healthcarereimagined

EDITORIAL TEAM

AUGUST 15, 2023

INTERNATIONAL, LEARN VLSI, SEMICONDUCTOR NEWS

Introduction

In the world of technology, innovation is a constant companion, pushing the boundaries of what’s possible. One of the latest marvels to capture the attention of tech enthusiasts and experts alike is the concept of “chiplets.” 

Imagine these as tiny, specialized building blocks that can be combined in various ways to create powerful and efficient electronic devices. In this blog post, we’ll delve into the world of chiplets, breaking down the complex concepts into easy-to-understand terms, using a simple analogy to help even a novice grasp the potential they hold for transforming the semiconductor industry.

“Chiplets are a game-changer for the semiconductor industry. They offer a way to improve performance, reduce power consumption, and increase design flexibility.” – 

~Pat Gelsinger, CEO of Intel

The Basics: What are Chiplets?

To put it simply, chiplets are like Lego pieces for computers. Imagine you have a collection of Lego bricks, each designed for a specific function – some are engines, some are wheels, and some are windows. You can take these different pieces and snap them together to create a custom vehicle that suits your needs. 

Similarly, chiplets are tiny electronic components, each with its own unique functionality. These chiplets can be combined to create powerful and specialized devices, just like Lego pieces can be combined to build intricate structures.

Read more: Microprocessors vs. Microcontrollers: A Cake Analogy

The Lego-Like Assembly

Think of a computer or any electronic device as a puzzle, with each piece contributing to the overall functionality. Traditionally, these pieces (or chips) were large, monolithic structures that contained all the necessary functions in one piece. 

However, this approach had limitations. It was like trying to build an entire vehicle with just one type of Lego brick – it could work, but there was limited room for customization and optimization.

Enter chiplets, the Lego pieces of the tech world. Instead of using one giant chip, designers now use smaller chiplets, each dedicated to a specific task. 

Just as you’d use different types of Lego pieces for different parts of a vehicle, chiplets can be made using different manufacturing processes to optimize their performance. This allows for more efficient use of the available silicon and results in improved overall performance.

Early days and evolution

The term “chiplet” was coined by John Wawrzynek, a professor at the University of California, Berkeley, in 2006. However, the concept of chiplets has been around for much longer. 

In the early 1990s, IBM developed a technology called Multichip Module (MCM) that allowed multiple chips to be interconnected on a single substrate. MCMs were used in some high-performance computing systems, but they were not widely adopted due to their high cost.

In recent years, there has been renewed interest in chiplets due to the challenges of scaling traditional monolithic chips. As the size of transistors continues to shrink, it becomes increasingly difficult to manufacture monolithic chips with high yields. 

Chiplets offer a potential solution to this problem, as they can be made using different manufacturing processes and then interconnected on a single substrate.

AMD was one of the first companies to adopt chiplet technology. In 2017, the company released its  Ryzen 7 1800X processor, which uses chiplets to combine different cores and cache memory.

AMD has since released several other products that use chiplets, including its Epyc CPUs and its Instinct GPUs.

Intel is also working on chiplet-based designs. The company is expected to release its first chiplet-based CPU in 2023. Other companies that are working on chiplet technology include IBM, NVIDIA, and Qualcomm.

Moore’s Law: A Quick Recap

Moore’s Law, named after Intel co-founder Gordon Moore, observes that the number of transistors on an integrated circuit (IC) doubles approximately every two years. This exponential growth has fueled the astonishing progress in computing power over the decades. 

However, as transistors approach atomic scales, the challenges of maintaining this pace become formidable.

Chiplets: A Synergy with Moore’s Law

Enter chiplets, the architects of a harmonious dance with Moore’s Law. Imagine Moore’s Law as a fast-paced race, and chiplets as versatile teammates that ensure the race continues despite hurdles. How do they achieve this?

Enhanced Performance: Moore’s Law has propelled the integration of more transistors on a single chip, but chiplet technology takes this further. By utilizing chiplets with diverse manufacturing processes, the available silicon is utilized optimally. This synergy results in superior performance, a crucial facet in high-demand applications.

Power Efficiency: The relentless quest to reduce power consumption finds an ally in chiplets. Just as a skilled athlete conserves energy with precise movements, chiplets can be customized for energy efficiency. This is particularly vital for mobile devices and other power-sensitive applications.

Flexibility in Design: Think of Moore’s Law as a roadmap, and chiplets as customizable vehicles navigating it. Chiplets empower designers to create tailored devices by handpicking components, akin to choosing specific Lego bricks for a unique structure. This design flexibility ensures compatibility with the evolving landscape of Moore’s Law.

The Synergy in Action: Real-World Instances

In the context of Moore’s Law, consider the following real-world instances:

AMD’s  Ryzen and Epyc CPUs:Chiplets play a pivotal role in enhancing these CPUs’ capabilities. Different chiplets, each containing distinct computing elements, are combined to form powerful processors. This approach complements Moore’s Law by maximizing performance within its bounds.

IBM’s Power10 CPU: Chiplets shine here by optimizing the interplay of processing units, memory controllers, and I/O blocks. This intelligent orchestration harmonizes with Moore’s Law, achieving enhanced scalability and efficiency.

NVIDIA’s Hopper GPU: Chiplets collaborate seamlessly to construct potent GPUs with various compute engines and memory controllers. This amalgamation supports Moore’s Law by ensuring high-performance computing while accommodating its trends.

The Future: Chiplets and the Evolution of Moore’s Law

In essence, chiplets are the skillful dancers that keep Moore’s Law’s rhythm steady. As the law faces mounting challenges, chiplets offer a means to sustain progress. They counteract diminishing returns from traditional monolithic chips by offering performance improvements, power efficiency, and design adaptability. While Moore’s Law has set the stage, chiplets step onto it, embracing the challenges and ushering in a new era of technological evolution.

Conclusion

The tapestry of technology is woven with innovation, and chiplets emerge as a testament to this ceaseless progress. Their symbiotic relationship with Moore’s Law paints a vivid picture of adaptability and advancement. As chiplets evolve and synergize with Moore’s Law, the world of computing stands poised to embark on an exhilarating journey, where customizability, performance, and energy efficiency harmonize to shape the future of electronics. Just as Lego pieces yield endless possibilities, chiplets unfurl a limitless canvas upon which the next chapter of computing history is written.

