Archive for the ‘VIP’ Category

Leading Industry 4.0

April 9, 2021

FountainBlue’s April 9 VIP Roundtable was on the topic of ‘Leading Industry 4.0’. As usual, our participating executives represented a wide breadth of backgrounds and perspectives. The conversation focused not just on the supply chain, process improvement, automation and robotics which are typical for Industry 4.0 discussion, but also focused on the data management and strategy around it.The first comments are of course about the increasingly larger volumes of data and the increased pressure to respond more quickly and more strategically based on that data. Below are some best practices on how to successfully manage that data.

  • Collect data for strategic reasons, focused around corporate goals, around customer current and anticipated needs, market trends.
  • Create and reinforce a culture where data share and best practice sharing is the norm, where everyone helps everyone else solve problems and make decisions.
  • Share your data and your learnings with other products, other divisions, other organizations, etc., but use your best judgment to ensure that you maintain a competitive advantage and are engaged in win-win collaborations.
  • Analyze the data anomalies as they may point to opportunities or current or pending challenges.
  • Move from Reactive to Proactive mode, going beyond generating and reporting on data, but looking beyond and beneath that to address questions such as
    • what are the data trends
    • what are the implications based on data
    • what are the underlying causes for the data
    • what kinds of decisions should we make based on data

Below are additional best practices for managing Industry 4.0.

  • Add value across the value chain within and across companies, products, roles and geographies. The more of the right partners and leaders participate, the more value for all. 
  • Focus on both the performance of the hardware/software/solution while also ensure that the user interface is intuitive and meets the preferences and needs of the targeted profile audience.
  • The more energy and power you can channel the better, within reason, but make sure that you’re focused on solving the problem and creating the solution which fits your corporate goals and your customers’ needs.
  • Think ahead at all the things which might impact how you can custom-design, create, distribute, manage, support, etc., your solutions to manage the ripple effect. We all learned the lesson about the well during the pandemic 
  • Look not just at the data generated real-time today, but also at the decades of data we’ve amassed to help us better manage the needs of others.
  • With that said, the need for privacy, security and access is of primary concern. No solution is complete and effective without folding in these elements.
  • Look not just at how products are manufactured, but look also at how the innovations around Industry 4.0 will help leaders from all industries better deliver exceptional value to their very demanding customers. 
  • Leverage the latest technologies to keep current, realizing the impact of Industry 4.0 advantages, including Digital Twin and 3D Printing, data analytics, robotic automation, etc.,

Industry 4.0 is in its infancy, as we work to be more efficient while being more excellent, leveraging technology and collaboration. The challenge has been how to vacillate between looking at the strategy and big picture while also focusing on the weeds of the data, the details of the process, the needs of the individual customers and each individual person involved in delivering customized solutions for these customers. 

Innovating on the Edge

March 12, 2021

Innovating on the Edge

FountainBlue’s March 12 VIP Roundtable on the ‘Innovating on the Edge’ topic included executives representing a wide breadth of backgrounds and perspectives. We began with some definitions about the ‘Edge’. In the ‘old days’, the Edge might be defined by where the wires end – at the point of going wireless. But today, most things are not hard-wired, and we look more at the edge of the cloud – where the cloud meets the sensors and devices.Our executives agree that it’s becoming increasingly more important to innovate and manage on the edge, but there are many business and logistical challenges for doing this well. 

  • Data Challenges: The volume of data generated by devices on the edge is immense and the challenges are varied.
  • Relevancy: getting filtered and relevant data to the right reports and programs
  • Latency: the time it takes to get the data to the right report and programs
  • Movement: moving data from the edge to other areas in the network and in the cloud can be complicated and takes time
  • Storage: management and maintenance of current and past data can be complicated and expensive

Problems beyond the data include:

  • Computational Issues: programs processing of the volumes of data to understand what’s relevant, what the implications are
  • Outdated and Sub-optimal Programs: legacy applications and other programs on enterprise networks may not be as effective and may even pose security challenges and additional expenses
  • Communicating between moving entities: car-to-car, car-to-object, car-to-people communication has its unique challenges which require collaboration between many stakeholders – from cities to auto dealers, from government officials to 5G developers, from business leaders to consumers
  • Infrastructure and Policy Challenges: we need the policies, support and funding so that we can invest in infrastructure upgrades which further allow for innovating on the edge

Below are best practices for innovating on the edge.

