Does More Intelligent Tomorrow: a DataRobot Podcast accept guests?

More Intelligent Tomorrow: a DataRobot Podcast does not accept guests

How often does More Intelligent Tomorrow: a DataRobot Podcast upload new episodes

More Intelligent Tomorrow: a DataRobot Podcast uploads weekly

How many episodes does More Intelligent Tomorrow: a DataRobot Podcast have

More Intelligent Tomorrow: a DataRobot Podcast has 68 episodes

More Intelligent Tomorrow: a DataRobot Podcast

Q: When was the last time More Intelligent Tomorrow: a DataRobot Podcast uploaded a new episode

More Intelligent Tomorrow: a DataRobot Podcast latest episode was on August 10, 2022

Aug 10 · 1h 5m Writer, philosopher, and futurist - Gary F. Bengier

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Jul 14 · 44m Shifting the Landscape of Food Insecurity - Mick Ebeling, Sanjay Srivastava What if the solution to food insecurity is technology? In today’s conversation, host Ari Kaplan sits down with Mick Ebeling, Founder /CEO of Not Impossible Labs and Bento and Sanjay Srivastava, Chief Digital Officer of Genpact, to discuss how technological innovation is helping solve food insecurity amongst at-risk populations. Not Impossible Labs is a global innovation lab that has spent the last decade tackling issues they call “absurdities” and building solutions aptly named “technology for the sake of humanity.” They have done this for a myriad of absurdities, from creating low cost ways for a parlayzed to draw again using only his eyes, to launching the world's first 3D printed prosthetic lab in a war torn refugee camp in Sudan. For years Mick and the Not Impossible Labs team were obsessed with the absurdity around Food Insecurity in the United States. So he and the Not Impossible team built Bento, software that is improving health one text message at a time. Bento’s unique approach is to connect historically marginalized and under-resourced people with nutritious, stigma-free meals from nearby restaurants and grocery stores. In the short time Bento has been deployed, it has already garnered such accolades as being named as a Fast Company World Changing Idea, and Fortune Impact 20 company, and being named as a TIME Best Invention of 2021. AI has the potential to supercharge Bento’s ability to create impact at scale. Generally, when people think of artificial intelligence they think of self-driving cars or large data processing algorithms however, it does not always have to operate on such a large scale. Bento is carefully architecting the foundation of its data to more efficiently identify and connect participants' needs to available resources that drive valuable health outcomes. The takeaway message from this invigorating conversation is the power of combining data, technology, and people to innovate and solve big problems in society. It is the belief of both guests that being able to orchestrate people, processes, data, and technology in a synchronized fashion is what drives real change in our societies. Tune in to learn more about the superpower of people and technology with true innovators that are making real change. Key Points From This Episode: The definition of food insecurity and how it manifests in society. Reasons for our guests getting involved with solving food insecurity. What the differences are between invention and innovation. The impact that Bento has and the potential it has to grow in the future. Technology and data challenges that they have or may need to overcome. Outcomes that they have achieved and hope to achieve in the future.

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More Intelligent Tomorrow: a DataRobot Podcast Jul 7 · 59m Creating Rhythm with Algorithms - Alex Mitchell Technology has always played a key role in the world of music, with things like digital production software, loop pedals, and multi-track recording transforming the industry forever. Artificial intelligence (AI), however, is about to be the most disruptive technology in music. In today’s episode, Ben Taylor sits down with Alex Mitchell, Founder, and CEO of Boomy, to discuss the future of AI in music. Boomy is an industry leader, using artificial intelligence to create "instant music.” Alex shares how he has tried to solve the question of what good music is with data and why he believes that, ultimately, good music is whatever people want to listen to. The music industry is evolving as we move into a time of growth, as evidenced by the explosion of content on TikTok and the billions of streams that have resulted from it, and why he believes that the least efficient way to invest in musicians today would be to start a traditional record label. Boomy presents the opportunity for a new consumption dynamic, using AI to look forward, not backward, and develop a native format for the next generation of listeners. On the topic of AI, Alex reflects on whether this emerging technology helps or hinders artists, saying that Boomy is not designed to replace musicians but rather provide the necessary tools to those who don’t have the same resources and access that many musicians do. In the end, great songs aren’t created by optimization algorithms, but by people with the tools to express themselves! Taking a minute to look toward the future of technology in music, Alex speaks to the likelihood of using real-time EEG neurofeedback or brain-computer interfaces (BCI) to accelerate trends, saying that, while there are companies that are already working on this technology, we probably won’t see it realized in our lifetime. When asked about the future impact of AI on music, Alex explains how Boomy facilities personalization or what he calls ‘context-aware algorithmic music’, which he believes is currently hindered by the difficulty to monetize it. What is missing, in his opinion, is a way to incentivize the business models of those systems, which is one of Boomy’s core goals. Reflecting on the two components of what musicians do, the first being skill and the second bringing taste, Alex explains how Boomy is building an interface where technology fills in the skill gap and allows everyone to become a composer in their own right. As the technology evolves, there will be greater opportunities to create and consume hyper-personalized content more rapidly and, as our appetite for short-form video content grows, Alex also thinks that the way the music industry operates will continue to evolve too. Regardless of what we think is ‘good' music or ‘bad’ music, the only data we have to go on is consumption and Alex believes that it’s what we each bring to music that makes it good, not some set of universal criteria. As a musician himself, Alex isn’t on a mission to replace musicians; he is leveraging the latest technology to create a whole new generation of creators and explore a new consumption dynamic that blurs the line between performer and audience. Key Points From This Episode: Defining what good (and bad) music is and what the future of the music industry looks like. How Boomy uses AI to present the opportunity for a new music consumption dynamic. Creating a new generation of artists, not replacing existing ones. Future technologies in music and how AI facilitates context-aware algorithmic music. Why there are no universal criteria that defines good or bad music; it’s personal!

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Jun 30 · 38m Data is Only as Good as its Ability to Drive Value - Dan Merzlyak “Data is only as good as its ability to drive value,” is the core belief of today’s guest, Dan Merzlyak, Head of Business Intelligence at BlackRock. BlackRock is the world’s largest asset management firm with over $10 trillion in assets and Dan is focusing on building a new data conversion and business intelligence strategy for the company’s core alternatives platform offering. The three different types of analytics that can be used to drive a business are descriptive, predictive, and prescriptive. As Dan explains in more detail in today’s episode, his approach to business intelligence is to first identify the business problems that need solving and then work backward towards the data. Artificial intelligence becomes useful at the prescriptive analytics stage and we’re only just scratching the surface of the potential of this tool to drive value. In big companies, there is more often than not a gap between the people who are driving the analytics and the people who are creating the analytics. However, Dan believes that in the near future it will be essential to have a greater degree of collaboration throughout the company, and for business leaders to adopt business intelligence tools in their daily workflows as opposed to relying on operational teams to present them with data. To enable true transformation in a business setting, people and processes deserve equal attention. Dan’s wide range of experience working in different companies across many industries has allowed him to witness the trends that are taking place in the business world. Based on these trends, Dan explains the importance of focusing on seamlessness workflow to attract customers. The world we live in is constantly changing, and changing fast. Data analytics has the potential to drive enormous value for an organization and keep it relevant in an ever-evolving environment. If you’re interested in hearing about the transformational power of data, you’ve come to the right place! Listen to this episode of More Intelligent Tomorrow to learn: Factors that have driven BlackRock’s astounding growth. Why Dan believes in focusing on driving business value first, and working back to the data. How different types of analytics can be used to drive a business. The importance of investing in the people behind your technology. How to build a data culture within your organization.

