Today’s guest is a Developer Advocate and Machine Learning Growth Engineer at Roboflow who has the pleasure of providing Roboflow users with all the information they need to use computer vision products optimally. In this episode, Piotr shares an overview of his educational and career trajectory to date; from starting out as a civil engineering graduate to founding an open source project that was way ahead of its time to breaking the million reader milestone on Medium. We also discuss Meta’s Segment Anything Model, the value of packaged models over non-packaged ones, and how computer vision models are becoming more accessible. Key Points From This Episode:What Piotr’s current roles at Roboflow entail.An overview of Piotr’s educational and career journey to date.The Medium milestone that Piotr recently achieved.The motivation behind Piotr’s open source project, Make Sense (and the impact it has had). Piotr’s approach to assessing computer vision models. The issue of lack of support in the computer vision space. Why Piotr is an advocate of packaged models. What makes Meta’s Segment Anything Model so novel and exciting. An example that highlights how computer vision models are becoming more accessible. Piotr’s thoughts about the future potential of ChatGPT.Tweetables:“Not only [do] I showcase [computer vision] models but I also show people how to use them to solve some frequent problems.” — Piotr Skalski [0:10:14]“I am always a fan of models that are packaged.” — Piotr Skalski [0:15:58]“We are drifting towards a direction where users of those models will not necessarily have to be very good at computer vision to use them and create complicated things.” — Piotr Skalski [0:32:15]Links Mentioned in Today’s Episode:Piotr Skalski on LinkedInPiotr Skalski on MediumMake SenseRoboflowSegment Anything by Meta AIHow to Use the Segment Anything ModelHow AI HappensSama

In our conversation, we learn about her professional journey and how this led to her working at DataRobot, what she realized was missing from the DataRobot platform, and what she did to fill the gap. We discuss the importance of bias in AI models, approaches to mitigate models against bias, and why incorporating ethics into AI development is essential. We also delve into the different perspectives of ethical AI, the elements of trust, what ethical “guard rails” are, and the governance side of AI. Key Points From This Episode:Dr. Mahmoudian shares her professional background and her interest in AI.How Dr. Mahmoudian became interested in AI ethics and building trustworthy AI.What she hopes to achieve with her work and research. Hear practical examples of how to build ethical and trustworthy AI.We unpack the ethical and trustworthy aspects of AI development.What the elements of trust are and how to implement them into a system.An overview of the different essential processes that must be included in a model.How to mitigate systems from bias and the role of monitoring.Why continual improvement is key to ethical AI development.Find out more about DataRobot and Dr. Mahmoudian’s multiple roles at the company.She explains her approach to working with customers.Discover simple steps to begin practicing responsible AI development.Tweetables:“When we talk about ‘guard rails’ sometimes you can think of the best practice type of ‘guard rails’ in data science but we should also expand it to the governance and ethics side of it.” — @HaniyehMah [0:11:03]“Ethics should be included as part of [trust] to truly be able to think about trusting a system.” — @HaniyehMah [0:13:15]“[I think of] ethics as a sub-category but in a broader term of trust within a system.” — @HaniyehMah [0:14:32]“So depending on the [user] persona, we would need to think about what kind of [system] features we would have .” — @HaniyehMah [0:17:25]Links Mentioned in Today’s Episode:Haniyeh Mahmoudian on LinkedInHaniyeh Mahmoudian on TwitterDataRobotNational AI Advisory CommitteeHow AI HappensSama

