healthcarereimagined

BY: Ki Lee

Mar 11, 2022 | 9:00 am

The Department of Defense (DoD) last month released its Software Modernization Strategy, an important step to unifying existing technology and directing a more joint approach to systems of the future. It notes that our competitive posture is “reliant on strategic insight, proactive innovation, and effective technology integration enabled through software capabilities.” Further, it asks DoD entities that are driving software development to address management and governance of the 29 software factories spread across the services.

The promise of software factories is significant. The construct has quickly demonstrated agile development of critical capabilities, while also delivering the speed and flexibility that DoD requires. This has been achieved with commitment to automation, modular and open architecture, and continuous authority to operate (cATO). Given how much progress has been achieved, software factories will undoubtedly play an important role in modernizing DoD technology, through a common framework which will drive efficiencies and provide oversight, best practices, and baseline software for reuse.

Industry’s Role  

The industry can offer lessons learned and best practices from supporting software factories over the past five years, and the Software Modernization Strategy makes several mentions of the role of industry within the acquisition process. Booz Allen has worked in partnership with the DoD to rapidly develop, integrate and field cutting edge mission capabilities. We know what works, and what it will take to mature the software factory ecosystem to bring more efficient, innovative software to the DoD.

The software factory ecosystem cannot be a one-size-fits-all approach. Given different mission requirements (i.e., from IT to OT), acquisition strategy and program maturity (i.e., inception to sustainment), bringing all of these software factories into a single factory or even entity will likely be both inefficient and ineffective.

DoD leaders would benefit from establishing an ecosystem that streamlines across today’s software factories and those that will exist in the future. We would offer that the DoD Chief Information Office should consider the following as a construct aligning software factories within a framework for governance:

Mission Purpose. Each software factory was purposefully designed to cover a broad spectrum of mission requirements. Some entities support complex combat requirements and require deep hardware integration for use on the battlefield driving additional layers of policy, cyber, security and technical specifications. Others focus on IT system development that requires less hardware integration, but instead requires standards for open architecture and system interoperability. Aligning against a core set of mission categories will help streamline baseline software and regulations.

Developmental Maturity of System. DoD must also consider the maturity of the software that’s being developed in applying governance to the software factories. For instance, a software factory for a system as mature as the F-35 program has very different needs (e.g., cyber regulations) than an entity that is incubating new software for prototyping and testing capabilities with more pipeline requirements (e.g., Navy’s Rapid Autonomy Integration Lab (RAIL) software factory).

Operations Model. Finally, the framework should address the variety of different models of ownership and accountability. Depending on how the DoD and industry partner together, the risks and benefits can vary significantly. In our experience, there are three broad models for operations that work effectively for managing cost, risk, and innovation: Government-Owned, Government-Operated (GoGo); Government-Owned, Contractor-Operated (GoCo); and Contractor-Owned, Contractor-Operated (CoCo). We have supported all of these models across the federal government and have lessons learned from each, including retaining government data rights, ensuring accountability and driving a culture of collaboration.

As the DoD works on the implementation plan for the Software Modernization Strategy and creates a framework for how to manage and govern its software factory ecosystem, aligning and categorizing its existing resources will be critical for delivering agile, mission critical software to current and future weapons systems.

Further, it is incumbent on the industry to continue to streamline and drive efficiencies in its software development. Across the Defense Industrial Base, we must commit to building open, reusable baseline software that can be extended and augmented across multiple use cases. Whether we are developing software on corporate research and development budgets or on government use cases, creating software that can be refined to meet mission needs is absolutely critical. From our own experience, we are committed to taking our investments and integrating them to build for custom mission solutions. Our AI/ ML software factories and cyber software factories are built consistently from a horizontal perspective so that they can be leveraged and reapplied for more vertical mission sets.

Through both a more consistent government framework for software factories built on real use cases and industry commitment to re-baselining, executing this new strategy will be critical for continuing to modernize technology to support today and tomorrow’s warfighter.

Article link: https://www.meritalk.com/creating-an-effective-framework-for-dods-software-factories/

Written by Mark Pomerleau
Mar 11, 2022 | FEDSCOOP

U.S. Cyber Command is looking to more tightly link cyberspace “effects” operations with information operations, according to its deputy commander.

When asked where he sees the command headed in the next five to 10 years, Lt. Gen. Charles Moore said he hopes it will broaden the concept of a “truly synchronized and integrated information warfare approach.”

“Without a doubt, what we have learned is that cyber effects operations in … more of a combined arms approach with what we call traditionally information operations, is an extremely powerful tool,” he said Thursday at Cybercom’s legal conference.

Cybercom has reportedly begun engaging in some level of combined operations along these lines. It has been reported that the command has sent messages to Russian cyber operatives letting them know the U.S. military knows who they are and what they’re doing as a means of deterring their potential future malicious activity aimed against the U.S.

Many of the organization’s subordinate commands have taken on a larger rolein info warfare for their respective services, reorganizing and consolidating similar capabilities to provide an integrated approach.

Former officials have pushed for a stronger alignment of cyber ops and information ops.

“When you can perform information operations to influence adversaries’ perceptions and maybe their behavior in conjunction with those effects, you can position yourself to see real-time reflections on how they’re assessing what you’re conducting. And then you can stay ahead of their decision cycle, inside of their OODA loop: their observe, orient, decide and action [cycle]. And you can do that at speed over time,” Moore said. “That’s where we want to be. That’s an extremely powerful tool inside the Department of Defense’s toolkit.”

Previously, Moore had explained that under DOD’s authorities and ability to gain access to different types of networks and social media, it can be positioned to see what the adversary is thinking.

“What that allows us to do is in a very precise manner to target individuals or groups of messages that we know will have an effect. And then when you combine that with actual cyber effects itself in a very combined arms approach … [it allows Cybercom] to drive the enemy’s decision calculus and cycle,” he said during an event hosted by C4ISRNET last year.

While cyber operators are experts in networks, not information ops, the services have begun aligning information operations specialists to work alongside cyber operators.

Despite having the necessary policies and authorities in place to execute these types of operations, Moore said DOD will need to harness emerging technologies such as artificial intelligence, big data analytics and even quantum computing to truly be successful in the future.

“You’re going to need these types of capabilities to execute across the breadth, the scope, the depth and the speed that’s going to be necessary to stay ahead of the adversaries and really drive their decisions cycles, drive their perceptions as opposed to being driven,” he said.

A better grasp of information warfare and what that looks like across the federal government and Department of Defense is warranted, Moore said. He explained that the Pentagon and the nation need to focus on a strategy aimed at affecting adversary perceptions.

Such a strategy or an approach should inform the government on what types of things to invest in, what to reveal and conceal, what and when to exercise capabilities and concepts, and when to conduct troop movements.

“All of these types of things ought to be part of that broader information warfare strategy leading back to the specific objectives and goals that you hope to achieve,” Moore said. “I think in the department, a lot of times, we have [been] a little bit backwards. We have pockets of excellence and those pockets are getting bigger. That’s the good news. But I think in general, they’re not completely synchronized.”

Article link: https://www.fedscoop.com/cyber-command-moving-toward-an-integrated-information-warfare-approach/

By Timothy Prickett Morgan

Some of the most important luminaries in the HPC sector have spoken from on high, and their conclusions about the future of the HPC market are probably going to shock a lot of people.

In a paper called Reinventing High Performance Computing: Challenges and Opportunities, written by Jack Dongarra at the University of Tennessee and Oak Ridge National Laboratory – Jack, didn’t you retire? –  and Dan Reed at the University of Utah and Dennis Gannon formerly of Indiana University and Microsoft, we get a fascinating historical view of HPC systems and then some straight talk about how the HPC industry needs to collaborate more tightly with the hyperscalers and cloud builders for a lot of technical and economic reasons.

