The Boundless Podcast

💬 “The tone at the investment conferences over the last two years has changed from everybody being super excited to get access to new sources of data, to now, much more focused and critical questions. There’s more demand for demonstrations, people want proof there is value in the data before they purchase it.”

This is a conversation with Stefan Jansen

🎙️ Stefan is the founder and CEO of Applied AI. He advises Fortune 500 companies, investment firms, and startups across industries on data & AI strategy, building data science teams, and developing end-to-end machine learning solutions for a broad range of business problems.
Before his current venture, he was a partner and managing director at an international investment firm, where he built the predictive analytics and investment research practice. He was also a senior executive at a global fintech company with operations in 15 markets, advised Central Banks in emerging markets, and consulted for the World Bank.

He holds Master's degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin, and a CFA Charter. He has worked in six languages across Europe, Asia, and the Americas and taught data science at Datacamp and General Assembly.

My co-host for this episode is Michael Berns, Michael is a Director at PwC where he leads the AI and FinTech Practice. He is an AI Thought Leader & FinTech Veteran, with 17 years of international experience on five continents.

🎧 In this episode, Stefan talked about the 2nd edition of his book Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python.

There’s a significant amount of financial data to incorporate into machine learning models, and Stefan explained how to navigate this so that you can take action and makes decisions in the trading market. Stefan gives a retrospective view of what has changed with machine learning, freely available data, paywalled data and algorithmic trading over the last two decades.
The new edition of the book looks at modelling historical share prices developments over time to try out prediction models for future changes. Stefan told me that, beyond the trading models, finance generally attracts intellectually curious people, because it's sophisticated, there's math involved and when you add more varied datasets and computer science and machine learning to it, it takes this field of study to a whole new level.

For example he mentions new data sources such as satellite images that track how busy shopping malls are and the implications of this on retail and real estate share prices. This allows more creativity into this work too, he says. Innovation, technology and books like this from Stefan are enabling more democratic access to and use of information and this is enabling smaller investment shops to be more quantitatively driven. With his book, Stefan is not advocating for automated funds, but to help portfolio managers to be more data driven, systematic and create more sophisticated strategies.

Finally Stefan talks about the trends in data and how we’ve moved from a data land grab, to more sophisticated questions being asked and more proof of value being sought.

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💬 “Companies can deploy APIs to analyse digital meetings and use machine learning on the language, intonation, facial expressions, and gestures to build up a picture of all of the conversations in their enterprise. This has incredible possibilities and of course incredible dangers in terms of security and privacy.”

🎙️ My Friday Co-Host is @Dr Naeema Pasha. She works for Henley Business School partly focussing on future strategy, vision and operations of business education. Everything from people engagement to tech adoption.

🎧 In this episode Naeema and I discussed our reactions to this week’s episodes with @Max Knupfer and @Mark Sears. Our topics included:

💡 1.) the pros and cons of conversational AI
💡 2.) the developments in different types of User Interfaces
💡 3.) understanding your technology quotient
💡 4.) CloudFactory and their unique operating model
💡 5.) how to reduce blind spots in corporate innovation

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💬 “When we talk about humanity, we've learned a lot at CloudFactory. We’ve learned that we’re all poor and that we're all rich in different ways. And when we can pull together a huge number of diverse people who are passionate, to grow together, you can see amazing things happen.”

This is a conversation with Mark Sears

🎙️ Mark is CEO and Founder of CloudFactory. He’s a computer scientist who is driven daily by his passion for technology, business, and people. CloudFactory helps companies to automate and outsource routine back-office data work. They aim to connect 1 million people to online work, while raising them up as leaders to address poverty in their own communities. Their workforce is recruited from talent “hot spots” around the globe such as Nepal and Kenya where they say they can hire the best and brightest in areas where there are thousands of talented people, but limited opportunities for meaningful and sustainable employment.

🎧 In this episode, Mark tells the CloudFactory story that started in a café in Nepal with his wife and led to a thriving company with over 5,000 employees. I actually learned about CloudFactory many months ago and wanted to bring Mark onto the show to explore the company’s history and its ethos. I was fascinated by the way that CloudFactory is leaning towards social good, and I wanted to explore what this meant for their culture and especially their ability to think, act and innovate differently to their peers. Does a company that helps people to earn, learn and serve in developing countries have better foresight, broader perspectives, and less blind spots? Mark was very gracious with us, to allow vulnerability and honestly into this conversation. What he has done is a superb achievement, literally incredible, but in this conversation, he was humble about what they are doing and the challenges a company like this has to find ways to completely support their employees.

