Impact AI

Biodiversity is not just an ecological concern. As you’ll learn in this episode, it has tangible economic implications too. Today on Impact AI, I'm joined by Dr. Noelia Jimenez Martinez, Head of Insights and Machine Learning at NatureMetrics, to talk about biodiversity monitoring. NatureMetrics is a global nature intelligence technology company providing end-to-end nature monitoring and impact reporting. Powered by eDNA, their Nature Intelligence Platform allows any company to manage its impacts and dependencies on biodiversity at scale, translating the complexities of nature into simple insights that help to inform the best decisions for both the planet and business. Tuning in, you’ll learn about the importance of NatureMetrics’ work, the role that machine learning plays in their technology, and some of the challenges that come with working with sometimes unpredictable data from nature. In my conversation with Noelia, we also touched on why biodiversity is an increasingly urgent imperative for businesses of all kinds, how NatureMetrics is democratizing biodiversity monitoring, and much more!Key Points:Insight into Noelia's background in astrophysics and how it led her to NatureMetrics.What NatureMetrics does, what eDNA is, and why it’s so important for sustainability.The major role that machine learning plays in NatureMetrics' technology.Specific examples of the types of models that NatureMetrics trains.How Jurassic Park helps us understand what eDNA data looks like.Different ways that this data is gathered depending on the relevant project.Unique challenges of sampling for eDNA and training models based on those datasets.How NatureMetrics measures the impact of its technology and makes biodiversity monitoring more accessible and achievable.Noelia’s urgent and common sense advice for other leaders of AI-powered startups.What the future holds for NatureMetrics and how their impact will continue to grow.Quotes:“I couldn't focus too much on solving galaxy formation with the amount of bad news I was seeing in the climate space and biodiversity collapse. I made a transition – [to] looking for jobs to apply [my astrophysics skills to] related problems in climate and biodiversity.” — Noelia Jiménez Martínez“Nature does not seem to behave [as well] as we would want. It might be that you have exactly the same covariates and your model is predicting species, and then you go, and it's not there.” — Noelia Jiménez Martínez“[Most companies] will have to report on their sustainability strategies in the world to keep on functioning. In that context, what we can do here is make biodiversity monitoring achievable and democratically easy to access.” — Noelia Jiménez Martínez“The success of [an AI startup is] – tied up to the diverse, strong teams you build.” — Noelia Jiménez MartínezLinks:NatureMetricsDr. Noelia Jiménez Martínez on LinkedInResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

In today’s episode, I am joined by Simon Arkell, the visionary CEO and co-founder of Ryght, to talk about copilots and the application of generative AI in life sciences. Ryght is dedicated to revolutionizing the field of life sciences through the power of AI. By leveraging cutting-edge technology and innovative solutions, Ryght aims to empower professionals and organizations within the life sciences industry to streamline processes, enhance productivity, and drive meaningful outcomes.In our conversation, we discuss Simon's entrepreneurial background, the various companies he has founded, and what led him to create Ryght. We delve into the pivotal role of enterprise-scale, secure AI solutions in healthcare, and learn how Ryght's platform is reshaping the landscape of drug development and clinical research. Discover the intricate workings of generative AI copilots, the challenges of minimizing hallucinations and validating AI models, and why the utility of the approach at the enterprise level is essential. Simon also shares Ryght’s long-term goals and invaluable advice for leaders of AI startups. Join us, as we explore a world where healthcare and life sciences are transformed by cutting-edge technology with Simon Arkell from Ryght!Key Points:Hear about Simon’s background and his path to founding Ryght.Ryght’s generative AI approach, its potential in life sciences, and the role of copilots.The importance of enterprise-scale, secure AI solutions in healthcare.How generative AI copilots accelerate drug development processes.Differences between training models for life sciences versus generic AI models.Discover the challenges encountered in AI-powered solutions.Explore the company’s approach to customer feedback and model validation.Strategic considerations and advice for leaders of AI startups.Ryght’s mission to transform the healthcare and life sciences industry.Where to find more information about Ryght and connect with Simon.Quotes:“We built an enterprise-secure version of Generative AI that has many different features that allow large companies and small companies to very securely benefit from Generative AI without all of the issues that a very insecure, non-industry-trained solution might create.” — Simon Arkell“With this type of [generative AI] technology, you have the ability to completely unlock new formulas, and new molecules that could be life-changing.” — Simon Arkell“Improving the utility of the platform comes down to the efficacy of the output. It comes down to the in-context learning, the ensembling, and the prompting. But at the end of the day, a human has to determine, in many cases, the accuracy and relevance of a specific answer.” — Simon Arkell“It's not really about building models. It's about making sure that the right models are being utilized for the copilot.” — Simon ArkellLinks:Simon Arkell on LinkedInRyghtRyght on LinkedInRyght on XRyght on YouTubeResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