Many in the HPC market have no doubt been thinking along the same lines. It is in the zeitgeist, being transported on the cosmic Ethernet. (And sometimes InfiniBand where low latency matters.) And by the way, we are not happy about any of this, as we imagine you are not either. We like the diversity of architectures, techniques, and technologies that the HPC market has developed over the years. But we also have to admit that the technology trickle down effect where advanced designs eventually make their way down into large enterprises and then everywhere else did not happen at the speed or to the extent that we had hoped it might over the decades we have been watching this portion of the IT space.

As usual, the details of the scenario painted in this paper and the conclusions that the authors draw are many and insightful, and we agree wholeheartedly that there are tectonic forces at play in the upper echelons of computing. Frankly, we founded The Next Platform with this idea in mind and used the same language, and in recent years have also wondered how long the market for systems that are tuned specifically for HPC simulation and modeling would hold out against the scale of compute and investment by the hyperscalers and cloud builders of the world.

The paper’s authors have a much better metaphor for contrasting large-scale HPC system development, and that is to look at it like a chemical reaction. HPC investments, especially for capability-class machines, are endothermic, meaning they require infusions of capital from governments and academia to cover the engineering costs of designing and producing advanced systems. But investments in large-scale machinery at the hyperscalers and cloud builders are exothermic, meaning they generate cash – among the Magnificent Seven of Amazon, Microsoft, Google, Facebook, Alibaba, Baidu, and Tencent, it is enormous amounts of money. We would go so far as to say that the reaction is volcanic among the hyperscalers and cloud builders, which is exothermic with extreme attitude. Enough to melt rock and build mountains.

The geography of the IT sector has been utterly transformed by these seven continents of compute, and we all know it, and importantly, so does the HPC community that is trying to get to exascale and contemplating 10 exascale and even zettascale.

“Economies of scale first fueled commodity HPC clusters and attracted the interest of vendors as large-scale demonstrations of leading edge technology,” the authors write in the paper. “Today, the even larger economies of scale of cloud computing vendors has diminished the influence of high-performance computing on future chip and system designs. No longer do chip vendors look to HPC deployments of large clusters as flagship technology demonstrations that will drive larger market uptake.”

The list of truisms that Dongarra, Reed, and Gannon outline as they survey the landscape is unequivocal, and we quote:

• Advanced computing of all kinds, including high-performance computing, requires ongoing non-recurring engineering (NRE) investment to develop new technologies and systems.
• The smartphone and cloud services companies are cash rich (i.e., exothermic), and they are designing, building, and deploying their own hardware and software infrastructure at unprecedented scale.
• The software and services developed in the cloud world are rich, diverse, and rapidly expanding, though only some of them are used by the traditional high-performance computing community.
• The traditional computing vendors are now relatively small economic players in the computing ecosystem, and many are dependent on government investment (i.e., endothermic) for the NRE needed to advance the bleeding edge of advanced computing technologies.
• AI is fueling a revolution in how businesses and researchers think about problems and their computational solution.
• Dennard scaling has ended and continued performance advances increasingly depend on functional specialization via custom ASICs and chiplet-integrated packages.
• Moore’s Law is at or near an end, and transistor costs are likely to increase as features sizes continue to decrease.
• Nimble hardware startups are exploring new ideas, driven by the AI frenzy.
• Talent is following the money and the opportunities, which are increasingly in a small number of very large companies or creative startups.

There is no question that the HPC and hyperscaler/cloud camps have been somewhat allergic to each other over the past decade or two, although there has been some cross pollination in recent years, with both people and technologies from the HPC sector being employed by the hyperscalers and cloud – mostly to attract HPC simulation and modeling workloads, but also because of the inherent benefits of technologies such as MPI or InfiniBand when it comes to driving the key machine learning workloads that have made the hyperscalers and cloud builders standard bearers for the New HPC. They didn’t invent the ideas behind machine learning – the phone company did – but they did have the big data and the massive compute scale to perfect it, and they are also going to be the ones building the metaverse – or metaverses – that are really just humungous simulations, driven by the basic principles of physics, done in real time.

What it comes down to is that standalone HPC in the national and academic labs takes money and has to constantly justify the architectures and funding for the machines that run their codes, and that the traditional HPC vendors – so many of them are gone now – could not generate enough revenue, much less profit, to stay in the game. HPC vendors were more of a public-private partnership than Wall Street ever wanted to think about or the vendors ever wanted to admit. And when they made any profits, it was never sustainable – just like being a server OEM is getting close to not being sustainable due to the enormous buying power of the hyperscalers and cloud builders.

We will bet a Cray-1 supercomputer assembled from parts acquired on eBay that the hyperscalers and cloud builders will figure out how to make money on HPC, and they will do it by offering applications as a service, not just infrastructure. National and academic labs will partner there and get their share of the cloud budget pool, and in some cases where data sovereignty and security are particularly high, the clouds will offer HPC outposts or whole dedicated datacenters, shared among the labs and securely away from other enterprise workloads. And moreover, the cloud makers will snap up the successful AI hardware vendors – or design their own AI accelerator chips as AWS and Google do – and the HPC community will learn to port their routines to these devices as well as CPUs, GPUs, and FPGAs. In the longest of runs, people will recode HPC algorithms to run on a Google TPU or an AWS Tranium. No, this will not be easy. But HPC will have to ride the coattails of AI because otherwise it will diverge from the same hardware path and not be an affordable endeavor.

As they prognosticate about the future of HPC, Dongarra, Reed, and Gannon outline the following six maxims that should be used to guide its evolution:

Maxim One: Semiconductor constraints dictate new approaches.There are constraints from Moore’s Law slowing and Dennard scaling stopping, but it is more than that. We have foundry capacity issues and geopolitical problems arising from chip manufacturing, as well as the high cost of building chip factories, and there will need to be standards of interconnecting chiplets to allow ease of integration of diverse components.

Maxim Two: End-to-end hardware/software co-design is essential. This is a given for HPC, and chiplet interconnect standards will help here. But we would counter that the hyperscalers and cloud builders limit the diversity of their server designs to drive up volumes. So just like AI learned to run on HPC iron back in the late 2000s, HPC will have to learn to run on AI iron of the 2020s. And that AI iron will be located on the clouds.

Maxim Three: Prototyping at scale is required to test new ideas. We are not as optimistic as Dongarra, Reed, and Gannon that HPC-specific systems will be created – much less prototyped at scale – unless one of the clouds corners the market on specific HPC applications. Hyperscalers bend their software to fit cheaper iron, and they only create unique iron with homegrown compute engines when they feel they have no choice. They will adopt mainstream HPC/AI technologies every time, and HPC researchers are going to have to make do. In fact, that will be largely what the HPC jobs of the future will be: Making legacy codes run on new clouds.

Maxim Four: The space of leading edge HPC applications is far broader now than in the past. And, as they point out, it is broader because of the injection of AI software and sometimes hardware technologies.

Maxim Five. Cloud economics have changed the supply chain ecosystem.Agreed, wholeheartedly, and this changes everything, even if cloud capacity costs 5X to 10X as much as running it on premises, the cloud builders have so much capacity that every national and academic lab could be pulling in the same direction as they modernize codes for cloud infrastructure – and where it is sitting doesn’t matter. What matters is changing from CapEx to OpEx.

