Episódios
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In the latest episode of Inference – an AI Business Podcast by Silo AI, our CTO Niko Vuokko and COO Jaakko Vainio sit down to discuss how companies can build real value and a competitive edge by scaling AI initiatives that redefine companies.
Tune in to hear our COO's and CTO's outlooks on the main challenges companies face when trying to transition from experimenting with AI to integrating AI into the core of their products, services, or processes.
Learn more at www.silo.ai
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S2 E9: Niko Vuokko, CTO of Silo AI, talks about the current and upcoming trends in AI.
Niko Vuokko is the CTO of Silo AI, leading Silo AI’s technology strategy and IP. Niko is an experienced technology leader of multiple successful enterprises, such as Eniram, Sharper Shape and Metrify, as well as an International Mathematical Olympiad competitor and national team coach.
In this episode, we talk about the current trends of AI, as well as the trends that are on the horizon of 2023 and beyond. We talk about the scalability on global natural language resources, ML’s impact to life sciences, state of ML embedding and more. Catch Niko’s recommendations for companies that are adjusting their AI vision and strategy.
Get all the above and Niko’s recommendations for companies that are adjusting their AI vision and strategy by listening to the episode today.
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Sabrina Maniscalco, a Professor and the CEO of Algorithmiq, talks about the quantum computing and AI.
S2 E8: Sabrina Maniscalco is a quantum information and logic professor at the University of Helsinki. She is also the co-founder and CEO of Algorithmiq - a pioneering startup developing real world impact challenges through quantum computing algorithmics.
In this episode, we talk about the current state of quantum computing, as well as the close relationship quantum computing has with AI. Sabrina explains state of quantum algorithmic development and discusses its first practical applications.
Catch Sabrina’s synopsis of quantum computing’s trajectory, an outline of how AI and quantum computing interact and hear her prediction on the next trends in quantum computing development.
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In this episode, Lina Weichbrodt discusses AI Post Mortems and shares her favorite stories regarding machine learning damage control.
Lina Weichbrodt is an experienced ML engineer. Having built her career at Zalando and Deutsche Kreditbank, she has developed a specialty for debugging and understanding performance risks of large-scale, deployed systems.
Catch Lina’s recap on debugging near-invisible edge cases, customer preference inclusion, and her takeaways for ML debugging and monitoring ops.
#AIforPeople #InferencePodcast
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Meet Michael Munn from Google, discussing the Advanced Solutions Lab.
In the latest episode of Inference, Ville Hulkko of Silo AI sits down with engineering specialist of ML design pattern and explainable AI, Michael Munn. Michael walks us through the operations behind Google’s Advanced Solutions Lab.
Catch Michael’s insights on the future of organizational ML adoption, and learn about the evolution behind AI education and the democratization of AI in the industrial space.
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S2 E5: Errol Koolmeister, CEO of The AI Framework, talks about the role of strategic AI advisory.
Errol is one of the most well-known figures in the Nordic AI scene. Known for his work as the AI lead at H&M, Vodafone, and Nordea, Errol has developed his expertise in pioneering practical large-scale AI implementations.
Recently, Errol has “gone rogue”, helping companies make intelligent AI management decisions. In this episode, we learn about the managerial challenges of real-world AI implementations and dive into the recurring themes of AI problem-solving.
Catch Errol's take on AI in talent acquisition, infrastructure, and the optimal AI management models, as well as his predictions on the upcoming trends of AI management.
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Douglas Castro, NeuralDSP: AI transforming an industry.
In S2 E4 of Inference we deep dive into audio processing as NeuralDSP’s CEO Douglas Castro discusses how Machine Learning has transformed an entire industry’s approach to technology.
NeuralDSP is the market leader in professional grade audio processing. Their guitar- and bass amplification modeling software, as well as Quad Cortex computation device, have been adopted by both industry professionals as well as advanced hobbyists globally. Their approach to combine DSP with Machine Learning has enabled the creation of extreme efficiency and high quality products.
Doug is a known pioneer in the industry. Prior to foundation of NeuralDSP, his previous company, Darkglass Electronics, created the de facto bass amplification system of modern rock and metal music, used by artists such as Faith No More, Periphery and Foo Fighters.
Catch Doug’s description how a scientifically extremely complex processes could be scaled with Machine Learning technologies, how the research adoption rate of Machine Learning in audio processing, and learn his predictions regarding the future trends of AI in professional audio.
#AIforPeople #InferencePodcast
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Timo is a professor at Aalto University ’s Department of Industrial Engineering and Management. Timo is also the co-author of Platform Strategy: a recent book discussing business transformations enabled by AI and platform business models.
With Timo, we discuss about the implications that AI will have in management, peopleops, talent matching and leadership.
Tune in to catch Timo’s insights into the “whats and hows” of an AI-as-a-manager organization, learn about the key challenges of skill classification and learn Timo’s predictions on the future developments of AI in management processes.
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S2 E2: Lina Weichbrodt of Deutsche Kreditbank discusses AI diagnostics, usability and post mortems.
