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:Elettra Damaggio explains what StoneX Group does and how she ended up there. Her professional journey and how she acquired her skills. The state of neural networks while she was studying them, why she was drawn to the subject, and how it’s changed. StoneX’s data science and ML capabilities when she arrived, and Elettra’s role in the system. Her first experience of being thrown into the deep end of data science, and how she swam. A data scientist’s tools for success. The multidisciplinary leaders and departments that she sought to learn from when she entered data science.  Defining data science, and why many do not fully understand the industry. How Elettra knows when her data set is inadequate. The recent projects and ML models that she’s been working on. Exploring the types of guardrails that are needed when training chatbots to be compliant.Elettra’s advice to those following a similar career path as hers. 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 LinkedInStoneX GroupHow AI HappensSama

 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:Mike Miller’s professional background, and how he got into AI and AWS. How Mike and his team decide on the products to bring to market for developers. Where PartyRock came from and how it fits into AWS’s strategy. How AWS decided on the timing to make PartyRock accessible to all.   What AWS’s products mean for those with zero coding experience. The level of oversight that is required to service clients who have no technical background. Taking a closer look at AWS’s strategy for generative AI. How customers are using PartyRock, and what Mike has learned from these observations.The challenges that the team faced whilst developing PartyRock, and how they persevered. Trying to understand the future of generative prompt engineering. A reminder that PartyRock is free, so go try it out! 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 LinkedInAmazon Web ServicesAWS DeepLensAWS DeepRacerAWS DeepComposerPartyRockAmazon BedrockHow AI HappensSama

Key Points From This Episode:Welcoming Seth Walker to the podcast. Why Seth jokes about being chaotic in his approach to machine learning. The importance of being agile in AI. All about Seth’s company, Carrier, and what they do. Seth tells us about his background and how he ended up at Carrier. How Seth goes about unlocking the power of AI.The different levels of success when it comes to AI creation and how to measure them. Seth breaks down the different things Carrier focuses on. The importance of prompt engineering.What makes him excited about the new iterations of machine learning. 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 LinkedInCarrierHow AI HappensSama

 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:Some background on Philip Moyer and his role as part of Google’s AI engineering team.What he learned from speaking with 371 customers from 15 different countries about AI.Philip shares his insights on how governments and companies are approaching AI.Recognizing the risks and requirements of models and how to manage them.Adversarial datasets; what they are and why they need to be audited.Understanding how adversarial datasets can vary between industries.A breakdown of Google’s approach to adversarial datasets in different languages.The most relevant takeaways from Philip’s cross-continental survey.How 2024 resembles the technological and competitive business landscape of 1997.Google’s partnership with Nvidia and how they are providing technologies at every layer.The new class of applications that come with generative AI.Using a company’s proprietary data to train generative AI models.The collective challenges we are all facing when it comes to creating generative AI at scale.Understanding the vectorization of knowledge and why it will need to be auditable.Philip shares what he is most excited about when it comes to AI.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 LinkedInHow AI HappensSama

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:Joelle Pineau's professional background and how she ended up at Meta.The aspects of AI robotics that fascinate her the most.Why elegance is an important element in Joelle's machine learning systems.How asking the right question is the most vital part of research and how to get better at it.FRESCO: how Joelle chooses which projects to work on.The relationship between her work, AI, and the end users of her final products.What success looks like for her and her team at Meta.World models versus word models and her summation of information modalities.What Joelle thinks about responsibility in the current high-stakes of technology development.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 LinkedInJoelle Pineau on XMetaHow AI HappensSama

Key Points From This Episode:Amii’s machine learning project management tool: MLPL.Amii’s ultimate goal of building capacity and how it differs from an agency model. Asking the right questions to ascertain the appropriate use for AI. Instances where AI is not a relevant solution. Common challenges people face when adopting AI strategies. Mara’s perspective on the education necessary to excel in a career in machine learning.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 CairoMaria Cairo on LinkedInAlberta Machine Intelligence UnitHow AI HappensSama

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:Bryan’s professional background and his role in the company.Unpack the concept of “industrial AI” and its various applications.The current state and trends of AI in the industrial landscape.Deep reinforcement learning (DRL) and how it applies to industrial AI.Why deep RL is a game-changer for solving industrial problems.Learn about autonomous AI, machine teaching, and explainable AI.Discover the approach for replicating human expertise with machines.Opportunities and challenges of using machine teaching techniques.Differences between monolithic deep learning and standard deep learning.His perspective on current trends and the future of generative AI. 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 LinkedInBryan DeBois EmailRoviSysRoviSys AIDesigning Autonomous AIHow AI HappensSama