AI in healthcare is one of the most researched areas today, particularly on the clinical side of healthcare. Sean Cassidy is the Co-Founder and CEO of Lucem Health. Having worked in digital health for the last twenty years, he joins me today to talk about identifying chronic diseases. Tune in to hear how AI and machine learning are creating efficiencies for different forms of healthcare data, and how changes and challenges are being addressed to improve the process. Going beyond workflow support, we discuss considerations to bear in mind when integrating AI into healthcare systems and how to meaningfully measure efficacy in a clinical context. Sean shares some hard-earned wisdom about leading an AI startup, reveals his big vision for the future of Lucem Health, and much more.Key Points:Introducing guest Sean Cassidy, who co-founded Lucem Health. Defining digital health through an overview of Sean’s history in this industry. The founding idea behind Lucem Health. Different forms of healthcare data and how AI and machine learning can support them. Navigating changes in external variables and patient circumstances.The downstream diagnosis process and why patients are rarely re-assessed.How Lucem Health’s approach facilitates doctors as they continue as they always have. Considerations to bear in mind with the clinical adoption of AI beyond workflow.How efficacy is measured in a clinical context.Advice for leaders in AI startups.A vision for the future of Lucem Health. Quotes:“We are focused on early disease detection almost exclusively, and so that is using AI and machine learning algorithms to, at any point in time, evaluate the risk that a patient may have a certain disease.” — Sean Cassidy“Workflow is really important, but there are also other considerations that matter in terms of AI being more widely adopted in clinical settings and healthcare.” — Sean Cassidy“We are always evaluating and trying to get a deep understanding of whether what we said was going to happen with respect to the performance of the solution is actually manifesting itself in the real world.” — Sean CassidyLinks:Sean Cassidy on LinkedInLucem HealthResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

In this episode, I sit down with Jean-Baptiste Schiratti, Medical Imaging Group Lead and Lead Research Scientist at Owkin, to discuss the application of self-supervised learning in drug development and diagnostics. Owkin is a groundbreaking AI biotechnology company revolutionizing the field of medical research and treatment. It aims to bridge the gap between complex biological understanding and the development of innovative treatments. In our conversation, we discuss his background, Owkin's mission, and the importance of AI in healthcare. We delve into self-supervised learning, its benefits, and its application in pathology. Gain insights into the significance of data diversity and computational resources in training self-supervised models and the development of multimodal foundation models. He also shares the impact Owkin aims to achieve in the coming years and the next hurdle for self-supervised learning.Key Points:Introducing Jean-Baptiste Schiratti, his background, and path to Owkin.Details about Owkin, its mission, and why its work is significant.The application of self-supervised learning in drug development and diagnostics.Examples of the different applications of self-supervised learning.Discover the process behind training self-supervised models for pathology.Explore the various benefits of using self-supervised learning.His approach for structuring the data used for self-supervised learning.Unpack the potential impact of self-supervised AI models on pathology.Gain insights into the next frontier of foundation model development.He shares his hopes for the future impact of Owkin.Quotes:“To be able to train efficiently, computer vision backbones, you actually need to have a lot of compute and that can be very costly.” — Jean-Baptiste Schiratti“There are some models that are indeed particular to specific types of tissue or specific sub-types of cancers and also the models can have different architectures and different sizes, they come in different flavors.” — Jean-Baptiste Schiratti“The more diverse the [training] data is, the better.” — Jean-Baptiste Schiratti“I’m convinced that the foundation models will play a very important role in digital pathology and I think this is already happening.” — Jean-Baptiste SchirattiLinks:Jean-Baptiste Schiratti on LinkedInJean-Baptiste Schiratti on XOwkinPhikonResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