Lina Weichbrodt is the Machine Learning Lead Engineer at Deutsche Kreditbank, formerly at Zalando. She has pioneered Machine Learning -focused quality management, debugging and post mortem practices throughout her career and shares her insights regarding ML quality & problem discovery. Machine Learning quality monitoring can be notoriously difficult, due to an absence of the “it either works or doesn’t” -logic common in traditional software applications.
Tune in to catch Lina’s recommendations on which PeopleOps principles have a strong indirect affect on algorithmic quality, how ML quality ownership should be allocated, and tips for the best toolkits & recommendations for product owners on how ML quality should be approached.
#aiforpeople #inferencepodcast #siloai
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NEW SEASON: Episode 1 - Motherbrain, Daniel Wroblewski, EQT
Profile photo colorized by a Generative Adverserial Network.EQT is one of the leading private capital powerhouses in Europe, investing heavily in AI and other cutting-edge technologies. To elevate their investment ops, EQT created the “Motherbrain” data & AI platform. Daniel Wroblewski, EQT:s former Chief Architect and Chief of Staff and the head of Motherbrain, walks us through the “why:s and how:s” of creating a private capital AI platform from scratch.
Since the recording of the episode, Daniel has moved to operate as Managing Director of Alpha Generation Lab of CPP Investments.
Tune in to catch Daniel’s insights into the importance of human-centric design, ways to overcome early adoption challenges with a win-win Active Learning loop, and catch his predictions on the upcoming private capital and AI trends.
Hosted by Ville Hulkko of Silo AI.
https://silo.ai/inference
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Inference #20 out - State of AI in Denmark with Stephen Alstrup, Supwiz
With the release of the State of AI in Nordics 2021 report, we’ll be taking a look at the Danish AI ecosystem. Professor Stephen Alstrup is one of the most prominent figures of Danish digital affairs. Stephen is a professor of algorithms at University of Copenhagen, the chair of the national digital advisory as well as the CEO of Supwiz.
With Stephen, we’ll be taking a look at the state of AI in Denmark within the private sector, public sector as well as academia.
Tune in to catch Stephen’s suggestions for AI policy making, POC:s vs. production level implementations and learn Stephen’s recommendations for next concrete steps to build Denmark into a more advanced digital society.
Hosted by Ville Hulkko of Silo AI.
https://silo.ai/inference
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Jouni is a senior AI engineer at Silo AI and the Nordic champion of the globally renowned DeepRacer competition. An enthusiast of signal processing, cloud + local data management, Jouni jumped into the deep end of Reinforcement Learning and simulation-based training to take on the challenges creating an autonomously piloting system.
Tune in to catch Jouni’s shock and recovery during his first physical race, learn about the deeper implications of DeepRacer and catch Jouni’s predictions of the upcoming applications of Digital Twins.
https://silo.ai/
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Tero Ojanperä discusses intelligent platforms & platform strategy.
Tero Ojanperä is a co-founder and chair of Silo AI, and a professor of practice at Aalto University. This fall, Tero and his co-author, Professor Timo Vuori, released a book called Platform Strategy: Transform Your Business with AI, Platforms, and Human Intelligence. The book describes seven steps to converting a business into a platform.
Tune in to catch Tero’s thoughts on upcoming transformation trends caused by platform thinking, understand AI’s role within platforms, and learn from multiple practical case studies on real-life platform strategies.
https://intelligentplatforms.ai/
https://silo.ai/
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Inference #17: ML Design Patterns with Michael Munn from Google
Information can be lost in translation. As new technologies require a unified framework for discussion, they too require it for semantics. Michael Munn, along with his co-authors Valliappa Lakshmanan and Sara Robinson, released a book called ML Design Patterns to help codify common modeling- and engineering problems, solutions, and approaches into uniform language, aiming to democratize ML comprehension.
Michael is a professor and mathematician by background. At Google, he works with Google Cloud Platform’s customer-facing projects and is one of the driving forces behind Google’s Advanced Solutions Lab.
Tune in to learn about the difference between academic vs. democratic ML, best practice sharing between Google’s teams and matching of Google’s MLOps principles to customer ways of working, and upcoming MLOps trends.
https://youtu.be/hNtJJ5R_T9s
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Regulation, ethics & geopolitics of AI.
AI development advances with staggering speeds across the globe, while the democratization of tools grow the number of developers in every corner of the planet.
"Through the explosion of smart devices, more people arguably have access to Artificial Intelligence than clean water today", says Mark Caine from World Economic Forum. In a world where transformative technologies outrun human procedure, defining regulative and ethical boundaries can be an overwhelming challenges. World Economic Forum, the Switzerland-based NGO, has made strong advances in researching the implications of AI politics, as well as spreading the best practices of AI implementation and regulation to the public and private sectors alike.
Mark Caine is the head of World Economic Forum’s Global AI Action Alliance (GAIA). In this episode, we discuss the state of AI development across the globe, the different approaches to regulating AI as well as the state of AI ehtics discussion.
