How AI Happens

After describing the work done at StoneX and her role at the organization, Elettra explains what drew her to neural networks, defines data science and how she overcame the challenges of learning something new on the job, breaks down what a data scientist needs to succeed, and shares her thoughts on why many still don’t fully understand the industry. Our guest also tells us how she identifies an inadequate data set, the recent innovations that are under construction at StoneX, how to ensure that your AI and ML models are compliant, and the importance of understanding AI as a mere tool to help you solve a problem. Key Points From This Episode: Quotes: “The best thing that you can have as a data scientist to be set up for success is to have a decent data warehouse.” — Elettra Damaggio [0:09:17] “I am very much an introverted person. With age, I learned how to talk to people, but that wasn’t [always] the case.” — Elettra Damaggio [0:12:38] “In reality, the hard part is to get to the data set – and the way you get to that data set is by being curious about the business you’re working with.” — Elettra Damaggio [0:13:58] “[First], you need to have an idea of what is doable, what is not doable, [and] more importantly, what might solve the problem that [the client may] have, and then you can have a conversation with them.” — Elettra Damaggio [0:19:58] “AI and ML is not the goal; it’s the tool. The goal is solving the problem.” — Elettra Damaggio [0:28:28] Links Mentioned in Today’s Episode: Elettra Damaggio on LinkedIn StoneX Group How AI Happens Sama

Mike Miller is the Director of Project Management at AWS, and he joins us today to share about the inspirational AI-powered products and services that are making waves at Amazon, particularly those with generative prompt engineering capabilities. We discuss how Mike and his team choose which products to bring to market, the ins and outs of PartyRock including the challenges of developing it, AWS’s strategy for generative AI, and how the company aims to serve everyone, even those with very little technical knowledge. Mike also explains how customers are using his products and what he’s learned from their behaviors, and we discuss what may lie ahead in the future of generative prompt engineering. Key Points From This Episode: Quotes: “We were working on AI and ML [at Amazon] and discovered that developers learned best when they found relevant, interesting, [and] hands-on projects that they could work on. So, we built DeepLens as a way to provide a fun opportunity to get hands-on with some of these new technologies.” — Mike Miller [0:02:20] “When we look at AIML and generative AI, these things are transformative technologies that really require almost a new set of intuition for developers who want to build on these things.” — Mike Miller [0:05:19] “In the long run, innovations are going to come from everywhere; from all walks of life, from all skill levels, [and] from different backgrounds. The more of those people that we can provide the tools and the intuition and the power to create innovations, the better off we all are.” — Mike Miller [0:13:58] “Given a paintbrush and a blank canvas, most people don’t wind up with The Sistine Chapel. [But] I think it’s important to give people an idea of what is possible.” — Mike Miller [0:25:34] Links Mentioned in Today’s Episode: Mike Miller on LinkedIn Amazon Web Services AWS DeepLens AWS DeepRacer AWS DeepComposer PartyRock Amazon Bedrock How AI Happens Sama

Key Points From This Episode: Quotes: “In many ways, Carrier is going to be a necessary condition in order for AI to exist.” — Seth Walker [0:04:08] “What’s hard about generating value with AI is doing it in a way that is actually actionable toward a specific business problem.” — Seth Walker [0:09:49] “One of the things that we’ve found through experimentation with generative AI models is that they’re very sensitive to your content. I mean, there’s a reason that prompt engineering has become such an important skill to have.” — Seth Walker [0:25:56] Links Mentioned in Today’s Episode: Seth Walker on LinkedIn Carrier How AI Happens Sama

Philip recently had the opportunity to speak with 371 customers from 15 different countries to hear their thoughts, fears, and hopes for AI. Tuning in you’ll hear Philip share his biggest takeaways from these conversations, his opinion on the current state of AI, and his hopes and predictions for the future. Our conversation explores key topics, like government and company attitudes toward AI, why adversarial datasets will need to be audited, and much more. To hear the full scope of our conversation with Philip – and to find out how 2024 resembles 1997 – be sure to tune in today! Key Points From This Episode: Quotes: “What's been so incredible to me is how forward-thinking – a lot of governments are on this topic [of AI] and their understanding of – the need to be able to make sure that both their citizens as well as their businesses make the best use of artificial intelligence.” — Philip Moyer [0:02:52] “Nobody's ahead and nobody's behind. Every single company that I'm speaking to, has about one to five use cases live. And they have hundreds that are on the docket.” — Philip Moyer [0:15:36] “All of us are facing the exact same challenges right now of doing [generative AI] at scale.” — Philip Moyer [0:17:03] “You should just make an assumption that you're going to be somewhere on the order of about 10 to 15% more productive with AI.” — Philip Moyer [0:25:22] “[With AI] I get excited around proficiency and job satisfaction because I really do think – we have an opportunity to make work fun again.” — Philip Moyer [0:27:10] Links Mentioned in Today’s Episode: Philip Moyer on LinkedIn How AI Happens Sama