Joining me today are Linda Chung and Michael Mullarkey to discuss the transformative potential of AI in mental health care. Linda is the co-CEO and Co-Founder of Aiberry, a groundbreaking AI company redefining mental healthcare accessibility. With a background in speech-language pathology, Linda pioneered telehealth services and now leads Aiberry in leveraging innovative technology for objective mental health screenings. Michael, the Senior Clinical Data Scientist at Aiberry, is dedicated to translating complex data science into tangible human value. His unique background in clinical psychology merged with a passion for coding drives his mission to address pressing human concerns through data.In our conversation, we explore the fascinating intersection of clinical expertise and artificial intelligence, unlocking personalized insights and proactive strategies for mental well-being. Hear about Aiberry’s innovative chatbot “Botberry” and how it helps provide insights into the user’s mental health. We also get into the weeds and unpack how Aiberry develops its models, data source challenges, the value of custom models, mitigating model biases, and much more! Our guests also provide invaluable advice for other startups and share their future vision for the company. Tune in and discover AI technology at the forefront of mental health innovation with Linda Chung and Michael Mullarkey from Aiberry!Key Points:Linda’s healthcare background and motivation for starting Aiberry.Michael's transition from clinical psychology to AI at Aiberry.Aiberry's AI-powered mental health assessment platform and its unique approach.The role of machine learning in Aiberry's technology.Model development, data collection challenges, and custom model creation.Addressing bias in models trained on patient interview data.Measuring impact and success metrics at AiberryAdvice for leaders of AI-powered startups.The vision for Aiberry's impact in the next three to five years.Quotes:“We know that early detection leads to early intervention and better outcomes.” — Linda Chung “Our models take the messy, natural human way that people talk about their mental health, and we turn it into systematic data that are necessary for the healthcare industry and report it back to the user.” — Linda Chung“As a health tech company, we have to take the health and the tech elements of our business equally seriously. So, one of our guiding principles from a health perspective is we have to keep people's data secure.” — Michael Mullarkey“We really value letting people talk about their mental health in their own words, and that can lead to some unexpected outcomes on the modeling side of the operation.” — Michael MullarkeyLinks:Linda Chung on LinkedInMichael Mullarkey on LinkedInAiberryResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

Machine learning can be used as an innovative method to contribute to climate change resiliency. Today on Impact AI, I am joined by the co-founder and CTO of Vibrant Planet, Guy Bayes, to discuss how they are using AI to revitalize forests. Listening in, you’ll hear all about our guest’s background, why he started Vibrant Planet, what the company does, how they apply machine learning to their work, and a breakdown of how they collect the four sets of data they need. We delve into any problem areas they face in their individual and integrated data types before Guy tells us how they cross-validate their models. We even talk about how the teams collaborate, how machine learning and forest knowledge come together, and where he sees the company in the next three to five years. Finally, our guest shares some pearls of wisdom for any leaders of AI-powered startups.Key Points:A warm introduction to today’s guest, Guy Bayes. Guy tells us about his background and what led him to create Vibrant Planet. What Vibrant Planet does and how it contributes to climate change resiliency. How Vibrant Planet applies machine learning to the work.A breakdown of the four sets of data they need and how they collect it. The challenges they face when it comes to collecting and integrating all their data. How Guy makes sure that their models work in different geographic regions. Incorporating forest knowledge into data modeling and machine learning development. How the Vibrant Planet teams work together and collaborate to achieve their goal. What Vibrant Planet does to measure the impact of this technology. New AI advancements Guy is particularly excited about for Vibrant Planet. Guy shares some advice for leaders of AI-powered startups.Where he sees Vibrant Planet’s impact in the next three to five years. Quotes:“Getting the forest back into a state that's more able to tolerate fire and more able to produce low-intensity fire rather than high-intensity fire is [Vibrant Planet’s] goal.” — Guy Bayes“We have – not only super good engineers but also very talented ecological scientists and people that have done physical hands-on forestry for their careers. – This mix of those three personas – work together pretty harmoniously actually because we all share a common goal.” — Guy Bayes“I don't think you can ever find one person who has all that in their head, but you can find a team that does.” — Guy Bayes“You will not have an impact without having a combined team that all respects each other and brings different things to the table.” — Guy BayesLinks:Guy Bayes on LinkedInVibrant PlanetVibrant Planet on LinkedInResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