Mark is a diplomat by background with a keen interest for emerging markets. At WEF, Mark leads government affairs at the Center of Fourth Industrial Revolution alongside GAIA. A former Research Fellow at London School of Economics, his work with sustainable energy policy within African Union Commission has gained global attention. Mark is also a known lecturer at LSE, Stanford, UCL, and more.
Tune in to catch Mark’s insights on emergent AI hubs outside of “the big three”, the paradigms of AI regulation vs. AI-inclusive regulation, and catch Mark’s predictions on the future development of AI politics.
https://youtu.be/bYh5J8kwGA0
https://silo.ai/
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Anna Mossberg – Managing Director, Silo AI Sweden, ex-Google and Telia
Choosing the first AI path of an organization can be a daunting task. Which challenge to tackle first? Where to get the skills and tools to do it? How to manage and budget the process? Anna Mossberg walks us through the steps that organizations both lean and large need to take to successfully embark on their AI journey and implement data strategies.
Anna is a technology leadership professional, with a solid background in companies like Google and Telia. Throughout her career, Anna has been the driving force behind data strategies of multiple listed companies, and is a strong advocate of rapid technological experimentation.
Tune in to hear Anna’s view on first selecting the first AI path, the role of data strategy, and what different organizational methods exist for early AI adoption. We’ll also get to hear Anna’s concrete three-step program for taking AI from talking to action.
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Ville Tuulos - Manager, ML Infrastructure at Netflix, author, entrepreneur
Machine Learning is defined by a need for rapid experimentation. To achieve an environment of fast, iterative and low-risk experimentation, both hard aspects (tools and platforms) and soft aspects (culture, ways of working) of the ecosystem need to be aligned. Ville Tuulos, the driving force behind Netflix's Metaflow platform, explains how Netflix has managed to tackle both sides of the coin to build a truly experimentation-oriented organization.
Ville is among the most renowned of Finnish data scientists. Spinning out of self-organising map research in Finland, he quickly landed in Silicon Valley at the first dawn of AI market emergence, focusing on the development and implemenation of deployment infrastructures. This led him to push platform development at Netflix, which later became Metaflow, Netflix's Open Source model design and deployment framework. Today, he's spinning out a development platform company, and will be releasing his book, Effective Data Science Infrastructure, this spring.
Tune in to hear Ville's point of view on the how a culture of freedom and responsibility is created, the role of processes (or lack thereof), and how the bridge between a thousands of great ideas for ML application should be validated and proven. Get Ville's predictions on the future trends of ML management and human-machine collaboration.
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Data monitoring & quality - Patrik Tran, CEO of Validio
“We have good quality data” is a phrase spoken by most organizations. Theory and practice differ, however, as Validio approaches data from a different perspective: all data is flawed by nature, and thus our focus should be on monitoring, understanding and tolerating it.
Patrik Tran, PhD, is the co-founder and CEO of Validio, working on a toolkit for data monitoring. With a stellar background in academia and upper management data consultancy, Patrik evangelises a key message: Data is never perfect.
Tune in to learn about the famously mismanaged approach to data quality, difference between academic and practical data quality, how data engineers should be empowered within organizations, and hear Patrik’s three predictions on the future of data monitoring within the next years.
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Predictive maintenance with a human in the loop - Patrik Strand, General Manager, Product Management – Performance Services at Wärtsilä
Predictive maintenance is a concept everyone talks about, but few are actually applying. Wärtsilä, an industry leading martitime & energy sector technology and power source company, executes a data-driven strategy through their "Expert Insight" predictive maintenance product. With a strong focus on matching data insights with the deep knowledge base of their engineers, Wärstilä chose a human-in-the-loop driven approach to direct the learning of Expert Insight.
Patrik is the general manager of performance service product management at Wärtsilä. With an engineering background, Patrik oversaw the augmenting of their pre-existing Condition Based Maintenance -ops with predictivity, and has a clear vision of the future of predictive maintenance solutions.
Tune in to discover how Patrik views the importance of customer collaboration, how interactivity can be translated to annotations, and what Patrik's vision for the future of predictive maintenance solutions is.
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Internal AI Center-of-Excellence - Girish Agarwal, Director AI Lab at Husqvarna
Among the most interesting management frameworks of AI development is the emergence on internal AI Centers-of-Excellence. Husqvarna is a Swedish global industry leader of power products and robotics, with a product portfolio ranging from forestry and landscaping consumer equipment to industrial scale solutions. With autonomous operations being a cornerstone of Husqvarna’s strategy, the AI lab was established to enable the creation of such systems.
Girish is the director of AI Lab at Husqvarna. Tasked with the goal of figuring out for the big data masses collected throughout Husqvarna's operations, Girish oversaw the establishment and management of AI resources and working models within Husqvarna. As a result, Husqvarna is now running one of the most advanced - and successful - internal AI COE:s in the Nordics.
Tune in to discover what Girish views as the golden ratio of early AI projects initiated vs. deployed with their ASD Scoring, how Husqvarna's AI management has matured over the years and how exploratory vs. tangible AI initiatives should be approached.
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