How AI Happens

 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

Wayfair uses AI and machine learning (ML) technology to interpret what its customers want, connect them with products nearby, and ensure that the products they see online look and feel the same as the ones that ultimately arrive in their homes. With a background in engineering and a passion for all things STEM, Wayfair’s Director of Machine Learning, Tulia Plumettaz, is an innate problem-solver. In this episode, she offers some insight into Wayfair’s ML-driven decision-making processes, how they implement AI and ML for preventative problem-solving and predictive maintenance, and how they use data enrichment and customization to help customers navigate the inspirational (and sometimes overwhelming) world of home decor. We also discuss the culture of experimentation at Wayfair and Tulia’s advice for those looking to build a career in machine learning.Key Points From This Episode:A look at Tulia’s engineering background and how she ended up in this role at Wayfair.Defining operations research and examples of its real-life applications.What it means for something to be strategy-proof.Different ways that AI and ML are being integrated at Wayfair.The challenge of unstructured data and how Wayfair takes the onus off suppliers.Wayfair’s North Star: detecting anomalies before they’re exposed to customers.Preventative problem-solving and how Wayfair trains ML models to “see around corners.”Examples of nuanced outlier detection and whether or not ML applications would be suitable.Insight into Wayfair’s bespoke search tool and how it interprets customers’ needs.The exploit-and-explore model Wayfair uses to measure success and improve accordingly.Tulia’s advice for those forging a career in machine learning: go back to first principles!Tweetables:“[Operations research is] a very broad field at the intersection between mathematics, computer science, and economics that [applies these toolkits] to solve real-life applications.” — Tulia Plumettaz [0:03:42]“All the decision making, from which channel should I bring you in [with] to how do I bring you back if you’re taking your sweet time to make a decision to what we show you when you [visit our site], it’s all [machine learning]-driven.” — Tulia Plumettaz [0:09:58]“We want to be in a place [where], as early as possible, before problems are even exposed to our customers, we’re able to detect them.” — Tulia Plumettaz [0:18:26]“We have the challenge of making you buy something that you would traditionally feel, sit [on], and touch virtually, from the comfort of your sofa. How do we do that? [Through the] enrichment of information.” — Tulia Plumettaz [0:29:05]“We knew that making it easier to navigate this very inspirational space was going to require customization.” — Tulia Plumettaz [0:29:39]“At its core, it’s an exploit-and-explore process with a lot of hypothesis testing. Testing is at the core of [Wayfair] being able to say: this new version is better than [the previous] version.” — Tulia Plumettaz [0:31:53]Links Mentioned in Today’s Episode:Tulia Plumettaz on LinkedInWayfairHow AI HappensSama

Bob highlights the importance of building interdepartmental relationships and growing a talented team of problem solvers, as well as the key role of continuous education. He also offers some insight into the technical and not-so-technical skills of a “data science champion,” tips for building adaptable data infrastructures, and the best career advice he has ever received, plus so much more. For an insider’s look at the data science operation at FreeWheel and valuable advice from an analytics leader with more than two decades of experience, be sure to tune in today!Key Points From This Episode:A high-level overview of FreeWheel, Bob’s role there, and his career trajectory thus far.Important intersections between data science and the organization at large.Three indicators that FreeWheel is a data-driven company.Why continuous education is a key component for agile data science teams.The interplay between data science and the development of AI technology.Technical (and other) skills that Bob looks for when recruiting new talent to his team.Bob’s perspective on the value of interdepartmental collaboration.Insight into what an adaptable data infrastructure looks like.The importance of asking yourself, “What more can we do?”Tweetables:“As a data science team, it’s not enough to be able to solve quantitative problems. You have to establish connections to the company in a way that uncovers those problems to begin with.” — @Bob_Bress [0:06:42]“The more we can do to educate folks – on the type of work that the [data science] team does, the better the position we are in to tackle more interesting problems and innovate around new ideas and concepts.” — @Bob_Bress [0:09:49]“There are so many interactions and dependencies across any project of sufficient complexity that it’s only through [collaboration] across teams that you’re going to be able to hone in on the right answer.” — @Bob_Bress [0:17:34]“There is always more you can do to enhance the work you’re doing, other questions you can ask, other ways you can go beyond just checking a box.” — @Bob_Bress [0:23:31]Links Mentioned in Today’s Episode:Bob Bress on LinkedInBob Bress on TwitterFreeWheelHow AI HappensSama