Kristen is also the founder of Data Moves Me, a company that offers courses, live training, and career development. She  hosts The Cool Data Projects Show, where she interviews AI, machine learning (ML), and deep learning (DL) experts about their projects. Points From This Episode:Kristen’s background in the data science world and what led her to her role at Comet.What it means to be a developer advocate and build community.Some of the coolest AI, ML, and DL ideas from The Cool Data Projects Show!One of the computer vision projects Kristen is working on that uses Kaggle datasets.How Roboflow can help you deploy a computer vision model in an afternoon.The amount of data that is actually needed for object detection.Solving the challenge of contextualization for computer vision models.A look at attention mechanisms in explainable AI and how to tackle large datasets.Insight into the motivations behind Kristen’s school bus project.The value of learning through building and solving “real” problems.How Kristen’s background as a data scientist lends itself to computer vision.Free and easily-available resources that others in the space have created to assist you.Advice for those forging their own careers: get involved in the community!Tweetables:“I’m finding people who are working on really cool things and focusing on the methodology and approach. I want to know: how did you collect your data? What algorithm are you using? What algorithms did you consider? What were the challenges that you faced?” — @DataMovesHer [0:05:55]“A lot of times, it comes back to [the fact that] more data is always better!” — @DataMovesHer [0:15:40]“I like [to do computer vision] projects that allow me to solve a problem that is actually going on in my life. When I do one, suddenly, it becomes a lot easier to see other ways that I can make other parts of my life easier.” — @DataMovesHer [0:18:59]“The best thing you can do is to get involved in the community. It doesn’t matter whether that community is on Reddit, Slack, or LinkedIn.” — @DataMovesHer [0:23:32]Links Mentioned in Today’s Episode:Data Moves MeCometThe Cool Data Projects ShowMothers of Data ScienceKristen Kehrer on LinkedInKristen Kehrer on TwitterKristen Kehrer on InstagramKristen Kehrer on YouTubeKristen Kehrer on TikTokKaggleRoboflowKangas LibraryHow AI HappensSama

 In this episode, we learn the benefits of blue-collar AI education and the role of company culture in employee empowerment. Dr. Borne shares the history of data collection and analysis in astronomy and the evolution of cookies on the internet and explains the concept of Web3 and the future of data ownership. Dr. Borne is of the opinion that AI serves to amplify and assist people in their jobs rather than replace them and in our conversation, we discover how everyone can benefit if adequately informed.Key Points From This Episode:Data scientist and astrophysicist, Dr. Kirk Borne’s vast background.The history of data collection and analysis in astronomy.How Dr. Borne fulfills his passion for educating others.DataPrime’s blue-collar AI education course.How AI amplifies your work without replacing it.The difference between efficiency and effectiveness.The difference between educating blue-collar students and graduate students.The goal of the blue-collar AI course. The ways in which automation and digital transformation are changing jobs.Comparison between the AI revolution (the fourth industrial revolution) and previous industrial revolutions.The role of company culture in employee empowerment.Dr. Borne’s approach to teaching AI education.Dr. Borne shares a humorous Richard Feynman anecdote.The concept of Web3 and the future of data ownership.The history and evolution of cookies on the internet.The ethical concerns of AI.Tweetables:“[AI] amplifies and assists you in your work. It helps automate certain aspects of your work but it’s not really taking your work away. It’s just making it more efficient, or more effective.” — @KirkDBorne [0:11:18]“There’s a difference between efficiency and effectiveness … Efficiency is the speed at which you get something done and effective means the amount that you can get done.” — @KirkDBorne [0:11:29]“There are different ways that automation and digital transformation are changing a lot of jobs. Not just the high-end professional jobs, so to speak, but the blue-collar gentlemen.” — @KirkDBorne [0:18:06]“What we’re trying to achieve with this blue-collar AI is for people to feel confident with it and to see where it can bring benefits to their business.” — @KirkDBorne [0:24:08]“I have yet to see an auto-complete come over your phone and take over the world.” — @KirkDBorne [0:26:56]Links Mentioned in Today’s Episode:Kirk Borne, Ph.D.Kirk Borne, Ph.D. on LinkedInKirk Borne, Ph.D. on TwitterRichard FeynmanJennyCoAlchemy ExchangeBooz Allen HamiltonDataPrimeHow AI HappensSama

