SIERRA & LENNYby 

• by the Editors

February 24, 2021

This list marks 20 years since we began compiling an annual selection of the year’s most important technologies. Some, such as mRNA vaccines, are already changing our lives, while others are still a few years off. Below, you’ll find a brief description along with a link to a feature article that probes each technology in detail. We hope you’ll enjoy and explore—taken together, we believe this list represents a glimpse into our collective future.

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Messenger RNA vaccines

We got very lucky. The two most effective vaccines against the coronavirus are based on messenger RNA, a technology that has been in the works for 20 years. When the covid-19 pandemic began last January, scientists at several biotech companies were quick to turn to mRNA as a way to create potential vaccines; in late December 2020, at a time when more than 1.5 million had died from covid-19 worldwide, the vaccines were approved in the US, marking the beginning of the end of the pandemic.

The new covid vaccines are based on a technology never before used in therapeutics, and it could transform medicine, leading to vaccines against various infectious diseases, including malaria. And if this coronavirus keeps mutating, mRNA vaccines can be easily and quickly modified. Messenger RNA also holds great promise as the basis for cheap gene fixes to sickle-cell disease and HIV. Also in the works: using mRNA to help the body fight off cancers. Antonio Regalado explains the history and medical potential of the exciting new science of messenger RNA.2021

10 Breakthrough Technologies

Messenger RNA VaccinesGPT-3Data trustsLithium-metal batteriesDigital contact tracingHyper-accurate positioningRemote everythingMulti-skilled AITikTok recommendation algorithmsGreen hydrogenAdvertisementhttps://49416f223e9128323cd6b38c4493b8e8.safeframe.googlesyndication.com/safeframe/1-0-37/html/container.html

GPT-3

Large natural-language computer models that learn to write and speak are a big step toward AI that can better understand and interact with the world. GPT-3 is by far the largest—and most literate—to date. Trained on the text of thousands of books and most of the internet, GPT-3 can mimic human-written text with uncanny—and at times bizarre—realism, making it the most impressive language model yet produced using machine learning.

But GPT-3 doesn’t understand what it’s writing, so sometimes the results are garbled and nonsensical. It takes an enormous amount of computation power, data, and money to train, creating a large carbon footprint and restricting the development of similar models to those labs with extraordinary resources. And since it is trained on text from the internet, which is filled with misinformation and prejudice, it often produces similarly biased passages. Will Douglas Heaven shows off a sample of GPT-3’s clever writing and explains why some are ambivalent about its achievements.

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TikTok recommendation algorithms

Since its launch in China in 2016, TikTok has become one of the world’s fastest-growing social networks. It’s been downloaded billions of times and attracted hundreds of millions of users. Why? Because the algorithms that power TikTok’s “For You” feed have changed the way people become famous online.

While other platforms are geared more toward highlighting content with mass appeal, TikTok’s algorithms seem just as likely to pluck a new creator out of obscurity as they are to feature a known star. And they’re particularly adept at feeding relevant content to niche communities of users who share a particular interest or identity.

The ability of new creators to get a lot of views very quickly—and the ease with which users can discover so many kinds of content—have contributed to the app’s stunning growth. Other social media companies are now scrambling to reproduce these features on their own apps. Abby Ohlheiser profiles a TikTok creator who was surprised by her own success on the platform.   

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Lithium-metal batteries

Electric vehicles come with a tough sales pitch; they’re relatively expensive, and you can drive them only a few hundred miles before they need to recharge—which takes far longer than stopping for gas. All these drawbacks have to do with the limitations of lithium-ion batteries. A well-funded Silicon Valley startup now says it has a battery that will make electric vehicles far more palatable for the mass consumer.

It’s called a lithium-metal battery and is being developed by QuantumScape. According to early test results, the battery could boost the range of an EV by 80% and can be rapidly recharged. The startup has a deal with VW, which says it will be selling EVs with the new type of battery by 2025.

The battery is still just a prototype that’s much smaller than one needed for a car. But if QuantumScape and others working on lithium-metal batteries succeed, it could finally make EVs attractive to millions of consumers. James Temple describes how a lithium-metal battery works, and why scientists are so excited by recent results.Advertisement

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Data trusts

Technology companies have proven to be poor stewards of our personal data. Our information has been leaked, hacked, and sold and resold more times than most of us can count. Maybe the problem isn’t with us, but with the model of privacy to which we’ve long adhered—one in which we, as individuals, are primarily responsible for managing and protecting our own privacy.

Data trusts offer one alternative approach that some governments are starting to explore. A data trust is a legal entity that collects and manages people’s personal data on their behalf. Though the structure and function of these trusts are still being defined, and many questions remain, data trusts are notable for offering a potential solution to long-standing problems in privacy and security. Anouk Ruhaak describes the powerful potential of this model and a few early examples that show its promise.

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Green hydrogen

Related Story

How falling solar costs have renewed clean hydrogen hopesAs nations do the hard math on how to meet their climate goals, green hydrogen increasingly appears essential.

Hydrogen has always been an intriguing possible replacement for fossil fuels. It burns cleanly, emitting no carbon dioxide; it’s energy dense, so it’s a good way to store power from on-and-off renewable sources; and you can make liquid synthetic fuels that are drop-in replacements for gasoline or diesel. But most hydrogen up to now has been made from natural gas; the process is dirty and energy intensive.

The rapidly dropping cost of solar and wind power means green hydrogen is now cheap enough to be practical. Simply zap water with electricity, and presto, you’ve got hydrogen. Europe is leading the way, beginning to build the needed infrastructure. Peter Fairley argues that such projects are just a first step to an envisioned global network of electrolysis plants that run on solar and wind power, churning out clean hydrogen.2021

10 Breakthrough Technologies

Messenger RNA VaccinesGPT-3Data trustsLithium-metal batteriesDigital contact tracingHyper-accurate positioningRemote everythingMulti-skilled AITikTok recommendation algorithmsGreen hydrogen

Digital contact tracing

As the coronavirus began to spread around the world, it felt at first as if digital contact tracing might help us. Smartphone apps could use GPS or Bluetooth to create a log of people who had recently crossed paths. If one of them later tested positive for covid, that person could enter the result into the app, and it would alert others who might have been exposed.

