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In a near-future war—one that might begin tomorrow, for all we know—a soldier takes up a shooting position on an empty rooftop. His unit has been fighting through the city block by block. It feels as if enemies could be lying in silent wait behind every corner, ready to rain fire upon their marks the moment they have a shot.

Through his gunsight, the soldier scans the windows of a nearby building. He notices fresh laundry hanging from the balconies. Word comes in over the radio that his team is about to move across an open patch of ground below. As they head out, a red bounding box appears in the top left corner of the gunsight. The device’s computer vision system has flagged a potential target—a silhouetted figure in a window is drawing up, it seems, to take a shot.

The soldier doesn’t have a clear view, but in his experience the system has a superhuman capacity to pick up the faintest tell of an enemy. So he sets his crosshair upon the box and prepares to squeeze the trigger.

In different war, also possibly just over the horizon, a commander stands before a bank of monitors. An alert appears from a chatbot. It brings news that satellites have picked up a truck entering a certain city block that has been designated as a possible staging area for enemy rocket launches. The chatbot has already advised an artillery unit, which it calculates as having the highest estimated “kill probability,” to take aim at the truck and stand by.

According to the chatbot, none of the nearby buildings is a civilian structure, though it notes that the determination has yet to be corroborated manually. A drone, which had been dispatched by the system for a closer look, arrives on scene. Its video shows the truck backing into a narrow passage between two compounds. The opportunity to take the shot is rapidly coming to a close.

For the commander, everything now falls silent. The chaos, the uncertainty, the cacophony—all reduced to the sound of a ticking clock and the sight of a single glowing button:

“APPROVE FIRE ORDER.”

To pull the trigger—or, as the case may be, not to pull it. To hit the button, or to hold off. Legally—and ethically—the role of the soldier’s decision in matters of life and death is preeminent and indispensable. Fundamentally, it is these decisions that define the human act of war.

It should be of little surprise, then, that states and civil society have taken up the question of intelligent autonomous weapons—weapons that can select and fire upon targets without any human input—as a matter of serious concern. In May, after close to a decade of discussions, parties to the UN’s Convention on Certain Conventional Weapons agreed, among other recommendations, that militaries using them probably need to “limit the duration, geographical scope, and scale of the operation” to comply with the laws of war. The line was nonbinding, but it was at least an acknowledgment that a human has to play a part—somewhere, sometime—in the immediate process leading up to a killing.

But intelligent autonomous weapons that fully displace human decision-making have (likely) yet to see real-world use. Even the “autonomous” drones and ships fielded by the US and other powers are used under close human supervision. Meanwhile, intelligent systems that merely guide the hand that pulls the trigger have been gaining purchase in the warmaker’s tool kit. And they’ve quietly become sophisticated enough to raise novel questions—ones that are trickier to answer than the well-­covered wrangles over killer robots and, with each passing day, more urgent: What does it mean when a decision is only part human and part machine? And when, if ever, is it ethical for that decision to be a decision to kill?

For a long time, the idea of supporting a human decision by computerized means wasn’t such a controversial prospect. Retired Air Force lieutenant general Jack Shanahan says the radar on the F4 Phantom fighter jet he flew in the 1980s was a decision aid of sorts. It alerted him to the presence of other aircraft, he told me, so that he could figure out what to do about them. But to say that the crew and the radar were coequal accomplices would be a stretch.

That has all begun to change. “What we’re seeing now, at least in the way that I see this, is a transition to a world [in] which you need to have humans and machines … operating in some sort of team,” says Shanahan.