Article link: https://techovedas.com/how-chiplets-can-change-the-future-by-extending-moores-law/

By

• Bryan FinsterDistinguished Engineer @Defense Unicorns

reviewed by

• Ben LindersTrainer / Coach / Adviser / Author / Speaker @BenLinders.com

Key Takeaways

• We saw negative outcomes from agile scaling frameworks. Focusing on “Why can’t we deliver today’s work today?” forced us to find and fix the technical and process problems that prevented agility.
• We couldn’t deliver better by only changing how development was done. We had to restructure the organization and the application.
• The ability to deliver daily improved business delivery and team morale. It’s a more humane way to work.
• Optimize for operations and always use your emergency process to deliver everything. This ensures hotfixes are safe while also driving improvement in feature delivery.
• If you want to retain the improvement and the people who did it, measure it to show evidence when management changes, or you might lose it and the people.

Struggling with your “agile transformation?” Is your scaling framework not providing the outcomes you hoped for? In this article, we’ll discuss how teams in a large enterprise replaced heavy agile processes with Conway’s Law and better engineering to migrate from quarterly to daily value delivery to the end users.

Replacing Agile with Engineering

We had a problem. After years of “Agile Transformation” followed by rolling out the Scaled Agile Framework, we were not delivering any better. In fact, we delivered less frequently with bigger failures than when we had no defined processes. We had to find a way to deliver better value to the business. SAFe, with all of the ceremonies and process overhead that comes with it, wasn’t getting that done. Our VP read The Phoenix Project, got inspired, and asked the senior engineers in the area to solve the problem. We became agile by making the engineering changes required to implement Continuous Delivery (CD).

Initially, our lead time to deliver a new capability to the business averaged 12 months, from request to delivery. We had to fix that. The main problem, though, is that creating a PI plan for a quarter, executing it in sprints, and delivering whatever passes tests at the end of the quarter ignores the entire reason for agile product development: uncertainty.

Here is the reality: the requirements are wrong, we will misunderstand them during implementation, or the end users’ needs will change before we deliver. One of those is always true. We need to mitigate that with smaller changes and faster feedback to more rapidly identify what’s wrong and change course. Sometimes, we may even decide to abandon the idea entirely. The only way we can do this is to become more efficient and reduce the delivery cost. That requires focusing on everything regarding how we deliver and engineering better ways of doing that.

Why Continuous Delivery?

We wanted the ability to deliver more frequently than 3-4 times per year. We believed that if we took the principles and practices described in Continuous Delivery by Jez Humble and Dave Farley seriously, we’d be able to improve our delivery cadence, possibly even push every code commit directly to production. That was an exciting idea to us as developers, especially considering the heavy process we wanted to replace.

When we began, the minimum time to deliver a normal change was three days. It didn’t matter if it was a one-line change to modify a label or a month’s worth of work — the manual change control process required at least three days. In practice, it was much worse. Since the teams were organized into feature teams and the system was tightly coupled, the entire massive system had to be tested and released as a single delivery. So, today’s one-line change will be delivered, hopefully, in the next quarterly release unless you miss merge week.

We knew if we could fix this, we could find out faster if we had quality problems, the problems would be smaller and easier to find, and we’d be able to add regression tests to the pipeline to prevent re-occurrence and move quickly to the next thing to deliver. When we got there, it was true. However, we got something more.

We didn’t expect how much better it would be for the people doing the work. I didn’t expect it to change my entire outlook on the work. When you don’t see your work used, it’s joyless. When you can try something, deliver it, and get rapid feedback, it brings joy back to development, even more so when you’ve improved your test suite to the point where you don’t fear every keystroke. Getting into a CD workflow made me intolerant of working the way we were before. I feel process waste as pain. I won’t “test it later when we get time.” I won’t work that way ever again. Work shouldn’t suck.

Descale and Decouple for Speed

We knew we’d never be able to reach our goals without changing the system we were delivering. It was truly monstrous. It was the outcome of taking three related legacy systems and a fourth unrelated legacy system and merging them, with some splashy new user interfaces, into a bigger legacy system. A couple of years before this improvement effort, my manager asked how many lines of code the system was. Without comments, it was 25 million lines of executable code. Calling the architecture “spaghetti” would be a dire insult to pasta. Where there were web services, the service boundaries were defined by how big the service was. When it got “too big,” a new service, Service040, for example, would be created.

We needed to break it up to make it easier to deliver and modernize the tech stack. Step one was using Domain Driven Design to start untangling the business capabilities in the current system. We aimed to define specific capabilities and assign each to a product team. We knew about Conway’s Law, so we decided that if we were going to get the system architecture we needed, we needed to organize the teams to mirror that architecture. Today, people call that the “reverse Conway maneuver.”  We didn’t know it had a name. I’ve heard people say it doesn’t work. They are wrong. We got the system architecture we wanted by starting with the team structure and assigning each a product sub-domain. The internal architecture of each team’s domain was up to them. However, they were also encouraged to use and taught how to design small services for the sub-domains of their product.

We also wanted to ensure every team could deliver without the need to coordinate delivery with any other team. Part of that was how we defined the teams’ capabilities, but having the teams focus on Contract Driven Development (CDD) and evolutionary coding was critical. CDD is the process where teams with dependencies collaborate on API contract changes and then validate they can communicate with that new contract before they begin implementing the behaviors. This makes integration the first thing tested, usually within a few days of the discussion. Also important is how the changes are coded.

The consumer needs to write their component in a way that allows their new feature to be tested and delivered with the provider’s new contract but not activated until that contract is ready to be consumed. The provider needs to make changes that do not break the existing contract. Working this way, the consumer or provider can deliver their changes in any order. When both are in place, the new feature is ready to release to the end user.

By deliberately architecting product boundaries, the teams building each product, focusing on evolutionary coding techniques, and “contract first” delivery, we enabled each team to run as fast as possible. SAFe handles dependencies with release trains and PI planning meetings. We handled them with code. For example, if we had a feature that also required another team to implement a change, we could deploy our change and include logic that would activate our feature when their feature was delivered. We could do that either with a configuration change or, depending on the feature, simply have our code recognize the new properties in the contract were available and activate automatically.  