But digital contact tracing largely failed to make much impact on the virus’s spread. Apple and Google quickly pushed out features like exposure notifications to many smartphones, but public health officials struggled to persuade residents to use them. The lessons we learn from this pandemic could not only help us prepare for the next pandemic but also carry over to other areas of health care. Lindsay Muscato explores why digital contact tracing failed to slow covid-19 and offers ways we can do better next time.

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Hyper-accurate positioning

We all use GPS every day; it has transformed our lives and many of our businesses. But while today’s GPS is accurate to within 5 to 10 meters, new hyper-accurate positioning technologies have accuracies within a few centimeters or millimeters. That’s opening up new possibilities, from landslide warnings to delivery robots and self-driving cars that can safely navigate streets.

China’s BeiDou (Big Dipper) global navigation system was completed in June 2020 and is part of what’s making all this possible. It provides positioning accuracy of 1.5 to two meters to anyone in the world. Using ground-based augmentation, it can get down to millimeter-level accuracy. Meanwhile, GPS, which has been around since the early 1990s, is getting an upgrade: four new satellites for GPS III launched in November and more are expected in orbit by 2023. Ling Xin reports on how the greatly increased accuracy of these systems is already proving useful.

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Remote everything

The covid pandemic forced the world to go remote. Getting that shift right has been especially critical in health care and education. Some places around the world have done a particularly good job at getting remote services in these two areas to work well for people.

Snapask, an online tutoring company, has more than 3.5 million users in nine Asian countries, and Byju’s, a learning app based in India, has seen the number of its users soar to nearly 70 million. Unfortunately, students in many other countries are still floundering with their online classes.

Meanwhile, telehealth efforts in Uganda and several other African countries have extended health care to millions during the pandemic. In a part of the world with a chronic lack of doctors, remote health care has been a life saver. Sandy Ong reports on the remarkable success of online learning in Asia and the spread of telemedicine in Africa.

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Multi-skilled AI

Despite the immense progress in artificial intelligence in recent years, AI and robots are still dumb in many ways, especially when it comes to solving new problems or navigating unfamiliar environments. They lack the human ability, found even in young children, to learn how the world works and apply that general knowledge to new situations.

One promising approach to improving the skills of AI is to expand its senses; currently AI with computer vision or audio recognition can sense things but cannot “talk” about what it sees and hears using natural-language algorithms. But what if you combined these abilities in a single AI system? Might these systems begin to gain human-like intelligence? Might a robot that can see, feel, hear, and communicate be a more productive human assistant? Karen Hao explains how AIs with multiple senses will gain a greater understanding of the world around them, achieving a much more flexible intelligence.

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For a look at what technologies made our 10 Breakthrough Technologies lists in previous years, check out this page, which starts with 2020’s list.

Article link: 10 Breakthrough Technologies 2021 | MIT Technology Review

In an effort to help improve healthcare organizations’ resilience against ransomware, MITRE this week unveiled its new Ransomware Resource Center, offering an array of tools and strategies for IT and infosec professionals to better guard against the growing epidemic of costly malware.

WHY IT MATTERS
The Ransomware Resource Center tailors its many offerings around the role of the healthcare professional who might be accessing them – whether business manager, technical manager or IT or cybersecurity practitioner – and also around the five stages of the National Institute of Standards and Technology (NIST) Cybersecurity Framework: Identify, protect, detect, respond and recover.

It also offers a well-stocked resource library that’s searchable and can be filtered for the materials that might be the most useful.

The tools are drawn from MITRE’s own expertise, from government sources and from provider best practices. The goal is to convene a variety of resources in a single accessible and intuitive location, say MITRE officials, and to help “network defenders, IT administrators and business managers better prepare for, respond to, and recover from ransomware attacks.”

In a Q&A on the MITRE website, Joanne Fitzpatrick, lead cybersecurity engineer in MITRE’s Cyber Solutions Innovation Center, said particular attention was paid to small and/or underfunded IT and security staff.

“There are two key considerations,” she explained.

“First, such organizations typically have smaller IT and security departments, with a handful of talented people wearing many hats, and each responsible for several major operational IT areas. Staff tend to be experienced in the operations of their own organization, but often have little access to growth/training/professional development on cybersecurity issues, such as threats and attacks. Lack of time or budget is usually the reason.”

Moreover, at under-resourced organizations, there’s often “little-to-no extra staff available to dedicate to specialty cyber topics, such as threat modeling or attack surface assessments,” said Fitzpatrick.

“Second, we recognize that both small and large healthcare organizations may be targets for adversaries. Size does not matter. We’ve witnessed successful attacks at all types of health organizations. Adversaries may even exploit a smaller hospital as part of their attack navigation to exploit a larger, partnering organization.”

THE LARGER TREND
MITRE points to a recent report that showed 560 healthcare facilities suffered a successful ransomware attack in 2020 – and another that saw a 45% increase in exploitation attempts just in the past four months.

“We are currently fighting not only the COVID-19 pandemic, but also an epidemic that is spreading through cyberspace: ransomware,” said newly appointed Homeland Security Director Alejandro Mayorkas recently. “In addition to disrupting city governments, schools and companies, ransomware has also been disrupting hospitals and health care facilities, who are already strained, going above and beyond the call of duty during this ongoing crisis.

“Last October, CISA, together with other government agencies, warned of the growing threat of ransomware targeting the healthcare and public health sector,” he added. “Previous ransomware attacks illustrate the risk to COVID-19 vaccine deployment efforts that depend on key production and logistics facilities.”

Healthcare IT News recently put together a primer for how healthcare organizations should respond to such an attack.