Goodbye Passwords, Hello Biometrics with George WilliamsEpisode 61: Show Notes.Is it really safer to have a system know your biometrics rather than your password? If so, who do you trust with this data? George Williams, a silicon valley tech veteran who most recently served as Head of AI at SmileIdentity, is passionate about machine learning, mathematics, and data science. In this episode, George shares his opinions on the dawn of AI, how long he believes AI has been around, and references the ancient Greeks to show the relationship between the current fifth big wave of AI and the genesis of it all. Focusing on the work done by SmileIdentity, you will understand the growth of AI in Africa, what and how biometrics works, and the mathematical vulnerabilities in machine learning. Biometrics is substantially more complex than password authentication, and George explains why he believes this is the way of the future.Key Points From This Episode:Georges's opinions on the genesis of AI.The link between robotics and AI.The technology and ideas of the Ancient Greeks, in the time of Aristotle.George’s career past: software engineer versus mathematics.What George’s role is within SmileIdentity.How Africa is skipping passwords and going into advanced biometrics.How George uses biometrics in his everyday life,Quantum supremacy: how it works and its implications.George’s opinions on conspiracy theories about the government having personal information.Why understanding the laws and regulations of technology is important.The challenges of data security and privacy.Some ethical, unbiased questions about biometrics, mass surveillance, and AI.George explains ‘garbage in, garbage out’ and how it relates to machine learning.How SmileIdentity is ensuring ethnic diversity and accuracy.How to measure an unbiased algorithm.Why machine learning is a life cycle. The fraud detection technology in SmileIdentity biometric security.The shift of focus in machine learning and cyber security.Tweetables:“Robotics and artificial intelligence are very much intertwined.” — @georgewilliams [0:02:14]“In my daily routine, I leverage biometrics as much as possible and I prefer this over passwords when I can do so.” — @georgewilliams [0:08:13]“All of your data is already out there in one form or another.” — @georgewilliams [0:10:38]“We don’t all need to be software developers or ML engineers, but we all have to understand the technology that is powering [the world] and we have to ask the right questions.” — @georgewilliams [0:11:53]“[Some of the biometric] technology is imperfect in ways that make me uncomfortable and this technology is being deployed at massive scale in parts of the world and that should be a concern for all of us.” — @georgewilliams [0:20:33]“In machine learning, once you train a model and deploy it you are not done. That is the start of the life cycle of activity that you have to maintain and sustain in order to have really good AI biometrics.” — @georgewilliams [0:22:06]Links Mentioned in Today’s Episode:George Williams on TwitterGeorge Williams on LinkedInSmileIdentityNYU Movement LabChatGPTHow AI HappensSama

Our discussion today dives into the climate change related applications of AI and machine learning, and how organizations are working towards mobilizing them to address the climate problem. Priya shares her thoughts on advanced technology and creating a dystopian version of humanity, what made her decide on her Ph.D. topic, and what she learned touring the world interviewing power grid experts around the world.Key Points From This Episode:Priya shares her take on ChatGPT.We talk about ChatGPT guard rails and whether it should be done manually or with built in technology that automatically detects issues.Concerns with the concept of advanced technology and creating a dystopian version of humanity. What made Priya want to get into her particular Ph.D. topic.What surprised her about her tour around the world interviewing people. Priya explains what she means by a 'systems problem.'Machine learning and AI in power grids; what is the thrift of opportunity?Priya speaks to the reason why she found a climate change AI organization.Narrowing the focus, in AI and Climate Change, as an organization.Priya shares an example of what work looks like for someone in a role with machine learning and climate change. Recent wins in the climate change world and how they measure the success of their progress. The gap between the vision of where she is now and where she wants to be in the medium term. Tweetables:“When we are working on climate change related problems, even ones that are “technical problems” every problem is basically a socio-political technical problem, and really understanding that context when we move that forward can be really important.” — @priyald17 [0:10:02]“Machine learning in power grids and really in a lot of other climate relevance sectors can contribute along several themes or in several ways.” — @priyald17 [0:12:18]“What prompted us to found this organization, Climate Change AI, [is] to really help mobilize the AI machine learning community towards climate action by bringing them together with climate researchers, entrepreneurs, industry, policy, all of these players who are working to address the climate problems and sort of to do that together.” — @priyald17 [0:17:21]Longer quote“So the whole idea of Climate Change AI is rather than just focusing on what can we as individuals who are already in this area do to do research projects or deployment projects in this area, how can we sort of mobilize the broader talent pool and really help them to connect with entities that are really wanting to use their skills for climate action.” — @priyald17 [0:19:17]Links Mentioned in Today’s Episode:Priya DontiPriya Donti on TwitterPutting the Smarts in the Smart GridClimate Change AIClimate Change AI Interactive SummariesHow AI HappensSama