  • Practice Data Gravity, which treats data at its origination site, rather than moving it to another location before working with it. This addresses the data issues around latency, storage and movement.
  • When filtering for relevant data on the edge, the algorithms don’t need to be that precise, just identifying data that’s in the right ballpark.
  • Invest in solutions which minimize latency, especially when lives are at stake –  for example for healthcare and automotive solutions.
  • Design smaller form factors but with more functionality and more control.
  • Get immediate, deep and broad visibility on security exposure and breaches.
  • Create and join partnerships with carriers, vendors, providers, regulators to support the infrastructure needed to innovate on the edge.
  • Look to the AI for the historical trends and integrate that into your solution,  while also looking at Machine Learning to make predictions based on past behaviors and information.
  • Optimize for tiny computers on the Edge, which can do much more processing more quickly. 
  • Design more sophisticated environmental sensors which would give real-time feedback, monitoring for specific issues. 
  • Pay close attention to the user experience – what’s intended and what’s experienced. 

The bottom line is that we will all continue to innovate on the edge, and companies and consumers will continue to reap the benefits of it in our day-to-day lives. 
Resource: March 8, 2021: Global Mission Critical Communications Market Report 2021: AI-powered IoT Critical Communication Market in Public Safety will Surpass $20 Billion by 2028 https://www.prnewswire.com/news-releases/global-mission-critical-communications-market-report-2021-ai-powered-iot-critical-communication-market-in-public-safety-will-surpass-20-billion-by-2028-301242381.html

Bigger Solutions with Smaller Devices

February 12, 2021

FountainBlue’s February 12 VIP Roundtable on the ‘Bigger Solutions with Smaller Devices’ topic included executives representing a wide breadth of backgrounds and perspectives, but they agreed on many things.

  • Hardware will continue to become smaller while gathering a wider assortment and larger amount of data real-time. Software will become bigger to optimize functionality and performance around the data and ensure its ongoing usefulness for all stakeholders.
  • The Future of Work will be remote, and hardware and software options need to fit the functionality, accessibility and security requirements of the (internal and external) customers. 
  • Optimizing managed services will help enterprise IT departments focus on the employee needs and the employee experience, rather than on the infrastructure functionality like privacy, security and access.
  • Enterprises need to proactively update and integrate all in-house and partner solutions, especially legacy solutions, to ensure their ongoing usability, performance and security. Reactively responding to issues could waste a lot of time and money and reflect poorly on the corporate brand.
  • Enterprise and government infrastructure must be enablers of the hardware and software functionality adopted by staff and citizens, and this infrastructure must be upgraded to meet the ongoing needs of these customers.
  • The functionality will expand with the demands of the customers, demands of the market. Form factors such as the body or a car offer guardrails for the range of solutions created – they must fit the user/target! But the connectedness between the solutions and the functions themselves can be more adaptable and fluid. 
  • Solutions may become much more complex, but users still want the solutions to be easy to use and customers expect a great immersive experience.

Below are some opportunities in this space.

  • As we provide more software functionality into smaller form factors, the phone and other ubiquitous devices must provide even greater functionality – including healthcare monitoring applications, decision-making of real-world devices and equipment based on real-time reporting, and other functionality. This is particularly true with the roll-out of 5G.
  • There will be an increasingly HUGE appetite for optimized hardware and software solutions which are high-performing, scalable, true-to-spec, and even self-monitoring/self-correcting. 
  • We will all continue to develop hardware solutions which are flexible, small and sleek, with a shape and size that would fit the target destination. 
  • Individual hardware solutions will be integrated with multiple software solutions to optimize functionality, usability, and form factor. 

The bottom line is that the hardware will get smaller, the software will get more integrated, more scalable, more versatile.