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💬 “Middleware is going to be the greatest growth area for technology in the next few years. It is where AI is going to deliver its greatest benefits because it will be the layer that allows us to stitch together all of the disparate technologies that we interact with and allow us to put them together in a way that will make our lives easier, better, simpler and more fun.”

This is a conversation with Max Knupfer

🎙️ Max is Chief Strategy Officer at Volume.ai. Helping business apply technology imaginatively to problems that matter. Volume is a company that specialises in the development of Conversational AI platforms and the AI middle-ware that helps businesses self-manage and scale their chatbot real estate.

🎧 In this episode, Max talks about the opportunities for efficiency and productivity in our lives and our work by exploring middleware – middleware is software that acts as a bridge between an operating system or database and applications. He says that this is the layer that allows us to stitch together all of the disparate technologies that we interact with and allow us to put them together in a way that will make our lives easier, better, simpler, more fun. We spoke about how to adapt and integrate new technologies into our lives and the importance of outsourcing everyday tasks to machines so that we can effectively change with the times. Max also spoke about how the ways we are interacting with technologies is changing including how we may move beyond the keyboard into further into voice input.

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💬 “Sometimes in data and in AI and data scientists, a lot of the job descriptions are quite gender-coded. So they'll say we're looking for Ninja. We're looking for a rock star, we're looking for a quarterback. And these descriptions can cloud what the job is, and it discourages women from applying to certain posts.” Naeema Pasha

🎙️ My Friday Co-Host is @Dr Naeema Pasha. She works for Henley Business School partly focussing on future strategy, vision and operations of business education. Everything from people engagement to tech adoption.

🎧 In this episode, Naeema and I discussed our reactions to this week’s episodes with @Violet Snell and @Kim Nilsson. Our topics included:

💡 1.) whether we need more engineering than science to solve global challenges
💡 2.) computer vision opportunities
💡 3.) diversity and bias in AI teams
💡 4.) AI hype
💡 5.) AI ethics

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💬 “There’s great curiosity in the data science community to learn what to do when we feel morally compromised. To help with this, there has been calls for accreditation. I think that’s a good idea, although I’m not sure who would come up with it or agree on it, but then at least ethics would be part of such an accreditation.”

This is a conversation with Dr. Kim Nilsson

🎙️ Kim is an Astrophysics PhD, MBA and Entrepreneur. She is the CEO of Pivigo; supporting organisations to innovate with data by connecting them with a global community of the best and brightest freelancing data scientists. She is passionate about people, data and connecting the two.

🎧 In this episode, Kim described the real-world daily work of a data scientist. How they have to spend their time at present, mostly trying to fix data problems, but also what might change in the future. I asked her what makes a successful data scientist and how we might introduce more ethical thinking into the profession. Talking about ethics led onto a fascinating conversation about Cambridge Analytica and the recent A levels scandal here in the UK. I asked Kim to tell us in her own words want went wrong on both of these occasions. I asked her whether companies engage her businesses services to try to reduce their blind spots, biases and stereotypes and whether she thought open-innovation programmes such as crowdsourcing data scientists to solve problems was worth exploring for corporates. Finally, I asked Kim what inspiring message she had for new and aspiring data scientists.

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💬 “There are some AI reasoning systems and there are some data-driven learning systems and we haven't quite cracked hybridizing them. But there are some interesting developments, in some very technical areas, which combined elements of reasoning with data-driven learning. So I think we'll get there, it is still very early days.” @Violet Snell

This is a conversation with Dr Violet Snell

🎙️ Violet has a doctorate in computer vision and machine learning and a background in video processing and software development. She is currently leading a team of researchers producing deep learning solutions for traffic safety applications.

🎧 In this episode, Violet talks about her work as Head of AI at Jenoptik Traffic Solutions UK, I asked how good the current crop of road safety cameras are, even when faced with partially hidden number plates and we spoke a little about the development of this particular type of computer vision technology.

The conversation moved to facial recognition, can cameras see through window screens to identify the driver and whether satellite-based camera systems will replace the local camera networks that we currently use.

Another specialized area for Violet is diversity in AI teams, I asked her to explain why this is so important. She said it’s actually critical for successful AI projects to scale out into the real world. She identified one particular challenge that COVID-19 has caused to distributed teams of data scientists and I asked her whether companies could benefit from hiring more psychologists and ethicists to work in or with AI teams.

We moved onto Violet’s core subject which is engineering – she has a master’s degree in Engineering and Computing Science from the University of Oxford. We talked about the engineering challenges associated with AI such as when is good enough, good enough, trade offs, and where do you draw the line. I asked Violet to explain how humans and machines learn, the differences and the similarities and the possible future of logical machines. I think you’ll enjoy this brief exchange between Violet and I about whether and why we should be trying to build human level AI.