 Genetec has been a software provider for the physical security industry for over 25 years, earning its spot as the world’s number one software provider in video management. We are pleased to be joined today by Florian Matusek, Genetec’s Director of Video Analytics and the host of Video Analytics 101 on YouTube. Florian explains how his company is driving innovation in the market and what his specific role is before divining into the importance of maintaining both security and privacy, this new wave of special analytics, and why real-time improvements are more difficult than back-end adjustments. Our guest then lists all the exciting things he is witnessing in the world of video analytics and what he hopes to see in re-identification and gait analysis in the future. We discuss synthetic data and whether it will ever be commoditized and close with an exploration of the probable future of grocery stores without any employees. Key Points From This Episode:A warm welcome to the Director of Video Analytics at Genetec, Florian Matusek.How the Video Analytics 101 YouTube channel was formed. The purpose of his YouTube channel and its ideal viewer. What his company does and what his role entails. How Genetec has transformed as a company from its inception until now. The insights Florian hopes to provide to his customers through video analytics.Genetec’s new technology that upholds both security and privacy. Exploring the new wave of spatial analytics. The difference between real-time improvements and gradual, back-end adjustments. New use cases, techniques, and trends that Florian finds exciting. The perks and problems of re-identification. Whether the current technology of gait analysis is reliable and how it relates to re-identification. How technology is evolving to include time-based data collection. The difficulties he experiences in collecting video data to train his models. Whether there’s an opportunity for synthetic data to augment his data strategy.Florian’s thoughts on synthetic data becoming commoditized. Some interesting ways that Genetec’s clients are using its technology. The video analytics behind the automated drinks system at the Denver Broncos stadium. How close we are to a future of grocery stores with no employees or cash registers. Tweetables:“Nowadays, it's about automation. It's about operational efficiency. It's about integrating video and access control, and license plate recognition, IoT sensors, all into one platform, and providing the user a single pane of glass.” — Florian Matusek [0:05:11]“We will always build products that benefit our users, which is the security operators, the ones purchasing it. But at the same time, we see it as our responsibility to also do everything possible to protect the privacy of the citizens that our customers are recording.” — Florian Matusek [0:09:03]“What gets me excited are solutions that are really targeted for a specific purpose and made perfect for this purpose.” — Florian Matusek [0:11:24]“You need both synthetic data and real data in order to make the real applications work really well.” — Florian Matusek [0:21:42]“It's really funny how customers come up with creative ways to solve their specific problems.” — Florian Matusek [0:26:36]Links Mentioned in Today’s Episode:Florian Matusek on LinkedInVideo Analytics 101 on YouTubeGenetecGenetec on YouTubeHow AI HappensSama

Navrina shares why trust and transparency are crucial in the AI space and why she believes  having a Chief Ethics Officer should become an industry standard. Our conversation ends with a discussion about compliance and what AI tech organizations can do to ensure reliable, trustworthy, and transparent products. To get 30 minutes of uninterrupted knowledge from The National AI Advisory Committee member, Mozilla board of directors member, and World Economic Forum young global leader Navrina Singh, tune in now!Key Points From This Episode:Welcoming today’s guest, CEO and Founder of Credo AI, Navrina Singh. A look at Navrina’s recent background and why she decided to start Credo AI.Why it’s important to take responsibility for the technology you create.The reasons why the AI technology industry chose to create its own systems of oversight.Why trust is a crucial part of the AI technology sector. How Credo AI helps companies engage with issues of transparency and trust. The people at various companies who are in charge of AI governance that Credo deals with. Who Navrina thinks should be responsible for AI governance at every company. Where Credo’s clients usually fall short when it comes to compliance.What AI technology companies should be thinking about beyond compliance. Navrina’s view on what organizations can do to ensure reliable, trustworthy, and transparent tech.Tweetables:“I always saw technology as the tool that would help me change the world. Especially growing up in an environment where women don’t have the luxury that some other people have, you tend to lean on things that can make your ideas happen, and technology was that for me.” —@navrinasingh [0:01:17]“As technologists, it’s our responsibility to make sure that the technologies we are putting out in the world that are becoming the fabric of our society, we take responsibility for it.” —@navrinasingh [0:04:04]“By its very nature, trust is all about saying something and then consistently delivering on what you said. That’s how you build trust.” —@navrinasingh [0:08:58]“I founded Credo AI for a reason, to bring more honest accountability in artificial intelligence.” —@navrinasingh [0:10:45]“We are going to see more trust officers and trust functions emerge within organizations, but I am not really sure if a chief ethics officer is going to emerge as a core persona, at least not in the next two to three years. Is it needed? Absolutely, it’s needed.” —@navrinasingh [0:17:32]Links Mentioned in Today’s Episode:Navrina Singh on TwitterNavrina Singh on LinkedInCredo AIThe National AI Advisory CommitteeWorld Economic ForumDr. Fei-Fei Li on LinkedInHow AI HappensSama