ON THE RECORD
“We hope the Ransomware Resource Center will make two key contributions,” said Fitzpatrick. “It will inform hospitals and healthcare organizations about how to prepare, respond to and recover from such an attack. It also will share freely with the broader community the unbiased guidance and best practices that MITRE cybersecurity and cyber resiliency professionals have provided for years to our many federal government sponsors.”

Twitter: @MikeMiliardHITN
Email the writer: mike.miliard@himssmedia.com
Healthcare IT News is a publication of HIMSS Media

Article link: MITRE launches ransomware support hub for hospitals and health systems | Healthcare IT News

Jan. 15, 2021 | BY C. Todd Lopez , DOD News

While the defense industrial base is healthy, there are single points of failure and dependencies on overseas suppliers that must be addressed, the undersecretary of defense for acquisition and sustainment said.

“Over a period of years, we have offshored many, many sources of supply,” Ellen M. Lord said during an online discussion Thursday with the Hudson Institute. “It’s not for one reason; it’s for a variety of reasons, whether it be regulations, whether it be labor costs, whether it be government support of different industries.”

The deindustrialization of the U.S. over the last 50 years, the end of the Cold War and the focus it gave the U.S. on defeating the Soviet Union, digital technology and the rise of China have all created challenges to national defense.

In the newly released Fiscal Year 2020 Industrial Capabilities Report to Congress, Lord said the department looked into those challenges and their effects on the defense industrial base and proposed key actions to address them.

“What we did in this report was try to really capture those risks, look at the opportunities and come up with some specific steps that we can really take to reform how we go about looking at that supply chain and, in the endgame, really get capability downrange to the warfighter as quickly and cost-effectively as possible,” she said.

First, Lord said, the U.S. must re-shore more of its industrial base — bring it back to the U.S. and U.S. allies.

“There are a couple [of] key areas there with shipbuilding, as well as microelectronics — fundamental to our capability,” she said.

Development of a modern manufacturing and engineering workforce along with a more robust research and development base is also critical. Declines in U.S. science, technology, engineering and mathematics education and industrial jobs hurt the ability of the defense industrial base to innovate, Lord said.

“We want to make sure that we have modern manufacturing and engineering expertise,” she said. “We do not have nearly the number of scientists and engineers as China has. We need to make sure that we develop our talent to be able to leverage on these critical areas.”

The department must also reform and modernize the defense acquisition process to better meet the realities of the 21st century, Lord said.

“We’ve started with a number of those, but there’s much further to go,” she said. “We want to make sure that our traditional defense industrial base is widened to get all of those creative, innovative companies. We know the small companies are where most of our innovation comes from, and the barriers to entry — sometimes to getting into the Department of Defense — are rather onerous.”

Lord said part of modernizing and reforming defense acquisition is the recently announced Trusted Capital Marketplace, which will match potential defense suppliers — many of them small companies that have never done business with DOD — with the investors they need to keep operating and innovating. The Trusted Capital Marketplace will vet investors to ensure foreign ownership, control and influence is nonexistent.

Finally, Lord said, the department must find new ways to partner private sector innovation with public sector resources and demand.

“We, as the government, I believe, need to work with industry to make sure that we diversify that industrial base and, also, that we much more quickly translate technological capability into features of current platforms and weapon systems, as well as incorporate it in new ones,” Lord said.

Article link: https://www.defense.gov/Explore/News/Article/Article/2474015/dod-aims-to-bring-industrial-base-back-to-us-allies/

Report link: Health Data Sharing Special Publication – National Academy of Medicine (nam.edu)

After Amazon’s three-week re:Invent conference, companies building AI applications may have the impression that AWS is the only game in town. Amazon announced improvements to SageMaker, its machine learning (ML) workflow service, and to Edge Manager — improving AWS’ ML capabilities on the edge at a time when serving the edge is considered increasingly critical for enterprises. Moreover, the company touted big customers like Lyft and Intuit.

But Mohammed Farooq believes there is a better alternative to the Amazon hegemon: an open AI platform that doesn’t have any hooks back to the Amazon cloud. Until earlier this year, Farooq led IBM’s Hybrid multi-cloud strategy, but he recently left to join the enterprise AI company Hypergiant.Ad: (2:07)Skip AdMicrosoft says hackers viewed source code, didn’t change it, and other top stories in technology from January 05, 2021.

Here is our Q&A with Farooq, who is Hypergiant’s chair, global chief technology officer, and general manager of products. He has skin in the game and makes an interesting argument for open AI.

VentureBeat: With Amazon’s momentum, isn’t it game over for any other company hoping to be a significant service provider of AI services, or at the least for any competitor not named Google or Microsoft? 

Mohammed Farooq: On the one hand, for the last three to five-plus years, AWS has delivered outstanding capabilities with SageMaker (Autopilot, Data Wrangler) to enable accessible analytics and ML pipelines for technical and nontechnical users. Enterprises have built strong-performing AI models with these AWS capabilities.

On the other hand, the enterprise production throughput of performing AI models is very low. The low throughput is a result of the complexity of deployment and operations management of AI models within consuming production applications that are running on AWS and other cloud/datacenter and software platforms.

Enterprises have not established an operations management system — something referred to within the industry as ModelOps. ModelOps are required and should have things like lifecycle processes, best practices, and business management controls. These are necessary to evolve the AI models and data changes in the context of the underlying heterogeneous software and infrastructure stacks currently in operation.

AWS does a solid job of automating an AI ModelOps process within the AWS ecosystem. However, running enterprise ModelOps, as well as DevOps and DataOps, will need not only AWS, but multiple other cloud, network, and edge architectures. AWS is great as far as it goes, but what is required is seamless integration with enterprise ModelOps, hybrid/multi-cloud infrastructure architecture, and IT operations management system.