Accelerating Forces Learning

It took us about 18 months after forming the first pilot product teams to get the initial teams to daily delivery. I learned from doing CD in the real world that you are not agile without CD. How can you claim to be agile if it takes two or more weeks to validate an idea? You’re emotionally invested by then and have spent too much money to let the idea go.

You cannot execute CD without continuous integration (CI). Because we took CI seriously, we needed to make sure that all of the tests required to validate a change were part of the commit for that change. We had to test during development. However, we were blocked by vague requirements. Focusing on CI pushed the team to understand the business needs and relentlessly remove uncertainty from acceptance criteria.

On my team, we decided that if we needed to debate story points, it was too big and had too much uncertainty to test during development. If we could not agree that anyone on the team could complete something in two days or less, we decomposed the work until we agreed. By doing this, we had the clarity we needed to stop doing exploratory development and hoping that was what was being asked for. Because we were using Behavior Driven Development (BDD) to define the work, we also had a more direct path from requirement to acceptance tests. Then, we just had to code the tests and the feature and run them down the pipeline.

You need to dig deep into quality engineering to be competent at CD. Since the CD pipeline should be the only method for determining if something meets our definition of “releasable,” a culture of continuous quality needs to be built. That means we are not simply creating unit tests. We are looking at every step, starting with product discovery, to find ways to validate the outcomes of that step. We are designing fast and efficient test suites. We are using techniques like BDD to validate that the requirements are clear. Testing becomes the job. Development flows from that.

This also takes time for the team to learn, and the best thing to do is find people competent at discovery to help them design better tests. QA professionals who think, “What could go wrong?” and help teams create strategies to detect that, instead of the vast majority who are trained to write test automation, are gold. However, under no circumstances should QA be developing the tests because they become a constraint rather than a force multiplier. CD can’t work that way.

The most important thing I learned was that it’s a more humane way of working. There’s less stress, more teamwork, less fear of breaking something, and much more certainty that we are probably building the right thing. CD is the tool for building high-performing teams.

Optimize for Operations

All pipelines should be designed for operations first. Life is uncertain — production breaks. We need the ability to fix things quickly without throwing gasoline on a dumpster fire. I carried a support pager for 20 years.  The one thing that was true for most of that time was that we always had some workaround process for delivering things in an emergency. This means that the handoffs we had for testing for normal changes were bypassed for an emergency. Then, we would be in a dumpster fire, hoping our bucket contained water and not gasoline.

With CD, that’s not allowed. We have precisely one process to deliver any change: the pipeline. The pipeline should be deterministic and contain all the validations to certify that an artifact meets our definition of “releasable.” Since, as a principle, we never bypass or deactivate quality gates in the pipeline for emergencies, we must design good tests for all of our acceptance criteria and continue to refine them to be fast, efficient, and effective as we learn more about possible failure conditions. This ensures hotfixes are safe while also driving improvement in feature delivery. This takes time, and the best thing to do is to define all of the acceptance criteria and measure how long it takes to complete them all, even the manual steps. Then, use the cycle time of each manual process as a roadmap for what to automate next.

What we did was focus on the duration of our pipeline and ensure we were testing everything required to deliver our product. We, the developers, took over all of the test automation. This took a lot of conversation with our Quality Engineering area since the pattern before was for them to write all the tests. However, we convinced them to let us try our way. The results proved that our way was better. We no longer had tests getting out of sync with the code, the tests ran faster, and they were far less likely to return false positives. We trusted our tests more every day, and they proved their value later on when the team was put under extreme stress by an expanding scope, shrinking timeline, “critical project.”

Another critical design consideration was that we needed to validate that each component was deliverable without integrating the entire system. Using E2E tests for acceptance testing is a common but flawed practice. If we execute DDD correctly, then any need to do E2E for acceptance testing can be viewed as an architecture defect. They also harm our ability to address impacting incidents.

For example, if done with live services, one of the tests we needed to run required creating a dummy purchase order in another system, flowing that PO through the upstream supply chain systems, processing that PO with the legacy system we were breaking apart, and then running our test. Each test run required around four hours. That’s a way to validate that our acceptance tests are valid occasionally, but not a good way to do acceptance testing, especially not during an emergency. Instead, we created a virtual service. That service could return a mock response when we sent a test header so we could validate we were integrating correctly. That test required milliseconds to execute rather than hours.

We could run it every time we made a change to the trunk (multiple times per day) and have a high level of confidence that we didn’t break anything. That test also prevented a problem from becoming a bigger problem. My team ran our pipeline, and that test failed. The other team had accidentally broken their contract, and the test caught it within minutes of it happening and before that break could flow to production. Our focus on DDD resulted in faster, more reliable tests than any of the attempts at E2E testing that the testing area attempted. Because of that, CD made operations more robust.

Engineering Trumps Scaling Frameworks

We loved development again when we were able to get CD off the ground. Delivery is addictive, and the more frequently we can do that, the faster we learn. Relentlessly overcoming the problems that prevent frequent delivery also lowers process overhead and the cost of change. That, in turn, makes it economically more attractive to try new ideas and get feedback instead of just hoping your investment returns results. You don’t need SAFe’s PI planning when you have product roadmaps and teams that handle dependencies with code. PI plans are static.

Roadmaps adjust from what we learn after delivering. Spending two days planning how to manage dependencies with the process and keeping teams in lock-step means every team delivers at the pace of the slowest team. If we decouple and descale, teams are unchained. Costs decrease. Feedback loops accelerate. People are happier. All of these are better for the bottom line.

On the first team where we implemented CD, we improved our delivery cadence from monthly (or less) to several times per day. We had removed so much friction from the process that we could get ideas from the users, decompose them, develop them, and deliver them within 48 hours. Smaller tweaks could take less than a couple of hours from when we received the idea from the field. That feedback loop raised the quality and enjoyment level for us and our end users.

Measure the Flow!