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’s CodeWhisperer (CW) is among the best in the game. We are joined today by the GM of CW and Director of Software Development at Amazon, 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 CW 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 CW 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 GM of Amazon CodeWhisperer (CW), 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 CW. What CodeWhisperer 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 CW. How to use CW to its full potential. The way recommendations change and improve once CW has access to a code base. Examples that reiterate how AI is not here to take your job but to do the jobs you hate.Guardrails that CW 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. Quotes:“CodeWhisperer sort of straddles the worlds of being a developer productivity tool and a generative AI-based product.” — @dseven [0:01:51]“Big effort can come from small groups of people with intentional motivations around what they want to go and build.” — @dseven [0:03:02]“Lots of problems are yet to be solved and we’ll use generative AI to help us solve them. In this case, it’s about helping us solve them faster.” — @dseven [0:14:31]“The idea here is that AI will replace the mundane parts of your job – the parts that are repetitive – as almost all innovation in the world has done — and allow you more capacity to do the things that are novel and interesting.” — @dseven [0:21:07]“When we approached the idea of building AI that could participate in software development processes – taking a very responsible approach was really important to us.” — @dseven [0:22:51]Links Mentioned in Today’s Episode:Doug SevenDoug Seven on LinkedInAmazon CodeWhispererHow AI HappensSama

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

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:An introduction to Chris Doe, Product Management Leader at Veritone.How Veritone is helping clients orchestrate their AI workflows.The four verticals Chris oversees: media, entertainment, sports, and advertising.Building solutions that infuse AI from beginning to end.An overview of the type of AI that Veritone is infusing.How they are helping their clients navigate the expansive landscape of cognitive engines.Fine-tuning generative AI to be use-case-specific for their clients.Why now is the time to be testing and defining proof of concept for generative AI.How LLMs have transformed chatbots to be significantly more sophisticated.Creating bespoke chatbots for clients that can navigate complex enterprise applications.The most common challenges clients face when it comes to integrating AI applications.Chris’s advice on taking stock of an organization and figuring out where to apply AI.Tips on how to identify potential AI use cases within an organization.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 LinkedInChris Doe on XVeritoneHow AI HappensSama

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:An introduction to today’s guest, Valeria Sadovykh. Valeria tells us about her studies at the University of Auckland and her Ph.D. The problems with using the internet to assist in decision making. How ethical and responsible AI frames Valeria’s career. What she is doing to encourage AI leaders to prioritize responsible design. The dangers of lack of authenticity, creativity, and emotion in AI. Whether we need human interaction or not and if we want to preserve it. What responsibility companies developing this technology have, according to Valeria. She tells us about her job at Microsoft and what large organizations are doing to be ethical. What kinds of AI organizations need to be most conscious of ethics and responsible design.Other common challenges companies face when they plug in other technology.How those challenges show up in people, processes, and technology when deploying AI.Why Valeria expects some costs to decrease as AI technology democratizes over time.The importance of innovating and being prepared to (potentially) fail. Why the future of emotional AI and the ability to be authentic fascinates Valeria. 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 LinkedInValeria Sadovykh on InstagramValeria Sadovykh on TwitterHow AI HappensSama

 Key Points From This Episode:She shares her professional journey that eventually led to the founding of Gradient Ventures.How Anna would contrast AI Winter to the standard hype cycles that exist.Her thoughts on how the web and mobile sectors were under-hyped.Those who decide if something falls out of favor; according to Anna.How Anna navigates hype cycles.Her process for evaluating early-stage AI companies. How to assess whether someone is a tourist or truly committed to something.Approaching problems and discerning whether AI is the right answer.Her thoughts on the best application for AI or MLR technology. Anna shares why she is excited about large language models (LLMs).Thoughts on LLMs and whether we should or can we approach AGIs.A discussion: do we limit machines when we teach them to speak the way we speak?Quality AI and navigating fairness: the concept of the Human in the Loop.Boring but essential data tasks: whose job is that?How she feels about sensationalism.  What gets her fired up when it is time to support new companies. Advice to those forging careers in the AI and ML space. 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’GradientHow AI HappensSama