Maxim Six: The societal implications of technical issues really matter. This has always been a hard sell to the public, even if scientists get it, and the politicians of all the districts where supercomputing labs exist certainly don’t want HPC centers to be Borged into the clouds. But, they will get to brag about clouds and foundries, so they will adapt.

“Investing in the future is never easy, but it is critical if we are to continue to develop and deploy new generations of high-performance computing systems, ones that leverage economic shifts, commercial practice, and emerging technologies. Let us be clear. The price of innovation keeps rising, the talent is following the money, and many of the traditional players – companies and countries – are struggling to keep up.”

Welcome to the New HPC.

Author’s Note: We read a certain amount of technical papers here at The Next Platform, and one of the games we like to play when we come across an interesting paper is to guess what it will conclude before we even read it.

This is an old – and perhaps bad – habit learned from a physics professor many decades ago, where he admonished us to figure out the nature of the problem and estimate an answer, in ours heads and out loud before the class, before we actually wrote down the first line to solve the problem. This was a form of error detection and correction, which is why we were taught to do this. And it kept you on your toes, too, because the class was at 8 am and you didn’t know you were going to have to solve a problem until the professor threw a piece of chalk to you. (Not at you, but to you.)

So when we came across the paper outlined above, we immediately went into speculative execution mode and this popped out:

“Just like we can’t really have a publicly funded mail service that works right or a publicly funded space program that works right, in the long run we will not be able to justify the cost and hassle of bespoke HPC systems. The hyperscalers and cloud builders can now support HPC simulation and modeling and have the tools, thanks to the metaverse, to not only calculate a digital twin of the physical world – and alternate universes with different laws of physics if we want to go down those myriad roads – but to allow us to immerse ourselves in it to explore it. In short, HPC centers are going to be priced out of their own market, and that is because of the fundamental economics of the contrast between hyperscaler and HPC center. The HPC centers of the world drive the highest performance possible for specific applications at the highest practical budget, whereas the hyperscalers always drive performance and thermals for a wider set of applications at the lowest cost possible. The good news is that in the future HPC sector, scientists will be focusing on driving collections of algorithms and libraries, not on trying to architect iron and fund it.”

We were pretty close.

Article link: https://www.nextplatform.com/2022/03/09/will-hpc-be-eaten-by-hyperscalers-and-clouds/

March 7, 2022 | Podcast

By Michael Chui and Mark Collins

McKinsey research shows that adoption of IoT technologies has increased exponentially the past five years—but successful implementation still eludes some. Here’s how to get it right.

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In this episode of The McKinsey Podcast, McKinsey partners Michael Chui and Mark Collins share their thoughts with Roberta Fusaro on the findings of McKinsey’s latest Internet of Things report, including how to successfully integrate IoT, the situations in which the most value is being created, and what companies continue to get wrong.

After, it’s expected a piece of space debris hit the moon on March 4, 2022. Hear about the implications of all the other pieces of space junk in orbit from McKinsey associate partner Chris Daehnick and podcast managing producer Laurel Moglen. The following transcript has been edited for clarity.

The McKinsey Podcast is cohosted by Roberta Fusaro and Lucia Rahilly.

Podcast link: https://f.bktrksfn.com/users/proj/84c9fcbe-9b03-11ec-b58b-0e001522e58d/src/.bt_download/mp-iot-comes-of-age.mp3?t=1646319998617

Defining the Internet of Things

Roberta Fusaro: How do you define the Internet of Things?

Michael Chui: The Internet of Things, or IoT, is when you embed digital technologies into the physical world. For example, we’re seeing digital technologies embedded in cars and in buildings. You connect those physical objects through digital network connections back to computers, and that simply is it. That’s the Internet of Things.

Roberta Fusaro: Mark, compared with McKinsey’s previous research on the technology, what were some of the most surprising findings from the new IoT research? What were the areas where you found great uptake or where the use cases were more prevalent than in others?

Mark Collins: Looking at the settings or physical locations where IoT is deployed, of 99 individual cases, we found five represented 52 percent of the value in 2020. So a really big concentration in terms of where value is created.

The second thing that I found surprising is the massive growth of the connected home. Rewind the clock back five years ago, and the average American house had one connected device in it. We fast forward five years, and homes have over five connected devices on average.

This shift has been born in part out of real innovation in technology with smart speakers, as well as us being caught in our homes for the last two years because of COVID-19. We’re investing in the area around us, both to improve and make our lives easier.

Roberta Fusaro: You talked about how five areas accounted for more than half of the value that’s being generated. What are the other four areas where you’re seeing value created?

Mark Collins: Consumer applications is one of the fastest-growing areas, but it’s not the largest as we think about it overall. And so when we look at the largest overall, what stood out to us were use cases related to human health in hospital, acute-care, and residential-care settings.

Also, we saw growth in operations and optimizations. So, how can we really drive greater efficiency, greater efficacy? And that was across the settings of factories, cities, and work sites. We also saw a real excitement around human productivity in the retail setting to really enhance what the experience can be for shoppers while also enabling companies to optimize both their revenue growth and their cost price.

Michael Chui: One of the remarkable things we’ve seen is that companies are deriving value from using these technologies. Whether it’s in the factory, in healthcare, or in the automotive industry, we’re seeing more and more cases where real value is growing and being created.

Getting the integration of IoT right

Roberta Fusaro: Are there examples of companies that are truly getting the integration of IoT right?

Michael Chui: The World Economic Forum has identified a set of factories around the world that they describe as lighthouses. They are lighthouses in the sense that they are truly being forward-thinking in terms of the application of IoT within their factories.

For instance, they’ve deployed condition-based or predictive maintenance: rather than waiting for a tool in a factory to go down and bring down an entire assembly line, they’re continuously monitoring the performance of that tool so as to avoid unplanned downtime.

So that lowers cost in terms of the maintenance itself, but more importantly, you don’t lose the output of that entire line, which can be millions of dollars per day. And this is true in the chemicals industry as well.

Roberta Fusaro: I’m curious about one aspect of the consumer applications: It feels to me like one of the larger impediments to greater adoption is this privacy issue. How did that factor into the results of the research? And how can companies help resolve that tension?

Michael Chui: Privacy is absolutely a factor as we think about consumer preferences. And it’s one which has increasingly become top of mind as we’ve seen very public incidents over the last few years. Some companies make their value proposition based on privacy as they go forward. But what we also see is a tension for users because they want the convenience that many of these IoT devices can bring. They’re seeking to balance these trade-offs between privacy, ease of use, reliability, ease of installation, et cetera.

Some companies make their value proposition based on privacy as they go forward. But what we also see is a tension for users because they want the convenience that many of these IoT devices can bring.

Michael Chui 

The impact of IoT on cities

Roberta Fusaro: What impact have you seen IoT have on cities?

Michael Chui: Cities are near and dear to my heart as a former municipal CIO [chief information officer]. One of the challenges that we’ve seen in a number of large cities around the world is the amount of traffic congestion in the center of cities.

A number of different cities have applied congestion pricing. They are tracking when vehicles are in the center of the city and charging for the times when congestion is highest. That doesn’t necessarily make the driver happy, but we have seen material changes in traffic patterns within those cities that have invested in congestion pricing. You can think about a city being like a dynamic, living organism, and IoT can help it adjust.

Mark Collins: Taking the example of the living organism, we’re seeing a real trend toward environmental, social, and governance [ESG] and sustainability right now. As someone who lived through the days of orange skies in San Francisco as a result of the fires a few years ago, I think we’re seeing real focus now on things like air quality monitoring and water quality monitoring.