Metrics is a deep topic and one I talk about frequently. One big mistake we made was not measuring the impact of our improvements on the business. When management changed, we didn’t have a way to show what we were doing was better. To be frank, the new management had other ideas – poorly educated ideas. Things degraded, and the best people left. Since then, I’ve become a bit obsessed with measuring things correctly.

For a team wanting to get closer to CD, focus on a few things first:

1. How frequently are we integrating code into the trunk? For CI, this should be at least once per day per team member on average. Anything less is not CI. CI is a forcing function for learning to break changes into small, deliverable pieces.
2. How long does it take for us, as a team, to deliver a story? We want this to be two days maximum. Tracking this and keeping it small forces us to get into the details and reduce uncertainty. It also makes it easy for us to forecast delivery and identify when something is trending later than planned. Keep things small.
3. How long does it take for a change to reach the end of our pipeline? Pipeline cycle time is a critical quality feedback loop and needs to keep improving.
4. How many defects are reported week to week? It doesn’t matter if they are implementation errors, “I didn’t expect it to work this way,” or “I don’t like this color.” Treat them all the same. They all indicate some failure in our quality process. Quality starts with the idea, not with coding.

Since this journey, I’ve become passionate about continuous delivery as a forcing function for quality. I’ve seen on multiple teams in multiple organizations what a positive impact it has on everything about the work and the outcomes. As a community, we have also seen many organizations not take a holistic approach, throw tools at the problem, ignore the fact that this is a quality initiative, and hurt themselves. It’s important that people understand the principles and recommended practices before diving in head first.

You won’t be agile by focusing on agile frameworks. Agility requires changing everything we do, beginning with engineering our systems for lower delivery friction. By asking ourselves, “Why can’t we deliver today’s work today?” and then relentlessly solving those problems, we improve everything about how we work as an organization. Deploy more and sleep better.

Article link: https://www.infoq.com/articles/replace-process-dogma-engineering/

Cathy Li

Head, AI, Data and Metaverse; Member of the Executive Committee, World Economic Forum

• This round-up brings you key digital technology stories from the past fortnight.
• Top headlines: UN’s call to big tech companies over product harm; Nvidia briefly becomes most expensive company in the world; McDonald’s to end test run of AI chatbots at drive-thrus.

UN chief warns tech firm to ‘acknowledge the damage’ their products are causing

António Guterres, Secretary-General of the United Nations (UN), has called on technology firms to “acknowledge the damage your products are inflicting on people and communities”.

Speaking at the launch of the UN’s Global Principles for Information Integrity, he said that algorithms on social media platforms had the ability to “push people into information bubbles and reinforce prejudices including racism, misogyny and discrimination of all kinds” through opaque algorithms.

UN Secretary-General António Guterres launches new UN Global Principles on Information Integrity. Image: UN Photo/Eskinder Debebe

“You have the power to mitigate harm to people and societies around the world,” he said. “You have the power to change business models that profit from disinformation and hate”.

His comments come after several high-profile calls for social media companies to do more to protect vulnerable people, especially children.

Earlier in the month, New York passed legislation to protect children from addictive social media content. Similarly, the US Surgeon General, Vivek Murthy, called on social media apps to add warning labels reminding users they can cause harm to young people.

Writing in an op-ed for the New York Times, he said that while this would not make the platforms safe, it could increase awareness among young people and influence their behaviour.

And in the UK, a group of parents is calling on social media companies to grant access to their children’s data following their deaths. One ended their life after viewing harmful content, while another may have died after participating in a social media ‘challenge’.

Misinformation and disinformation is predicted to be the biggest global risk over the next two years. Image: World Economic Forum Global Risk Report 2024

AI boom sees Nvidia briefly become world’s most valuable company

Nvidia became the world’s most valuable company in June, overtaking Microsoft and Apple to hit a market value of $3.34 trillion. The company’s microchips are playing a key part in the development and advancement of artificial intelligence (AI) technology. Microsoft retook the top spot a few days later following a stock selloff.

Despite the fall, many investors expect Nvidia to continue rising in valuation in the future. The business has seen its stock surge 180% in 2024 alone. At the time of writing, The Guardian reported a 2.8% rise in the company’s shares in early trading on 25 June. 

The company started the month by unveiling its new generation of processors – Rubin – less than three months after launching its predecessor, the Blackwell chip.

News in brief: Digital technology stories from around the world

McDonald’s is to halt testing of AI chatbots at drive-thrus. The systems, which had been implemented in over 100 US locations, featured an AI voice that could respond to customer orders. The company has not given a reason for ending its test run, but shortcomings in the technology including multiplying items or incorrectly adding items have been shared on social media.

A film credited to ChatGPT 4.0 has had its premiere cancelled. The Last Screenwriter was due to debut at the Prince Charles Cinema in London, but a backlash has seen the screening withdrawn. Speaking to the Daily Beast, director Peter Luisi said: “I think people don’t know enough about the project. All they hear is ‘first film written entirely by AI’ and they immediately see the enemy, and their anger goes towards us.”

SAP is restructuring 8,000 jobs to focus on AI-driven business areas, the company announced. It will spend €2 billion ($2.2 billion) to retrain employees or replace them through voluntary redundancy programs.

And US record labels are suing two prominent AI music generators, Suno and Udio, reports Wired. Universal Music Group, Warner Music Group and Sony Music Group filed lawsuits in US federal court, alleging copyright infringement. Speaking in a press release, Recording Industry Association of America chair and CEO Mitch Glazier said: “Unlicenced services like Suno and Udio that claim it’s ‘fair’ to copy an artist’s life’s work and exploit it for their own profit without consent or pay set back the promise of genuinely innovative AI for us all.” 

More on digital technology on Agenda

Digital twin technologies have the potential to drive innovation in the industrial sector, allowing for improvements in efficiency without sacrificing productivity. See how the technology could support integrated digital ecosystems and help the energy sector in this piece.

As we face the fourth industrial revolution, better knowledge exchange between businesses and governments will help facilitate faster growth in tech-enabled industries. See how regions like Karnataka in India can become attractive locations for businesses developing AI solutions in this article.

