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Estimating the long-term cost of data breaches to brands and businesses today.

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By: Jenn Webb
Title: Invisible tech. Real impact.
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Published Date: Thu, 11 Nov 2021 21:45:40 +0000

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I received the new gene-editing drug for sickle cell disease. It changed my life.

MIT Oleghere Matt Odom Photo002c scaled

On a picturesque fall day a few years ago, I opened the mailbox and took out an envelope as thick as a Bible that would change my life. The package was from Vertex Pharmaceuticals, and it contained a consent form to participate in a clinical trial for a new gene-editing drug to treat sickle cell disease.

A week prior, my wife and I had talked on the phone with Haydar Frangoul, an oncologist and hematologist in Nashville, Tennessee, and the lead researcher of the trial. He gave us an overview of what the trial entailed and how the early participants were faring. Before we knew it, my wife and I were flying to the study site in Nashville to enroll me and begin treatment. At the time, she was pregnant with our first child.

I’d lived with sickle cell my whole life—experiencing chronic pain, organ damage, and hopelessness. To me, this opportunity meant finally taking control of my life and having the opportunity to be a present father.

The drug I received, called exa-cel, could soon become the first CRISPR-based treatment to win approval from the US Food and Drug Administration, following the UK’s approval in mid-November. I’m one of only a few dozen patients who have ever taken it. In late October, I testified in favor of approval to the FDA’s advisory group as it met to evaluate the evidence. The agency will make its decision about exa-cel no later than December 8.

I’m very aware of how privileged I am to have been an early recipient and to reap the benefits of this groundbreaking new treatment. People with sickle cell disease don’t produce healthy hemoglobin, a protein that red blood cells use to transport oxygen in the body. As a result, they develop misshapen red blood cells that can block blood vessels, causing intense bouts of pain and sometimes organ failure. They often die decades younger than those without the disease.

After I received exa-cel, I started to experience things I had only dreamt of: boundless energy and the ability to recover by merely sleeping. My physical symptoms—including a yellowish tint in my eyes caused by the rapid breakdown of malfunctioning red blood cells—virtually disappeared overnight. Most significantly, I gained the confidence that sickle cell disease won’t take me away from my family, and a sense of control over my own destiny.

Today, several other gene therapies to treat sickle cell disease are in the pipeline from biotech startups such as Bluebird Bio, Editas Medicine, and Beam Therapeutics as well as big pharma companies including Pfizer and Novartis—all to treat the worst-suffering among an estimated US patient population of about 100,000, most of whom are Black Americans.

But many people who need these treatments may never receive them. Even though I benefited greatly from gene editing, I worry that not enough others will have that opportunity. And though I’m grateful for my treatment, I see real barriers to making these life-changing medicines available to more people.

grueling process

I feel very fortunate to have received exa-cel, but undergoing the treatment itself was an intense, monthslong journey. Doctors extracted stem cells from my own bone marrow and used CRISPR to edit them so that they would produce healthy hemoglobin. Then they injected those edited stem cells back into me.

It was an arduous process, from collecting the stem cells, to conditioning my body to receive the edited cells, to the eventual transplant. The collection process alone can take up to eight hours. For each collection, I sat next to an apheresis machine that vigorously separated my red blood cells from my stem cells, leaving me weakened. In my case, I needed blood transfusions after every collection—and I needed four collections to finally amass enough stem cells for the medical team to edit.

The conditioning regimen that prepared my body to receive the edited cells was a whole different challenge. I underwent weeks of chemotherapy to clear out old, faulty stem cells from my body and make room for the newly edited ones. That meant dealing with nausea, weakness, hair loss, debilitating mouth sores, and the risk of exacerbating the underlying condition.

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By: Jimi Olaghere
Title: I received the new gene-editing drug for sickle cell disease. It changed my life.
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Published Date: Mon, 04 Dec 2023 13:30:00 +0000

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The Download: cleantech 2.0, and ‘jury duty’ on Chinese delivery apps


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.

Climate tech is back—and this time, it can’t afford to fail

A cleantech bust in 2011 left almost all the renewable-energy startups in the US either dead or struggling to survive.

Over a decade on, the excitement around cleantech investments and manufacturing is back, and the money is flowing again. A recent analysis estimates that total green investments reached $213 billion in the US during the 12 months beginning July, 2022.

However, as ‘cleantech 2.0’ startups inch towards commercialization, many of them still face the same issues that tripped up the green revolution a decade ago. Can they succeed where their predecessors failed? Read the full story.

—David Rotman

Users are doling out justice on a Chinese food delivery app 

Jury trials are plentiful on Chinese apps—especially Meituan, the country’s most popular food delivery service.

Offered as a way for restaurants to appeal bad reviews they believe are unreasonable, Meituan crowdsources help from users by showing them the review, details of the order, and notes from the restaurant. Then users can vote on whether to take down the review from the restaurant’s public page. More than six million users have now participated in ‘jury duty’ on the app.

Even though it has existed for a few years, many people have only recently become aware of Meituan’s public jury feature. It’s now frequently a viral topic on social media—and a source of joy for those nosy enough to weigh in on other people’s business. Read the full story.

—Zeyi Yang

Meet the 15-year-old deepfake victim pushing Congress into action

In October, Francesca Mani was one of reportedly more than 30 girls at Westfield High School in New Jersey who were victims of deepfake pornography. Boys at the school had taken photos of Francesca and her classmates and used AI to create sexually explicit images of them without their consent.

The practice is actually stunningly commonplace, but we rarely hear such stories—at least in part because many victims understandably don’t want to talk publicly. But, within just a day of learning about the violation, 15-year-old Francesca started speaking out and calling on lawmakers to do something about the broader problem. Her efforts are already starting to pay off with new momentum for legislation.