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Jun 24 · 58m Seeking Deeper Connection through Augmented Reality - Nicolas Robbe Nicolas Robbe is the CEO of Hoverlay in Acton, Massachusetts. In 2016, he left his job as Chief Marketing Officer at Dynatrace and re-engaged with one of his passions—augmented reality. The space was going through a profound transformation, and his goal was to create new technology and cool new tools for the public. In this episode of More Intelligent Tomorrow, host Dave Anderson talks to Nicolas about his work bringing augmented reality to screens. Nicolas describes how AR can bring deeper meaning to our experiences, associated opportunities and challenges, and the need for technologies that have a positive influence—promoting empathy and understanding instead of distancing people from one another. AR, he says, has the power to activate cognitive circuitry to convey emotion, promoting community connection and generating empathy and understanding. “As we live our lives, we're seeking meaning, looking for a deeper connection with the places we go and with the people around us. This medium is designed extremely well for that task, the same way the web is well-suited for sharing information and transacting.” Nicolas has always had a passion for understanding the nature of interactions between humans and systems and reducing the cognitive load on people when trying to transmit information to them. He describes multiple layers. One involves figuring out how to use visual metaphors to communicate and bring information into the physical world through a screen. Another involves getting off the screen to where the camera becomes a sort of browser—something a user can place content into just as they place images on a webpage. He explains how it works from a user perspective: They hold their phone, open the app, and see the video feed go through—as if they were taking photos. However, with AR, the camera is able to insert pixels and content into the field. “The magic is that the user creates the illusion of presence to help them feel the content. There are many techniques for creating the illusion so the brain accepts that it’s real. The user holds their phone, sees the content, and plays along. The content is usually meaningful to their location.” Asked how virtual reality compares with augmented reality, Nicolas says, “VR is about taking you away from your reality and giving you, perhaps, a better reality or an experience you couldn’t have in the physical world, with everything that entails. AR, on the other hand, tries to reconnect you with your physical environment. It could be a park, a building, (or) a kitchen. But it starts and ends with the location—the context—then augments it so your experience of that moment and location is more meaningful, more fun, more engaged. It's trying to connect you with the moment and the space versus taking you away from it.” Listen to this episode of More Intelligent Tomorrow to learn more about: How augmented reality deepens our understanding of history How AR can be used to create connection and understanding How context affects the experience and retention of information How augmented reality is different from virtual reality How ethical concerns relate to AR, including fake news and virtual trespassing

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Jun 14 · 45m You Can't Be in Business and Not Take Risks - H.P. Bunaes This episode of More Intelligent Tomorrow brings together H.P. Buanes, the Executive Director for AI. & Machine Learning at JPMorgan Chase and Diego Oppenheimer, the Executive Vice President of Machine Learning Ops at DataRobot for a conversation about artificial intelligence, machine learning, and their roles in the financial services industry. Data analytics and AI/ML wasn't a thing in 1983 when H.P. Buanes started his career. But since then, it’s become a core element for businesses who want to succeed in the new economy. "It's never been a better time to be in data and analytics than it is right now." With that being the case, Diego wonders why banks, who have so much data, still struggle with implementing AI/ML. H.P. believes it’s because executive management delegates the task to technical teams and hopes for the best. Successful implementation of AI/ML requires leadership from the very top. It also requires selecting the right project to apply AI/ML. The temptation is to start with big, high visibility projects. But you have to walk before you can run. Starting with overly complex projects will lead to disappointment and wasted resources. Don't just pick what's shiny and new. Come at AI/ML from a business perspective and select a project where there’s potential for attainable and measurable success. Another crucial decision is to have the right people working on your projects. There are few data scientists who understand banking, and you need someone who not only knows the data, but also your business needs. H.P. suggests imagining the press release for your project and working backward from there. Know the end goal and build your solution to reach it. Too often there’s a tendency to look at the data available and see what you can learn from it. Instead, figure out what you need and build your AI/ML solution to meet that need. Turning specifically to the world of banking and financial services, Diego asks H.P. where he sees opportunity. Credit. It's where analytics began in the world of financial services, and it remains a deep well of opportunity. From predicting default to more advanced areas like collateral valuation, severity loss, forecasting, and reserve analysis, there are a lot of possibilities. Using AI/ML to differentiate risk and go deeper into the credit spectrum is a terrific way to separate yourself from your competitors. AI/ML should be used to manage risk rather than to minimize it. If you design your control infrastructure for the highest risk, you'll find you can't move. Make a control infrastructure that's sensitive to the data, and you'll be more agile. "You can't be in business and not take risks." Much of the financial services industry resists moving to modern AI/ML solutions. The cloud is still an unknown for a lot of executive leadership, and there’s an inertial bias toward proven legacy policies and procedures. Moving to the cloud means having to rethink those processes. For a company to survive, moving to the cloud is inevitable. With access to unlimited computing power, endless storage, and the latest machine learning models in the cloud, the legacy model of an on-site data center is becoming obsolete. The companies who do AI/ML right will separate themselves from the rest of the pack and be more competitive. You must have the right talent, the right support, and the right goals, all implemented to work with as little friction as possible. Get educated. Know what AI/ML can achieve. Be able to spot an opportunity and understand what your analytics teams are working on and why.