Spatial computing, blockchain and AI have all generated excitement in recent years. But their potential is set to grow further as synergies between the technologies are realized. Learn about some of the industries already witnessing the transformative impact of these key technologies as they converge.

Article link: https://www.weforum.org/agenda/2024/07/ai-regulation-digital-news-july-2024/

An internet protocol called C2PA adds a “nutrition label” to images, video, and audio.

By Tate Ryan-Mosley

July 28, 2023

The White House wants big AI companies to disclose when content has been created using artificial intelligence, and very soon the EU will require some tech platforms to label their AI-generated images, audio, and video with “prominent markings” disclosing their synthetic origins. 

There’s a big problem, though: identifying material that was created by artificial intelligence is a massive technical challenge. The best options currently available—detection tools powered by AI, and watermarking—are inconsistent, impermanent, and sometimes inaccurate. (In fact, just this week OpenAI shuttered its own AI-detecting tool because of high error rates.)

But another approach has been attracting attention lately: C2PA. Launched two years ago, it’s an open-source internet protocol that relies on cryptography to encode details about the origins of a piece of content, or what technologists refer to as “provenance” information. 

The developers of C2PA often compare the protocol to a nutrition label, but one that says where content came from and who—or what—created it. 

The project, part of the nonprofit Joint Development Foundation, was started by Adobe, Arm, Intel, Microsoft, and Truepic, which formed the Coalition for Content Provenance and Authenticity (from which C2PA gets its name). Over 1,500 companies are now involved in the project through the closely affiliated open-source community, Content Authenticity Initiative (CAI), including ones as varied and prominent as Nikon, the BBC, and Sony.

Recently, as interest in AI detection and regulation has intensified, the project has been gaining steam; Andrew Jenks, the chair of C2PA, says that membership has increased 56% in the past six months. The major media platform Shutterstock has joined as a member and announced its intention to use the protocol to label all its AI-generated content, including its DALL-E-powered AI image generator. 

Sejal Amin, chief technology officer at Shutterstock, told MIT Technology Review in an email that the company is protecting artists and users by “supporting the development of systems and infrastructure that create greater transparency to easily identify what is an artist’s creation versus AI-generated or modified art.”

What is C2PA and how is it being used?

Microsoft, Intel, Adobe, and other major tech companies started working on C2PA in February 2021, hoping to create a universal internet protocol that would allow content creators to opt in to labeling their visual and audio content with information about where it came from. (At least for the moment, this does not apply to text-based posts.) 

Crucially, the project is designed to be adaptable and functional across the internet, and the base computer code is accessible and free to anyone. 

Truepic, which sells content verification products, has demonstrated how the protocol works with a deepfake video with Revel.ai. When a viewer hovers over a little icon at the top right corner of the screen, a box of information about the video appears that includes the disclosure that it “contains AI-generated content.” 

Adobe has also already integrated C2PA, which it calls content credentials, into several of its products, including Photoshop and Adobe Firefly. “We think it’s a value-add that may attract more customers to Adobe tools,” Andy Parsons, senior director of the Content Authenticity Initiative at Adobe and a leader of the C2PA project, says. 

C2PA is secured through cryptography, which relies on a series of codes and keys to protect information from being tampered with and to record where information came from. More specifically, it works by encoding provenance information through a set of hashes that cryptographically bind to each pixel, says Jenks, who also leads Microsoft’s work on C2PA. 

C2PA offers some critical benefits over AI detection systems, which use AI to spot AI-generated content and can in turn learn to get better at evading detection. It’s also a more standardized and, in some instances, more easily viewable system than watermarking, the other prominent technique used to identify AI-generated content. The protocol can work alongside watermarking and AI detection tools as well, says Jenks. 

The value of provenance information 

Adding provenance information to media to combat misinformation is not a new idea, and early research seems to show that it could be promising: one project from a master’s student at the University of Oxford, for example, found evidence that users were less susceptible to misinformation when they had access to provenance information about content. Indeed, in OpenAI’s update about its AI detection tool, the company said it was focusing on other “provenance techniques” to meet disclosure requirements.

That said, provenance information is far from a fix-all solution. C2PA is not legally binding, and without required internet-wide adoption of the standard, unlabeled AI-generated content will exist, says Siwei Lyu, a director of the Center for Information Integrity and professor at the University at Buffalo in New York. “The lack of over-board binding power makes intrinsic loopholes in this effort,” he says, though he emphasizes that the project is nevertheless important.

What’s more, since C2PA relies on creators to opt in, the protocol doesn’t really address the problem of bad actors using AI-generated content. And it’s not yet clear just how helpful the provision of metadata will be when it comes to media fluency of the public. Provenance labels do not necessarily mention whether the content is true or accurate. 

Ultimately, the coalition’s most significant challenge may be encouraging widespread adoption across the internet ecosystem, especially by social media platforms. The protocol is designed so that a photo, for example, would have provenance information encoded from the time a camera captured it to when it found its way onto social media. But if the social media platform doesn’t use the protocol, it won’t display the photo’s provenance data.

The major social media platforms have not yet adopted C2PA. Twitter had signed on to the project but dropped out after Elon Musk took over. (Twitter also stopped participating in other volunteer-based projects focused on curbing misinformation.)  

C2PA “[is] not a panacea, it doesn’t solve all of our misinformation problems, but it does put a foundation in place for a shared objective reality,” says Parsons. “Just like the nutrition label metaphor, you don’t have to look at the nutrition label before you buy the sugary cereal.

“And you don’t have to know where something came from before you share it on Meta, but you can. We think the ability to do that is critical given the astonishing abilities of generative media.”

This piece has been updated to clarify the relationship between C2PA and CAI.

Article link: https://www-technologyreview-com.cdn.ampproject.org/c/s/www.technologyreview.com/2023/07/28/1076843/cryptography-ai-labeling-problem-c2pa-provenance/amp/

by

Brian Eastwood

 Nov 29, 2023

Why It Matters

Content labels are one way to identify content generated with artificial intelligence. A new study looks at what wording is most effective. Share 

In late October, President Joe Biden issued a wide-ranging executive order on AI security and safety. The order includes new standards and best practices for clearly labeling AI-generated content, in part to help Americans determine whether communications that appear to be from the government are authentic.