The rise of machine learning, in particular, has set off a paradigm shift in how militaries use computers to help shape the crucial decisions of warfare—up to, and including, the ultimate decision. Shanahan was the first director of Project Maven, a Pentagon program that developed target recognition algorithms for video footage from drones. The project, which kicked off a new era of American military AI, was launched in 2017 after a study concluded that “deep

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By: Arthur Holland Michel
Title: Inside the messy ethics of making war with machines
Sourced From: www.technologyreview.com/2023/08/16/1077386/war-machines/
Published Date: Wed, 16 Aug 2023 09:00:00 +0000

Did you miss our previous article…
https://mansbrand.com/this-uk-startup-engineered-a-clever-way-to-reuse-waste-heat-from-cloud-computing/

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Inside the hunt for new physics at the world’s largest particle collider

In 1977, Ray and Charles Eames released a remarkable film that, over the course of just nine minutes, spanned the limits of human knowledge. Powers of Ten begins with an overhead shot of a man on a picnic blanket inside a one-square-­meter frame. The camera pans out: 10, then 100 meters, then a kilometer, and eventually all the way to the then-known edges of the observable universe—1024 meters. There, at the farthest vantage, it reverses. The camera zooms back in, flying through galaxies to arrive at the picnic scene, where it plunges into the man’s skin, digging down through successively smaller scales: tissues, cells, DNA, molecules, atoms, and eventually atomic nuclei—10-14 meters. The narrator’s smooth voice-over ends the journey: “As a single proton fills our scene, we reach the edge of present understanding.”

During the intervening half-century, particle physicists have been exploring the subatomic landscape where Powers of Ten left off. Today, much of this global effort centers on CERN’s Large Hadron Collider (LHC), an underground ring 17 miles (27 kilometers) around that straddles the border between Switzerland and France. There, powerful magnets guide hundreds of trillions of protons as they do laps at nearly the speed of light underneath the countryside. When a proton headed clockwise plows into a proton headed counterclockwise, the churn of matter into energy transmutes the protons into debris: electrons, photons, and more exotic subatomic bric-a-brac. The newly created particles explode radially outward, where they are picked up by detectors.

In 2012, using data from the LHC, researchers discovered a particle called the Higgs boson. In the process, they answered a nagging question: Where do fundamental particles, such as the ones that make up all the protons and neutrons in our bodies, get their mass? A half-­century earlier, theorists had cautiously dreamed the Higgs boson up, along with an accompanying field that would invisibly suffuse space and provide mass to particles that interact with it. When the particle was finally found, scientists celebrated with champagne. A Nobel for two of the physicists who predicted the Higgs boson soon followed.

But now, more than a decade after the excitement of finding the Higgs, there is a sense of unease, because there are still unanswered questions about the fundamental constituents of the universe.

Perhaps the most persistent of these questions is the identity of dark matter, a mysterious substance that binds galaxies together and makes up 27% of the cosmos’s mass. We know dark matter must exist because we have astronomical observations of its gravitational effects. But since the discovery of the Higgs, the LHC has seen no new particles—of dark matter or anything else—despite nearly doubling its collision energy and quintupling the amount of data it can collect. Some physicists have said that particle physics is in a “crisis,” but there is disagreement even on that characterization: another camp insists the field is fine and still others say that there is indeed a crisis, but that crisis is good. “I think the community of particle phenomenologists is in a deep crisis, and I think people are afraid to say those words,” says Yoni Kahn, a theorist at the University of Illinois Urbana-Champaign.

The anxieties of particle physicists may, at first blush, seem like inside baseball. In reality, they concern the universe, and how we can continue to study it—of interest if you care about that sort of thing. The past 50 years of research have given us a spectacularly granular view of nature’s laws, each successive particle discovery clarifying how things really work at the bottom. But now, in the post-Higgs era, particle physicists have reached an impasse in their quest to discover, produce, and study new particles at colliders. “We do not have a strong beacon telling us where to look for new physics,” Kahn says.

So, crisis or no crisis, researchers are trying something new. They are repurposing detectors to search for unusual-looking particles, squeezing what they can out of the data with machine learning, and planning for entirely new kinds of colliders. The hidden particles that physicists are looking for have proved more elusive than many expected, but the search is not over—nature has just forced them to get more creative.

n almost-complete theory

As the Eameses were finishing Powers of Ten in the late ’70s, particle physicists were bringing order to a “zoo” of particles that had been discovered in the preceding decades. Somewhat drily, they called this framework, which enumerated the kinds of particles and their dynamics, the Standard Model.