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Jun 9 · 51m Applied AI - Innovation That Matters - Debanjan Saha In this episode of More Intelligent Tomorrow , we hear from Debanjan Saha, the President and Chief Operating Officer for DataRobot. He sat down with host Ben Taylor to discuss how he got to where he is and what he thinks is in store for artificial intelligence and machine learning. Ben starts off by asking Debanjan about the unique journey that brought him to the world of AI/ML. While Debanjan had originally planned to be a professor, a summer job at IBM Research started him on a climb up the technology stack. From systems and networking at IBM and a couple of start-ups, to databases and data lakes work in Amazon, and analytics with Google, the next logical step was to take on AI/ML. That brought him to DataRobot. He says IBM Research was filled with talented technologists, Amazon was all about executing growth at scale, and Google taught him about building innovative distributed systems. He learned that experience is a key skill, and it takes time. "There's no compression algorithm for experience." - Andy Jassy Every technology goes through a hype cycle, where it’s the “next big thing.” It was the internet in the 80s, the dotcom boom in the 90s, and big data at the start of the new century. This decade looks to belong to artificial intelligence and machine learning. The good thing about excitement around innovative technology is that it can lead to more investments, and investments can compress development time. It’s part of the growth phase. "We will use AI without knowing that we are using AI. It's probably going to be in pretty much every decision-making process that we have.” For AI/ML to succeed, we need to set proper expectations. AI can help you get to better decision-making, but there must be a return on investment to make it worthwhile. Another expectation to be set is that it’s not just about technology. It's also about process and company culture. Software alone isn’t going to solve your process problems or your culture problems. It will take time and experimentation to see how AI/ML will work best for any given situation. And don’t forget that failure is part of the process. Fail fast, iterate quickly. Find what doesn't work, and move on from there. In healthcare, Debanjan notes that AI/ML is augmenting what humans can do in symptom analysis, image processing, and remote diagnostics. And in the field of sustainability research, it’s being used to develop sustainable harvesting solutions. Debanjan goes on to share with Ben some of the most memorable lessons he has learned working in technology. First is that plans never work. But a close second is to be curious. Do that, and you'll end up in the right spot. "I think what’s most important for a technologist is to be intellectually curious and continuously learning." Ben wanted to know how Debanjan's experiences in sales and marketing have influenced his technical decision-making. His advice is to focus on customers and how you can delight them. Know the problem the customer is trying to solve. Also, think big. Don't think 10% bigger, instead think 10x bigger. Finally, Ben asks what advice Debanjan would give to people starting out. Imagine where the world will be five years from now and work toward that. Think Big. Take risks. Be curious and learn. Listen to the full episode to hear Debanjan speak on: His tech journey What he thinks of the hype around artificial intelligence The keys to successful AI/ML integration His thoughts on the future for AI/ML Why he suggests getting advice from outside your circle

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Jun 1 · 1h 3m The Cost and Complexity of Last Mile Delivery - Dr. Matthias Winkenbach If the pandemic has taught us anything, it’s the value of having goods delivered right to our front door. From fast food ordered on an app, to next day shipping from an online store, the distance from click to delivery is getting shorter all the time. What most of us don’t realize is that the hardest part of delivering an order is the last mile. More Intelligent Tomorrow host Ben Taylor asked researcher Dr. Matthias Winckenbach to explain what he means by “the last mile” and why it’s so difficult to cover. The term “the last mile” refers to the home stretch of the supply chain where goods are moved from the final distribution location to the customer. It can represent as much as 40% of the overall supply chain costs despite almost always being the shortest segment. This is why there’s a lot of effort going into optimizing it. "The cost and complexity of last mile delivery very much comes from the fragmentation of shipments." Customer expectations for faster, more flexible deliveries, with shorter lead times are running up the cost of covering the last mile. But financial costs aren’t the only concern. Our increase in demand might be having a negative impact on the environment as well. Increased demand means fewer opportunities for suppliers to consolidate, and that means more trips are needed to deliver the additional load of packages. Those extra trips use more fuel and put more traffic on the road. The challenge is that you're adding complexity to the system. Now you're managing inventory across many more locations while trying to predict demand in any one location. With this move to hyper localized inventory, the traditional methods of optimization must be updated. There’s a theoretical optimal solution to routing a package. It’s sometimes referred to as the Traveling Salesman Problem. But variables like traffic, customer availability, parking, and even the mood of the driver can impact the delivery in ways the optimal solution can’t predict. And all these things can change once the delivery is underway, meaning the optimal solution may no longer be valid. AI/ML can complement traditional planning methods to create better routes. Ben wonders if aerial delivery drones are the answer. They can fly directly to a location and don't have to deal with things like traffic and parking. Matthias thinks the answer seems to be no, at least not right away. There are regulatory, technological, and societal issues we need to overcome first. The density of deliveries in an urban area could lead to large swarms of drones buzzing overhead. The sky would fill up quickly. From an economic point of view, it’s hard to beat an effectively designed ground delivery route. Drones are still expensive, and you’d need up to 300 of them to match the capacity of a single truck. But one day, there might be a hybrid solution where a drone and delivery truck could collaborate to deliver your package. "A lot of people don't realize how difficult it is to get that package to the doorstep every morning." Listen to this episode of More Intelligent Tomorrow to learn: What the last mile means How it increases the cost of goods The impact the last mile has on the environment If drones might be the answer to the problem What’s needed so AI/ML can help solve the problem If delayed gratification is the answer

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More Intelligent Tomorrow: a DataRobot Podcast May 20 · 42m Disrupting a Market is Rarely an Overnight Transformation - Peter Coffee Disrupting a market is rarely an overnight transformation. And the right leaders see disruption as an ongoing process, not a one-time event. Peter Coffee was the first person at Salesforce with the word “platform” in his title. This was in an era where the idea of cloud computing was still in its infancy. He’s spent the last 15 years helping transform Salesforce from a company that sold CRM software into one that offers an entire cloud platform designed to enable companies to offer complete solutions to their customers. MIT host Dave Anderson caught up with Peter and asked him about how he thinks the future of the workforce will shape up over the coming years. 76% of workers say they are unequipped for the future of work. The traditional way to address this problem would have been to hire new talent out of college who would come in equipped with the tools and skills needed for the new landscape of the business world. But Peter feels new talent doesn't yet have the experience developed over time to really help customers solve their problems. He feels the answer to this problem is continuous education within the workforce. That’s the idea behind Salesforce’s Trailhead product. "We need to get back to a much more organic idea of what it means to learn and to adapt." Usually, AI projects fail because of a team’s ability to implement, learn, and change the way they work. Changing the idea of ongoing skill learning requires it to be part of a company’s culture. And to change that culture, you need to hire people who can change it. Peter says, if you hire for talent, you're hiring for skills that can be taught. But if you hire for fit, you're hiring for someone who won't challenge you in your thinking. He suggests you hire someone who will challenge you. "People are so much more capable than they're treated as being." Dave wonders if a lack of urgency is part of the reason more companies don't make similar changes. Too many companies focus on what they do and not on why. If they open themselves up to asking why they do what they do, then they're open to creating disruptive solutions. Companies tend to focus on what leads to being better, faster, and cheaper than their competitor. But there’s a physical limit to those factors. Offering solutions to your customers opens limitless opportunities. The iPod is a great example. Steve Jobs’s vision wasn’t about making a digital music player, it was about putting thousands of songs in your pocket. It was about offering the whole solution. This meant the iPod could adapt and grow with each generation to be an ecosystem and not just a single purpose device. Listen to this episode to learn about: Preparing the workforce for the future What it takes to be a better leader Creating moments of transition A new concept for continuous learning in the workplace The keys to giving customers a differentiating experience