We’re seeing cities intervene by deploying sensors to detect these things. We’re also seeing democratization of that with private individuals paying for sensors that tell them the levels of air pollution in their region. These sensors allow governments to think about their response to pollution. And that can feed into decisions, such as what factories are going to be opened, where they’re going to be opened, and what their hours are.

What companies get wrong about IoT

Roberta Fusaro: So those are areas that show high promise and high activity. What are companies still getting wrong about IoT?

Mark Collins: I think one of the big findings when we looked back, relative to the research of five years ago, was that now largely we can say the technology is actually good enough. We’ve had huge maturation in things like network connectivity, battery power, computer advanced analytics, et cetera. What we saw happen all too often was IoT being treated as a technology project, often run by the CIO or by a small business unit or factory plant all by themselves.

And so the technology has changed, but the actual way of work has not. When we look at some of the lighthouse factories that Michael referenced earlier from the World Economic Forum, we see that they treat the integration of IoT as a holistic operating model transformation.

When they look at how systems and processes are going to change on the factory floor, for example, they think about how they may need to motivate individuals working within that system differently. And then to support that, they think about what are the KPIs and reporting that you need to be looking at on a forward basis. And we find when you bring these operating-model factors together with some of the fantastic technology that you have today, you really can produce some pretty magical results.

Michael Chui: As a former CIO, I feel the technology leadership is still important, though. We just need to make sure we have business leadership, too.

Roberta Fusaro: That’s a good point, Michael. How should technology and business leadership be working differently to enable IoT initiatives?

Michael Chui: This is an old story. In any business application of technology, you need business leaders to partner closely with the technology leaders. To ensure smooth integration of IoT—because the technology has advanced a lot, you really need that coleadership.

At the end of the day, you need the business leader also to be able to say what’s being targeted, for example, “We’re going to target unplanned downtime. We’re going to target inventory management cost. We’re going to target our employees’ safety.” Whatever it is. That’s the business result you’re going for. Hopefully, the technology people are learning about that at the same time that the business people are learning about technology.

Mark Collins: To build off that, one of the themes that we heard in our client service is that the integration of IoT is often easier in greenfield settings. It’s easy to build in the technology and the business icons from the start, or if you’re a new IoT company being set up from scratch. Where we see this being a challenge is when you’re trying to integrate it into legacy or brand field environments.

And that’s not just the case of where you have a factory that’s 50 years old that suddenly needs to be connected to the internet for the first time, but it’s also in terms of companies that have traditionally sold unconnected products. How do they now move to a world which is much more software-driven, where they want to sell connected products going forward? In these situations, the connection between business leaders and technology leaders can have stunning results.

The role of talent in the adoption of IoT

Roberta Fusaro: What role does talent play in the way that IoT is being adopted? Or is it holding the technology back?

Mark Collins: We see talent playing an absolutely critical role. And it’s not just in terms of core academic disciplines, where there’s a huge need for talent such as data engineers, data scientists, computer scientists, et cetera, who underpin all of the operations of analytics and IoT.

We also see it in terms of ways of working. We see companies doing really well when they’re taking a design thinking methodology and working backward from their customers’ needs all the way through to how they deliver it.

As we thought about the implications for governments or regulators, one of the key reflections that we had is how do governments and regulators think about turbocharging the talent that is needed to unlock this potential going forward. And that’s going to require a whole raft of interventions all the way from primary and secondary and tertiary education through to thinking about skill development.

Michael Chui: Folks who know how to implement IoT and capture value from it in a business are in high demand. It is one of the bases of competition. And because IoT is not exclusive to the technology industry, you see automotive companies vying for the same talent as the tech industry.

Avoid a fragmented approach to IoT

Roberta Fusaro: What’s a typical example of some things that companies get wrong when they try to use IoT or implement IoT into their corporate strategies?

Mark Collins: What we sometimes see with large multinational companies that have global footprints is that there isn’t a clear owner of IoT within the organization. And that leads to fragmented and decentralized decision making when it comes to IoT.

I remember an example: I was speaking with a colleague about a company that had multiple factories across the globe. And almost every factory had a bespoke application, a bespoke vendor for providing one single discrete use case.

Each of them worked well in terms of their individual silos, but when it came to looking across the company as a whole, it was next to impossible to get an aggregate view across the entirety of the company. It meant as you thought about scaling those solutions, you were structurally limited and almost had to go back to the start and reengineer.

I see a real theme around who owns the IoT agenda, particularly from multinational companies, and how being thoughtful about the short term and the long term, at a global and a local level, will allow you to capture value.

We’ve seen lots of examples where people buy a new piece of technology and it’s used for the first week and then sits idle. And it’s through uniting the business side and the technology side and changing the day-to-day ways of working that you get to real change for companies.

The classic example that Michael and I often talk about are the repairers, who, until recently, were the heroes of the hour when they fixed the repair. In the future, the hero of the hour will be the person who makes sure the repair is never needed in the first place. And so, you need to think totally differently about the KPIs, the incentives, and the performance management of people on a very practical level.

Michael Chui: Another thing that companies get wrong is they don’t ensure that the machines they buy are connected to each other. A lot of companies that sell connected equipment to factories say that they’ll do predictive maintenance, but unless all of the machines talk to each other, you’re often going to underoptimize the performance of the whole factory.

When we did the analysis, well over 50 percent of the value that IoT can unlock requires interoperability. Unless the factory manager or the procurement manager specifies that connectivity and interoperability occurs, you’ll have IoT devices that don’t talk to each other.

Business ecosystems are critical

Roberta Fusaro: What role do business ecosystems play in the changes you’ve seen? Is there more collaboration? Are these ecosystems bigger or better?

Mark Collins: I think ecosystems are absolutely critical as we think about the landscape going forward. And to give a very tangible example of this: One company I work with wants to deploy sensors within their production environment, and they want to make sure that those sensors are secure. Making sure those sensors are secure requires having security designed in from the start. How is the network on which those chipsets communicate information secured? In the servers where that data is stored, how do we ensure that they are secure as well? And so, no single vendor can secure an entire IoT value chain. Rather, we need people who work together to integrate almost each layer of the stack to bring this together.

We have seen the emergence of platforms that are at scale that everyone can integrate with. We have not seen that emerge in IoT as of yet. And the emergence of that through an ecosystem play or through some other play is an unlock that we would be excited about.

Applying IoT to supply chain and public-health issues

Roberta Fusaro: If we do this research again in another two to five years, what are some trends that you could imagine developing?

Michael Chui: We need our factories to be better performing. Look at all the supply chain issues that we’ve had. It has been, in some cases, a setting that ended up being slower growing than we thought back in 2015. At the same time, it actually is the largest potential source of value.

I’m not saying IoT can solve all of the problems, but if you have better visibility in your supply chain, if you have the ability to ramp up and down in volumes in your manufacturing, we can have a much more resilient and productive economy.

Hopefully, we will be able to manage the next pandemic, which I hope is a long way away, by better understanding how public health is doing and how we are all individually doing. Hopefully, our homes will become more energy efficient.

Mark Collins: We wrote this report in the middle of a pandemic. In many cases, the world is different now than when we started writing this report 18 months ago.

One CEO referred to there being a decade of progress in digital transformation in under ten months. And so, I think about how many of my clients are now achieving things at a speed that they would never have thought possible in equivalent time frames before. I also see the ambition levels increasing as they think about what they want to do, how they want to link the physical and digital worlds together and make concerted investments in things like digital twins and other technologies that not only would enable them to be better prepared for the future but also enable them to capture value and serve their customers better.

It’s time to go all in

Roberta Fusaro: What is one key takeaway you would offer business leaders to accelerate their adoption of IoT?

