:08:35]“The most important skill set [of a data scientist] is to be able to [develop] creative approaches to problem-solving. That is why we are called scientists.” — Dalia Shanshal [0:11:24]“I think it is very important for data scientists to keep up to date with the science. Whenever I am [faced] with a problem, I start by researching what is out there.” — Dalia Shanshal [0:22:18]“One of the things that is really important to me is making sure that whatever [data scientists] are doing has an impact.” — Dalia Shanshal [0:33:50]Links Mentioned in Today’s Episode:Dalia ShanshalDalia Shanshal on LinkedInDalia Shanshal on GitHubDalia Shanshal EmailBellGeekFest 2023 | BellCanadian Conference on Artificial Intelligence (CANAI)‘Towards an Automated Framework of Root Cause Analysis in the Canadian Telecom Industry’Ohm Dome ProjectHow AI HappensSama

 EXAMPLE: AgriSynth Synthetic Data-- Weeds as Seen By AIData is the backbone of agricultural innovation when it comes to increasing yields, reducing pests, and improving overall efficiency, but generating high-quality real-world data is an expensive and time-consuming process. Today, we are joined by Colin Herbert, the CEO and Founder of AgriSynth, to find out how the advent of synthetic data will ultimately transform the industry for the better. AgriSynth is revolutionizing how AI can be trained for agricultural solutions using synthetic imagery.  He also gives us an overview of his non-linear career journey (from engineering to medical school to agriculture, then through clinical trials and back to agriculture with a detour in Deep Learning), shares the fascinating origin story of AgriSynth, and more. Key Points From This Episode:Colin’s career trajectory and the surprising role that Star Wars plays in AgriSynth’s origin story.Reasons that the use of AI in agriculture is still limited, despite its vast potential.Ways that AgriSynth seeks to bridge these gaps in the industry using synthetic imagery.Insight into the vast amount of parameters and values required.What synthetic data looks like in AgriSynth’s “closed-loop train/test system.”Why photorealistic data is completely unnecessary for AI models.How AgriSynth is working towards eliminating human cognition from the process.Dispelling some of the criticism often directed at synthetic data.Just a few of the many applications for AgriSynth’s tech and how their output will evolve.Why real-world images aren’t necessarily superior to synthetic data!Quotes:“The complexity of biological images and agricultural images is way beyond driverless cars and most other applications [of AI].” — Colin Herbert [0:06:45]“It’s parameter rich to represent the rules of growth of a plant.” — Colin Herbert [0:09:21]“We know exactly where the edge cases are – we know the distribution of every parameter in that dataset, so we can design the dataset exactly how we want it and generate imagery accordingly. We could never collect such imagery in the real world.” — Colin Herbert [0:10:33]“Ultimately, the way we look at an image is not the way AI looks at an image.” — Colin Herbert [0:21:11]“It may not be a real-world image that we’re looking at, but it will be data from the real world. There is a crucial difference.” — Colin Herbert [0:32:01]Links Mentioned in Today’s Episode:Colin Herbert on LinkedInAgriSynthHow AI HappensSama

Jennifer is the founder of Data Relish, a boutique consultancy firm dedicated to providing strategic guidance and executing data technology solutions that generate tangible business benefits for organizations of diverse scales across the globe. In our conversation, we unpack why a data platform is not the same as a database, working as a freelancer in the industry, common problems companies face, the cultural aspect of her work, and starting with the end in mind. We also delve into her approach to helping companies in crisis, why ‘small’ data is just as important as ‘big’ data, building companies for the future, the idea of a ‘data dictionary’, good and bad examples of data culture, and the importance of identifying an executive sponsor.Key Points From This Episode:Introducing Jennifer Stirrup and an overview of her professional background.Jennifer’s passion for technology and the exciting projects she is currently working on.Alan Turing’s legacy in terms of AI and how the landscape is evolving.The reason for starting her own business and working as a freelancer.Forging a career in the technology and AI space: advice from an expert.Challenges and opportunities of working as a consultant in the technology sector.Characteristics of AI that make it a high-pressure and high-risk environment.She breaks down the value and role of an executive sponsor.Common hurdles companies face regarding data and AI operations.Circumstances when companies hire Jennifer to help them.Safeguarding her reputation and managing unrealistic expectations. Advice for healthy data practices to avoid problems in the future.Why Jennifer decided on the name Data Relish.Discover how good and reliable data can help change lives.Quotes:“Something that is important in AI is having an executive sponsor, someone who can really unblock any obstacles for you.” — @jenstirrup [0:08:50]“Probably the biggest [challenge companies face] is access to the right data and having a really good data platform.” — @jenstirrup [0:10:50]“If the crisis is not being handled by an executive sponsor, then there is nothing I can do.” — @jenstirrup [0:20:55]“I want people to understand the value that [data] can have because when your data is good it can change lives.” — @jenstirrup [0:32:50]Links Mentioned in Today’s Episode:Jennifer StirrupJennifer Stirrup on LinkedInJennifer Stirrup on XData RelishHow AI HappensSama