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May 13 · 40m Having Your Own Genetic Personal Trainer - FOXO Technologies More Intelligent Tomorrow host Dave Anderson got a chance to catch up with FOXO Technologies ’ Tyler Danielson and Nichole Rigby to discuss longevity, epigenetics, and rethinking life insurance. Tyler Danielson is the Chief Technology Officer at FOXO Technologies with a history of working in the financial services and commerce industries. Nichole Rigby is the Director of Data Science and Bioinformatics at FOXO Technologies. She’s a data scientist specializing in analyzing genetic and other biological data. FOXO Technologies is a company that’s bringing epigenetics together with AI to disrupt the insurance industry. It’s creating models to classify human health based on epigenetic data and using those models for the goal of underwriting life insurance. Since epigenetics is a new term for most of us, Dave asked Nichole to explain it. At the most basic level, epigenetics are the chemical modifications that exist along the outside of DNA strands and affect gene expression. They’re not your genes, which are coded into your DNA and don’t change. Epigenetics are chemicals that sit on the gene and are responsible for the expression of those genes. We’re still learning what controls and influences epigenetics. But we do know they change over a person’s lifetime. Some are naturally expressed, and others are affected by factors such as diet and exercise. David says he’s been hearing more and more about epigenetics lately. Is this new science, or are we just noticing it more? While it’s a new field of study, the public is getting familiar with the idea of individual genetic data thanks to the rise of consumer genetic testing services. An epigenetic test starts with collecting a saliva sample, so it’s less invasive than a traditional blood test. When your doctor runs a blood test, they’re usually looking for specific indicators which will tell, for example, if you’re a smoker. The advantage of epigenetic testing is that it returns a much wider range of information. For an insurance company, the results could be useful for determining if it should insure someone and for how much. "Human made epigenetics risk classification is a challenge." This is a new approach to risk classification. Because the insurance industry already has a long-standing history of using existing actuary tables, the acceptance of epigenetic testing as a tool for risk classification is proving to be one of the biggest challenges for FOXO. But the use of AI has helped them uncover new insights. With just a single sample, FOXO can quantify more than 850,000 sites along the genome overnight. "I'm here to do really cool science with the potential … to help humans and make the world a better place." FOXO is unique in using epigenetics as a wellness report. It’s the first company to combine epigenetics, wellness, and insurance. Dave wants to know how we get to a more intelligent tomorrow with epigenetics. Nichole’s answer is with lots and lots of data. Tyler wants insurance companies to take a step back and reconsider how to help its customers live longer. In this episode, learn more about how FOXO Technologies is seeking to disrupt the insurance industry including: Learn about epigenetics and how it differs from DNA Discover how using epigenetics can help you live longer Hear the reasons epigenetics is changing the insurance industry

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May 6 · 52m Health Data is Medicine - CEO & Co-Founder of Seqster, Ardy Arianpour In a world where companies like Meta, Google, and Apple collect and benefit from vast amounts of data about you, what would it be like if you were in control of your data instead? Specifically, what would it be like if you were in control of your health data? And what if you had it all in one easy to access place? CEO and Co-Founder Ardy Arianpour came on the podcast to tell Dave Anderson how and why Seqster is giving people that kind of control over their own health data. Adry says patient-centric data interoperability is healthcare’s biggest challenge and it’s his number one mission. Seqster is a technology company working to break down the silos within the world of healthcare and make health data interoperability easy and universal. Making Health Data Interoperable Data is the gold of the twenty-first century. But interoperability of data is the moonshot. It’s not enough to collect the data, it also needs to be accessible and usable, and it turns out interoperability is hard to do. Ardy says that Seqster is the first company to make the idea work. Dave asks why interoperability is so hard to do. It’s because lab data is different from wearable data, which is different from data from your doctor, which is different from data from your dentist, and so on. All your data needs to be extracted and keyed in such a way it can be cross referenced. But putting it all together isn't enough. You must also think about the patient’s experience. “How do you connect the dots quickly and how do you visualize this data?" The data needs to be easy for both patients and providers to access and read. "What are they going to do with my data?” You're already giving your doctor and other providers access to your data. But you’re not really in control of it from that point on. This year, the U.S. Centers for Medicare & Medicaid Services interoperability rules stated that every single patient must have access to their healthcare data. Seqster wants to put the patient in control. Dave follows up by asking what is being done with all this data? Who is using it and how? It’s mostly clinical and decentralized trials, Ardy says. By using these data sets, both the cost and the time required to complete trials and develop new pharmaceuticals and therapies are vastly reduced. Imagine what happens when you can collect a million patients’ data in an hour versus 18 months and then look at them all in one place? Ardy says we get more accurate information which results in better and faster development of new medicines and vaccines. Breaking down the silos that contain health data allows for a bigger picture of health for all of us. With enough data, from enough patients, and with the right funding, Ardy thinks cancer is a problem we can solve. Dave wonders if health data could be like being an organ donor. Could we mark a box on a form and agree to donate our health data to science after our deaths? Listen to this episode to learn more about: The problems around siloed health data and his solution How to address privacy concerns around healthcare data Ardy’s personal journey that brought him to this field Why Big Pharma isn't evil Bill Gates's advice to Ardy on getting Seqster to scale The decade of biological revolution The business model to make this work

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Apr 28 · 24m The New Economy Will Require Empathy - Brian Solis More Intelligent Tomorrow host Dave Anderson sits down with Brain Solis to talk about interesting ways to get creative solutions to our long-standing challenges. Dave Anderson is a keynote speaker, tech evangelist, and podcast host with a refreshing perspective on marketing, analytics, and technology. Brian Solis is a digital analyst, speaker, and author. Brian currently serves as the Global Innovation Evangelist at Salesforce. His work focuses on thought leadership and research into studying disruptive technologies. They begin their conversation talking about what disruption is and what it isn’t. At the heart of it, disruption isn't about technology. It's about changing the norm. It’s about shaking people out of their comfortable ways of thinking to find new and better ways to approach problems. Doing the same thing over and over can saturate a market. Eventually it calls out for something new. Just like a wildfire can renew a forest, disruption becomes a means for reinvigorating a market. But companies today are still using legacy systems designed for scale and efficiency to protect themselves from disruption in the future. They're still the wrong tools for the job. “76% of all employees say that they don't feel that they have the digital skills necessary to work in this new economy.” We don't have all the answers. We can only scenario plan so much. That's the nature of disruption. If we’re going to fix this skill gap and be ready for change to come, everyone needs to feel involved. Everyone needs to feel relevant. Agreeing to change requires empathy toward everyone involved. Empathy is just as important as intelligence. By being empathetic to what the workforce needs to succeed in the future, we can build these skills into our education system. We can bring up a generation that’s prepared to take on new disruptions and thrive. The best way to accelerate creativity and innovation within organizations is to be empathetic and give people the room they need to follow ideas that are outside their normal work. Companies like Google with their 20% program not only understand this, but they practice it. Empathy is understanding how someone else sees the world. A lack of empathy is one of the reasons we don’t respond well to change. Without empathy, we lack a sense of urgency to change that doesn't immediately impact us. The gift of empathy is a powerful tool. An anecdote from Brian about the TV show, Ted Lasso, brings him around to talking about how company management needs to develop more empathy. A problem with current leadership is that they don't stop to ask questions. They aren't curious. They don't put themselves in their customer or employee's positions. They need to learn to ask “why” over and over. Kids do it naturally, and we should embrace it as a path toward continuous evolution. Dave closed out the show by asking Brian how he thinks we get more intelligent. “Try not to be better than anyone else, try to be better than you were yesterday.” This episode includes discussion of: Embracing disruption as an agent of positive change Asking if we're ready for the changes ahead of us or planning for the a future using tools of the past. Leveraging empathy to find creative solutions to our problems The role of empathy in our thinking about education Asking, “why” over and over as a tool to achieving continuous evolution

