This week we interviewed Daniel Young, Senior Manager of Product Standards at Toshiba.  Toshiba provides innovative and cost-efficient technology solutions for multiple industries. Daniel is part of Toshiba's global Digital Innovation team, promoting digital transformation strategies and best practices across all of Toshiba's operating companies.

In this episode, we discussed trends in manufacturing technology adaptation and common adoption challenges to address. We also explored how standards and microservices enable the development of solutions with significantly lower system integration costs and timelines.

Key Questions: 

How would you characterize the state of manufacturing in the US today?

How do you assess the financial viability of use cases and optimize them to meet ROI targets?

What are common challenges in digitalizing industrial automation processes that are operated with legacy technology?

In this episode, we interviewed Brian Sathianathan, co-founder and CTO of Iterate AI. Iterate AI is a low-code rapid prototyping platform set up in 2013 that helps accelerate innovation projects with large enterprises by developing and employing digital solutions faster using the 465 pre-built software components.

In this talk, we discussed how a low-code platform simplifies the completion of tasks by using components of AI and IoT tech stacks that are already pre-built. We also covered how low-code platforms can transform the go-to-market path for edge and cloud AI solutions.

Key Questions:

• What is the difference between the traditional AI-development process and low-code platforms for enterprises?
• What are the unique challenges of deploying a platform for edge AI versus cloud AI?
• How can high volumes of data be efficiently tagged to train AI algorithms?

In this week's episode, we interviewed Taylor Cooper, CEO of MistyWest. MistyWest is an engineering design consultancy that creates technically complex, intelligent connective devices that accelerate the world's transition to a more sustainable future.

In this talk, we discussed embedded computing, an essential system that helps electronics and industrial devices function well. We also dive deep into the current state of embedded computing and share the best practices for crossing the valley of death from concept to production for a connected device.

Key Questions:

• What has changed in embedded computing over the past ten years?

• How is the current chip shortage impacted companies that are bringing new IoT solutions to market?

• Which IoT segments are red oceans today, and where is there potential to discover blue oceans?

• How do you explore opportunities for new solutions?

In this episode, we interviewed Andy Chatham, CEO and Co-founder of DIMO. DIMO is an open platform that connects data producers like cars, consumers and business applications to unlock next-generation mobility services. 

Today, we explored the potential of Web3 as an enabling technology for the automotive industry. In particular, we discussed how Web3 provides a convenient infrastructure to conduct business transactions on which innovative business models can be designed. 

Key Questions: 

• What technology and market changes have made DIMO’s business model feasible today? 
• What are the challenges of hardware-software integration for applications used in cars? 
• When does the Web3 approach make sense as part of a business model?
• How does government regulation impact the development of Web3 business models? 

This week we interviewed Ross Sheil, Senior VP of Global Revenue at Infogrid. Infogrid is a mission-based, AI-intelligence-powered platform that gathers and analyzes data from its Internet of Things (IoT) sensor network to drive its mission of making every building healthy, efficient, and sustainable. 

In this episode, we discussed how the demand for building intelligence to reduce energy consumption and improve human well-being has increased. Infogrid works with Fortune 500 companies helping them to reimagine their smart-building strategies for corporate real estate.  

Key questions: 

• What significant changes in the building tech industry have you observed in the past years? 
• How do regulations impact the use of technology in buildings? 
• How do you measure the impact of AI solutions on building owners and managers? 
• Which stakeholders do you typically work with in a smart building project? 
• How do you deal with issues of privacy in collecting data related to employees or customers in commercial buildings? 

In this episode, we interviewed Gijs Meuleman, CEO of Sensorfy, specialist in predictive solutions for industrial companies. Sensorfy designs and develops predictive maintenance solutions to processes sensor data near the source, with machine learning on the edge. 

We discuss how to transform a transactional business model into service-based, data-enabled business that is more closely aligned with benefits realized by the end customer. We also discuss how predictive maintenance impacts operations cost structures to allow leaner maintenance organizations while sustaining service levels.  

Key Questions: 

• Why is predictive maintenance one of the most widely adopted IoT use cases? 
• How do you evaluate the different parameters to decide if predictive maintenance makes sense in a specific situation?  
• How do predictive analytics enable business model innovation? 
• What is the current level of maturing in industrial equipment markets in terms of adopting service based business models? 

In this week’s episode we were joined by Gregory Garrett, CEO of CGS Advisors, a strategic transformation advisory firm that helps companies plan and execute initiatives to compete in a connected world. He is also the host of the highly rated podcast You, Me and Your Top Three.  

In a change to our typical format, Gregory kicked off by interviewing Erik about his experience as an entrepreneur in China. They then switched roles and Gregory shared his perspective on the importance of establishing an ecosystem in order to create digital solutions that scale. 

 Key Questions: 

• How do you keep your knowhow fresh when you work in a dynamic industry? 
• What is it like to be an American entrepreneur in China?  
• Who should you surround yourself with in order to become a better leader? 
• How do you build a vision for your company’s future? And when should you be open to changing that vision? 

In this episode, we interview Brian Gilmore, director of IoT and Emerging Technology at InfluxData. InfluxData is the creator of InfluxDB, a pioneering time series platform that allows developers to build real-time IoT, analytics, and cloud applications with time-stamped data. They handle massive volumes of data produced by sensors, applications, and systems that change over time. 

Today, we discuss how next-generation databases create new opportunities by enabling organizations to seamlessly integrate real-time IoT data streams with cloud databases. We also dive deep into the relationships between database technology and adjacent innovations in AI, AR, and blockchain.  

Key Questions: 

• What is the right way to think about “real time” from the perspective of a user? 
• What are the unique uses of time series data, and what challenges does it present? 
• How are AI, AR and blockchain being integrated into IoT systems? 
• What recent database developments are improving management of complex IoT systems?  

This week we interviewed Tim Wagner, CEO of Vendia. Vendia is a real-time data infrastructure provider that enables companies to securely share data across organizations, applications, and data lakes. 

In this talk, we discussed the use of next-generation blockchain and server list infrastructure to solve data sharing problems related to security, privacy, and data management. We also explored the three body problem - how conventional IT, private ledgers, and public blockchains will work together to usher in the Web 3.0 era. 

Key Questions: 

• What are the characteristics of a suitable use case for blockchain architecture? 
• How to distinguish between private and public ledgers? 
• How do data ownership and sharing mechanisms work for private blockchains? 
• What is the functionality and business logic of next generation blockchains?

Data annotation is the hidden champion of machine learning. It is the process of tagging image, video, text, and other data in order to prepare it for training a model. The quality of your data annotation makes the difference between insight and noise.  

In this week’s episode, we interview Tigran Petrosyan, co-founder & CEO of SuperAnnotate. We discuss how to manage and scale your annotation workflow, quickly spot quality issues in your data, and seamlessly integrate new data sets into your existing pipeline. We also explore how specialized agencies and AI are collaborating to accurately tag the high volume of data that AI training requires. 

Key questions: 

• How to manage the key steps of the annotation process - annotate, manage, automate, curate, and integrate? 
• How can you deliver ML projects faster without compromising on quality? 
• How should you balance the efforts of internal teams, freelancers, and automated tagging to achieve the right cost structure and performance?