Failures in experimentation are the result of average time needed to create a model. Today, successful AI models that deliver value and that business leaders trust take 6-12 months to build. According to the Deloitte MLOps Industrialized AI Report (released in December 2020), an average AI team can build and deploy, at best, two AI models in a year. At this rate, industrializing and scaling AI in the enterprise will be a challenge. An enterprise ModelOps process integrated with the rest of enterprise IT is required to speed up and scale AI solutions in the enterprise.

I would argue that we are on the precipice of a new era in artificial intelligence — one where AI will not only predict but recommend and take autonomous actions. But machines are still taking actions based on AI models that are poorly experimented with and fail to meet defined business goals (key performance indicators).

VentureBeat: So what is it that holds the industry back? Or asked a different way, what is that holds Amazon back from doing this?

Farooq: To improve development and performance of AI models, I believe we must address three challenges that are slowing down the AI model development, deployment, and production management in the enterprise. Amazon and other big players haven’t been able to address these challenges yet. They are:

AI data: This is where everything starts and ends in performant AI models. Microsoft [Azure] Purview is a direct attempt to solve the data problems of the enterprise data governance umbrella. This will provide AI solution teams (consumers) valuable and trustworthy data.

AI operations processes: These are enabled for development and deployment in the cloud (AWS) and do not extend or connect to the enterprise DevOps, DataOps, and ITOps processes. AIOps processes to deploy, operate, manage, and govern need to be automated and integrated into enterprise IT processes. This will industrialize AI in the enterprise. It took DevOps 10 years to establish CI/CD processes and automation platforms. AI needs to leverage the assets in CI/CD and overlay the AI model lifecycle management on top of it.

AI architecture: Enterprises with native cloud and containers are accelerating on the path to hybrid and multi-cloud architectures. With edge adoption, we are moving to pure distributed architecture, which will connect the cloud and edge ecosystem. AI architecture will have to operate on distributed architectures across hybrid and multi-cloud infrastructure and data environments. AWS, Azure, Google, and VMWare are effectively moving towards that paradigm.

To develop the next phase of AI, which I am calling “industrialized AI in the enterprise,” we need to address all of these. They can only be met with an open AI platform that has an integrated operations management system.

VentureBeat: Explain what you mean by an “open“ AI platform.

Farooq: An open AI platform for ModelOps lets enterprise AI teams mix and match required AI stacks, data services, AI tools, and domain AI models for different providers. Doing so will result in powerful business solutions at speed and scale.

AWS, with all of its powerful cloud, AI, and edge offerings, has still not stitched together a ModelOps that can industrialize AI and cloud. Enterprises today are using a combination of ServiceNow, legacy systems management, DevOps tooling, and containers to bring this together. AI operations adds another layer of complexity to an already increasingly complex model.

An enterprise AI operations management system should be the master control point and system of record, intelligence, and security for all AI solutions in a federated model (AI models and data catalogs). AWS, Azure, or Google can provide data, process, and tech platforms and services to be consumed by enterprises.

But lock-in models, like those currently being offered, harm enterprise’s ability to develop core AI capabilities. Companies like Microsoft, Amazon, and Google are hampering our ability to build high-caliber solutions by constructing moats around their products and services. The path to the best technology solutions, in the service of both AI providers and consumers, is one where choice and openness is prized as a pathway to innovation.

You have seen companies articulate a prominent vision for the future of AI. But I believe they are limited because they are not going far enough to democratize AI access and usage with the current enterprise IT Ops and governance process. To move forward, we need an enterprise ModelOps process and an open AI services integration platform that industrializes AI development, deployment, operations, and governance.

Without these, enterprises will be forced to choose vertical solutions that fail to integrate with enterprise data technology architectures and IT operations management systems.

VentureBeat: Has anyone tried to build this open AI platform? 

Farooq: Not really. To manage AI ModelOps, we need a more open and connected AI services ecosystem, and to get there, we need an AI services integration platform. This essentially means that we need cloud provider operations management integrated with enterprise AI operations processes and a reference architecture framework (led by CTO and IT operations).

There are two options for enterprise CIOs, CTOs, CEOs, and architects. One is vertical, and the other one is horizontal.

Dataiku, Databricks, Snowflake, C3.AI, Palantir, and many others are building these horizontal AI stack options for the enterprise. Their solutions operate on top of AWS, Google, and Azure AI. It’s a great start. However, C3.AI and Palantir are also moving towards lock-in options by using model-driven architectures.

VentureBeat: So how is the vision of what you’re building at Hypergiant different to these efforts?

Farooq: The choice is clear: We have to enable an enterprise AI stack, ModelOps tooling, and governance capabilities enabled by an open AI services integration platform. This will integrate and operate customer ModelOps and governance processes internally that can work for each business unit and AI project.

What we need is not another AI company, but rather an AI services integrator and operator layer that improves how these companies work together for enterprise business goals.

A customer should be able to use Azure solutions, MongoDB, and Amazon Aurora, depending on what best suits their needs, price points, and future agenda. What this requires is a mesh layer for AI solution providers.

VentureBeat: Can you further define this “mesh layer”? Your figure shows it is a horizontal layer, but how does it work in practice? Is it as simple as plugging in your AI solution on top, and then having access to any cloud data source underneath? And does it have to be owned by a single company? Can it be open-sourced, or somehow shared, or at least competitive?

Farooq: The data mesh layer is the core component, not only for executing the ModelOps processes across cloud, edge, and 5G, but it is also a core architectural component for building, operating, and managing autonomous distributed applications.

Currently we have cloud data lakes and data pipelines (batch or steaming) as an input to build and train AI models. However, in production, data needs to be dynamically orchestrated across datacenters, cloud, 5G, and edge end points. This will ensure that the AI models and the consuming apps at all times have the required data feeds in production to execute.

AI/cloud developers and ModelOps teams should have access to data orchestration rules and policy APIs as a single interface to design, build, and operate AI solutions across distributed environments. This API should hide the complexity of the underlying distributed environments (i.e., cloud, 5G, or edge).

In addition, we need packaging and container specs that will help DevOps and ModelOps professionals use the portability of Kubernetes to quickly deploy and operate AI solutions at scale.