This points to a concern that as generative AI becomes more widely used, manipulated content could easily spread false information. As the executive order indicates, content labels are one strategy for combatting the spread of misinformation. But what are the right terms to use? Which ones will be widely understood by the public as indicating that something has been generated or manipulated by artificial intelligence technology or is intentionally misleading?

A new working paper co-authored by MIT Sloan professor David Rand found that across the United States, Mexico, Brazil, India, and China, people associated certain terms, such as “AI generated” and “AI manipulated,” most closely with content created using AI. Conversely, the labels “deepfake” and “manipulated” were most associated with misleading content, whether AI created it or not.

These results show that most people have a reasonable understanding of what “AI” means, which is a good starting point. They also suggest that any effort to label content needs to consider the overarching goal, said Rand, a professor of management science and brain and cognitive sciences. Rand co-authored the paper with Ziv Epstein, SM ’19 and PhD ’23, a postdoctoral fellow at Stanford; MIT graduate researcher Cathy Fang, SM ’23; and Antonio A. Arechar, a professor at the Center for Research and Teaching in Economics in Aguascalientes, Mexico.

Rand also co-authored a recent policy brief about labeling AI-generated content. 

“A lot of AI-generated content is not misleading, and a lot of misleading content is not AI-generated,” Rand said. “Is the concern really about AI-generated content per se, or is it more about misleading content?”

Looking at how people understand various AI-related terms 

Governments, technology companies, and industry associations are wrestling with how to let viewers know that they are viewing artificially generated content, given that face-swapping and voice imitation tools can be used to create misleading content, and images can be generated that falsely depict people in compromising situations.

In addition to the recent executive order, U.S. Rep. Ritchie Torres has proposed the AI Disclosure Act of 2023, which would require a disclaimer on any content — including videos, photos, text, or audio — generated by AI. Meanwhile, the Coalition for Content Provenance and Authenticityhas developed an open technical standard for tracing the origins of content and determining whether it has been manipulated.

Disclaimers, watermarks, or other labels would be useful to indicate how content was created or whether it is misleading; in fact, studies have indicated that social media users are less likely to believe or share content labeled as misleading. But before trying to label content that is generated by AI, platforms and policymakers need to know which terms are widely understood by the general population. If labels use a term that is overly jargony or confusing, it could interfere with the label’s goal.

To look at what terms were understood correctly most often, the researchers surveyed more than 5,100 people across five countries in four languages. Participants were randomly assigned one of nine terms: “AI generated,” “generated with an AI tool,” “artificial,” “synthetic,” “deepfake,” “manipulated,” “not real,” “AI manipulated,” or “edited.” They were then shown descriptions of 20 different content types and asked whether the assigned term applied to each type of content.

The phrases “AI generated,” “generated with an AI tool,” and “AI manipulated” were most closely associated with content generated using AI.

Alternatively, the researchers found that “deepfake” and “manipulated” were most closely associated with potentially misleading content. Terms such as “edited,” “synthetic,” or “not real” were not closely associated with either AI-generated content or misleading content.

The results were similar among the participants, regardless of age, gender, education, digital literacy, and familiarity with AI.

“The differences between ‘AI manipulated’and ‘manipulated’ are quite striking: Simply adding the ‘AI’ qualifier dramatically changed which pieces of content participants understood the term as applying [to],” the researchers write.

The purpose of an AI label 

Content labels could serve two different purposes. One is to indicate that content was generated using AI. The other is to show that the content could mislead viewers, whether created by AI or not. That will be an important consideration as momentum builds to label AI generated content.

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“It could make sense to have different labels for misleading content that is AI-generated, versus content that’s not AI-generated,” Rand said.

How the labels are generated will also matter. Self-labeling has obvious disadvantages, as few creators will willingly admit that their content is intentionally misleading. Machine learning, crowdsourcing, and digital forensics are viable options, though relying on those approaches will become more challenging as the lines between content made by humans and generated by computers continue to blur. And under the principle of implied authenticity, the more content that gets labeled, the more that content without a label is assumed to be real.

Finally, researchers found that some labels will not work everywhere. For example, in the study, Chinese speakers associated the word “artificial” with human involvement, whereas the term connotes automation in English, Portuguese, and Spanish.

“You can’t just take labels shown to work well in the United States and blindly apply them cross-culturally,” Rand said. “Testing of labels will need to be done in different countries to ensure that terms resonate.”

READ THE PAPER: WHAT LABEL SHOULD BE APPLIED TO CONTENT PRODUCED BY GENERATIVE AI?

READ NEXT: STUDY GAUGES HOW PEOPLE PERCEIVE AI-GENERATED CONTENT

Article link: https://mitsloan.mit.edu/ideas-made-to-matter/how-should-ai-generated-content-be-labeled?

Safeguarding Large Language Models and Why This Matters for the Future of Geopolitics

Event Details

Date:

July 18, 2024

Time:

4:00–6:00 p.m. Eastern

Location:American Association for the Advancement of Science (AAAS)auditorium
12th St. NW and H St. NW, Washington, D.C.

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Program

Given the dramatic, rapid, and unpredictable rate of change of AI capabilities, there is an urgent need for robust, forward-thinking strategies to ensure the security of AI systems. As many national governments have acknowledged, AI models may soon be critical for national security: They could potentially drive advantages in strategic competition—and, in the wrong hands, enable significant harm.

Please join RAND on Thursday, July 18, 4:00 Eastern, for a moderated panel discussion on the increasingly important topic of securing AI, and the implications for national and homeland security. A brief reception will follow the discussion and Q&A.

Speakers

Vijay Bolina

Chief Information Security Officer (CISO), Google DeepMind

Lisa Einstein

Senior Advisor for AI and Executive Director of the Cybersecurity Advisory Committee, CISA

Sella Nevo

Director, RAND Meselson Center and RAND Senior Information Scientist

Learn More about AI Security

A recent RAND study, Securing AI Model Weights: Preventing Theft and Misuse of Frontier Models, focuses on the potential theft and misuse of foundation AI model weights and details how promising security measures can be adapted specifically for model weights.

For more from RAND on AI, see our collection of featured research and commentary related to artificial intelligence topics.