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Apr 21 · 58m Anticipating the Singularity - Daniel Hulme Singularities have been explored in science fiction; authors such as William Gibson and Phillip K. Dick have speculated what life could be like in a world beyond them, and video games such as Cyberpunk 2077 have invited us to experience them firsthand. Singularities may have once been a philosophical flight of fancy, but today they are an important topic for consideration by scientists, politicians, and other thought leaders. In this episode of More Intelligent Tomorrow, artificial intelligence expert Daniel Hulme discusses singularities and their importance with host Ben Taylor. “I think we all have an innate desire to try to make the world better for the next generation.” Hulme starts the episode sharing the realization that he may only have another 500 months to live. That is not a lot of time in the grand scheme of things. It caused him to wonder what a person should do with the time they have left. He believes the meaning of life is to maximize good. Therefore, an ideal way to spend the time we each have left is to work towards an economic singularity, where humanity is free from the burden of having to meet our basic needs. This freedom would enable us to make the world a better place and enrich all of humanity. Together, Hulme and Taylor speculate on what might come out of an AI singularity. What would happen if we created an AI that was able to iterate on itself? Could it solve humanity's greatest puzzles or make Nobel Prize winning discoveries in a matter of moments? Or would it decide humanity’s existence stands in the way of the universe reaching a state of maximized good? “Once you birth a silicon god, it will be difficult to really understand how it operates.” Both agree however, that the risks of creating a digital consciousness are outweighed by the possibility of an AI being able to solve our long-standing problems as a species. Fields like medicine, mathematics, and farming could all benefit from an AI singularity and help to bring about an economic singularity. Taylor asks Hulme about the questions of ethics surrounding a sentient AI. How do we define what is right and wrong or good and evil and would an AI have the same views? Hulme shares his self-described controversial views on AI ethics, which leads to talking about how AI is used to track our digital existence and what that means for our privacy. He proposes the idea of an open-source social network aimed at improving humanity as an alternative to the way social media users are currently bought and sold as products to advertisers. A decentralized social network would allow people to connect for humanity’s betterment instead of maximizing profits for shareholders. Finally, they wrap up the episode by talking about the ways AI can be employed to personalize your educational experience. Imagine being taught lessons with methods that were individually tailored to your interests and presented in ways optimal to your modes of learning. Prepare for a fascinating discussion of singularities which include: What is a singularity and how many of them are there? The ramifications from achieving singularities. Will an AI singularity be a glorious event or humanity's biggest existential threat? Metaverses and how we might interact within them in the future. The ethics of creating an AI singularity. What it means to be human and how singularities might change this definition. Using social media to improve humanity’s experience. How AI can customize our educational experience.

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Apr 13 · 44m From the Menial and Mundane to the Meaningful and Humane - Anders Sörman-Nilsson Host Dave Anderson recently sat down with Anders Sörman-Nilsson, a Swedish-Australian Global Futurist, and made a striking observation about the very table where they were having their chat—there was no spittoon. This may not seem surprising in 2022, but prior to the Spanish flu pandemic in 1918, most restaurants and bars would have featured a spittoon next to the bar. “So things do change. I do think we're going to see that same change to the physical infrastructure, but also who's going to be living in cities, who's going to be investing in the central business district, et cetera.” Anders—who states that futurists are a bit like “reverse historians”—uses his love of history and deep knowledge of past and present technology to predict what the future and a more intelligent tomorrow might look like. Don’t Throw the Analog Baby Out with the Digital Bathwater A profound maxim lies at the heart of Anders’ philosophy: One need not necessarily throw out the analog baby—the physical baby—along with the digital bathwater. He believes that digital transformation can combine the best of both worlds and that the pandemic compressed 10 years worth of digital transformation into two—but that everything in the physical world needn’t go the way of the spittoon. Anders states that digital transformation, “doesn't mean that we're going to (fully) live our lives in the metaverse. It doesn't mean that we're going to disconnect from the physical world. (It) just means (that) now as the world is starting to open up that we're going to crave the best of the face-to-face or the best of the interface to interface.” Anders cites examples of his positive personal experiences of delivering in-person presentations compared with Zoom sync ups. “The physical world actually allows some of the perfect imperfections to come through a little bit better.” He also explains what it was like to take an online course through a traditional university, the University of Cambridge: “I think some of the learning experiences lift some things to be desired, but it just showed you (that) the way we consume now and keep reeducating ourselves as needed is evolving.” This experience is directly related to a core message he delivers when consulting various companies: “Content is really just chemistry wrapped in narrative—or content is chemistry wrapped in story.” Good Stewardship Protects the Environment and Attracts Investors The iPhone, meanwhile, represents the best of total digital transformation—particularly Apple’s massive investment into the circular economy through product stewardship and recycling. “They've got Liam and Daisy, the recycling robots. I think Daisy is built from parts of Liam, who was her predecessor. They recycle 200 iPhones every hour and split the components. And as a result, Apple's now one of the largest gold miners in the world.” Anders also observes that the only way to truly be sustainably profitable is to ensure that you’re not a climate risk so that investors will keep backing you. “Consumers will punish you as well. If your products and services are not seen as being sustainable and achieving environmental, social and governance (goals).” Listen to this episode of More Intelligent Tomorrow to learn about: Becoming a good storyteller in a digitally democratized world and giving people a platform to produce and publish new works. Understanding how technology is enabling the world to decouple from planetary constraints Finding ways to be sustainably profitable and to ensure that investors keep backing you Switching from fast thinking to slow thinking Pinpointing the overlapping concentric circles of doing something that you're really good at and also passionate about