Michael Chui: I’d say, bring all of the innovation that you have in digital to the physical world using IoT. It turns out that to transform your business, you actually have to do a lot of things. It doesn’t come down to one thing. That’s what we’ve discovered. And so you really have to bring the whole playbook if you’re actually going to transform using IoT.

Mark Collins: For me, it would be about thinking about how to start to sell and deliver outcomes versus technology.

Roberta Fusaro: In the research, you say, “Don’t dip your toe in the water.” What does that look like if you’re an executive and you’re maybe a little bit hesitant?

Mark Collins: Doing more things together means that you force change. And so, in the research that we’ve done every year, we’ve seen that those companies that deploy more use cases together in parallel consistently capture more value per individual use case. And that’s because you force the rethink around your operating model, processes, incentives, and reporting. And it really serves as an unlock for the company.

Companies that deploy more use cases together in parallel consistently capture more value per individual use case. That’s because you force the rethink around your operating model, processes, incentives, and reporting. And it really serves as an unlock for the company.

Mark Collins 

Roberta Fusaro: What does it look like to deploy multiple use cases?

Mark Collins: Multiple use cases may be occurring, but they may be occurring from different perspectives on different systems in different areas. And that’s why I think it’s so important to think about who is the IoT champion within your company. Who is the person that’s going to take on this mantle and lead forward? Because just by the act of appointing someone as the lead, you automatically enable greater coordination and greater visibility, which, as we’ve seen in our research, gives you a multiplier in terms of the outcomes you get.

IoT and the metaverse

Roberta Fusaro: What role could IoT play in fostering the metaverse or challenging the metaverse?

Michael Chui: First, I want to recognize that there are a number of different opinions on what the metaverse is. So it’s hard to come up with a definitive answer that matches everyone’s definition. We tend to exclude user interface devices as part of the IoT, but nevertheless, those things are, from a very practical standpoint, metaverse related.

Mark Collins: Think about how you can create almost near-perfect replicas of things like telecom networks or electricity networks or factory floors, where you can trial and test and get real-time feedback.

Sensors and IoT devices within these environments will provide the information to enable those digital twins to run in real time and give you the ability to think about how you can optimize. And these deliver benefits not just in terms of cost opportunity savings optimization but also in terms of situations such as telecom networks in the face of mass weather events when cell towers are falling over.

Michael Chui: If you have a digital twin of a factory and you can go into the factory and simulate what the operators might be able to do, you can start to train and you can start to optimize all those sorts of things.

What IoT can do for construction and healthcare

Roberta Fusaro: Before we sign off, is there anything that you wanted to touch on that we’ve missed?

Michael Chui: One of the other settings that I’m excited about in which these technologies could be deployed is worksites. Productivity in construction has actually declined slightly over several years.

We badly need to do things such as building more housing. The hope is that IoT and other practices can improve the productivity of things like construction. That would be incredibly beneficial, not only to our economy but also for people.

Mark Collins: Michael, with you taking the efficiency angle, let me take the efficacy angle for a second. We spoke earlier about the impact of COVID-19 on the deployment of IoT. One area that has foundationally changed forever is the provision of healthcare.

The pandemic has reset how we interact with our healthcare providers. We’re now in a world where the default is, “I’ll engage with my primary care over unified communications (UC) or over my phone through videoconferencing. No longer am I going into a clinic or waiting room.”

If you think about what IoT is enabling off the back of that, it’s the ability to do diagnostic tests that were previously only possible in acute-care settings. Now, suddenly, I can do these with a wearable on my wrist and be done in 30 seconds. If I think about the potential for that not just to improve wellness but also for patients with acute and chronic diseases, it can make a real difference in terms of both quality of life and also detecting potential disease or deterioration earlier—helping to prolong life as well as expanding its quality.

Segment Two: Look out for space junk

Laurel Moglen: At the time of this recording, a piece of space debris is expected to hit Earth’s moon—if it hasn’t already. That piece is not alone. Chris Daehnick, a McKinsey associate partner in the Aerospace & Defense Practice, says many thousands more are in orbit.

Chris Daehnick: The US Space Force tracks about 27,000 pieces of debris.

Laurel Moglen: That’s 27,000 pieces of debris that are traceable. Chris says those pieces are—

Chris Daehnick: Roughly the size of a softball.

Laurel Moglen: What about the number of pieces that are not traceable?

Chris Daehnick: Several hundred thousand pieces, or at least a large multiple of the 27,000, are untraceable.

Laurel Moglen: What exactly are those thousands of pieces of junk?

Chris Daehnick: Space junk starts with pieces of rockets or old satellites that have outlived their usefulness. Everything from things that have fallen off the International Space Station, or the Space Shuttle in the past, or other spacecraft. In a few cases where someone has intentionally destroyed a satellite with an antisatellite weapon, that creates a huge amount of additional junk. You have things like paint flecks. There’s even an astronaut’s glove floating around out there.

Laurel Moglen: A paint fleck here on Earth is pretty innocent, but in space, it’s a projectile that packs a punch.

Chris Daehnick: Somewhat notoriously, one of the space shuttles got a chip in one of its windows. It was a noticeable defect in the window when it came back to Earth. But didn’t cause any greater damage.

Laurel Moglen: But greater damage is a possibility if space pollution increases?

Chris Daehnick: There is the potential over the long run that if you have enough pieces of junk in orbit and they begin to collide with each other, you could have almost a chain reaction. This was first theorized by a NASA scientist. It was called the Kessler Syndrome. We haven’t quite reached that critical level yet.

Laurel Moglen: To avoid getting to that critical level, Chris says there are a few things we can do.

Chris Daehnick: Don’t create debris intentionally. Improve the mechanisms for removing satellites once they’re no longer active and have requirements that enhance the ability to get things out of orbit when they’re a problem—when they’re dead, literally.

Laurel Moglen: A couple of entities are taking an interest in the issue.

Chris Daehnick: The United Nations has taken this on. NASA has proposed some standards. I think it’s safe to say there’s interest from any space-faring nation. This is a common problem. The question, perhaps, is more about who is willing to take any sort of action.

Laurel Moglen: For now, that action depends mostly on the willingness of companies and individual nations.

Chris Daehnick: About all I can say is that we still don’t have any agreement on that because now you’re getting into sovereign rights and what a country is willing to limit for itself.

Laurel Moglen: Looking forward, it’s expected there will be increased activity in space—from launching new communication constellations, greater human exploration, and space tourism. This heightens the chances of collisions.

Chris Daehnick: It’s not going to be enough in the future to just hope that nothing bad happens. We are going to have to take a more active role in limiting debris.

Laurel Moglen: And, what about that piece of debris hitting Earth’s moon? Chris says the moon will be okay. But it will spread a bunch of space debris around the surface and create a small impact crater.

Article link: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/iot-comes-of-age?

ABOUT THE AUTHOR(S)

Michael Chui and Mark Collins are partners in McKinsey’s Bay Area office. Chris Daehnick is an associate partner in the Denver office. Roberta Fusaro is an executive editor in the Waltham, Massachusetts, office. Laurel Moglen is a managing producer/editor in the Southern California office. Lucia Rahilly is the global editorial director based in New York City

Global health groups are turning to supply chain software to help patients get better access to treatment and testing.

By 

• Will Douglas Heaven

March 9, 2022

Running a healthcare system is like juggling bees. Millions of moving pieces—from mobile clinics to testing kits—need to be in the right places at the right time. That’s even harder to do in countries with limited resources and endemic disease.

But getting stuff where it’s needed is a problem big companies deal with all the time. Now global healthcare groups are adopting some of their techniques: a number of the world’s poorer countries have started to use AI-powered supply-chain management tools to help people get better access to testing and treatment.