Data Meets Healthcare

January 15, 2021

FountainBlue’s January 15 VIP Roundtable on the ‘Data Meets Healthcare’ topic included executives representing a wide breadth healthcare industries – from pharma to biotech, from healthcare services to digital health, and even tech companies in semiconductor and consumer electronics had perspectives on the topic. We all agreed that the advances in technology and in healthcare have facilitated the amazing response to a worldwide pandemic and other pervasive and emerging health issues.Although their backgrounds and strategies varied, our participating executives agreed on the following regarding data meeting healthcare.

  1. Data will continue to be pervasive and overwhelmingly available. And the challenge will continue to be in selecting the relevant, true and actionable data which would best serve stakeholders across the ecosystem, which protecting their privacy, and providing selective and immediate access.
  2. Focusing on getting the data right alone can help improve how quickly and accurately we diagnose, treat and care for our patients. Indeed, it could also help prevent diseases and conditions and help mitigate risks.
  3. There may be opportunities to extrapolate from large volumes of data to draw conclusions and help develop initial diagnosis and treatment strategies.
  4. Proactively managing the usage of equipment and materials and pharmaceuticals with data will help ensure logistical and operational excellence in support of the healthcare needs of our patients, and the bottom line of the providers and caretakers across the ecosystem.
  5. The more data we gather about people with similar conditions, the more we learn about each individual person, and the more generalities we might be able to make about a particular disease. But every person will respond differently to different things, so respect the collected data without making direct correlations and conclusions on how any one person might react and respond to any particular scenario.

We all remarked on the value of collaboration across the ecosystem so that we can all better benefit. Below is a compilation of thoughts on the market opportunities ahead, which might benefit from collaboration.

  • Personalized medicine can be a real opportunity if we can overcome the privacy, security and access challenges and if the technology continues to evolve so that we can design custom diagnosis and treatment for patients.
  • Empowering the consumer with data will help them as patients better partner with others across the ecosystem to make better decisions in researching their own conditions, and also in making treatment and care decisions.
  • Identifying and treating a niche market may have ripple effects in supporting others. The example of an expectant mother comes to mind.

Throughout the discussion, there was a message of hope as we all grapple with the different challenges and opportunities offered as data meets healthcare. It’s significant that companies far outside the healthcare sector are looking closely at how data meets healthcare, and what the business and market  opportunities are, and also significant that each executive is exploring how we can better connect and provide more impact for all.
Please also see Frost & Sullivan’s Top 10 predictions for healthcare in 2021.

Smart Cities, Smart Buildings

December 11, 2020
FountainBlue’s December 11 VIP roundtable : ‘Smart Cities, Smart Buildings’

FountainBlue’s December 11 VIP roundtable was on the topic of ‘Smart Cities, Smart Buildings’, conducted online with introductory remarks provided by our host company at Amazon. Below are notes from the conversation.

Today’s participating executives represented a wide breadth of industries and roles, across the spectrum of technology – from semiconductors to fabs to end consumers. Each brought an interesting perspective about the opportunities for smart buildings and smart cities.  

We launched the discussion talking about the technology innovation explosion around semiconductors, networking, storage, software (AI, Advanced AI, ML) which facilitates the adoption of Smart Building/Smart City solutions. The pandemic and its implications are also accelerating the development and adoption of technology.  

  • Whether it’s on the edge, in the fog or in the cloud, it’s always about the data. But we need to make sure that data is relevant, timely, filtered, and targeted in its delivery in order to provide maximum benefit for all stakeholders, with actionable recommendations based on pre-defined requirements and parameters.
  • Balancing privacy, security and access remains of paramount concern. Individuals, cities, countries, companies will differ in terms of their sensitivity levels and options for managing these elements, but each entity must take these factors into account.
  • Whereas we may have many ‘Smart Building’ use cases now, to help us manage our day-to-day activities at home for example, we are farther away from the management of ‘Smart Cities‘ as this would take coordinated infrastructure development and collaboration. This might be easier in China, where there might be mandates and requirements, but in Western countries where many parties get a vote, it’s a much greater challenge. 