Roughly speaking, the Standard Model separates fundamental particles into two types: fermions and bosons. Fermions are the bricks of matter—two kinds of fermions called up and down quarks, for example, are bound

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By: Dan Garisto
Title: Inside the hunt for new physics at the world’s largest particle collider
Sourced From: www.technologyreview.com/2024/02/20/1088002/higgs-boson-physics-particle-collider-large-hadron-collider/
Published Date: Tue, 20 Feb 2024 10:00:00 +0000

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Transforming document understanding and insights with generative AI

Adobe AI

At some point over the last two decades, productivity applications enabled humans (and machines!) to create information at the speed of digital—faster than any person could possibly consume or understand it. Modern inboxes and document folders are filled with information: digital haystacks with needles of insight that too often remain undiscovered.

Adobe AI Assistant 1000px 1

Generative AI is an incredibly exciting technology that’s already delivering tremendous value to our customers across creative and experience-building applications. Now Adobe is embarking on our next chapter of innovation by introducing our first generative AI capabilities for digital documents and bringing the new technology to the masses.

AI Assistant in Adobe Acrobat, now in beta, is a new generative AI–powered conversational engine deeply integrated into Acrobat workflows, empowering everyone with the information inside their most important documents.

ccelerating productivity across popular document formats

As the creator of PDF, the world’s most trusted digital document format, Adobe understands document challenges and opportunities well. Our continually evolving Acrobat PDF application, the gold standard for working with PDFs, is already used by more than half a billion customers to open around 400 billion documents each year. Starting immediately, customers will be able to use AI Assistant to work even more productively. All they need to do is open Acrobat on their desktop or the web and start working.

With AI Assistant in Acrobat, project managers can scan, summarize, and distribute meeting highlights in seconds, and sales teams can quickly personalize pitch decks and respond to client requests. Students can shorten the time they spend hunting through research and spend more time on analysis and understanding, while social media and marketing teams can quickly surface top trends and issues into daily updates for stakeholders. AI Assistant can also streamline the time it takes to compose an email or scan a contract of any kind, enhancing productivity for knowledge workers and consumers globally.

Innovating with AI—responsibly

Adobe has continued to evolve the digital document category for over 30 years. We invented the PDF format and open-sourced it to the world. And we brought Adobe’s decade-long legacy of AI innovation to digital documents, including the award-winning Liquid Mode, which allows Acrobat to dynamically reflow document content and make it readable on smaller screens. The experience we’ve gained by building Liquid Mode and then learning how customers get value from it is foundational to what we’ve delivered in AI Assistant.

Today, PDF is the number-one business file format stored in the cloud, and PDFs are where individuals and organizations keep, share, and collaborate on their most important information. Adobe remains committed to secure and responsible AI innovation for digital documents, and AI Assistant in Acrobat has guardrails in place so that all customers—from individuals to the largest enterprises—can use the new features with confidence.

Like other Adobe AI features, AI Assistant in Acrobat has been developed and deployed in alignment with Adobe’s AI principles and is governed by secure data protocols. Adobe has taken a model-agnostic approach to developing AI Assistant, curating best-in-class technologies to provide customers with the value they need. When working with third-party large language models (LLMs), Adobe contractually obligates them to employ confidentiality and security protocols that match our own high standards, and we specifically prohibit third-party LLMs from manually reviewing or training their models on Adobe customer data.

The future of intelligent document experiences

Today’s beta features are part of a larger Adobe vision to transform digital document experiences with generative AI. Our vision for what’s next includes the following:

Insights across multiple documents and document types: AI Assistant will work across multiple documents, document types, and sources, instantly surfacing the most important information from everywhere.AI-powered authoring, editing, and formatting: Last year, customers edited tens of billions of documents in Acrobat. AI Assistant will make it simple to quickly generate first drafts, as well as

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By: Deepak Bharadwaj
Title: Transforming document understanding and insights with generative AI
Sourced From: www.technologyreview.com/2024/02/20/1088584/transforming-document-understanding-and-insights-with-generative-ai/
Published Date: Tue, 20 Feb 2024 16:08:01 +0000

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Tech

The Download: hunting for new matter, and Gary Marcus’ AI critiques

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This is today’s edition of The Download our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Inside the hunt for new physics at the world’s largest particle collider

In 2012, using data from CERN’s Large Hadron Collider, researchers discovered a particle called the Higgs boson. In the process, they answered a nagging question: Where do fundamental particles, such as the ones that make up all the protons and neutrons in our bodies, get their mass?