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More Intelligent Tomorrow: a DataRobot Podcast Apr 7 · 34m Building Augmented Reality Into a Contact Lens - Mike Wiemer, Brian Lemoff, Mojo Vision In this episode of More Intelligent Tomorrow, Global AI Evangelist, Ari Kaplan, sits down with Brian Lemoff, Fellow and Head of Optics at Mojo Vision, and Mike Wiemer, CTO at Mojo Vision, to discuss augmented reality, the future of wearable technology and the technology behind building a contact lens with built in display. Brian and Mike work for Mojo Vison, a company hard at work developing the first smart contact lens. The technology behind Mojo Lens is staggering, with the lens containing roughly 14,000 pixels per inch, with pixels less than two microns apart, sensors that track where your eye is pointing, and a display less than half a millimeter wide. But, how do you actually build something so functional in such a small space? Brian walks us through what it was like to start working at Mojo Vision; “I think the first few months I was with the company, all we did was brainstorm. A day in the life of the early company was come in the morning, talk about how we are going to do this, come up with ideas, go off and test out theories. And, we'll do back of the envelope calculations, a little simulation, come back, here's what's not working. And over time, come up with an idea that you think, Hey, this could actually hold together. ” - Brian Lemoff It turns out that the secret of building something revolutionary is to make changes incrementally over time. When asked about the future of wearable augmented reality, Mike outlines his predictions, which include a belief that most people will have some form of augmented wearable in the future, but the experience must be “invisible” for it to be successful - which is why Mike calls this approach “invisible computing.” “Our view is to create information systems that are invisible, and we call it invisible computing. It’s about trying to get all that hardware out of the way and let you maintain your ability to be a social creature and interact with other humans, and yet still have access to that kind of information. That's the vision of where we're trying to go.” - Mike Wiemer Listen to this episode of More Intelligent Tomorrow to learn about: Augmented reality and how it can help people with disabilities The use cases for augmented reality in sport How to innovate in a constrained environment How to develop a user interface for an invisible computing experience

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Apr 1 · 48m Changing the DNA of How We Serve Customers - Harveer Singh In this episode of More Intelligent tomorrow, Global AI Evangelist, Ari Kaplan, sits down with Harveer Singh, Chief Data Architect and Head of Data Engineering and Architecture at Western Union to discuss the customer experience, blockchain and cryptocurrency, and how to digitally transform a 170 year old company. Harveer was born in India and has traveled the world through his career - first moving to Sydney for his masters degree, then to various cities in the United States and is now in Denver. Growing up in Delhi in the 80’s, Harveer had his life uprooted and his family was forced to leave their homes. “They were tough times. When people's lives are challenged by the politics that are played at a very high level, it's very hard to cope. I was fortunate that we didn't have any family harmed, but we lost our house. We lost everything. And we were forced to run in the streets to save our lives and for water, because we were from a minority community.” Experiencing this hardship led Singh to strive and protect his family, while also remaining thankful of what he has been able to achieve and who he has met throughout his life. “If we were not forced to move away, I would have probably not tried as hard in my life that I did because I needed to make the amends that I needed to, to make sure my family is safe.” Western Union is in the business of moving money across the globe, which can prove to be complicated. Handling money globally is also a data rich environment that Western Union has used to prevent fraud and human trafficking, all with the help of AI. Money is also an interesting space right now, with the advent and increasing popularity of Web3 and cryptocurrency. When asked about his opinion on cryptocurrency, Harveer stated that he doesn’t currently think that blockchain is stable enough for it to be a currency used by big banks and financial institutions, but there are some benefits to the innovations around cryptocurrency. “Our financial services have become more and more complex over a period of time. I think blockchain simplifies some of those. But the underlying education is extremely important right now.” Western Union is a 170 year old company, and while the age of a company shows stability, it also can prove to be a difficult roadblock to overcome when modernizing and building a digital product. Harveer and team were able to move Western Union’s operations to the cloud, overcoming many obstacles and building global solutions along the way. His biggest piece of advice is to lean into the existing culture of the company when making changes. “So some of the things that you have to think about when you deal with a company that is that old, is what is the culture of the organization? If you approach it that I'm going to change it, then you know, that is a disaster to start with. … If you approach it from that particular angle, I'm going to embrace it, but then I’m going to bring about change.” Listen to this episode of More Intelligent Tomorrow to learn about: The challenges of innovating in a 170 year old company, and how to approach digital transformation on a global stage. Blockchain, cryptocurrency and the future of currency online. Building customer loyalty programs that encourage stickiness and make customers come back. What will shape the future, and what we may fear from advanced technology and machine intelligence.

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Mar 24 · 50m Can Science Fiction Save Humanity? - David Brin Soylent Green, the movie based on Harry Harrison's novel, Make Room! Make Room!, interpreted what a future of pollution, poverty, overpopulation, and depleted resources could mean for humanity and ended up recruiting millions of people to environmentalism. Nineteen Eighty-Four was a cautionary tale about the consequences of totalitarianism that alerted hundreds of millions of people around the world to fear “big brother”. Movies like Dr. Strangelove, War Games, and The Day After portended the different ways that nuclear war might happen accidentally. Each of these works of science fiction became relevant at a time when the dystopian outcomes they illustrated seemed imminently possible. David Brin is a highly influential scientist and renowned science fiction author who speaks, advises, and writes about artificial intelligence and human augmentation. In this episode of More Intelligent Tomorrow, Ari Kaplan and David Brin delve into the role of science fiction in our society, and the role Hollywood plays in science fiction. They explore underlying themes that shape the way we look at everything, from the safety of our institutions, to trust in our neighbors, to foresights that help us avoid catastrophe. “Our job as science fiction authors is not to predict the future. It is to prevent it.” David also serves on the advisory council of NASA's Innovative and Advanced Concepts (NIAC) program, and advises government agencies and corporations on topics from national defense to astronomy and space exploration, nanotechnology, philanthropy, and predicting the future. Ari and David direct their conversation toward the latest advances in space exploration technologies like the Webb telescope, along with upcoming missions that could provide unprecedented insight into our universe, such as the hycean class of exoplanets and the possibility of Martian moon exploration. They also talk about the work that is happening today in the Search for Extraterrestrial Intelligence (SETI), and possible reasons why we don't yet see signs, such as the very complexity of a sapient species. “There may be lots of life out there. There may even be lots of complex life, but the leap to our type of intelligence may be very difficult.” Don’t miss this fascinating and provocative episode of More Intelligent Tomorrow, densely packed with insights on space exploration, artificial intelligence, human augmentation, including: The secondary mirror and wonders of the cosmos The Project Artemis and the great age of lunar tourism The eight phase of the American civil war Polemical Judo and the war on science GPT-3 and the race toward advanced intelligence