Below is a compilation of best practices to ensure sustainable implementation and execution of Smart Building initiatives.

  • Minimize latency issues so that selected users can efficiently receive not just the data, but the recommended actions based on the data received.
  • Focus on the most relevant use case, and solve that specific problem, which can be expanded to solve other problems.
  • Focus on the short term wins around Smart Buildings, then expand to other use cases which leverage the same technology, but outside the home, or at work. 
  • Focus on the long term play around Smart Cities, but build standards and collaboration to ensure the coordinated implementation of solutions.
  • Create model solutions including model cities and intersections to test solutions and technologies and their relevance and effectively for individual types of users. 
  • Develop integrated solutions to help employees better manage their logistics and planning around meetings and development or operations.
  • Create Smart Home/Smart City solutions which focus on the goals of safety, efficiency and quality of life, while conserving energy and optimizing performance.
  • Take advantage of smart building solutions which speak to current pandemic-related challenges including robotic automation for warehouses, disinfection management which keeps humans safe, commercial deep cleaning.

We ended the roundtable with some key open-ended questions:

  • What are the opportunities around the challenges posed by 2020 – the year of the pandemic and the myriad of resulting implications?
  • What are the long-game solutions around Smart Buildings/Smart Cities which take minimal investment dollars, so that we can prove value/warrant investment?

Please join me in thanking our hosts at Amazon, and our participating executives. 

Data is the New Black

November 16, 2020

FountainBlue’s November 13 VIP roundtable was on the topic of ‘Data is the New Black’. Please join me in thanking our participating executives and our hosts at Micron for framing the discussion.

November 13, 2020 Roundtable: Data is the New Black

It’s a given that each executive from each industry is impacted by data, so all were challenged to provide a provocative perspective on how data is and will be impacting our businesses, our industries, our daily lives. Below is a summary of my discussion.

The executives agreed that the data volume, velocity and variety is constantly growing. The challenge and the opportunity is to collaboratively delivering VALUE, while focusing on the needs of our customers’ needs.

Data is plentiful and growing, but finding the relevant and true data quickly will make the difference. Indeed, solutions which lead to valid conclusions, information, and solutions with limited data will become increasingly important. This is, in fact how the human brain functions… The trick is finding the most relevant kernels of true data.

One idea was to generate ‘fast data’ using AI and ML algorithms to make quick decisions based on specific criteria – like whether someone with little credit history is at high risk for defaulting on a loan.  
Others brought up the importance of examining edge cases – double-clicking not on what’s regular and normal, but finding patterns and learnings from the exceptional and different scenarios. From these anomalies, you may at times make a broader statement or conclusion – or just identify the anomaly as a one-off, a fluke. 
What’s important is to focus on the validity, the relevance, and the immediacy of the data, for if you’re not working with this type of data, you’ll have Garbage In, which would lead to Garbage Out (commonly known as GI-GO). The executives were in violent agreement that collaborating with others to validate data sources would lead to better decisions, better applications, better solutions. 
Several executives mentioned the need to focus on HOW data can solve many problems, and the importance of leveraging data to solve the most relevant problems, and the need to customize solutions, for there is no one-size-fits-all scenario.
Additional best practices include the aggregation of data from across departments, organizations, product lines and organizations will bring larger, broader perspectives which are relevant for all involved, and the need for ongoing collaboration to ensure elegant and ongoing data integrity, especially when milliseconds of time make a huge difference. An example is validating the signature between a nurse and a patient so that prescriptions are more quickly distributed, when timing is critical.
Everyone agrees that the data generated and gathered can help organizations run more efficiently, can help customers better understand what’s working, can help predict and capitalize on new strategies, can help deliver more relevant and richer customer experiences. 
With the rising dependence on the generation, storage and management of data, and especially now as the data is integrated into everyone’s day-to-day lives, we’re finding huge volumes of data generated on the edge. Responding quickly to the data on the edge will be a challenge, and filtering out the most relevant data will make the solution more effective and more efficient.
We ended with a call for collaboration, for standardization, for policy upgrades around data generation, privacy and security. The semiconductor solutions which gave Silicon Valley its name continues to develop the chips that power servers, machines, tools and devices. Innovations in the semiconductor industry in turn can support the data collection, storage, distribution, etc., tasks normally attributed to software solutions. 
Our executives concluded that Data can’t be the new oil, for oil is organic and more predictable. Data is defined, created, standardized, used by humans, and humans have decided that data will be everywhere. So humans must decide how to use it well, to solve specific problems. The need is urgent, the need is immediate, the opportunities are vast.