Francesca and her mother, Dorota, say that their activism aims particularly to support women and girls who might be less equipped to push for change. Our senior reporter Tate Ryan-Mosley spoke to them both—read her write-up of their interview.

This story is from The Technocrat, our weekly newsletter all about power, politics, and Silicon Valley. Sign up to receive it in your inbox every Friday.

The must-reads

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

1 Inside the bitter feuds that will shape the future of AI
It seems most of today’s top AI companies were born out of arguments between rich, egomaniacal men. (NYT $)
How Microsoft navigated the recent OpenAI board turmoil. (New Yorker $)
OpenAI agreed to buy $51 million of AI chips from a startup backed by Sam Altman. (Wired $)
Adam D’Angelo helped to fire Altman. Now he has to work with him. (WSJ $)
Not every AI expert thinks superintelligence is on its way. (CNBC)

2 Satellite images suggest nearly 98,000 buildings in Gaza are damaged
The pictures were taken before the seven-day suspension of hostilities, which has now ended. (BBC)
 Inside the satellite tech being used to reveal the extent of Gaza’s destruction. (Scientific American $)

3 A group of 56 nations have agreed to phase out coal
Including the US, which sends a strong signal. (AP $)
Why the UN climate talks are a moment of reckoning for oil and gas companies. (MIT  Technology Review)
Climate experts are furious with the head of COP28 for spreading misinformation. (Sky)

4 We badly need to regulate AI in medicine
Here’s how we might approach that mammoth task. (Proto.Life)
+ Artificial intelligence is infiltrating health care. We shouldn’t let it make all the decisions. (MIT Technology Review)

5 Ozempic makes people want to drink less alcohol 
Researchers need to collect more data to understand why, but it’s a potentially promising finding. (Wired $)
 Weight-loss injections have taken over the internet. But what does this mean for people IRL? (MIT Technology Review)

6 As X descends into chaos, news outlets are turning to Reddit
The trouble is, it’s a very different beast. (WP $)
X is

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By: Charlotte Jee
Title: The Download: cleantech 2.0, and ‘jury duty’ on Chinese delivery apps
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Published Date: Mon, 04 Dec 2023 13:15:00 +0000

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Capitalizing on machine learning with collaborative, structured enterprise tooling teams

cap one abstract ML

Advances in machine learning (ML) and AI are emerging on a near-daily basis—meaning that industry, academia, government, and society writ large are evolving their understanding of the associated risks and capabilities in real time. As enterprises seek to capitalize on the potential of AI, it’s critical that they develop, maintain, and advance state-of-the-art ML practices and processes that will offer both strong governance and the flexibility to change as the demands of technology requirements, capabilities, and business imperatives change.

cap one abstract ML 1

That’s why it’s critical to have strong ML operations (MLOps) tooling, practices, and teams—those that build and deploy a set of software development practices that keep ML models running effectively and with agility. Capital One’s core ML engineering teams demonstrate firsthand the benefits collaborative, well-managed, and adaptable MLOps teams can bring to enterprises in the rapidly evolving AI/ML space. Below are key insights and lessons learned during Capital One’s ongoing technology and AI journey.

Standardized, reusable components are critical

Most MLOps teams have people with extensive software development skills who love to build things. But the continuous build of new AI/ML tools must also be balanced with risk efficiency, governance, and risk mitigation.

Many engineers today are experimenting with new generative AI capabilities. It’s exciting to think about the possibilities that something like code generation can unlock for efficiency and standardization, but auto-generated code also requires sophisticated risk management and governance processes before it can be accepted into any production environment. Furthermore, a one-size-fits-all approach to things like generating code won’t work for most companies, which have industry, business, and customer-specific circumstances to account for.

As enterprise platform teams continue to explore the evolution of ML tools and techniques while prioritizing reusable tools and components, they can look to build upon open-source capabilities. One example is Scikit-Learn, a Python library containing numerous supervised and unsupervised learning algorithms that has a strong user community behind it and which can be used as a foundation to further customize for specific and reusable enterprise needs.

Cross-team communication is vital

Most large enterprises have data scientists and engineers working on projects through different parts of the company. This means it can also be difficult to know where new technologies and tools are built, resulting in arbitrary uniqueness.

This underscores the importance of creating a collaborative team culture where communication about the big picture, strategic goals, and initiatives is prioritized—including the ability to find out where tools are being built and evolved. What does this look like in practice?

Ensure your team knows what tools and processes it owns and contributes to. Make it clear how their work supports the broader company’s mission. Demonstrate how your team can feel empowered not to build something from scratch. Incentivize reuse and standardization. It takes time and effort to create a culture of “innersourcing” innovation and build communications mechanisms for clarity and context, but it’s well worth it to ensure long-term value creation, innovation, and efficiency.

Tools must map to business outcomes

Enterprise MLOps teams have a broader role than building tools for data scientists and engineers: they need to ensure those tools both mitigate risk and enable more streamlined, nimble technology capabilities for their business partners. Before setting off on building new AI/ML capabilities, engineers and their partners should ask themselves a few core questions. Does this tool actually help solve a core problem for the business? Will business partners be able to use it? Will it work with existing tools and processes? How quickly can we deliver it, and is there something similar that already exists that we should build upon first?

Having centralized enterprise MLOps and engineering teams ask these questions can free up the business to solve customer problems, and to consider how technology can continue to support the evolution of new solutions and experiences.

Don’t simply hire unicorns, build

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By: Miriam Friedel
Title: Capitalizing on machine learning with collaborative, structured enterprise tooling teams
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Published Date: Mon, 04 Dec 2023 15:00:00 +0000

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