Healthcare organizations are using the software to help decide where to set up new clinics, how to allocate equipment and staff, and what spending to prioritize. And the same tools could soon end up helping to run health programs in the US, too.

Only 1% of clinics and 14% of hospitals in low- and middle-income countries have the equipment or staff they need to diagnose endemic diseases, such as HIV, tuberculosis, and malaria, according to a 2017 report in the Bulletin of the World Health Organization. A commission set up by the medical journal the Lancet in 2021 concluded that nearly half of the world’s population has limited or no access to testing services. Even when tests are available, the results are often inaccurate or come too late to be of clinical use. 

Global health organizations have realized that if you zoom out far enough, these shortcomings start to look a lot like a supply-chain problem. “Why is it that Coca-Cola can deliver ice-cold cola to some of the most remote places in the world but we can’t do something similar in health care?” asks John Sargent, a doctor and co-founder of BroadReach Group. The health care delivery organization manages one of the world’s largest HIV care and treatment programs using AI software, including digital twins—tools that simulate complex processes by mirroring real-world resources such as goods, warehouses, and transport links. Machine learning algorithms can use these simulations to predict problems and propose solutions.

Over the last few years, companies across industries from retail to manufacturing have started using digital twins to weather the worst of the world’s ongoing supply-chain disruptions. “We wanted to step back and look at a country’s whole health care network,” says Heidi Albert, head of FIND South Africa. “That’s what led us to supply-chain thinking.” FIND (Foundation for Innovative New Diagnostics) is a nonprofit based in Switzerland. Testing is one of the weakest links in global health care, says Albert: “Our aim is to make sure that everyone who needs a test has access to one.”

That involves allocating a limited number of resources across an entire health care system, turning it into an optimization problem: how to make the most of what’s available. By modeling resources and the dependencies between them, digital twins identify gaps in a service, anticipate disruptions, and tweak the setup to explore various possible scenarios.  

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FIND is working with US-based Coupa, a business-management software vendor, to build a tool for optimizing testing services. Coupa has been adapting its business software called OptiDx for health care applications for the last four years. By using a range of computational techniques to model and analyze complex processes, including machine learning, its tools predict demand and identify optimal setups to meet it.   

OptiDx is designed to use that technology to help health care managers, from government officials to nurses, allocate resources effectively, making sure that equipment and staff are in the best locations. By simulating increased demand in the digital twin of a country’s testing facilities, for example, the government can see what additional clinics and equipment it might need and tweak variables to explore hypothetical scenarios.

FIND and Coupa have already piloted the software in Zambia, Vietnam, Bangladesh, and Burkina Faso. In Zambia, the tool’s recommendations cut the average distance HIV test samples had to be transported to three miles—11 times less than before.

FIND is now rolling OptiDx out to 15 more countries, including Kenya, Lesotho, India, and the Philippines. In the Philippines the software has shown that one clinic could conduct the same number of tests with fewer instruments than expected, allowing the clinic to reallocate some of its budget.

FIND is monitoring whether the tool’s recommendations are useful as well as whether they are put into practice. “This needs to be something that countries can own themselves,” says Albert. “A key thing is to put the power of the data in the hands of the people [who] need to make the decisions.”

Catching up to smartphones

BroadReach uses a simulation tool called Vantage, built in partnership with Microsoft, to identify understaffed clinics and send health care workers where they are needed most. In 2020, during the first few weeks of the pandemic, BroadReach worked with FIND to assess how prepared two provinces in South Africa were for covid, flagging shortages in protective equipment and staff in more than 300 clinics in just three days. 

Sargent says he learned about health care systems in Africa firsthand when working in refugee camps before he went to medical school. He later founded BroadReach with fellow doctor Ernest Darkoh, who grew up in Tanzania and Kenya. “You can go into rural clinics in places like Zambia, and you will see patient records sitting around on paper,” says Sargent. Yet the technology is there: “The nurses are using smartphones and Facebook is suggesting posts they might like.”

In addition to monitoring shortages, BroadReach tracks individual patients across more than a thousand clinics in several African countries, monitoring whether they are getting the treatment they need as well as continuing to follow it. Clinics already do that, but Vantage also uses machine learning, trained on hundreds of thousands of anonymized clinical records and social data, to identify people who are likely to drop out of treatment and suggest that health care workers check in with them proactively.

The Institute of Virology Nigeria used Vantage in 2021 to predict which of 30,000 people being treated for HIV across three sites in Nigeria were at risk of stopping their medication. The tool found that 91% of those who received a call or visit from a health care worker as a result were up to date with their medication, compared with 55% of those who had not been contacted.

According to BroadReach, health care workers at a number of HIV clinics have said that the tool helps them maintain closer relationships with their patients by helping them focus on those most in need of intervention. 

The so-called developed world

BroadReach now wants to make its software available in the US. “Right around the time covid hit we sort of woke up to realize that a lot of quote-unquote developed-world health systems weren’t that great, and large swaths of the population were being left behind,” says Sargent. 

BroadReach is involved in four pilot projects with US health care providers and insurers. In one, it tackled low rates of vaccinations in parts of Colorado by using machine learning to predict where vaccination sites should be set up and which communities should be canvassed. Local health officials had assumed that resources should be focused on urban areas to vaccinate as many people as possible. But Vantage revealed that focusing on low-income rural, minority communities would have a greater impact. 

BroadReach is also working with an insurer in California that sees significant disparities in the way people in various groups keep to a treatment regimen of statins, drugs used to treat high cholesterol. By looking at the data, BroadReach wants to identify potential explanations for what the insurer is finding. Some communities have poor transport links to clinics, which may stop people from visiting their doctors to update prescriptions. Others simply have a longstanding distrust of the health care system, says Sargent. 

Eventually he wants to see Vantage predict risk factors for individual patients. For example, for a Spanish speaker who does not live near a clinic, the software would recommend that the insurer provide a Spanish-speaking social worker and a voucher for a taxi, he says. 

But it’s difficult to get hold of the data needed to train AI to make such predictions accurately. In the US, health care data is typically not shared between providers. Sargent says BroadReach is getting around this by combining medical records with socioeconomic data, such as people’s ZIP codes and credit histories. “We have partnerships with consumer data companies, because you can tell a lot about a patient if you know their patterns of behavior and what conditions they live in,” he says. “We stitch all of this together to get a view of an overall population and a view of each patient.” 

How people feel about this kind of surveillance will depend on what benefits it actually brings them. A range of bodies—including credit companies, hiring agencies, the police, and more—already use the socioeconomic data BroadReach draws on to predict the likely future behavior of individuals. Biases in these systems have rightly led to strong pushback from civil rights groups. 

Government proposals to share medical data in several countries have generated backlash, including in the US, the UK, and Australia, says Nicholson Price, who studies legal and ethical questions around the use of personal data at the University of Michigan. But that hasn’t stopped companies from combining medical and consumer data. “Companies have been doing this for several years, just at a lower profile,” says Price.

“There’s a sense of resignation that it’s happening and we appear to have no ability to stop it,” he says. “That said, maybe there’s a silver lining that some good stuff will come out of this, too, instead of just being advertised at and manipulated.”

Article link: https://www.technologyreview.com/2022/03/09/1046976/ai-is-helping-treat-healthcare-as-if-its-a-supply-chain-problem/

4 Major Programs & Offices Being Rolled Under John Sherman With DOD CDAO Appointment

In recent months, the Department of Defense has placed greater emphasis and importance on artificial intelligence. The technology has been included on the White House’s critical and emerging technologies list, and the department in December created a new position meant to oversee artificial intelligence, digital-focused initiatives and data.