Register for This Program

Please register to attend in person to join us in the auditorium at the American Association for the Advancement of Science (AAAS) in Washington, D.C., or register to join the livestream.

Contact securing-ai-panel@rand.org with questions about the event.

Article link: https://www.rand.org/events/2024/07/securing-ai.html?

https://hbr.org/2015/04/the-15-diseases-of-leadership-according-to-pope-francis

By ALEXANDRA KELLEYJUNE 24, 2024

As artificial intelligence and machine learning continue to intersect with sensitive research efforts, the Department of Homeland Security recommended increased communication and guidance to mitigate dangerous outcomes.

Artificial intelligence has the potential to unlock secrets to the development of weapons of mass destruction, particularly chemical and biological threats, to malicious actors, according to a Department of Homeland Security reportpublicly released last week.

The full report, which was teed up in April with the release of a fact sheet, examines the role of AI in aiding but also thwarting efforts by adversaries to research, develop and use chemical, biological, radiological and nuclear weapons. The report was required under President Joe Biden’s October 2023 executive order on AI.

“The increased proliferation and capabilities of AI tools … may lead to significant changes in the landscape of threats to U.S. national security over time, including by influencing the means, accessibility, or likelihood of a successful CBRN attack,” DHS’s Countering Weapons of Mass Destruction Office states in the report.

According to the report, “known limitations in existing U.S. biological and chemical security regulations and enforcement, when combined with increased use of AI tools, could increase the likelihood of both intentional and unintentional dangerous research outcomes that pose a risk to public health, economic security, or national security.”

Specifically, the report states that the proliferation of publicly available AI tools could lower the barrier to entry for malicious actors seeking information on the composition, development and delivery of chemical and biological weapons. While access to laboratory facilities is still a hurdle, the report notes that so-called “cloud labs” could allow threat actors to remotely develop components of weapons of mass destruction in the physical world, potentially under the cover of anonymity. 

CWMD recommended that the U.S. develop guidance covering the “tactical exclusion and/or protection of sensitive chemical and biological data” from public training materials for large language models, as well as more oversight governing access to remote-controlled lab facilities. 

The report also said that specific federal guidance is needed to govern how biological design tools and biological- and chemical-specific foundation models are used. This guidance would ideally include “granular release practices” for source code and specification for the weight calculations used to build a relevant language model.

More generally, the report seeks the development of consensus within U.S. government regulatory agencies on how to manage AI and machine learning technologies, in particular as they intersect with chemical and biological research.

Other findings include incorporating “safe harbor” vulnerability reporting practices into organizational proceedings, practicing internal evaluation and red teaming efforts, cultivating a broader culture of responsibility among expert life science communities and responsibly investigating the benefits AI and machine learning could have in biological, chemical and nuclear contexts. 

The report also envisions a role for AI in mitigating existing CBRN risks through threat detection and response, including via disease surveillance, diagnostics, “and many other applications the national security and public health communities have not identified.”

While the findings in this report are not enforceable mandates, DHS said that the contents will help shape future policy and objectives within the CWMD office. 

“Moving forward, CWMD will explore how to operationalize the report’s recommendations through existing federal government coordination groups and associated efforts led by the White House,” a DHS spokesperson told Nextgov/FCW. “The Office will integrate AI analysis into established threat and risk assessments as well as into the planning and acquisition that it performs on behalf of federal, state, local, tribal and territorial partners.” 

Article link: https://www.nextgov.com/artificial-intelligence/2024/06/dhs-report-details-ais-potential-amplify-biological-chemical-threats/397607/?

These are the Top 10 Emerging #Technologies which could significantly impact #society and the #economy in the next 3-5 years. This World Economic Forum report, produced in collaboration with Frontiers, draws on the expertise of scientists, researchers and futurists and covers applications in health, communication, infrastructure and sustainability. Learn more about #emergingtech24 here: https://lnkd.in/e9qfH9Mz #AMNC2

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The Top 10 Emerging Technologies report is a vital source of strategic intelligence. First published in 2011, it draws on insights from scientists, researchers and futurists to identify 10 technologies poised to significantly influence societies and economies. These emerging technologiesare disruptive, attractive to investors and researchers, and expected to achieve considerable scale within five years. This edition expands its analysis by involving over 300 experts from the Forum’s Global Future Councils and a global network of comprising over 2,000 chief editors worldwide from top institutions through Frontiers, a leading publisher of academic research.

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Engineers use the high-fidelity models to monitor operations, plan fixes, and troubleshoot problems.

By Sarah Scoles

June 10, 2024

In January 2022, NASA’s $10 billion James Webb Space Telescope was approaching the end of its one-million-mile trip from Earth. But reaching its orbital spot would be just one part of its treacherous journey. To ready itself for observations, the spacecraft had to unfold itself in a complicated choreography that, according to its engineers’ calculations, had 344 different ways to fail. A sunshield the size of a tennis court had to deploy exactly right, ending up like a giant shiny kite beneath the telescope. A secondary mirror had to swing down into the perfect position, relying on three legs to hold it nearly 25 feet from the main mirror. 

Finally, that main mirror—its 18 hexagonal pieces nestled together as in a honeycomb—had to assemble itself. Three golden mirror segments had to unfold from each side of the telescope, notching their edges against the 12 already fitted together. The sequence had to go perfectly for the telescope to work as intended.

“That was a scary time,” says Karen Casey, a technical director for Raytheon’s Air and Space Defense Systems business, which built the software that controls JWST’s movements and is now in charge of its flight operations. 

Over the multiple days of choreography, engineers at Raytheon watched the events unfold as the telescope did. The telescope, beyond the moon’s orbit, was way too distant to be visible, even with powerful instruments. But the telescope was feeding data back to Earth in real time, and software near-simultaneously used that data to render a 3D video of how the process was going, as it was going. It was like watching a very nerve-racking movie.

The 3D video represented a “digital twin” of the complex telescope: a computer-based model of the actual instrument, based on information that the instrument provided. “This was just transformative—to be able to see it,” Casey says.

The team watched tensely, during JWST’s early days, as the 344 potential problems failed to make their appearance. At last, JWST was in its final shape and looked as it should—in space and onscreen. The digital twin has been updating itself ever since.

