>For years, Michael Maxson spent more nights in hotels than his own bed, working on speaker systems for the titans of heavy rock on global tours. When Maxson decided to settle down with his wife and their two dogs, they chose the city where stadium rock spectacles took him more often than any other: Las Vegas.
After renting for several years, in 2021 he found a home he wanted to buy in Clark County—a place within easy reach of Vegas’s headline venues yet also quiet, an airy single-story stucco house on Dancing Avenue, which backs onto a 2,000-acre park. He dreamed of waking up each morning to look out across lakes and parkland. “It was a beautiful home,” says Maxson. “I mean, the fact you could see the mountains and the sun set and rise. Man.”
But Maxson’s house hunt was unexpectedly chaotic. House prices in Las Vegas leaped up 25% that year, and the market was awash with cheap mortgages and wolfish investors.
His dream home was not owned by a person but by a tech company. Zillow, the US’s largest real estate listings site, had begun buying up homes in 2018, predicting it could create a “one-click nirvana” for purchasing real estate. It estimated returns of $20 billion a year. Zillow Offers, its “instant buying” business, followed startups like Opendoor and Offerpad, which had pioneered “iBuying,” the so-called “high-tech flipping” model, which uses data systems to price houses and investor cash to buy them before fixing them up and selling them.
In 2021, iBuyers’ purchases jumped to double prepandemic levels, accounting for tens of billions of dollars in home sales. Las Vegas was among the top 10 markets where startups concentrated their investments. In a feverish summer, Maxson had already been outmuscled on two bids by cash offers from Zillow and Opendoor. On Dancing Ave., Zillow now acted as seller, having listed the home on June 24 for $470,000, nearly $60,000 more than it had paid less than two weeks before. But Maxson wanted it and agreed to close at just under asking price.
A Zillow listing for Maxson’s dream home on Dancing Avenue.
When he went to take a look at the property, however, he discovered a 37,000-gallon water leak that had eroded garden walls and flooded the neighbors’ yard. Seattle-based Zillow, which owned the home, was oblivious, but the city authorities weren’t—Maxson found a notice stuck to the garage door, threatening a fine for allowing green water to pool, attracting mosquitos carrying West Nile virus. This is one downside of having homes owned by “faceless” corporations, says Maxson: “The [owners] were disconnected from it, because it’s just a number on a spreadsheet.” Though he offered to handle the estimated $30,000 of repairs himself, and take it off Zillow’s books for $30,000 less than the list price, they said no. Maxson discovered soon after that the house had sold to another family, at the same price he had offeredHe estimates that he lost about $2,000 on inspections and other costs—the closest he came to securing a home in 22 attempts that summer.
But at the very same time, the startup that had profited from his dream home was discovering cracks in its own foundation. As it turned out, Zillow Offers had lost more than $420 million in three months of erratic house buying and unprofitable sales. As Zillow Offers shut down, analysts questioned whether other iBuyers were at risk or whether the entire tech-driven model is even viable. For the rest of us—neighbors, renters, or prospective buyers—the bigger question remains: Does the arrival of Silicon Valley tech point to a better future for housing or an industry disruption to fear?
By summer 2021, the US housing market had almost run out of records to break. The Washington Post reported house prices at all-time highs (with a median of $386,000 in June) as the number of homes listed hit record lows (1.38 million nationwide). The average home sold in 15 days that summer—half the time taken a year earlier—as cash-rich investors and second-home buyers bought more than ever before. By November, a New York Times headline asked: “Will Real Estate Ever Be Normal Again?”
Despite making just under 2% of home purchases nationwide during this period, iBuyers began to play a larger, and more unpredictable, role than most, leading to calls from city leaders in Los Angeles
By: Matthew Ponsford
Title: House-flipping algorithms are coming to your neighborhood
Sourced From: www.technologyreview.com/2022/04/13/1049227/house-flipping-algorithms-are-coming-to-your-neighborhood/
Published Date: Wed, 13 Apr 2022 09:00:00 +0000
Did you miss our previous article…
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
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
Transforming document understanding and insights with generative 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.
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
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
The Download: hunting for new matter, and Gary Marcus’ AI critiques
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.
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.
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
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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