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Mar 18 · Sharing the Mic in Cybersecurity - Lauren Zabierek In this episode of More Intelligent tomorrow, Global AI Evangelist, Ari Kaplan, sits down with Lauren Zabierek, Executive Director of the Cyber Project at Harvard Kennedy School's Belfer Center, and Sean Plankey, Director of Cyber Missions for DataRobot, to discuss the cybersecurity landscape, broadening the field of cyber security, and how to build a cybersecurity career. Both Lauren and Sean started their careers in the US government and military before pivoting to cybersecurity, with Lauren beginning her career in the United States Air Force, and Sean in the Coast Guard. Cybersecurity has been a big topic lately, with incidents of cybersecurity increasing as much as 50% over the last two years. While cyber attacks have been in the news, the full story of the impact that they can have can be monumental. “Every organization is at risk and our essential services and public safety could be impacted. And I think this is really evident in the different ransomware attacks, not only against critical infrastructure, but against our state governments.” - Lauren Zabierek Cybersecurity aslo has the complicating factor that it is one of the most data-rich environments, making it hard to find the signal in the noise. This also makes it a particularly ripe industry for the application of AI and machine learning. “How do we move to stopping a cyber incident before it occurs? That, to me, means getting after the unknown unknowns. … And I think AIML is the only way you can get there in a rapid manner.” - Sean Plankey But before we start to panic about the state of cybersecurity, privacy and safety, it’s important to know that there are small changes we can make to keep ourselves more protected. Adding two factor authentication to our accounts can prevent a significant number of successful attacks, as well as being more aware of where you’re vulnerable online. On top of improving personal cybersecurity, Lauren and Sean discuss how to broaden the field of cybersecurity itself. Lauren is the co-founder of the online social media movement called #ShareTheMicInCyber, which aims to dismantle racism in cybersecurity and privacy. She explains how the industry can become more inclusive and why it’s important to pull in cybersecurity professionals from a variety of backgrounds. Listen to this episode of More Intelligent Tomorrow to learn about: When we are most vulnerable to cybersecurity incidents and attacks How to make cybersecurity a more inclusive industry The cybersecurity workforce gap, and what can be done to solve it Applications for AI and Machine learning in the most data-rich cybersecurity environments Misconceptions about the background needed to work in cybersecurity, and how to get a job in cybersecurity

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Mar 11 · 49m Why “Know Thy Data” Is a Rallying Cry in Retail - Danielle Crop Danielle Crop is Chief Data Officer (CDO) at Albertsons Companies, a recent transition from her nine-year term as CDO for American Express. Throughout her career, she has orchestrated big data projects that have exponentially increased customer conversion rates and helped millions of customers make smarter purchasing decisions. In this episode of More Intelligent Tomorrow, Ben Taylor talks to Danielle about the role of creative design thinking in data, how ethics can help us avoid unconscious bias, and how data science mitigates retail shrinkage. Danielle shares astute observations about what data scientists and business leaders need to learn from one another, why “Know Thy Data” remains the first law of statistical inference, and why you can never replace human relationships in data science. Understanding Business Problems 85% of AI projects fail today, arguably due to a lack of understanding how an AI project should be driving toward business value. Yet often data science teams are far-removed from business teams. Danielle works to enable her data scientist teams to fully understand how their company makes money and what they can do to help their company make money. “If you don't understand the problem, you can't solve it. You have to enable that understanding for data scientists. It's about creating an opportunity to learn about the business. I make sure that my teams get into the stores and actually work there. ” Retail is very complex, both in the way that it makes money and in the way that it needs to be managed. There are a lot of people involved and there is a lot of room for error. Knowing how data is created and used, where it comes from, and how it can be used to optimize value can make or break the success of any AI project. “Know Thy Data is the first law of statistical inference. You have to know where your data comes from.” Shrink occurs throughout the retail supply chain and illustrates this complexity. Perishables expire, purchases are returned, or items arrive damaged from a vendor: this “entropy of retail” is extremely difficult to forecast and control. Having an intimate understanding of data sources and data gathering techniques has been shown to help control this endemic problem. In the end, retail is about people. Data science can be used in retail to help customers make informed, contextual choices at the point of a purchasing decision. For example, there is a lot of data available on the link between what you eat and how long you live. Making that data actionable could change peoples’ lives. Listen to this episode of More Intelligent Tomorrow to learn: How business value thinking can improve AI project outcomes The important distinction between the discovery aspect and the modeling aspect of data How ethics can tackle the problem of unconscious bias in data science How data science is used to mitigate the “entropy of retail” How AI can help people make better buying decisions

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Mar 2 · 1h 4m Bringing History and Foresight to Ethical AI - Meg Mitchell In this episode of More Intelligent Tomorrow, Meg talks to Michael Gilday about the opportunities in machine learning to create a more diverse, equitable, and inclusive future. Dr. Margaret Mitchell (Meg) is a researcher in ethics-informed AI with a focus on natural language generation, computer vision, and other augmentative and assistive technologies. Foresight is an indispensable tool for shaping and evaluating AI project outcomes. Instead of focusing on creating technology to improve something that already exists, a longer-term focus –one that is two, five, or ten years into the future–can help us understand what we should be working on today. It’s a fairly straightforward way of thinking, yet foresight is often brushed aside as incalculable. Foresight also can present a liability issue. If you’re working on a technology that will be less discriminatory, for example, that means your technology right now is discriminatory. Fear of impending regulation and of misinterpretations that hamper development can cause a troubling lack of imagination within development teams. Bringing in people who have a creative mindset or a different perspective can help technical teams see things in a more imaginative way. Science fiction writers, for example, are adept at bringing foresight into a project and help teams see how things might evolve over time. That, in turn, could help us be smarter about the kinds of development we do. But bringing people who are adept at foresight into a project, such as science fiction writers, creates an opportunity to think through how things might evolve over time. That, in turn, could help us be smarter about the kinds of development we do. Similarly, historians can shed light on patterns of development over time. Instead of focusing on how rapidly technology is changing, they can offer a reflection on corresponding power dynamics and sociological changes that can also inform how we develop a technology. A collaboration of humanities-oriented thinkers and science-oriented thinkers can help us think through the storyline of what a technology should be. There’s a need to focus not only on how well the model or system works in isolation but also how well it works in context. “Understanding how people use a technology–and therefore understanding people– is not something computer scientists are always good at. It requires different skill sets, which makes collaboration with subject matter experts critical.” To really understand what it means to have AI in our social contexts, we need social scientists, anthropologists, and historians. So, how do we bring a diversity of voices and experiences into these technological challenges and conversations? “Now is a great time to focus our attention on the science of diversity and inclusion. We’re on a global scale we haven’t been able to see before. We have infinitely better access to different cultures and perspectives on differences and similarities like we’ve never had before.” Listen to this episode of More Intelligent Tomorrow to learn about: The culture of ethical behavior and the bottom-up-top-down approach with regulators and corporations How no-code solutions are removing barriers in AI and machine learning work Malicious actors vs. irresponsible ones and why ignorance is the biggest problem we face Gender bias and progress bringing more women into in tech and STEM How transparency can be prioritized over the obfuscation that is prevalent right now