Now, with John Sherman’s confirmation as the Department of Defense’s chief information officer at the close of 2021, and his more recent appointment to the role of acting chief digital and AI officer in February, the intelligence community veteran has assumed responsibility for a number of AI-focused programs, initiatives and offices at the Pentagon.

Sherman, a 2022 Wash100 Award winner, now oversees the following key areas within the DOD:

Joint AI Center

The DoD’s Joint Artificial Intelligence Center, established in 2018, aims to leverage AI in its mission to “safeguard American citizens, support our allies and partners, and improve the affordability and effectiveness of military operations.”

Among JAIC’s recent projects is the creation of an integration layer that will help accelerate the development of AI algorithms for combatant commands. Ltg. Michael Groen, director of JAIC and a Wash100 Award recipient, said the DOD needs a central platform for data feeds in order to speed the application generation process and better support rapid decision making capabilities.

In February, the U.S. Army Contracting Command awarded 79 companies positions on a blanket purchase agreement to support the ethical, responsible development of AI systems. Awarded on behalf of JAIC, the BPA will set test and evaluation standards as well as best practices for automated testing of AI-enabled systems.

Defense Digital Service

Described by Sherman as the DOD’s “digital fire brigade,” the Defense Digital Service is a team of engineers, data scientists and software developers from government and industry that seek to rapidly improve technology across the department.

DDS in December played a vital role in the department’s pursuit of tracking down Log4j cyber vulnerabilities. The service leveraged its bug bounty competition to identify the vulnerabilities on public-facing military websites, The Record reported.

Chief Data Officer

The Office of the Chief Data Officer, occupied by Wash100 Award winner David Spirk, is also being rolled under the responsibility of John Sherman as CDAO.

Since his appointment to chief data officer in June 2020, Spirk has spearheaded initiatives to update the DoD’s data policies, implement new standards and streamline data access in alignment with the department’s overarching mission to become more data-centric.

Spirk said he has plans to optimize information sharing in military operations through expanded partnerships with U.S. allies and allied organizations.

CMMC

Released in 2020, the Cybersecurity Maturity Model Certification framework is comprised of cybersecurity requirements and protection standards to safeguard the defense industrial base from cyberattacks and bolster national security.

In November 2021, the second iteration of the CMMC was published, updating the program to respond to increasingly complex and severe cyber threats.

Sherman said CMMC 2.0 could help raise the “waterline” of the Defense Department’s cybersecurity defenses. The DOD CIO plans to more closely engage with small and mid-sized businesses and clarify requirements in an effort to strengthen the CMMC program.

Additionally, the CMMC’s realignment under the CIO office is expected to increase the program’s integration with other cybersecurity programs across the department.

Under dedicated governance, the above programs and offices are expected to work more collaboratively to deliver synchronized AI capabilities and systems to the Defense Department.

“Look at it as a continuum, an ecosystem, all the way from data discovery and collection through how we’re going to store, curate, make sense of that data, unlock that data, and then to run advanced analytics,” Sherman said of the realignment.

John Sherman is slated to keynote the Potomac Officers Club’s 3rd Annual CIO Summit on April 26. Sherman will join a roster of prominent CIOs from vital government agencies, who will gather to share their innovative insights and discuss the challenges, priorities, initiatives and technologies that drive their organizations.

Article link: https://www.govconwire.com/2022/03/4-major-programs-and-offices-being-rolled-under-john-sherman-with-dod-cdao/

Although often less appreciated than remote-access cyber threats, insider threats to U.S. critical infrastructure can be just as damaging. Learn how to defend against threats from witting or unwitting insiders at: https://lnkd.in/ewQS2Vd

Document link: https://www.dni.gov/files/NCSC/documents/nittf/20210319-Insider-Threat-Mitigation-for-US-Critical-Infrastru-March-2021updated-5Apr21b.pdf

Across the sprawling Defense bureaucracy, it’s not a stretch to say there are dozens of different organizations who each see themselves at the forefront of buying or building innovative technologies. So it makes a certain degree of sense for the Pentagon’s top technology official to start her tenure by asking who’s working on what.

Heidi Shyu, the undersecretary of Defense for research and engineering, says one of her top priorities since being sworn in three months ago has been to build a better understanding of what’s actually happening across DoD’s vast innovation ecosystem — not just for the benefit of her own office, but for those working inside the military services and DoD’s various innovation organizations.

To the greatest extent possible, she said, she wants to limit the number of Defense R&D dollars and time that go toward reinventing the wheel.

“We’re looking across the entire DoD to look at all the different innovation activity that’s ongoing. I’d like to get my arms around just how many innovation organizations that we have,” she said during an interview with Federal News Network at the annual Association of the U.S. Army conference. “We’re asking questions like, ‘What is your mission? What have you procured? What capabilities do these products have? What have you transitioned into the hands of the warfighter, and which company are you buying these products from?’ The second piece I would like to understand is what the best practices are in each organization. If we can share those across the board, that would be valuable.”

        Insight by Qlik: Federal News Network surveyed five agencies to detail the current and future impact of SaaS across their mission areas. Download the survey to learn more.

The discovery process is happening under the auspices of DoD’s Innovation Steering Group, which Deputy Defense Secretary Kathleen Hicks established earlier this year. Hicks appointed Shyu to lead the group after she took the post as undersecretary and chief technology officer in June.

In a related effort, Shyu said she has also begun asking the military services for specific prototype technologies that could fill “capability gaps” that have already been identified as joint requirements for the Pentagon.

After working with the Joint Staff and DoD’s global combatant commands to identify which requirements needed to be met most urgently, Shyu said the steering group got 203 white papers from the military services over the course of just five weeks, identifying prototypes they’d already started developing that could fill those gaps. Her office has recommended that 32 of them be funded and tested in joint exercises next fiscal year.

“We’ve briefed what we’re planning to do, and we were given the thumbs up. So one of the key things we’re doing now is going through the detailed planning. All the details of planning will be done in Fiscal 2022 to enable the execution in FY 23,” she said. “But the thing that’s exciting is three of those projects are actually joint projects with our allies. We definitely want to partner with allies and partner nations to be able to deliver capabilities much quicker.”

Ultimately, Shyu wants the outcome of those efforts to be a much easier discovery process for anyone in the Defense Department who’s looking for a particular capability. As of now, there’s no easy way for a program office to find out whether one of its counterparts is working on — or has already solved — an individual technology problem.

“R&E looks across every service. We’ve already gone to the Air Force, the Navy, the Marines, the Army, the [Defense Innovation Unit] and the [Strategic Capabilities Office] to try to understand what process they utilize and what companies they have gone out to,” she said. “So all of those things will be pulled together. We’d like to create a database we can tap into so that I can basically Google for a specific capability or product. Right now, you’re making a zillion phone calls.”

To the extent the department’s technology efforts are disjointed and decentralized, that’s not just a problem for DoD itself — but also for the sorts of nontraditional companies DoD said it wants to attract into its science and technology ecosystem. For small businesses, getting through the Pentagon’s front door has always been hard. Creating dozens of new and different doors doesn’t necessarily help, unless they’re well-marked.

Shyu, who previously served as assistant secretary of the Army for acquisition, logistics and technology during the Obama administration, said she learned that lesson directly during an interlude in private industry during the past four years.

        Read more: On DoD 

“Having worked with a number of small companies on the outside, it made me really realize how difficult it is to work with the Pentagon,” she said. “You don’t know who to talk to, and there’s no focal point. You talk to Person A, who will refer you to Person B and then Person C, and vendors go around and around and around before they find someone to go talk to and who’s interested in what they have.”

