How AI Happens

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

Curren is a curious, driven, and creative leader with vast experience in data science and AI. Her original background was in neuroscience and cognitive neuroscience but entered the industry when she realized how much she enjoyed programming, maths, and statistics. Additionally, her biology background gave her an advantage, making her a perfect fit for managing the neuroscience portfolio for Johnson & Johnson. In our conversation with Curren, we learn about her professional background, how her biology background is an advantage, and what she enjoys most about data science, as well as the important work she does at Johnson & Johnson. We then talk about AI in the pharmaceutical industry, how it is used, what it is used for, the benefits of AI both to the company and patients, and her approach to tackling data science problems. She also tells us what it was like moving into a leadership role and shares some advice for people wanting to take the plunge into leadership.  Key Points From This Episode:Curren’s professional background and how she ended up in her role at Johnson & Johnson.The connection between traditional neuroscience and neural networks in AI.Ways in which traditional scientific education in neurology informs AI.How much we currently understand about human learning.Curren explains her role and responsibilities in her position at Johnson & Johnson.What the term ‘precision’ means in her line of work and examples.Outline of Curren’s approach to data science and her role at Johnson & Johnson.We find out what Curren’s definition of success is.The significant benefits of optimizing processes and procedures.Curren outlines the various ways AI is deployed at Johnson & Johnson.Her experience moving from an individual contributor role into a leadership role.Advice Curren has for people who are considering entering a leadership role.The importance of trusting your team as a leader.Tweetables:“Finding new ways to use data to drive diagnosis is a big focus for us.” — @CurrenKatz [0:11:56]“In data science, it can be challenging to define success. But choosing the right problem to solve can make that a lot easier.” — @CurrenKatz [0:15:27]“I want the best data scientists in the world and to have those people on my team or the best managers in the world. I just need to give them the space to be successful.” — @CurrenKatz [0:23:55]Links Mentioned in Today’s Episode:Curren Katz on LinkedInCurren Katz on TwitterJohnson & JohnsonJohnson & Johnson on LinkedInSama

Dr. Kruft unpacks how she went from earning a Ph.D. focused on quantum chemistry, to working in AI and machine learning. She shares how she first discovered her love of data science, and how her Ph.D. equipped her with the skills she needed to transition into this new and exciting field. We also discuss the data science approach to problem-solving, deep learning emulators, and the impact that machine learning could have on the natural sciences.  Key Points From This Episode:Introducing today's guest, Bonnie Kruft, Senior Director at Microsoft’s AI for Science.A quick look at Bonnie’s background and the research she is currently doing.The work that Bonnie did on quantum chemistry for her Ph.D. dissertation.How quantum chemistry led to her working in the field of AI.An overview of the transferable skills that Bonnie picked up during her Ph.D.Learn about Bonnie’s work with pharmaceutical companies.How Bonnie became interested in data science and machine learning.The data science approach to problem-solving.The concept of falling faster and how to facilitate it.What the word ‘quantum’ means and how it applies to computing.How Bonnie’s Ph.D. prepared her for a career in machine learning.The impact that machine learning could have on the natural sciences.A breakdown of the four paradigms through which science has evolved.The emulator approach and how it can apply to anywhere data science is being done.Learn about Microsoft's AI for science and what they are doing with machine learning.What Bonnie’s typical day looks like.Tweetables:“Although I wasn't really working on machine learning, or data science during my Ph.D., there's a lot of transferable skills that I picked up along the way while I was working on quantum chemistry.” — Bonnie Kruft [0:03:00]“We believe that deep learning could have a really transformational impact on the natural sciences.” — Bonnie Kruft [0:13:02]“The idea is that deep learning emulators will be used for the things that are going to make the most impact on the world. Solving healthcare challenges, combating disease, combating climate change, and sustainability. Things like that.” — Bonnie Kruft [0:21:29]Links Mentioned in Today’s Episode:Bonnie Kruft on LinkedInMicrosoftHow AI HappensSama