When the particle was finally found, scientists celebrated with champagne. A Nobel for two of the physicists who predicted the Higgs boson soon followed.

But now, more than a decade later, there is a sense of unease. That’s because there are still so many unanswered questions about the fundamental constituents of the universe.

So researchers are trying something new. They are repurposing detectors to search for unusual-looking particles, squeezing what they can out of the data with machine learning, and planning for entirely new kinds of colliders. Read the full story.

—Dan Garisto

This story is from the upcoming print issue of MIT Technology Review, dedicated to exploring hidden worlds. Want to get your hands on a copy when it publishes next Wednesday? Subscribe now.

I went for a walk with Gary Marcus, AI’s loudest critic

Gary Marcus, a professor emeritus at NYU, is a prominent AI researcher and cognitive scientist who has positioned himself as a vocal critic of deep learning and AI. He is a divisive figure, and can often be found engaged in spats on social media with AI heavyweights such as Yann LeCun and Geoffrey Hinton (“All attempts to socialize me have failed,” he jokes.)

Marcus does much of his tweeting on scenic walks around his hometown of Vancouver. Our senior AI reporter Melissa Heikkilä decided to join him on one such stroll while she was visiting the city, to hear his thoughts on the latest product releases and goings-on in AI. Here’s what he had to say to her.

This story is from The Algorithm, our weekly newsletter all about AI. Sign up to receive it in your inbox every Monday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 A new class of satellites could change everything
🛰
They’re armed with cameras powerful enough to capture peoples’ individual features. (NYT $)
A big European satellite is set to return to Earth tomorrow. (Ars Technica)
A new satellite will use Google’s AI to map methane leaks from space. (MIT Technology Review)

2 How much electricity does AI consume?
It’s a lot—but working out exact sums can be tricky. (The Verge)
Making an image with generative AI uses as much energy as charging your phone. (MIT Technology Review)

3 How Silicon Valley learned to love the military
The world is feeling like a more dangerous place these days, and that’s drowning out any ethical concerns. (WP $)
Why business is booming for military AI startups. (MIT Technology Review)
+ SpaceX is getting closer to US intelligence and military agencies. (WSJ $)
Ukraine is in desperate need of better methods to clear land mines. (Wired $)

4 The EU is investigating TikTok over child safety
It alleges the company isn’t doing enough to verify users’ ages. (Mashable)

5 It’s hard to get all that excited about Bluesky
It’s just more of the same social media. (Wired $)
How to fix the internet. (MIT Technology Review)
Why millions of people are flocking to decentralized social media services. (MIT Technology Review)

6 Ozempic is taking off in China
A lack of official approval there yet isn’t stopping anyone. (WSJ $)
We’ve never understood how hunger works. That might be about to change. (MIT Technology Review)

7 Meet the people trying to make ethical AI porn
Sex work is a sector that’s already being heavily disrupted by AI. (The Guardian)

8 Why we need DNA data drives
We’re rapidly running out of storage space, but DNA is a surprisingly viable option. (IEEE Spectrum)

9 You don’t need to keep closing your phone’s background apps
It does nothing for your battery life. In fact, it can even drain it further. (Gizmodo)
Here’s another myth worth busting: you shouldn’t put your wet phone in rice. (The Verge)

10 The mysterious math of billiard tables
If you struggle to play pool, take comfort in the fact mathematicians get stumped by it too. (Quanta $)

Quote of the day

“We realized how easy it is for people to be against something, to reject something new.” 

—Silas Heineken, a 17-year-old from Grünheide, a suburb near Berlin in Germany, tells the New York

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By: Charlotte Jee
Title: The Download: hunting for new matter, and Gary Marcus’ AI critiques
Sourced From: www.technologyreview.com/2024/02/20/1088705/hunting-new-matter-gary-marcus/
Published Date: Tue, 20 Feb 2024 13:10:00 +0000

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