Automation Use Cases

October 9, 2020
FountainBlue’s October 9, 2020 VIP Roundtable: Automation Use Cases

FountainBlue’s October 9 VIP roundtable was on the topic of ‘Automation Use Cases’, conducted online with introductory remarks provided by our host company at Automation Anywhere. Below are notes from the conversation.

We were fortunate to have a diverse range of executives in attendance who shared a wide range of perspectives across organizations, across industries, across roles, across teams. 

It was exhilarating and exciting to hear of all the automation use cases envisioned, designed and implemented across industries by some smart, collaborative innovators and leaders. The future is bright for continued advancements in automation, providing more efficient, more effective support and information. Fundamental to the success of automation use cases is an understanding of:

  • the needs of the customer
  • the origination of, type of, format of and volume of the data produced
  • the processes underlying the solutions
  • the desired efficiency and effectiveness goal of the automation
  • the role of collaboration in the automation
  • AI to rapid process the volumes of available data and understand trends

One of the challenges for producing effective automation use cases is the latency involved in getting the right information to the right places/location/software/person. As we move from 4G to 5G, the latency get better addressed (going from 50 to 1 millisecond). Strategies for minimizing that latency were proposed.

Below is a list of potential innovations around automation.

  • Documenting transfer of property and information from one party to another
  • Humans training robots how to do what they do on the manufacturing floor for example
  • Further integration of 360 and other sophisticated cameras to better understand status before and after an event
  • Bridge between the AI which understands the data of the now/past to the Machine Learn – predicting a trend/action/pattern/event based on past data
  • Move between just the collection of data (what temperature is it) to the second degree of sensors which summarizes what a collection of data could mean and alerts the user (temperature plus respiration plus oxygen might mean infection)

Below are some best practices for producing automation use cases.

  • Test each software and hardware element independently and collectively.
  • Automate things that need to be frequently measured, processed in detail, etc.,
  • Provide actionable dashboards for the user.
  • Curate and collect data quickly and efficiently, filtering out what’s irrelevant.
  • Protect the privacy of users, but gather the aggregated data to make decisions, inform users, etc.,
  • Frequently track the time-to-value for your own company, and for your clients.
  • Figure out what’s true data, and only include the data that’s true within the offering you’re providing.

The discussion turned to the role of humans in automation use cases (process automation vs attended automation). We concluded that humans will never be obsolete and we listed specific circumstances where humans are needed.

  • Humans will have to be the decision-maker/fall-guy (or girl). This can not be delegated to a robot or hardware or software.
  • Humans decide what data is important and how important for what scenarios. In other words, the automation use case is generally designed by humans.
  • Humans need to evaluate the automation use case and make improvements. 
  • Humans define how to make the customer experience valuable for each type of customer – something which may not be inherently logical.
  • Humans have the creativity to solve a problem whose solution is not logical.
  • Humans know best how to bring the joy and passion to other humans.
  • Humans will have empathy for other humans, something difficult to program in. 

We conclude by looking at hybrid models, where humans and robots with hardware and software elements are fully integrated. What would it take to make this happen? 

All industries, all companies, all people will be progressively impacted in ways big and small by automation. Embrace this reality and do your own part in leading and innovating so automation is well integrated into your work and life. And while you’re doing it, make sure you’re focusing on the ‘human’ problems, not just the business opportunities which are profitable for some.

Please join me in thanking our hosts at Automation Anywhere, and our participating executives.

Power to the Grid

September 11, 2020
FountainBlue’s September 11, 2020 VIP Roundtable was on the topic of ‘Power to the Grid’.