In our conversation, we discuss Brandon's approach to problem-solving, the use of synthetic data, challenges facing the use of AI in drug development, why the diversity of both data and scientists is important, the three qualities required for innovation, and much more.Key Points From This Episode:We hear about Brandon’s unconventional background and professional career journey. Why he has a passion for combining AI and machine learning with biology.An outline of the Opal platform and how it is used for drug discovery.Brandon’s approach to innovating and improving various stages of pharmaceutical development.Whether or not he thinks his approach can be applied outside of pharmaceutical development.How data science is used in traditional companies and how this differs at Valo.What signs people should look out for to ensure they are at a data-driven organization. A brief discussion about the benefits of using non-traditional approaches. Ways in which Brandon sees synthetic data being used in the future.The biggest challenge currently limiting the use of synthetic data. A breakdown of the three competing qualities that are required to innovate.Reasons why Brandon thinks current algorithms and the underlying datasets need to be improved. Brandon shares his approach to ensuring fairness and rooting out bias in datasets.Another problem the industry faces with scientists: a lack of diversity.The value of re-weighting a training set.Innovations in AI and machine learning that keeps Brandon motivated and inspired.Tweetables:“Instead of improving the legacy, is there a way to really innovate and break things? And that’s the way we think about it here at Valo.” — @allg00d [0:08:46]“Here at Valo, if data scientists have good ideas, we let them run with them, you know? We let them commission experiments. That’s not generally the way that a traditional organization would work.” — @allg00d [0:11:31]“While you might be able to get synthetic data that represents the bulk, you are not going to get the resolution within those patients, within those subgroups, within the patient set.” — @allg00d [0:15:15]“We suffer right now from a lack of diversity of data, but then, on the other side, we also suffer as a field from lack of diversity in our scientists.” — @allg00d [0:19:42]Links Mentioned in Today’s Episode:Brandon AllgoodBrandon Allgood on LinkedInValoValo on LinkedInOpal platformDALI AllianceLogicaBrandon Allgood on TwitterRob Stevenson on LinkedInSama

In this episode, Heather shares her background in both farming and commerce, and explains how her in-field experience and insights aid both her and the AI team in the development cycle. We learn about the advantages of drone-based precision spraying, the function of the herbicides that Precision AI’s drones spray onto crops, and the various challenges of creating AI models that can recognize plant variations. Key Points From This Episode:Introducing Heather Clair, Product Manager at precision.ai.Heather’s background in farming and commerce; and what led her to precision.ai.precision.ai’s dramatically different approach to agriculture.The advantages of drone-based precision spraying, as opposed to land-based high-clearance spraying.The function of the herbicides that precision.ai’s drones spray onto crops.precision.ai’s use of AI to teach their drones to identify crops and distribute herbicides with precision.  The relationship between Heather, as product manager, and the AI experts at precision.ai.Heather’s involvement in the development cycle.Sama’s reliable accuracy rate.The challenge of creating AI models that recognize and can work with plant variations.How the varying colors of soil impact the AI models.The phenomenon of phenoplasticity and the challenge it presents to the AI team. The advantage Heather has of having in-field experience.Heather’s closing tip: how to have happier, healthier houseplants.Tweetables:“Up until now, everybody just went, ‘How do we get more efficient [with] fewer passes?’ But nobody questioned, ‘Are we doing the passes with the right equipment?’” — Heather Clair [0:07:07]“[precision.ai is] moving from land-based high-clearance sprayers to drone-based precision spraying.” — Heather Clair [0:07:24]“I never thought when I was a little farm kid that I would be playing with drones, but it is one of my favorite things to do.” — Heather Clair [0:07:45]“Trying to create these AI models that can work on any stage of plant can be a challenge.” — Heather Clair [0:21:15]“It's incredible how working with my AI team has opened up my eyes to being able to look at these plants from a very logical standpoint.” — Heather Clair [0:25:34]Links Mentioned in Today’s Episode:Heather Clair on LinkedInprecision.aiSama