 Joining us today to provide insight on how to put together a credible AI solutions team is Mike Demissie, Managing Director of the AI Hub at BNY Mellon. We talk with Mike about what to consider when putting together and managing such a diverse team and how BNY Mellon is implementing powerful AI and ML capabilities to solve the problems that matter most to their clients and employees.   To learn how BNY Mellon is continually innovating for the benefit of their customers and their employees, along with Mike’s thoughts on the future of generative AI, be sure to tune in! Key Points From This Episode:Mike’s background in engineering and his role at BNY Mellon.The history of BNY Mellon and how they are applying AI and ML in financial services.An overview of the diverse range of specialists that make up their enterprise AI team.Making it easier for their organization to tap into AI capabilities responsibly.Identifying the problems that matter most to their clients and employees.Finding the best ways to build solutions and deploy them in a scalable fashion.Insight into the AI solutions currently being implemented by BNY Mellon.How their enterprise AI team chooses what to prioritize and why it can be so challenging.The value of having a diverse set of use cases: it builds confidence and awareness.Their internal PR strategy for educating the rest of the organization on AI implementations.Insight into generative AI's potential to enhance BNY Mellon’s products and services.Ensuring the proper guardrails and regulations are put in place for generative AI.Mike’s advice on pursuing a career in the AI, ML, and data science space.Quotes:“Building AI solutions is very much a team sport. So you need experts across many disciplines.” —Mike Demissie [0:06:40]“The engineers need to really find a way in terms of ‘okay, look, how are we going to stitch together the various applications to run it in the most optimal way?’” —Mike Demissie [0:09:23]“It is not only opportunity identification, but also developing the solution and deploying it and making sure there's a sustainable model to take care of afterwards, after production — so you can go after the next new challenge.” —Mike Demissie [0:09:33]“There's endless use of opportunities. And every time we deploy each of these solutions [it] actually sparks ideas and new opportunities in that line of business.” —Mike Demissie [0:11:58]“Not only is it important to raise the level of awareness and education for everyone involved, but you can also tap into the domain expertise of folks, regardless of where they sit in the organization.” —Mike Demissie [0:15:36]“Demystifying, and really just making this abstract capability real for people is an important part of the practice as well.” —Mike Demissie [0:16:10]“Remember, [this] still is day one. As much as all the talk that is out there, we're still figuring out the best way to navigate and the best way to apply this capability. So continue to explore that, too.” —Mike Demissie [0:24:21]Links Mentioned in Today’s Episode:Mike Demissie on LinkedInBNY MellonHow AI HappensSama

Mercedes-Benz is a juggernaut in the automobile industry and in recent times, it has been deliberate in advancing the use of AI throughout the organization. Today, we welcome to the show the Executive Manager for AI at Mercedes-Benz, Alex Dogariu. Alex explains his role at the company, he tells us how realistic chatbots need to be, how he and his team measure the accuracy of their AI programs, and why people should be given more access to AI and time to play around with it. Tune in for a breakdown of Alex's principles for the responsible use of AI. Key Points From This Episode:A warm welcome to the Executive Manager for AI at Mercedes-Benz, Alex Dogariu.Alex’s professional background and how he ended up at Mercedes-Benz.When Mercedes-Benz decided that it needed a team dedicated to AI.An example of the output of descriptive analytics as a result of machine learning at Mercedes.Alex explains his role as Executive Manager for AI. How realistic chatbots need to be, according to Alex. The way he measures the accuracy of his AI programs. How Mercedes-Benz assigns AI teams to specific departments within the organization. Why it’s important to give people access to AI technology and allow them to play with it.  Using vendors versus doing everything in-house. Alex gives us a brief breakdown of his principles for the responsible use of AI.What he was trying to express and accomplish with his TEDx talk. Tweetables:“[Chatbots] are useful helpers, they’re not replacing humans.” — Alex Dogariu [09:38]“This [AI] technology is so new that we really just have to give people access to it and let them play with it.” — Alex Dogariu [15:50]“I want to make people aware that AI has not only benefits but also downsides, and we should account for those. And also, that we use AI in a responsible way and manner.” — Alex Dogariu [25:12]“It’s always a balancing act. It’s the same with certification of AI models — you don’t want to stifle innovation with legislation and laws and compliance rules but, to a certain extent, it’s necessary, it makes sense.” — Alex Dogariu [26:14]“To all the AI enthusiasts out there, keep going, and let’s make it a better world with this new technology.” — Alex Dogariu [27:00]Links Mentioned in Today’s Episode:Alex Dogariu on LinkedInMercedes-Benz‘Principles for responsible use of AI | Alex Dogariu | TEDxWHU’How AI HappensSama

Tarun  dives into the game-changing components of Watsonx, before delivering some noteworthy advice for those who are eager to forge a career in AI and machine learning. Key Points From This Episode:Introducing Tarun Chopra and a brief look at his professional background. His intellectual diet: what Tarun is consuming to stay up to date with technological trends. Common challenges in technology and AI that he encounters daily. The importance of fully understating what problem you want your new technology to solve.  IBM’s role in AI and how the company is helping to accelerate change in the space.Exploring IBM’s decision to remove facial recognition from its endeavors in biometrics. The development of IBM’s Watsonx and how it’s helping business tell their unique AI stories. Why IBM’s consultative approach to introducing their customers to AI is so effective. Tarun’s thoughts on computer power and all related costs. Diving deeper into the three components of Watsonx. Our guest’s words of advice to those looking to forge a career in AI and ML. Tweetables:“One of the first things I tell clients is, ‘If you don’t know what problems we are solving, then we’re on the wrong path.’” — @tc20640n [05:14]“A lot of our customers have adopted AI — but if the workflow is, let’s say 10 steps, they have applied AI to only one or two steps. They don’t get to realize the full value of that innovation.” — @tc20640n [05:24]“Every client that I talk to, they’re all looking to build their own unique story; their own unique point of view with their own unique data and their own unique customer pain points. So, I look at Watsonx as a vehicle to help customers build their own unique AI story.” — @tc20640n [14:16]“The most important thing you need is curiosity. [And] be strong-hearted, because this [industry] is not for the weak-hearted.” — @tc20640n [27:41]Links Mentioned in Today’s Episode:Tarun ChopraTarun Chopra on LinkedInTarun Chopra on TwitterTarun Chopra on IBMIBMIBM WatsonHow AI HappensSama