In this episode, I sit down with Erez Naaman, co-founder and CTO of Scopio Labs, to delve into the transformative potential of AI in healthcare, particularly in blood cell morphology analysis. Erez shares the intriguing journey behind the inception of Scopio Labs which was driven by a desire to revolutionize healthcare practices. Discover how Scopio Labs' platforms digitize and streamline the process of blood cell analysis and the pivotal role of machine learning in distinguishing and classifying various cell types. Gain insights into the significance of data collection and algorithm development, the evolution of AI infrastructure over the past decade, regulatory considerations on product development, and more. He also shares invaluable insights for AI startup leaders, the future trajectory of Scopio Labs, and the profound impact envisioned for the healthcare landscape. Join me as we explore the intersection of AI and healthcare innovation with Erez Naaman.Key Points:Eres shares his professional background and his path to founding Scopio Labs.Revolutionizing healthcare through AI-driven blood cell morphology analysis.The pivotal role machine learning plays in distinguishing and classifying various cell types.Discover the challenges of working with blood smear images; particularly for training models.Learn about the differences between regulated and nonregulated machine learning.AI infrastructure development and the associated regulatory considerations.Explore his approach to developing new machine learning products or features. Hear why he chooses to prioritize the end-user experience during development.Advice for budding entrepreneurs and the future trajectory of Scopio Labs.Quotes:“In terms of the approach [to AI], I think we saw it the same way that we do today in terms of its importance but I think that the infrastructure for using ML has greatly evolved.” — Erez Naaman“Getting a large enough data set to get a reliable classification on specific more rare cell types is the most difficult problem in my opinion.” — Erez Naaman“In a way, we look at it backward. Machine learning is a tool and not a goal. So, we always start with the patient in mind or the user.” — Erez Naaman“Everyone is dealing with AI and so the front runners are clearly becoming the leaders with time. So, it is much easier to choose the right tools for every task as time progresses.” — Erez NaamanLinks:Erez Naaman on LinkedInScopio LabsResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

Customer service calls often start and end at the operator’s headset, but there is so much untapped data from these conversations that could be used to improve business systems on a holistic level. Today’s guest, Amy Brown has seen the value of unlocking conversational data to improve healthcare systems across the country, and as the Founder and CEO of Authenticx, she has taken giant strides towards accomplishing this goal.Authenticx is an AI-powered platform that makes it possible for healthcare organizations to have a single source of conversational data, creating powerful and immersive customer insight analysis that informs business decisions. In today’s conversation, Amy explains why she founded Authenticx, what the company does, and why her business is important for healthcare.  We also learn about how the company uses machine learning in its processes, the challenges of working with conversational data, how Authenticx upholds a high ethical standard, and how the impact of its technology can be measured across healthcare systems nationwide. After sharing some important advice for other leaders of AI-powered startups, Amy explains why Authenticx will be a key player in healthcare for the foreseeable future. Key Points:A warm welcome to the Founder and CEO of Authenticx, Amy Brown. Amy’s professional background, and how she ended up founding Authenticx. What Authenticx does and why the company is important for healthcare. How the company uses machine learning to get better insights from conversational data.  A closer look at the conversational data that Authenticx works with. The challenges of working with and training models on conversational data.  Other ways that they validate their models. Mitigating biases and upholding ethics. How Amy measures the impact of Authenticx’s technology.Her advice to other leaders of AI-powered startups. Where Authenticx will be in the next three to five years, according to Amy.Quotes:“That’s really what I’m trying to get at; using technology to help explain customer and consumer perception of their care, and using that; putting that to work for the healthcare industry so it can start to improve its systems in a way that allows patients and consumers to actually get a better outcome.” — Amy Brown“Our data team has had to become extremely proficient at dealing with all kinds of messy data.” — Amy Brown“We’ve hired a diverse group of human beings because we want to make sure that we’re inclusive in our interpretations of what’s happening in these conversations.” — Amy Brown“You can never eliminate all bias – we would never purport of doing that – but we can be very intentional about how we train the data.” — Amy Brown“[The] dream scenario is that the healthcare system in this country starts to make room for and evolve in how it makes its business decisions to include the voices of their customers as a key source of insight, intel, and data.” — Amy BrownLinks:Amy Brown on LinkedInAmy Brown on XAuthenticxAuthenticx on InstagramResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