These data mesh APIs and packaging technologies need to be open sourced to ensure that we establish an open AI and cloud stack architecture for enterprises and not walled gardens from big providers.

By analogy, look at what Twilio has done for communications: Twilio strengthened customer relationships across businesses by integrating many technologies in one easy-to-manage interface. Examples in other industries include HubSpot in marketing and Squarespace for website development. These companies work by providing infrastructure that simplifies the experience of the user across the tools of many different companies.

VentureBeat: When are you launching this?

Farooq: We are planning to launch a beta version of a first step of that roadmap early next year [Q1/2020].

VentureBeat: AWS has a reseller policy. Could it could crack down on any mesh layer if they wanted to?

Farooq: AWS could build and offer their own mesh layer that is tied to its cloud and that interfaces with 5G and edge platforms of its partners. But this will not help its enterprise customers accelerate the development, deployment, and management of AI and hybrid/multi-cloud solutions at speed and scale. However, collaborating with the other cloud and ISV providers, as it has done with Kubernetes (CNCF-led open source project), will benefit AWS significantly.

As further innovation on centralized cloud computing models have stalled (based on current functionality and incremental releases across AWS, Azure, and Google), the data mesh and edge native architectures is where innovation will need to happen, and a distributed (declarative and runtime) data mesh architecture is a great place for AWS to contribute and lead the industry.

The digital enterprise will be the biggest beneficiary of a distributed data mesh architecture, and this will help industrialize AI and digital platforms faster — thereby creating new economic opportunities and in return more spend on AWS and other cloud provider technologies.

VentureBeat: What impact would such a mesh-layer solution have on the leading cloud companies? I imagine it could influence user decisions on what underlying services to use. Could that middle mesh player reduce pricing for certain bundles, undercutting marketing efforts by the cloud players themselves? 

Farooq: The data mesh layer will trigger massive innovation on the edge and 5G native (not cloud native) applications, middleware, and infra-architectures. This will drive the large providers to rethink their product roadmaps, architecture patterns, go-to-market offerings, partnerships, and investments.

VentureBeat: If the cloud companies see this coming, do you think they’ll be more inclined to move toward an open ecosystem more rapidly and squelch you? 

Farooq: The big providers in a first or second cycle of evolution of a technology or business model will always want to build a moat and lock in enterprise clients. For example, AWS never accepted that hybrid or multi-cloud was needed. But in the second cycle of cloud adoption by VMWare clients, VMWare started to preach an enterprise-outward hybrid cloud strategy connecting to AWS, Azure, and Google.

This led AWS to launch a private cloud offering (called Outposts), which is a replica for the AWS footprint on a dedicated hardware stack that has the same offerings. AWS executes its API across AWS public and Outposts. In short, they came around.

The same will happen to edge, 5G, and distributed computing. Right now, AWS, Google, and Azure are building their distributed computing platforms. However, the power of the open source community and the innovation speed is so great, the distributed computing architecture in the next cycle and beyond will have to move to an open ecosystem.

VentureBeat: What about lock-in at the mesh-layer level? If I choose to go with Hypergiant so I can access services across clouds, and then a competing mesh player emerges that offers better prices, how easy is it to move?

Farooq: We at Hypergiant believe in an open ecosystem, and our go-to-market business model depends on being at the intersection of enterprise consumption and provider offerings. We drive consumption economics, not provider economics. This will require us to support multiple data mesh technologies and create a fabric for interoperation with a single interface to our clients. The final goal is to ensure an open ecosystem, developer, and operator ease, and value to enterprise clients so that they are able to accelerate their business and revenue strategies by leveraging the best value and the best breed of technologies. We are looking at this from the point of view of the benefits to the enterprise, not the provider.

Article link: Amazon, we don’t need another AI tool or APl, we need an open AI platform for cloud and edge | VentureBeat

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Maj. Chuck Suslowicz , Jan Kallberg , and LTC Todd Arnold

The SolarWinds breach points out the importance of having both offensive and defensive cyber force experience.

The breach is an ongoing investigation, and we will not comment on the investigation. Still, in general terms, we want to point out the exploitable weaknesses in creating two silos — OCO and DCO.

The separation of OCO and DCO, through the specialization of formations and leadership, undermines broader understanding and value of threat intelligence. The growing demarcation between OCO and DCO also have operative and tactical implications. The Multi-Domain Operations (MDO) concept emphasizes the competitive advantages that the Army — and greater Department of Defense — can bring to bear by leveraging the unique and complementary capabilities of each service.

It requires that leaders understand the capabilities their organization can bring to bear in order to achieve the maximum effect from the available resources. Cyber leaders must have exposure to a depth and the breadth of their chosen domain to contribute to MDO.

Unfortunately, within the Army’s operational cyber forces, there is a tendency to designate officers as either offensive cyber operations (OCO) or defensive cyber operations (DCO) specialists. The shortsighted nature of this categorization is detrimental to the Army’s efforts in cyberspace and stymies the development of the cyber force, affecting all soldiers.

The Army will suffer in its planning and ability to operationally contribute to MDO from a siloed officer corps unexposed to the domain’s inherent flexibility.

We consider the assumption that there is a distinction between OCO and DCO to be flawed. It perpetuates the idea that the two operational types are doing unrelated tasks with different tools, and that experience in one will not improve performance in the other. We do not see such a rigid distinction between OCO and DCO competencies. In fact, most concepts within the cyber domain apply directly to both types of operations.

The argument that OCO and DCO share competencies is not new; the iconic cybersecurity expert Dan Geer first pointed out that cyber tools are dual-use nearly two decades ago, and continues to do so. A tool that is valuable to a network defender can prove equally valuable during an offensive operation, and vice versa.

For example, a tool that maps a network’s topology is critical for the network owner’s situational awareness. The tool could also be effective for an attacker to maintain situational awareness of a target network. The dual-use nature of cyber tools requires cyber leaders to recognize both sides of their utility.