Arize and its founding engineer, Tsion Behailu, are leaders in the machine learning observability space. After spending a few years working as a computer scientist at Google, Tsion’s curiosity drew her to the startup world where, since the beginning of the pandemic, she has been building breaking-edge technology. Rather than doing it all manually (as many companies still do to this day), Arize AI technology helps machine learning teams detect issues, understand why they happen, and improve overall model performance. During this episode, Tsion explains why this method is so advantageous, what she loves about working in the machine learning field, the issue of bias in machine learning models (and what Arize AI is doing to help mitigate that), and more! Key Points From This Episode:Tsions’s career transition from computer science (CS) into the machine learning (ML) space.What motivated Tsion to move from Google to the startup world.The mission of Arize AI.Tsion explains what ML observability is.Examples of the Arize AI tools and the problems that they solve for customers.What the troubleshooting process looks like in the absence of Arize AI.The problem with in-house solutions.Exploring the issue of bias in ML models.How Arize AI’s bias tracing tool works.Tsion’s thoughts on what is most responsible for bias in ML models and how to combat these problems.Tweetables:“We focus on machine learning observability. We're helping ML teams detect issues, troubleshoot why they happen, and just improve overall model performance.” — Tsion Behailu [0:06:26]“Models can be biased, just because they're built on biased data. Even data scientists, ML engineers who build these models have no standardized ways to know if they're perpetuating bias. So more and more of our decisions get automated, and we let software make them. We really do allow software to perpetuate real world bias issues.” — Tsion Behailu [0:12:36]“The bias tracing tool that we have is to help data scientists and machine learning teams just monitor and take action on model fairness metrics.” — Tsion Behailu [0:13:55]Links Mentioned in Today’s Episode:Tsion BehailuArize Bias Tracing ToolArize AIHow to Know When It's Time to Leave your Big Tech SWE Job -- Tsion BehauliHow AI HappensSama

Ian discusses what unique problems aerial automated vehicles face, how  segregations in the air affect flying, how the vehicles land, and how they know where to land. Animal Dynamics' goal is to phase out humans in their technology entirely and Ian explains the human involvement in the process before telling us where he sees this technology fitting in with disaster response in the future. Key Points From This Episode:An introduction to today’s guest, Ian Foster. A brief overview of Ian’s background and how he ended up at Animal Dynamics.Ian shares the mission of Animal Dynamics and how that’s being carried out.What the delivery mechanism is and what the technology is delivering.Why air is best for this kind of delivery and why it’s best not to use pilots.The challenges in an aerial automated vehicle. How segregations in the air affect this technology and how they’re combatting these issues. Ian tells us which is more difficult: to park a car autonomously or land a plane autonomously. How their vehicles land themselves. How they are training the technology to notice safe landing zones.How humans come into this AI technology and why they’re being phased out slowly. What Ian thinks the future and long-term opportunities are for Animal Dynamic’s technology.Tweetables:“Drawing inspiration from the natural world to help address problems is very much the ethos of what Animal Dynamics is all about.” — Ian Foster [0:02:06]“Data for autonomous aircraft is definitely a big challenge, as you might imagine.” — Ian Foster [0:16:17]We're not aiming to just jump straight to full autonomy from day one. We operate safely within a controlled environment. As we prove out more aspects of the system performance, we can grow that envelope and then prove out the next level.” — Ian Foster [0:19:01]“Ultimately, the desire is that the systems basically look after themselves and that humans are only involved in telling the thing where to go, and then the rest is delivered autonomously.” — Ian Foster [0:23:45]“The important thing for us is to get out there and start making a difference to people. So we need to find a pragmatic and safe way of doing that.” — Ian Foster [0:23:57]Links Mentioned in Today’s Episode:Ian Foster on LinkedInAnimal DynamicsHow AI HappensSama