FountainBlue’s September 11, 2020 VIP Roundtable was on the topic of ‘Power to the Grid’. We were fortunate to have a diverse range of executives in attendance who shared a wide range of perspectives across organizations, across industries, across roles, across teams. Despite the differences, we came to an agreement regarding getting power to the grid.
Today is actually the 19th anniversary of the 9/11 attacks, so we started by comparing where we were then and where we are now in terms of how we think about energy usage. 

  • Solar adoption, renewable energy generation and EV usage have gone from a novelty – a long-term vision, to a more standard practice, more in some locations than others.
  • The age of always-on vampire mode is now politically and economically incorrect.
  • Network infrastructure and data storage is now ubiquitous, the cloud commonplace now, with costs within reason for businesses large and small.
  • Similarly, the movement of data is now commonplace, relatively inexpensive and pervasive.
  • Energy storage is much more prevalent for enterprises, with consumers now taking a look at it.

There was much conversation about the challenges related to the management of energy generation, storage and distribution, and also agreement on how we can collaborate to make progress.
Energy Management is Complicated.

  • Our energy infrastructure involves an ecosystem of public-private partnerships.
  • The challenges are many and escalating, caused by: energy usage, climate change, distribution of energy generation/storage, political agendas, the vocal opinions of companies and consumers alike, aging power infrastructure, etc.,
  • The order of magnitude for energy projects is mind-bogglingly HUGE, taking many resources and much time (in the order of decades) to implement. 

Collaboration is key.

  • Policymakers must collaborate with all parties to ensure efficient energy generation, storage and distribution.
  • With energy, IT (including enterprise systems around HR, finance, etc.,) must meet OT – operational systems which physically bring the power to our systems and devices. Collaboration is a mandate, yet often a challenge.
  • Leveraging open source solutions around energy like the LF Energy system, may help build collaborations which are science-based and a-political while also proactively managing our energy consumption and our adoption of renewable energy sources.
  • Partnering with European countries in their use of technologies and processes may help US energy customers better manage their own generation, storage and usage. 
  • Find ways where all stakeholders can provide value and provide services in a way which is economically viable and even attractive.

Security across the grid is of paramount importance.

  • Security is at huge risk with the proliferation of devices attached to the grid from both enterprises and consumers. This is especially true as attacking our source of power puts regions and nations at risk.
  • Utilities may be forced to integrate legacy systems into overall energy management solutions. Keeping these solutions secure may be more challenging and complex.

Innovation is key. Below are some thoughts on how we could innovate.

  • Consider the adoption and integration of micro-services, especially for less populous areas with lower energy demands.
  • Create Software Defined Infrastructures to better manage energy usage, to better support the integration and management of all applications, including legacy applications.
  • Provide detailed real-time reports on energy usage easily accessible by enterprises and consumers. Gamify the solution so that we can make sustainable choices around energy usage.
  • Offer strategies for enterprises and consumers can be independent of the grid either when necessary (for rolling black-outs for example), or for efficiency.
  • There may be opportunities for cities to put power lines underground, and if they do so, perhaps they can set up data sub-stations while they’re at it, for areas which use higher volumes of energy.
  • Consider vehicle-to-grid opportunities around energy generation, storage and distribution.
  • AI and ML algorithms to identify patterns and make predictions around energy usage, so that we can proactively manage.
  • Consider providing aggregated reports of energy usage and renewable energy adoption which enterprises could use across their many global facilities. 
  • Devices on the edge of the grid can help manage and monitor the energy usage, and also help us understand current and anticipated needs based on specific scenarios – usage patterns, temperature readings, population density, industry trends…
  • Create solutions which would allow customers and enterprises to make local input, and global impact while creating jobs as well.

The bottom line is that there are models and predictions around energy usage, even as we are impacted by climate change. Rising above the political and economic agendas, we can come together and create solutions and forge policies which would be good for businesses, for customers, for the Earth.

What’s Next in Mobility

August 14, 2020

Mobility

FountainBlue’s August 14 VIP roundtable was on the topic of ‘What’s Next in Mobility’, conducted online with introductory remarks provided by our host company at Samsung.