So, a tool that does a beneficial job of visualizing key terrain to defend will create a high-quality roadmap for a devastating attack. Limiting officer experiences to only one side of cyberspace operations (CO) will limit their vision, handicap their input as future leaders, and risk squandering effective use of the cyber domain in MDO.

An argument will be made that “deep expertise is necessary for success” and that officers should be chosen for positions based on their previous exposure. This argument fails on two fronts. First, the Army’s decades of experience in officers’ development have shown the value of diverse exposure in officer assignments. Other branches already ensure officers experience a breadth of assignments to prepare them for senior leadership.

Second, this argument ignores the reality of “challenging technical tasks” within the cyber domain. As cyber tasks grow more technically challenging, the tools become more common between OCO and DCO, not less common. For example, two of the most technically challenging tasks, reverse engineering of malware (DCO) and development of exploits (OCO), use virtually identical toolkits.

An identical argument can be made for network defenders preventing adversarial access and offensive operators seeking to gain access to adversary networks. Ultimately, the types of operations differ in their intent and approach, but significant overlap exists within their technical skillsets.

Experience within one fragment of the domain directly translates to the other and provides insight into an adversary’s decision-making processes. This combined experience provides critical knowledge for leaders, and lack of experience will undercut the Army’s ability to execute MDO effectively. Defenders with OCO experience will be better equipped to identify an adversary’s most likely and most devastating courses of action within the domain. Similarly, OCO planned by leaders with DCO experience are more likely to succeed as the planners are better prepared to account for potential adversary countermeasures.

In both cases, the cross-pollination of experience improves the Army’s ability to leverage the cyber domain and improve its effectiveness. Single tracked officers may initially be easier to integrate or better able to contribute on day one of an assignment. However, single-tracked officers will ultimately bring far less to the table than officers experienced in both sides of the domain due to the multifaceted cyber environment in MDO.

Maj. Chuck Suslowicz is a research scientist in the Army Cyber Institute at West Point and an instructor in the U.S. Military Academy’s Department of Electrical Engineering and Computer Science (EECS). Dr. Jan Kallberg is a research scientist at the Army Cyber Institute at West Point and an assistant professor at the U.S. Military Academy. LTC Todd Arnold is a research scientist in the Army Cyber Institute at West Point and assistant professor in U.S. Military Academy’s Department of Electrical Engineering and Computer Science (EECS.) The views expressed are those of the authors and do not reflect the official policy or position of the Army Cyber Institute at West Point, the U.S. Military Academy or the Department of Defense.

Article link: Government cyber breach shows need for convergence (armytimes.com)

The letter, signed by nine members of Congress, sends an important signal about how regulators will scrutinize tech giants.

by Karen Hao December 17, 2020

The letter, signed by nine members of Congress, sends an important signal about how regulators will scrutinize tech giants.

Nine members of the US Congress have sent a letter to Google asking it to clarify the circumstances around its former ethical AI co-lead Timnit Gebru’s forced departure. Led by Representative Yvette Clarke and Senator Ron Wyden, and co-signed by Senators Elizabeth Warren and Cory Booker, the letter sends an important signal about how Congress is scrutinizing tech giants and thinking about forthcoming regulation.

Gebru, a leading voice in AI ethics and one of a small handful of Black women at Google, was unceremoniously dismissed two weeks ago, after a protracted disagreement over a research paper. The paper detailed the risks of large AI language models trained on enormous amounts of text data, which are a core line of Google’s research, powering various products including its lucrative Google Search. 

Citing MIT Technology Review’s coverage, the letter raises three issues: the potential for bias in large language models, the growing corporate influence over AI research, and Google’s lack of diversity. It asks Google CEO Sundar Pichai for a concrete plan on how it will address each of these, as well as for its current policy on reviewing research and details on its ongoing investigation into Gebru’s exit (Pichai committed to this investigation in an internal memo, first published by Axios). “As Members of Congress actively seeking to enhance AI research, accountability, and diversity through legislation and oversight, we respectfully request your request to the following inquiries,” the letter states.

In April of 2019, Clarke and Wyden introduced a bill, the Algorithmic Accountability Act, that would require big companies to audit their machine-learning systems for bias and take corrective action in a timely manner if such issues were identified. It would also require those companies to audit all processes involving sensitive data—including personally identifiable, biometric, and genetic information—for privacy and security risks. At the time, many legal and technology experts praised the bill for its nuanced understanding of AI and data-driven technologies. “Great first step,” wrote Andrew Selbst, an assistant professor at the University California Los Angeles School of Law, on Twitter. “Would require documentation, assessment, and attempts to address foreseen impacts. That’s new, exciting & incredibly necessary.”

The latest letter doesn’t tie directly to the Algorithmic Accountability Act, but it is part of the same move by certain congressional members to craft legislation that would mitigate AI bias and the other harms of data-driven, automated systems. Notably, it comes amid mounting pressure for antitrust regulation. Earlier this month, the US Federal Trade Commission filed an antitrust lawsuit against Facebook for its “anticompetitive conduct and unfair methods of competition.” Over the summer, House Democrats published a 449-page report on Big Tech’s monopolistic practices.

The letter also comes in the context of rising geopolitical tensions. As US-China relations have reached an all-time low during the pandemic, US officials have underscored the strategic importance of emerging technologies like AI and 5G. The letter also raises this dimension, acknowledging Google’s leadership in AI and its role in maintaining US leadership. But it makes clear that this should not undercut regulatory action, a line of argument popularized by Facebook CEO Mark Zuckerberg. “To ensure America wins the AI race,” the letter says, “American technology companies must not only lead the world in innovation; they must also ensure such innovation reflects our nation’s values.”

“Our letter should put everyone in the technology sector, not just Google, on notice that we are paying attention,” said Clarke in a statement to MIT Technology Review. “Ethical AI is the battleground for the future of civil rights. Our concerns about recent developments aren’t just about one person; they are about what the 21st century will look like if academic freedom and inclusion take a back seat to other priorities. We can’t mitigate algorithmic bias if we impede those who seek to research and study it.”