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More Intelligent Tomorrow: a DataRobot Podcast Feb 22 · 1h 2m How AI Evolves with National Intelligence - Robert Cardillo Robert Cardillo, former director of the National Geospatial Intelligence Agency joins Ben Taylor for this episode of the More Intelligent Tomorrow podcast. Robert Cardillo has delivered dozens of intelligence assessments that informed vitally important decisions on US national security. In his 35 years serving the intelligence community, he advised President George H.W. Bush during Panama’s Noriega regime, President Obama during the search for Osama bin Laden, and to understand North Korea’s nuclear intentions. As such, he has spent a lot of time in the Oval Office and the Pentagon contextualizing complicated, data-driven intelligence assessments to very important people. Conveying the data effectively, safely, and confidently often meant reporting on wide-ranging opinions among peers about what conclusions should be drawn. In 2010, he was faced with a challenge of how to present critical data to President Obama on North Korea, about which there was wide-ranging dissent among his peers in the intelligence community. He needed to decide whether to present the data including his peers’ diverse assessments of what it meant, or to wait until his peers could come to a consensus about its meaning. He decided not to wait. He included each different interpretation in his report. In the end, “President Obama told me, ‘I get really nervous when you all agree.’ So being willing to take on dissent is really healthy for the community.” Today in his work as Distinguished Fellow at Georgetown University’s Center for Security and Emerging Technology continues to focus on data-driven information sharing and collaboration between those who collect intelligence and those who analyze it. Listen in to hear Robert’s fascinating accounts of his 35-year service in the US Intelligence Community and its technological advances throughout his tenure. Topics include: Is there an inherent flaw in the term “Artificial Intelligence”? What’s the most effective way to scale? How can you make a thing that worked for 10 work for 10,000? Can enforced authentication systems like Blockchain protect us from synthetic data, data manipulation, and deep fakes? If a spaceship crashed on earth, would satellite sensing capabilities we have today detect it? Or Is there too much data to review?

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Feb 15 · 32m Making Data Science Bilingual - Najat Khan Featuring: Dr Najat Khan, Chief Data Science Officer, Janssen Global sits down with Ari Kaplan, AI Evangelist, DataRobot, for this episode of the More Intelligent Tomorrow podcast. Najat Khan thrives in her mission to solve the world's most challenging health problems. With data science, AI, and machine learning, she endeavors to bring critical medicines to the public with efficiency, effectiveness, and inclusivity. When COVID-19 hit, working for Janssen Global and with Johnson and Johnson, Khan set about constructing a team of more than 100 “bilingual” data scientists. Not only did they understand the language of data science, they also brought medical expertise like a PhD in neuroscience or a background in oncology. “Every single thing we do needs to be purpose driven. You can’t start with ‘I saw this really cool algorithm that was published. We should do something with it’. If you go after the shiny objects, it doesn't work. You have to continually ask, ‘What’s the business problem we’re trying to solve?’ To me that is the critical foundation.” With the business problem fully articulated, she embedded her team into every product, regulatory, clinical, and operational group tackling COVID-19. They worked “shoulder to shoulder” through every critical decision point along the vaccine building workflow. To accelerate the process of identifying global hotspots and potential risks for vaccine trials, they partnered with MIT to build what would become one of the world’s largest machine learning programs. The resulting longitudinal dataset included socioeconomic, racial, and health data, with higher-than-census diversity and inclusiveness factors. “The hardest part was not just the technical aspect, but also the cultural change management. When a company has never used one of the biggest machine learning programs, and all of humanity is watching to see if it is successful, it takes a lot of guts, will, and fortitude to see it all the way through to measurable impact.” Listen to this episode of More Intelligent Tomorrow, to learn: How data science can help us to be better prepared for the next global health crisis Where the life sciences research and industry is headed in the next 5, 10, and 50 years How data science, AI, and machine learning can change health care “We can never say what we're doing in data science today is enough. We need to constantly push boundaries.”

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Feb 10 · 54m From Retrospective to Prospective Analytics - Ikechi Okoronkwo Ari Kaplan sits down with Ikechi Okoronkwo of Mindshare for this episode to discuss using predictive analytics to make key future decisions. Mindshare is a global media agency dedicated to forging competitive marketing advantage for global clients like Unilever, General Mills, and Ford. Ikechi Okoronkwo heads Mindshare’s Business Intelligence & Analytics team, which is lauded in the industry for their fresh approach to analytics. His mission is to simplify and refine decision-making processes using data and analytics to drive good growth: growth that is ethical, unbiased, and beneficial. His team builds tools that shift the focus of analytics from retrospective to prospective–from looking backward to looking forward. “People think of analytics as reporting or dashboards charting progress against a target ROI. But the best types of insights are those that influence an imminent decision, whether that's a budgeting decision, an optimization problem, or any question about how to move forward.” Their analytics do, of course, have a quantitative aspect: using the scientific method to test and validate assumptions is critical. But Ikechi challenges his team to continually find ways to drive a balance between data and the creativity that empowers clients to push their boldest ideas. “Our job is to provide our clients simple frameworks to help them make smarter data-driven decisions in everything from creative to content to strategy.” They begin with qualitative consultative conversations, from which they build an outcomes framework and define successful key performance indicators (KPIs). Only at that point do they start to talk about the data that will be needed to serve as indicators of performance against their articulated outcomes. They then apply machine learning technologies to collect relevant data, leveraging proprietary methodologies to determine which types of data should be fused into each aspect of a client’s outcomes framework. While the industry has been focused on collecting as much data as possible on people's behaviors–what they're clicking on, which sites they're visiting–Ikechi’s team invests in AI and machine learning to understand what’s behind those rational signals. For example, neurological studies in a neurolab might validate the self-reporting in surveys. They might observe participants and their subconscious reactions to a specific format or moments of higher receptivity when ads are viewed. At the same time, data privacy and ethics are paramount to their solutions. They firmly believe clients should own their own data, and they’ve led the industry in their work to recognize bias in data. Their ethics tools enable continuous monitoring of how data is being collected, from where, and how it is used. In the end, this helps clients make objective decisions. “It's not a question of how much more data we can collect to be better at our job. It's really more about what outcomes our clients want to drive and how we can empower them as business partners.” Learn more about Ikechi’s innovative approach to analytics in this episode of More Intelligent Tomorrow. We’ll cover: How to define exactly what data is needed to make critical decisions and how best to protect it How to identify bias and know when you’re collecting the wrong data How to use data to test feedback in the creative process

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Feb 1 · 58m How the United States Will Shape the Future of AI - Robert Work

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Jan 25 · 56m Reshaping Reality Through Citizenship - Baratunde Thurston

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