Starting to catalogue the department’s capability gaps and innovation activities should help with that problem, Shyu said, even if DoD can’t always share the precise details of its most urgent capability gaps. The needs statements the Innovation Steering Group sent to the military services earlier this year, for example, were entirely classified.

“But once we get all of that information together, we can go the next step. Figuring out what we already have — pulling a database together to share across the board will speak volumes,” she said. “Then, we need to figure out if there is a specific entry point we need to highlight even more to the small businesses. It’s a stepwise process we’re going through.”

Article link: https://federalnewsnetwork-com.cdn.ampproject.org/c/s/federalnewsnetwork.com/on-dod/2021/10/dods-new-rd-chief-prioritizes-moving-prototypes-to-real-world-applications/?amp=1

Written by Billy Mitchell
Mar 7, 2022 | FEDSCOOP

Director of National Intelligence Avril Haines announced Adele Merritt as the new CIO of the intelligence community on Monday.

Merritt officially took the role, housed under the Office of the Director of National Intelligence, in late January.

Michael Waschull had been serving as acting CIO of the IC since January 2021 and will continue on in his official role as deputy CIO.

“Dr. Merritt will lead our ongoing modernization efforts to transform the IC Information Technology Enterprise, ensure the security of the IC’s Information Technology systems, and enhance IT cooperation within the IC,” Haines said in her announcement. “Dr. Merritt brings over 20 years of technical, analytic, and policy expertise in cyber and national security operations to the role. Her accomplishments span the U.S. government.”

Within that portfolio, she’ll also be responsible for the continued rollout of the IC multi-cloud offering, the Commercial Cloud Enterprise (C2E) program.

Merritt comes to the IC after serving most recently as a program manager for Dreamport, a nonprofit associated with the Maryland Innovation and Security Institute and created by U.S. Cyber Command. Prior to that, she spent time in government as principal deputy CIO for cyber at the Department of Energy and as acting senior director for intelligence programs on the National Security Council during the Obama administration.

She’ll be the first permanent CIO for the IC since Matthew Kozma stepped away from the role. Before that, John Sherman — now serving as the CIO at the Department of Defense — held the role for several years.

Article link: https://www.fedscoop.com/intelligence-community-gets-new-cio-in-adele-merritt/

-In this Story- 

Adele Merritt, intelligence community (IC), john sherman, Michael Waschull, Office of the Director of National Intelligence (ODNI)

Eric Walz Jun 22, 2021 11:00 AM WST

At the fourth annual Computer Vision and Pattern Recognition (CVPR 2021) conference this week, Tesla’s senior director of AI, Andrej Karpathy, shared details the company’s latest supercomputer that will be used to train deep neural networks (DNN) for Tesla’s Autopilot and Full Self-Driving (FSD) autonomous driving features. 

Tesla said its new supercomputer will be ready by the end of the year. It’s actually the predecessor to Tesla’s more powerful supercomputer nicknamed “Dojo”, which Tesla Chief Executive Elon Musk says should be ready by the end of the year.

“Project Dojo” was first announced at Tesla’s Autonomy Investor Day in April 2019. Musk mentioned the supercomputing power of “Dojo” will help Tesla better label visual data, which is a difficult and time consuming task for developers of  self-driving vehicles.

For autonomous vehicles, neural networks are used to train software for complex tasks, such as identifying street signs, detecting pedestrians and predicting their movements, as well as for safe navigation. A typical self-driving vehicle can use dozens of DNNs for perception, localization and path planning.

However training neural networks requires a hefty amount of processing power, which is why Tesla built its supercomputer using powerful Nvidia GPUs.

Tesla’s supercomputer uses 720 nodes of 8x NVIDIA A100 Tensor Core GPUs (5,760 GPUs total) to achieve an unparalleled 1.8 exaflops of performance, making it one of the world’s most powerful computers. This kind of processing power is mind boggling. One exaFLOP is one quintillion (1018) floating-point operations per second.

NVIDIA’s A100 GPUs power the world’s highest-performing data centers. The A100 GPU provides up to 20x higher performance over the prior generation, according to NVIDIA.

“This is a really incredible supercomputer,” Karpathy said during his presentation at CVRP 2021. “I actually believe that in terms of flops, this is roughly the No. 5 supercomputer in the world.”

Tesla uses the data from more than one million Tesla vehicles on the road to refine and build new autonomous driving features for continuous improvement. Having a fleet of connected vehicles to regularly collect data from gives Tesla an enormous advantage over other automakers in the development of autonomous driving technology.

Camera data from Tesla vehicles is continuously fed into the supercomputer in order to improve the software powering Autopilot and FSD. The neural networks are used to label 4D data from videos taken from eight onboard cameras that make up each vehicle’s 360-degree perception system. 

How Tesla’s Supercomputer is Put to Work

In a blog post, Nivida’s Senior Director of Automotive, Danny Shapiro, provided an overview of how Tesla’s supercomputer is used to train its deep neural networks for autonomous driving. Tesla’s cyclical development actually begins in the car, he said.

Whenever a Tesla vehicle is driving, a deep neural network running in the background known as “shadow mode” quietly perceives and makes predictions without actually controlling the vehicle, explained Shapiro.

The predictions are recorded, and any mistakes or misidentifications are logged. Tesla engineers then take this information and use each instance to create a training dataset of difficult and diverse scenarios to refine the DNN for improved performance.

The dataset is a collection of roughly one million 10-second clips recorded at 36 frames per second, equal to around 1.5 petabytes of data. The DNN runs through these scenarios in the data center over and over until it operates without a mistake. From there, it’s sent back to the vehicle and the process repeats.

Karpathy said training a DNN in this manner and on such a large amount of data requires a massive amount of compute power, which led Tesla to build and deploy the current generation supercomputer with NVIDIA’s high-performance A100 GPUs.

In addition to training DNNs, Tesla’s supercomputer provides vehicle engineers with the high performance needed to experiment and iterate in the development process.

Karpathy said the current DNN structure that Tesla is deploying allows a team of 20 engineers to work on a single network at once, isolating different features for parallel development, which is much faster.

These DNNs can then be run through training datasets at speeds faster than what has been previously possible for rapid iteration.

“Computer vision is the bread and butter of what we do and enables Autopilot,” said Karpathy. “For that to work, you need to train a massive neural network and experiment a lot.”That’s why we’ve invested a lot into the compute.”

Tesla’s autonomous driving system uses just radar and cameras, while most other developers of self-driving vehicles rely on supplemental lidar data. However, as Tesla’s computer vision capabilities improve, the radar will no longer be needed. Tesla said it would drop radar altogether, and began transitioning solely to the camera-based system in its Model 3 and Model Y vehicles starting in May.

Earlier this year, Musk said during the company’s fourth quarter earnings call in January that its upcoming Dojo supercomputer could potentially be offered as a service for other companies for training their neural networks.

“So some of the others need neural net training, we’re not trying to keep it to ourselves,” Musk said in January. “So I think there could be a whole line of business in and of itself.”

Tesla’s supercomputer is also a work in progress and the company plans to build an even more powerful one than the upcoming Dojo in the future. Although Karpathy declined to elaborate on the next iteration of Dojo, he said it will take Tesla’s supercomputing plans to the “next level”, which might get Musk closer to his target of level-5 autonomous driving, which requires no human supervision whatsoever.

Article link: https://m.futurecar.com/4698/A-Closer-Look-at-Teslas-Nvidia-powered-Supercomputer-for-Training-Deep-Neural-Networks