Article link: Congress wants answers from Google about Timnit Gebru’s firing | MIT Technology Review

Tech giants dominate research but the line between real breakthrough and product showcase can be fuzzy. Some scientists have had enough.

by Will Douglas Heaven

November 12, 2020

Last month Nature published a damning response written by 31 scientists to a study from Google Health that had appeared in the journal earlier this year. Google was describing successful trials of an AI that looked for signs of breast cancer in medical images. But according to its critics, the Google team provided so little information about its code and how it was tested that the study amounted to nothing more than a promotion of proprietary tech.

“We couldn’t take it anymore,” says Benjamin Haibe-Kains, the lead author of the response, who studies computational genomics at the University of Toronto. “It’s not about this study in particular—it’s a trend we’ve been witnessing for multiple years now that has started to really bother us.”

Haibe-Kains and his colleagues are among a growing number of scientists pushing back against a perceived lack of transparency in AI research. “When we saw that paper from Google, we realized that it was yet another example of a very high-profile journal publishing a very exciting study that has nothing to do with science,” he says. “It’s more an advertisement for cool technology. We can’t really do anything with it.”

Science is built on a bedrock of trust, which typically involves sharing enough details about how research is carried out to enable others to replicate it, verifying results for themselves. This is how science self-corrects and weeds out results that don’t stand up. Replication also allows others to build on those results, helping to advance the field. Science that can’t be replicated falls by the wayside.

At least, that’s the idea. In practice, few studies are fully replicated because most researchers are more interested in producing new results than reproducing old ones. But in fields like biology and physics—and computer science overall—researchers are typically expected to provide the information needed to rerun experiments, even if those reruns are rare.

Ambitious noob

AI is feeling the heat for several reasons. For a start, it is a newcomer. It has only really become an experimental science in the past decade, says Joelle Pineau, a computer scientist at Facebook AI Research and McGill University, who coauthored the complaint. “It used to be theoretical, but more and more we are running experiments,” she says. “And our dedication to sound methodology is lagging behind the ambition of our experiments.”

The problem is not simply academic. A lack of transparency prevents new AI models and techniques from being properly assessed for robustness, bias, and safety. AI moves quickly from research labs to real-world applications, with direct impact on people’s lives. But machine-learning models that work well in the lab can fail in the wild—with potentially dangerous consequences. Replication by different researchers in different settings would expose problems sooner, making AI stronger for everyone. 

AI already suffers from the black-box problem: it can be impossible to say exactly how or why a machine-learning model produces the results it does. A lack of transparency in research makes things worse. Large models need as many eyes on them as possible, more people testing them and figuring out what makes them tick. This is how we make AI in health care safer, AI in policing more fair, and chatbots less hateful.

What’s stopping AI replication from happening as it should is a lack of access to three things: code, data, and hardware. According to the 2020 State of AI report, a well-vetted annual analysis of the field by investors Nathan Benaich and Ian Hogarth, only 15% of AI studies share their code. Industry researchers are bigger offenders than those affiliated with universities. In particular, the report calls out OpenAI and DeepMind for keeping code under wraps.

Then there’s the growing gulf between the haves and have-nots when it comes to the two pillars of AI, data and hardware. Data is often proprietary, such as the information Facebook collects on its users, or sensitive, as in the case of personal medical records. And tech giants carry out more and more research on enormous, expensive clusters of computers that few universities or smaller companies have the resources to access.

To take one example, training the language generator GPT-3 is estimated to have cost OpenAI $10 to $12 million—and that’s just the final model, not including the cost of developing and training its prototypes. “You could probably multiply that figure by at least one or two orders of magnitude,” says Benaich, who is founder of Air Street Capital, a VC firm that invests in AI startups. Only a tiny handful of big tech firms can afford to do that kind of work, he says: “Nobody else can just throw vast budgets at these experiments.”

The rate of progress is dizzying, with thousands of papers published every year. But unless researchers know which ones to trust, it is hard for the field to move forward. Replication lets other researchers check that results have not been cherry-picked and that new AI techniques really do work as described. “It’s getting harder and harder to tell which are reliable results and which are not,” says Pineau.

What can be done? Like many AI researchers, Pineau divides her time between university and corporate labs. For the last few years, she has been the driving force behind a change in how AI research is published. For example, last year she helped introduce a checklist of things that researchers must provide, including code and detailed descriptions of experiments, when they submit papers to NeurIPS, one of the biggest AI conferences.

Replication is its own reward

Pineau has also helped launch a handful of reproducibility challenges, in which researchers try to replicate the results of published studies. Participants select papers that have been accepted to a conference and compete to rerun the experiments using the information provided. But the only prize is kudos.

This lack of incentive is a barrier to such efforts throughout the sciences, not just in AI. Replication is essential, but it isn’t rewarded. One solution is to get students to do the work. For the last couple of years, Rosemary Ke, a PhD student at Mila, a research institute in Montreal founded by Yoshua Bengio, has organized a reproducibility challenge where students try to replicate studies submitted to NeurIPS as part of their machine-learning course. In turn, some successful replications are peer-reviewed and published in the journal ReScience. 

“It takes quite a lot of effort to reproduce another paper from scratch,” says Ke. “The reproducibility challenge recognizes this effort and gives credit to people who do a good job.” Ke and others are also spreading the word at AI conferences via workshops set up to encourage researchers to make their work more transparent. This year Pineau and Ke extended the reproducibility challenge to seven of the top AI conferences, including ICML and ICLR. 

Another push for transparency is the Papers with Code project, set up by AI researcher Robert Stojnic when he was at the University of Cambridge. (Stojnic is now a colleague of Pineau’s at Facebook.) Launched as a stand-alone website where researchers could link a study to the code that went with it, this year Papers with Code started a collaboration with arXiv, a popular preprint server. Since October, all machine-learning papers on arXiv have come with a Papers with Code section that links directly to code that authors wish to make available. The aim is to make sharing the norm.