Using biological intelligence, human intelligence, and artificial intelligence, the company in the spotlight today aims to demystify health, make science accessible, and honor the biochemical individuality of every human.Today on Impact AI, I am joined by the founding CTO and Head of Discovery AI at Viome, Guru Banavar! He is here to talk all about AI and the human microbiome. As you tune in, you’ll hear about Guru’s background and what led to the creation of Viome, including what they do and why their work is crucial to chronic disease. He unpacks their use of machine learning to turn RNA data into insights for their customers, the challenges they face in training models for the work they do, and Guru sheds light on the early steps of their process for planning and developing new machine-learning products or features.  Be sure not to miss out on this insightful conversation about how Guru and the team at Viome are working to decode the human microbiome.Key Points:Learn about Guru’s background and what led to the creation of Viome.What Viome does and why it’s important for chronic disease.Using machine learning to turn RNA data into insights for customers at Viome.Guru highlights the challenges they face in training models based off of the work with RNA data and the large data set they’ve collected from customers. He unpacks the early steps in the process of planning and developing a new machine-learning product or feature.We talk about technological advancements that made it possible to build their technology. Guru’s advice to other leaders of AI-powered startups.His thoughts on the impact of Viome in the next 3-5 years.Quotes:“At some point in time, I decided that the impact that I wanted to make in the field of computational biology, life sciences, and healthcare could be done only if I joined a few of my friends from the broader community, and started a new company — [Viome].” — Guru Banavar“I am one of those AI people who believes that you first focus on the problem, and you bring all of the tools you need to solve the problem. AI, to me, is not just one thing, like the latest buzzword. For me, AI is an ML, a set of tools, and you take the right tool for the right problem.” — Guru Banavar“One of our core intellectual property elements is the meta-transcriptomic laboratory technology, which essentially, isolates, detects, and processes what we call the informative RNA molecules in any given sample. That required a number of sort of biochemistry-level technology breakthroughs.” — Guru Banavar“I would advise other leaders of AI-powered startups to be very careful about how you pick your solution toolset, based upon the problem that you want to solve.” — Guru BanavarLinks:Guruduth Banavar on LinkedInGuruduth Banavar on XViomeViome BlogResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

In this episode, I sit down with Konstantinos Kyriakopoulos, CEO of DeepSea, to discuss the transformative world of AI-powered shipping optimization. DeepSea focuses on enhancing vessel performance, fuel efficiency, and overall logistics management in the shipping and logistics industry. Konstantinos has been a key figure in advocating for digitalization in the maritime sector, pushing for technologies to streamline processes, cut costs, and reduce environmental impact.In our conversation, Konstantinos shares the captivating journey behind DeepSea's inception, revealing how its AI-driven solutions emerged from a desire to revolutionize the shipping industry's efficiency and environmental impact. We explore the intricate use of machine learning to predict fuel consumption, optimize vessel operations, and navigate the shift toward decarbonization.Gain insights into the intricacies of data architecture, the critical role of scalability, measuring impact, the future vision of the company, and much more. Don't miss out on discovering the cutting-edge applications of AI that are steering the shipping industry toward a more sustainable future with Konstantinos Kyriakopoulos. Tune in now!Key Points:Background about Konstantinos and DeepSea's inception.How AI is reshaping shipping efficiency and vessel operations.The role of DeepSea in the shipping industry and mitigating climate change.Insights into the challenges and hurdles of an evolving shipping industry.How DeepSea leverages AI, inputs into the model, and the overall aim.Approaches the company implements to ensure the integrity of its products.Why the explainability of machine learning models is critical. He shares DeepSea’s approach to model validation.Measuring impact: CO2 reduction and cost savings for clients.Konstantinos offers valuable advice for leaders of AI-powered startups.What the company has planned for the future.Quotes:“If you really want to create impact, it’s not enough to just show people what’s happening and give them analytics, but you also have to, in some way, produce a tangible ROI.” — Konstantinos Kyriakopoulos“The most important thing is to evaluate performance, so to make sure that the proof of performance is constantly being tested and you have good benchmarks and analytics.” — Konstantinos Kyriakopoulos“It’s really important to also be able to check internally what is going on but also how the customer wants to see what’s created.” — Konstantinos Kyriakopoulos“For us, the impact is actually very straightforward. It’s dollars and the metrics tonnes of CO2.” — Konstantinos Kyriakopoulos“I think what I always say when people talk to me about starting an AI company is to focus on your data architecture early.” — Konstantinos KyriakopoulosLinks:Konstantinos KyriakopoulosDeepSeaDeepSea on LinkedInResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.