Joelle further discusses the relationship between her work, AI, and the end users of her products as well as her summation of information modalities, world models versus word models, and the role of responsibility in the current high-stakes of technology development. Key Points From This Episode: Quotes: “Perhaps, the most important thing in research is asking the right question.” — @jpineau1 [0:05:10] “My role isn't to set the problems for [the research team], it's to set the conditions for them to be successful.” — @jpineau1 [0:07:29] “If we're going to push for state-of-the-art on the scientific and engineering aspects, we must push for state-of-the-art in terms of social responsibility.” — @jpineau1 [0:20:26] Links Mentioned in Today’s Episode: Joelle Pineau on LinkedIn Joelle Pineau on X Meta How AI Happens Sama

Key Points From This Episode: Quotes: “Amii is all about capacity building, so we’re not a traditional agent in that sense. We are trying to educate and inform industry on how to do this work, with Amii at first, but then without Amii at the end.” — Mara Cairo [0:06:20] “We need to ask the right questions. That’s one of the first things we need to do, is to explore where the problems are.” — Mara Cairo [0:07:46] “We certainly are comfortable turning certain business problems away if we don’t feel it’s an ethical match or if we truly feel it isn’t a problem that will benefit much from machine learning.” — Mara Cairo [0:11:52] Links Mentioned in Today’s Episode: Maria Cairo Maria Cairo on LinkedIn Alberta Machine Intelligence Unit How AI Happens Sama

Jerome discusses Meta's Segment Anything Model, Ego Exo 4D, the nature of Self Supervised Learning, and what it would mean to have a non-language based approach to machine teaching. For more, including quotes from Meta Researchers, check out the Sama Blog

Bryan discusses what constitutes industrial AI, its applications, and how it differs from standard AI processes. We explore the innovative process of deep reinforcement learning (DRL), replicating human expertise with machines, and the types of AI approaches available. Gain insights into the current trends and the future of generative AI, the existing gaps and opportunities, why DRL is a game-changer and much more! Join us as we unpack the nuances of industrial AI, its vast potential, and how it is shaping the industries of tomorrow. Tune in now! Key Points From This Episode: Quotes: “We typically look at industrial [AI] as you are either making something or you are moving something.” — Bryan DeBois [0:04:36] “One of the key distinctions with deep reinforcement learning is that it learns by doing and not by data.” — Bryan DeBois [0:10:22] “Autonomous AI is more of a technique than a technology.” — Bryan DeBois [0:16:00] “We have to have [AI] systems that we can count on, that work within constraints, and give right answers every time.” — Bryan DeBois [0:29:04] Links Mentioned in Today’s Episode: Bryan DeBois on LinkedIn Bryan DeBois Email RoviSys RoviSys AI Designing Autonomous AI How AI Happens Sama

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: 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 LinkedIn Jason Corso Jason Corso on LinkedIn Voxel51 2023 ML Pulse Report ChatGPT Bard DALL·E 3 How AI Happens Sama

Sama 2023 ML Pulse Report ML Pulse Report: How AI Happens Live Webinar AMD's Advancing AI Event Our 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: 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 Event Ian Ferreira on LinkedIn Ian Ferreira on X AMD AMD Software Stack Hugging Face Allen Institute Open AI How AI Happens Sama

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:

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: 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 Shanshal Dalia Shanshal on LinkedIn Dalia Shanshal on GitHub Dalia Shanshal Email Bell GeekFest 2023 | Bell Canadian Conference on Artificial Intelligence (CANAI) ‘Towards an Automated Framework of Root Cause Analysis in the Canadian Telecom Industry’ Ohm Dome Project How AI Happens Sama