Do such efforts make a difference? Pineau found that last year, when the checklist was introduced, the number of researchers including code with papers submitted to NeurIPS jumped from less than 50% to around 75%. Thousands of reviewers say they used the code to assess the submissions. And the number of participants in the reproducibility challenges is increasing.

Sweating the details

But it is only a start. Haibe-Kains points out that code alone is often not enough to rerun an experiment. Building AI models involves making many small changes—adding parameters here, adjusting values there. Any one of these can make the difference between a model working and not working. Without metadata describing how the models are trained and tuned, the code can be useless. “The devil really is in the detail,” he says.

It’s also not always clear exactly what code to share in the first place. Many labs use special software to run their models; sometimes this is proprietary. It is hard to know how much of that support code needs to be shared as well, says Haibe-Kains.

Pineau isn’t too worried about such obstacles. “We should have really high expectations for sharing code,” she says. Sharing data is trickier, but there are solutions here too. If researchers cannot share their data, they might give directions so that others can build similar data sets. Or you could have a process where a small number of independent auditors were given access to the data, verifying results for everybody else, says Haibe-Kains.

Hardware is the biggest problem. But DeepMind claims that big-ticket research like AlphaGo or GPT-3 has a trickle-down effect, where money spent by rich labs eventually leads to results that benefit everyone. AI that is inaccessible to other researchers in its early stages, because it requires a lot of computing power, is often made more efficient—and thus more accessible—as it is developed. “AlphaGo Zero surpassed the original AlphaGo using far less computational resources,” says Koray Kavukcuoglu, vice president of research at DeepMind.

In theory, this means that even if replication is delayed, at least it is still possible. Kavukcuoglu notes that Gian-Carlo Pascutto, a Belgian coder at Mozilla who writes chess and Go software in his free time, was able to re-create a version of AlphaGo Zero called Leela Zero, using algorithms outlined by DeepMind in its papers. Pineau also thinks that flagship research like AlphaGo and GPT-3 is rare. The majority of AI research is run on computers that are available to the average lab, she says. And the problem is not unique to AI. Pineau and Benaich both point to particle physics, where some experiments can only be done on expensive pieces of equipment such as the Large Hadron Collider.

In physics, however, university labs run joint experiments on the LHC. Big AI experiments are typically carried out on hardware that is owned and controlled by companies. But even that is changing, says Pineau. For example, a group called Compute Canada is putting together computing clusters to let universities run large AI experiments. Some companies, including Facebook, also give universities limited access to their hardware. “It’s not completely there,” she says. “But some doors are opening.”

Haibe-Kains is less convinced. When he asked the Google Health team to share the code for its cancer-screening AI, he was told that it needed more testing. The team repeats this justification in a formal reply to Haibe-Kains’s criticisms, also published in Nature: “We intend to subject our software to extensive testing before its use in a clinical environment, working alongside patients, providers and regulators to ensure efficacy and safety.” The researchers also said they did not have permission to share all the medical data they were using.

It’s not good enough, says Haibe-Kains: “If they want to build a product out of it, then I completely understand they won’t disclose all the information.” But he thinks that if you publish in a scientific journal or conference, you have a duty to release code that others can run. Sometimes that might mean sharing a version that is trained on less data or uses less expensive hardware. It might give worse results, but people will be able to tinker with it. “The boundaries between building a product versus doing research are getting fuzzier by the minute,” says Haibe-Kains. “I think as a field we are going to lose.” 

Research habits die hard

If companies are going to be criticized for publishing, why do it at all? There’s a degree of public relations, of course. But the main reason is that the best corporate labs are filled with researchers from universities. To some extent the culture at places like Facebook AI Research, DeepMind, and OpenAI is shaped by traditional academic habits. Tech companies also win by participating in the wider research community. All big AI projects at private labs are built on layers and layers of public research. And few AI researchers haven’t made use of open-source machine-learning tools like Facebook’s PyTorch or Google’s TensorFlow.

As more research is done in house at giant tech companies, certain trade-offs between the competing demands of business and research will become inevitable. The question is how researchers navigate them. Haibe-Kains would like to see journals like Nature split what they publish into separate streams: reproducible studies on one hand and tech showcases on the other.

But Pineau is more optimistic. “I would not be working at Facebook if it did not have an open approach to research,” she says. 

Other large corporate labs stress their commitment to transparency too. “Scientific work requires scrutiny and replication by others in the field,” says Kavukcuoglu. “This is a critical part of our approach to research at DeepMind.”

“OpenAI has grown into something very different from a traditional laboratory,” says Kayla Wood, a spokesperson for the company. “Naturally that raises some questions.” She notes that OpenAI works with more than 80 industry and academic organizations in the Partnership on AI to think about long-term publication norms for research.

Pineau believes there’s something to that. She thinks AI companies are demonstrating a third way to do research, somewhere between Haibe-Kains’s two streams. She contrasts the intellectual output of private AI labs with that of pharmaceutical companies, for example, which invest billions in drugs and keep much of the work behind closed doors.

The long-term impact of the practices introduced by Pineau and others remains to be seen. Will habits be changed for good? What difference will it make to AI’s uptake outside research? A lot hangs on the direction AI takes. The trend for ever larger models and data sets—favored by OpenAI, for example—will continue to make the cutting edge of AI inaccessible to most researchers. On the other hand, new techniques, such as model compression and few-shot learning, could reverse this trend and allow more researchers to work with smaller, more efficient AI.

Either way, AI research will still be dominated by large companies. If it’s done right, that doesn’t have to be a bad thing, says Pineau: “AI is changing the conversation about how industry research labs operate.” The key will be making sure the wider field gets the chance to participate. Because the trustworthiness of AI, on which so much depends, begins at the cutting edge. 

Article link: AI is wrestling with a replication crisis | MIT Technology Review