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💬 “We all saw Jack Ma and Elon Musk sitting on the stage together. Next time will we see a European person there as well? But what story are they going to tell? China has their AI story, the US has theirs and maybe it seems like they are building walls with AI. Rather than Europe building a wall, can it build a table that more people can sit at?” Richard Foster-Fletcher

🎙️ My Friday Co-Host is @Dr Naeema Pasha. She works for Henley Business School partly focussing on future strategy, vision and operations of business education. Everything from people engagement to tech adoption.

🎧 In this episode Naeema and I discussed our reactions to this week’s episodes with @Michael Kanaan, @Frits Bussemaker and @Ryan Moore. Our topics included:

💡 1.) the ongoing challenges experienced by distributed teams
💡 2.) breaking down siloes to deliver successful AI projects
💡 3.) AI geo-politics and the place for Europe
💡 4.) Rethinking AI and China
💡 5.) AI and its impacts on human liberties

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💬 “When I first started off with data science projects, I had this big poster up that said ‘if it makes money, it makes sense’. And every project, we were measuring it against that. When I look back, I realize that was a mistake and that ethos prevented us from seeing anything other than ROI. When we present the business case now, there's around seven business benefit areas included, because financial KPIs alone don’t cut it anymore.”

This is a conversation with Frits Bussemaker and Ryan Moore

Frits is the Chair of the Institute for Accountability in the Digital Age and Ryan Moore is Head of Data and Analytics at Aiimi.

🎧 In this episode, Frits and Ryan discuss their experiences with talking about AI in the Enterprise. The challenges they have witnessed around different language, context and nuances that can derail a successful project. We talk about the lack of data literacy in the boardroom, how to think about an AI strategy and how to avoid hype. The conversation moved on to accountability and specifically accountability by design. Both Ryan and Frits talked about the need for self-governance by organisations on their AI projects. Ryan talked openly about how we used to think in a very binary fashion about projects and their return on investment potential and why he no longer feels like this. How he has changed his mind about what matters. I finished the episode by asking these experts what is the biggest misconception about AI in the Enterprise. I hope you enjoy hearing their answers.

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💬 “Our focus now together as a society is on having a holistic and collective conversation to ensure that AI is only implemented in ways consistent with fundamental human dignity and only for purposes consistent with each of our personal democratic ideals and liberties. The call to action is I hope you'll be a part of that conversation because I don't want future societies or future groups to say. I wish our eyes had been more open on that topic when it mattered.”

This is a conversation with Michael Kanaan

🎙️ Michael is the co-chair of Artificial Intelligence for the US Air Force at the Pentagon. In that role, he guides the research, development, and implementation strategies for AI technology and machine learning activities across Air Force global operations. He is currently the Director of Operations for Air Force / MIT Artificial Intelligence. In recognition of his fast-rising career, he was named to the 2019 Forbes “30 Under 30” list and has received numerous other awards and prestigious honours—including the Air Force’s 2018 General Larry O. Spencer Award for Innovation as well as the US Government’s Arthur S. Flemming Award (an honour shared by past recipients Neil Armstrong, Robert Gates, and Elizabeth Dole).

🎧 Michael Kanaan is the author of T-Minus AI: Humanity’s Countdown to Artificial Intelligence and the New Pursuit of Global Power. In this episode, Mike talks about why he wrote the book, and he shared his thoughts on the confusion and misinformation around what is artificial intelligence. Especially what aspects of our lives will be changed by AI? Which of those changes will be beneficial and which have been harmful? And what can we do to ensure that AI is only used in legal, moral, and ethical ways?

How do we cultivate a reasonable understanding to the point in which we can all partake in meaningful conversations about the use of AI and to recognise the problems that Science Fiction and myths about artificial general intelligence have had on the value of these conversations.
Michael talked at length about biases and how these are represented in machine-based decision making and how we can review the impact of AI-based on plotting it on a graph for worldview and impact.

We spoke about the different lenses that people around the world see AI through depending on the use of it and the major technology corporations Head Quartered in their countries.
Michael compared the use of AI in the West to how it is deployed in China.
He closed with his call to arms for more people to get involved in understanding artificial intelligence so that they can join this important conversation.

I’ll quote his final comments that you will enjoy hearing again from his own voice at the end of the episode.

Our focus now together as a society is on having a holistic and collective conversation to ensure that AI is only implemented in ways consistent with fundamental human dignity and only for purposes consistent with each of our personal democratic ideals and liberties. The call to action is I hope you'll be a part of that conversation because I don't want future societies or future groups to say. I wish our eyes had been more open on that topic when it mattered.

Become a supporter of this podcast: https://www.spreaker.com/podcast/the-boundless-podcast--4077400/support.