EXAMPLE: AgriSynth Synthetic Data-- Weeds as Seen By AI Data 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: 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 LinkedIn AgriSynth How AI Happens Sama

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: 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 Stirrup Jennifer Stirrup on LinkedIn Jennifer Stirrup on X Data Relish How AI Happens Sama

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: 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 LinkedIn BNY Mellon How AI Happens Sama

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: 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 LinkedIn Mercedes-Benz ‘Principles for responsible use of AI | Alex Dogariu | TEDxWHU’ How AI Happens Sama

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: 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 Chopra Tarun Chopra on LinkedIn Tarun Chopra on Twitter Tarun Chopra on IBM IBM IBM Watson How AI Happens Sama

Creating AI workflows can be a challenging process. And while purchasing these types of technologies may be straightforward, implementing them across multiple teams is often anything but. That’s where a company like Veritone can offer unparalleled support. With over 400 AI engines on their platform, they’ve created a unique operating system that helps companies orchestrate AI workflows with ease and efficacy. Chris discusses the differences between legacy and generative AI, how LLMs have transformed chatbots, and what you can do to identify potential AI use cases within an organization. AI innovations are taking place at a remarkable pace and companies are feeling the pressure to innovate or be left behind, so tune in to learn more about AI applications in business and how you can revolutionize your workflow! Key Points From This Episode: Quotes: “Anybody who's writing text can leverage generative AI models to make their output better.” — @chris_doe [0:05:32] “With large language models, they've basically given these chatbots a whole new life.” — @chris_doe [0:12:38] “I can foresee a scenario where most enterprise applications will have an LLM power chatbot in their UI.” — @chris_doe [0:13:31] “It's easy to buy technology, it's hard to get it adopted across multiple teams that are all moving in different directions and speeds.” — @chris_doe [0:21:16] “People can start new companies and innovate very quickly these days. And the same has to be true for large companies. They can't just sit on their existing product set. They always have to be innovating.” — @chris_doe [0:23:05] “We just have to identify the most problematic part of that workflow and then solve it.” — @chris_doe [0:26:20] Links Mentioned in Today’s Episode: Chris Doe on LinkedIn Chris Doe on X Veritone How AI Happens Sama

AI is an incredible tool that has allowed us to evolve into more efficient human beings. But, the lack of ethical and responsible design in AI can lead to a level of detachment from real people and authenticity. A wonderful technology strategist at Microsoft, Valeria Sadovykh, joins us today on How AI Happens. Valeria discusses why she is concerned about AI tools that assist users in decision-making, the responsibility she feels these companies hold, and the importance of innovation. We delve into common challenges these companies face in people, processes, and technology before exploring the effects of the democratization of AI. Finally, our guest shares her passion for emotional AI and tells us why that keeps her in the space. To hear it all, tune in now! Key Points From This Episode: Tweetables: “We have no opportunity to learn something new outside of our predetermined environment.” — @ValeriaSadovykh [0:07:07] “[Ethics] as a concept is very difficult to understand because what is ethical for me might not necessarily be ethical for you and vice versa.” — @ValeriaSadovykh [0:11:38] “Ethics – should not come – [in] place of innovation.” — @ValeriaSadovykh [0:20:13] “Not following up, not investing, not trying, [and] not failing is also preventing you from success.” — @ValeriaSadovykh [0:29:52] Links Mentioned in Today’s Episode: Valeria Sadovykh on LinkedIn Valeria Sadovykh on Instagram Valeria Sadovykh on Twitter How AI Happens Sama

Key Points From This Episode: Tweetables: “When that hype cycle happens, where it is overhyped and falls out of favor, then generally that is – what is called a winter.” — @AnnapPatterson [0:03:28] “No matter how hyped you think AI is now, I think we are underestimating its change.” — @AnnapPatterson [0:04:06] “When there is a lot of hype and then not as many breakthroughs or not as many applications that people think are transformational, then it starts to go through a winter.” — @AnnapPatterson [0:04:47] @AnnapPatterson [0:25:17] Links Mentioned in Today’s Episode: Anna Patterson on LinkedIn ‘Eight critical approaches to LLMs’ ‘The next programming language is English’ ‘The Advice Taker’ Gradient How AI Happens Sama