We were fortunate to have a diverse range of executives in attendance who shared a wide range of perspectives across organizations, across industries, across roles.

The definitions around mobility varied greatly, and are all equally valid. Whether we’re talking about physical mobility or wireless mobility or virtual and immersive mobility, they are all forms of mobility.

And each form of mobility is increasingly adopting a more digital implementation. Indeed, mobility is the great differentiator, and leaders, companies, industries, and countries embracing digital transformation will continue to lead and succeed.

The converse is also true. Leaders, companies and industries which do not embrace the opportunities around digitization and mobility, those who do not embrace the opportunities around market disruptions will be left behind.

With physical mobility, there are opportunities around autonomous driving, clean energy, in-vehicle communication, and transportation in the air and in the water. Although the business case for autonomous driving may be years away, we are already proving that the efficient delivery of products to center hubs (e.g. not last mile) provides a huge market opportunity. 

Wireless mobility is enabling workers to remain in communication and remain productive despite the challenges of working from home. The mass adoption of wireless mobility in these days of sheltering at home shows that we can be amazingly productive, but also that wireless mobility helps us do things beyond work, like telehealth.

And virtual reality brings mobility to the next level with the immersion capabilities of augmented reality. Our panelists talked about several use cases where AR/VR, with the support of AI help enterprises to better manage, to better perform, even when we have to choose contact-free options, even when we are separated by great distances.

With that said, there are challenges to the mass adoption of digital solutions enabling mobility.

  • 5G and 6G mobile networks need to be adopted to get better, faster, more reliable access to bandwidth so that we can process the data. This will take a collaboration of industry, government (local, national), and community leaders.
  • Digital adoption requires endpoints/hardware like phones, tablets, laptops and computers. But having them is not enough. We must be trained on how to effectively and efficiently use these tools. (And those individuals and groups and communities not embracing the digital age most certainly will be left behind.)
  • Solutions must be secure and private, following protocols and policies which protect the rights of the users, while also protecting the greater community, the greater good.
  • Adoption by some industries, including oil and gas and energy and healthcare, will be slower than adoption by tech industries including hardware and software.

Below are some examples of opportunities ahead.

  • There will always be the opportunity to make individual solutions more seamless, more ubiquitous.
  • The more we help the less educated, the less informed, the more tech-philic we all become, the larger the market opportunity will be, the more empowered we will all feel.
  • Wireless mobility is making working from home possible when we are sheltering in place. Continuing advancements in this area will help us all be more productive with our work, and also be better able to connect virtually with others as we play and interact. (Nobody said that this replaces face to face interactions – humans are social animal.!)
  • While it may not yet be safe now to play most sports (unless we are in a closed loop environment), there are opportunities to develop virtual / online entertainment solutions which would resonate with huge volumes of users.
  • Optimizing supply chain and manufacturing costs will continue to be a huge opportunity.
  • Robots and humans will co-exist and mobility solutions will help them optimize how they work together.
  • Contact-free access to experts will be a growing ‘thing’, even when we’re past the pandemic. It’s just more efficient to connect experts to others located anywhere around the world.
  • Optimizing security, privacy while providing full access to users will always be an area of great need.
  • The volume of available data keeps growing. But the more data we have, the more efficiently we need to process the data, so that users can make informed decisions. There will always be opportunities to optimize secure and dynamic access to ‘true’ data.
  • Building user interfaces for applications focused on specific personas (types of customers) will be a huge opportunity. 
  • Chatbots and automation will continue to provide huge opportunities.
  • Mobility solutions are needed in clean room environments where cell phones are not allowed.
  • IoT solutions leveraging RISC-V, a free and open ISA can enable a new era of processor innovation through open standard collaboration, including customized ASICS for IoT.

Finally, there are comments on how we can each support what’s next in mobility.

  • Join advisories and collectives which will help create collaborations for technology adoption of technology standards.
  • Create cross-industry, cross-leader, cross-organization, cross-country partnerships to serve the needs of the customer.
  • Do your part to bridge the digital divide, helping those less fortunate to be better educated, better prepared, and better equipped to take advantage of the opportunities ahead.