The concept of building a full-scale replica of such a complicated bit of kit wasn’t new to Raytheon, in part because of the company’s work in defense and intelligence, where digital twins are more popular than they are in astronomy.

JWST, though, was actually more complicated than many of those systems, so the advances its twin made possible will now feed back into that military side of the business. It’s the reverse of a more typical story, where national security pursuits push science forward. Space is where non-defense and defense technologies converge, says Dan Isaacs, chief technology officer for the Digital Twin Consortium, a professional working group, and digital twins are “at the very heart of these collaborative efforts.”

As the technology becomes more common, researchers are increasingly finding these twins to be productive members of scientific society—helping humans run the world’s most complicated instruments, while also revealing more about the world itself and the universe beyond.  

800 million data points

The concept of digital twins was introduced in 2002 by Michael Grieves, a researcher whose work focused on business and manufacturing. He suggested that a digital model of a product, constantly updated with information from the real world, should accompany the physical item through its development. 

But the term “digital twin” actually came from a NASA employee named John Vickers, who first used it in 2010 as part of a technology road map report for the space agency. Today, perhaps unsurprisingly, Grieves is head of the Digital Twins Institute, and Vickers is still with NASA, as its principal technologist. 

Since those early days, technology has advanced, as it is wont to do. The Internet of Things has proliferated, hooking real-world sensors stuck to physical objects into the ethereal internet. Today, those devices number more than 15 billion, compared with mere millions in 2010. Computing power has continued to increase, and the cloud—more popular and powerful than it was in the previous decade—allows the makers of digital twins to scale their models up or down, or create more clones for experimentation, without investing in obscene amounts of hardware. Now, too, digital twins can incorporate artificial intelligence and machine learning to help make sense of the deluge of data points pouring in every second. 

Out of those ingredients, Raytheon decided to build its JWST twin for the same reason it also works on defense twins: there was little room for error. “This was a no-fail mission,” says Casey. The twin tracks 800 million data points about its real-world sibling every day, using all those 0s and 1s to create a real-time video that’s easier for humans to monitor than many columns of numbers. 

The JWST team uses the twin to monitor the observatory and also to predict the effects of changes like software updates. When testing these, engineers use an offline copy of the twin,  upload hypothetical changes, and then watch what happens next. The group also uses an offline version to train operators and to troubleshoot IRL issues—the nature of which Casey declines to identify. “We call them anomalies,” she says. 

Science, defense, and beyond

JWST’s digital twin is not the first space-science instrument to have a simulated sibling. A digital twin of the Curiosity rover helped NASA solve the robot’s heat issues. At CERN, the European particle accelerator, digital twins help with detector development and more mundane tasks like monitoring cranes and ventilation systems. The European Space Agency wants to use Earth observation data to create a digital twin of the planet itself. 

At the Gran Telescopio Canarias, the world’s largest single-mirror telescope, the scientific team started building a twin about two years ago—before they’d even heard the term. Back then, Luis Rodríguez, head of engineering, came to Romano Corradi, the observatory’s director. “He said that we should start to interconnect things,” says Corradi. They could snag principles from industry, suggested Rodríguez, where machines regularly communicate with each other and with computers, monitor their own states, and automate responses to those states.

The team started adding sensors that relayed information about the telescope and its environment. Understanding the environmental conditions around an observatory is “fundamental in order to operate a telescope,” says Corradi. Is it going to rain, for instance, and how is temperature affecting the scope’s focus? 

After they had the sensors feeding data online, they created a 3D model of the telescope that rendered those facts visually. “The advantage is very clear for the workers,” says Rodríguez, referring to those operating the telescope. “It’s more easy to manage the telescope. The telescope in the past was really, really hard because it’s very complex.”

Right now, the Gran Telescopio twin just ingests the data, but the team is working toward a more interpretive approach, using AI to predict the instrument’s behavior. “With information you get in the digital twin, you do something in the real entity,” Corradi says. Eventually, they hope to have a “smart telescope” that responds automatically to its situation. 

Corradi says the team didn’t find out that what they were building had a name until they went to an Internet of Things conference last year. “We saw that there was a growing community in industry—and not in science, in industry—where everybody now is doing these digital twins,” he says.

The concept is, of course, creeping into science—as the particle accelerators and space agencies show. But it’s still got a firmer foothold at corporations. “Always the interest in industry precedes what happens in science,” says Corradi.  But he thinks projects like theirs will continue to proliferate in the broader astronomy community. For instance, the group planning the proposed Thirty Meter Telescope, which would have a primary mirror made up of hundreds of segments, called to request a presentation on the technology. “We just anticipated a bit of what was already happening in the industry,” says Corradi.

The defense industry really loves digital twins. The Space Force, for instance, used one to plan Tetra 5, an experiment to refuel satellites. In 2022, the Space Force also gave Slingshot Aerospace a contract to create a digital twin of space itself, showing what’s going on in orbit to prepare for incidents like collisions. 

Isaacs cites an example in which the Air Force sent a retired plane to a university so researchers could develop a “fatigue profile”—a kind of map of how the aircraft’s stresses, strains, and loads add up over time. A twin, made from that map, can help identify parts that could be replaced to extend the plane’s life, or to design a better plane in the future. Companies that work in both defense and science—common in the space industry in particular—thus have an advantage, in that they can port innovations from one department to another.

JWST’s twin, for instance, will have some relevance for projects on Raytheon’s defense side, where the company already works on digital twins of missile defense radars, air-launched cruise missiles, and aircraft. “We can reuse parts of it in other places,” Casey says. Any satellite the company tracks or sends commands to “could benefit from piece-parts of what we’ve done here.”  

Some of the tools and processes Raytheon developed for the telescope, she continues, “can copy-paste to other programs.” And in that way, the JWST digital twin will probably have twins of its own.

Sarah Scoles is a Colorado-based science journalist and the author, most recently, of the book Countdown: The Blinding Future of Nuclear Weapons.

Article link: https://www.linkedin.com/posts/mit-technology-review_digital-twins-are-helping-scientists-run-activity-7210230903908790272-cIQ4?