It’s clear that What’s Next in Mobility is providing many opportunities to better communicate, collaborate, and celebrate together. The digital is bridging the physical – the more who embrace and join the revolution, the better it is for all.

Please join me in thanking our hosts at Samsung, and our participating executives.

The Next Generation Hardware

July 10, 2020

hardware

FountainBlue’s July 10 VIP roundtable was on the topic of ‘Next Generation Hardware’, conducted online with introductory remarks provided by AMD.

We were fortunate to have a diverse range of executives in attendance who shared a wide range of perspectives around the Next Generation of Hardware.

Chips have been powering not just servers, laptops and devices, but now progressively more the Cloud, Gaming, Automotive, AI/ML which requires intensive acceleration of performance, and response times, while also respecting the privacy and security of users.

The hardware – including CPUs, GPUs, Tensor Cores, Digital Signal processing devices, IoT devices – facilitates the generation of the data whereas the software ensures that the right data is captured to drive the application, to report and measure on specific outcomes, to enabled data-based decision-making.

Below is advice from our esteemed hardware executives.

  • As performance and response times as safety-critical – particularly in auto and health-related solutions – OR business-critical – particularly in manufacturing and production – the physical design of all the hardware involved in each solution must be scalable and flexible, working seamlessly with the software.
  • The hardware helps to collect the data, but must be designed so that AI and ML integrated into the software can efficiently collect the relevant data, and provide real-time information to relevant stakeholders.
  • The hardware must be modular enough to work with other hardware units, small enough to fit within a device, powerful enough to meet the needs of the customer, durable enough to withstand intensive usage, and efficient enough to work with minimal power.
    • As an example, the hardware must become even smaller and more portable, so the functionality is provided for demanding customers, in small form factors such as the phones which fit in our pockets!
  • IoT devices will increasingly need to do some processing on the edge, especially when performance is critical. This is the ‘Empowered Edge’.
  • Sort the data in terms of what’s most relevant, most urgent and to what audience, and give actionable real-time reports which would help them make critical decisions.
  • Design the hardware to keep up with the explosion of data, and design it to be flexible enough to work with the software. 

Below are examples of specific enterprise use cases involving augmented reality hardware:

  • Remote assistance, so that the expert can support the user to do everything from monitor or fix or manage equipment or devices from a distant location
  • Guided Workflow, which supports the adoption of efficient processes
  • Digital Collaboration on design and implementation

Below are examples of proactive management solutions related to the production of hardware.

  • Predictive Maintenance to proactively manage when equipment needs parts or service
  • Proactive management of Supply chain to ensure no one part is a limiting factor for production

Below are some thoughts about future trends and things to think about:

  • Much as there has been a consolidation of architectures and GPUs and DSPs, there will also be a consolidation of AI accelerators. Create a software ecosystem to support the AI accelerator, to increase the likelihood of becoming a hardware standard.
  • Leverage biological constructs to design solutions which can store and process immense amounts of data.
  • Hardware does everything from managing the batteries on your phone to navigating home. How can the hardware work with the software to increase performance and accuracy? to do it with a smaller, more powerful footprint? to integrate with other functionality?
  • The Work-From-Home phenomena resulting from the pandemic is exacerbating the adoption of laptop and smartphone hardware innovations as well. With everyone working (or not working) from home, the volume of data is amplified, the adoption of unstructured video data is magnified, and the demand for immediate and accurate response and support is urgent. 
    • What does this mean for chip designers, manufacturers, retailers, distributors? What kinds of innovations would suit this immense and quickly growing WFH user base?
    • What does this mean for the executive who wants to maximize operational and minimize IT issues, while addressing privacy, access and security issues?

We close with some provocative thoughts which might not be too far in the future.

  • What’s next after the smart phone?
  • How do we create an electronic mask for protection?
  • How do we sanitize our clothing between washes?
  • How can we leverage Lidar to better navigate our surroundings?
  • How do we make brain computing a reality?

The pendulum swings back and forth between the hardware and the software, and both will always be important.