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How our new AI feature earned 5% adoption in its first week

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Since the launch of ChatGPT, a stampede of technology company leaders has been chasing the buzz: Everywhere I turn, another company is trumpeting their pioneering AI feature. But real business value comes from delivering product capabilities that matter to users, not just from using hot tech.

We achieved a 10x better return on engineering effort with AI by starting with core principles for what users need from your product, building an AI capability that supports that vision, and then measuring adoption to make sure it hits the mark.

Our first AI product feature was not aligned with this idea, and it took a month to reach a disappointing 0.5% adoption among returning users. After recentering on our core principles for what our users need from our product, we developed an “AI as agent” approach and shipped a new AI capability that exploded to 5% adoption in the first week. This formula for success in AI can be applied to almost any software product.

The waste of hype haste

Many startups, like ours, are often tempted by the allure of integrating the latest technology without a clear strategy. So after the groundbreaking release of the various incarnations of generative pretrained transformer (GPT) models from OpenAI, we began looking for a way to use large language model (LLM) AI technology in our product. Soon enough, we’d secured our spot aboard the hype train with a new AI-driven element in production.

This first AI capability was a small summarization feature that uses GPT to write a short paragraph describing each file our user uploads into our product. It gave us something to talk about and we made some marketing content, but it didn’t have a meaningful impact on our user experience.

Many startups are often tempted by the allure of integrating the latest technology without a clear strategy.

We knew this because none of our key metrics showed an appreciable change. Only 0.5% of returning users interacted with the description in the first month. Moreover, there was no improvement in user activation and no change in the pace of user signups.

When we thought about it from a wider perspective, it was clear that this feature would never move those metrics. The core value proposition of our product is about big data analysis and using data to understand the world.

Generating a few words about the uploaded file is not going to result in any significant analytical insight, which means it’s not going to do much to help our users. In our haste to deliver something AI-related, we’d missed out on delivering actual value.

Success with AI as agent: 10x better return

The AI approach that gave us success is an “AI as agent” principle that empowers our users to interact with data in our product via natural language. This recipe can be applied to just about any software product that is built on top of API calls.

After our initial AI feature, we’d checked the box, but we weren’t satisfied because we knew we could do better for our users. So we did what software engineers have been doing since the invention of programming languages, which was to get together for a hackathon. From this hackathon, we implemented an AI agent that acts on behalf of the user.

The agent uses our own product by making API calls to the same API endpoints that our web front end calls. It constructs the API calls based on a natural language conversation with the user, attempting to fulfill what the user is asking it to do. The agent’s actions are manifested in our web user interface as a result of the API calls, just as if the user had taken the actions themselves.



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Webb Telescope Just Found The Holy Grail In A Famous Supernova

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At long last scientists believe they have the answer to what happened to a star that died in a famous supernova explosion not far from home.

The James Webb Space Telescope detected strong evidence supporting the existence of a neutron star, one of the densest objects in space, in its infancy. While some supernovas result in a new black hole, others create neutron stars when the core of a massive star collapses.

Though astronomers have known about neutron stars for decades, no one had actually seen one of these objects being formed before. The hunt for a neutron star within this close supernova remnant has been regarded as a holy grail quest.

“With this observatory, we have now found direct evidence for emission triggered by the newborn compact object, most likely a neutron star,” said Claes Fransson of Stockholm University, the lead author of the study, in a statement released by NASA.

Scientists first saw this stellar explosion — dubbed SN 1987A — with the naked eye nearly 40 years ago in the Large Magellanic Cloud, a small satellite galaxy of the Milky Way about 160,000 light-years away. Since then, they’ve investigated it at radio, gamma-ray, and X-ray wavelengths — searching for clues among the ashes for what came of the deceased star.

But supernovas, by their very nature, churn out a lot of dust, clouding the view. Stars on the verge of dying and supernovas are element factories: They make carbon, for instance, the same chemical on which humans and much of life on Earth are based. Then they spread elements like calcium found in bones and iron in blood across interstellar space.

This dispersal seeds new generations of stars and planets, but scientists admit they have much to learn about the early stages of the process.

The James Webb Space Telescope has observed the best evidence yet for emissions from a neutron star in supernova remnant SN 1987A.
Credit: NASA / ESA / CSA / STScI / Claes Fransson / Mikako Matsuura / M. Barlow / Patrick Kavanagh / Josefin Larsson

Webb, the leading infrared telescope, was finally able to “see” what other telescopes couldn’t in the aftermath. The new study, published this week in the journal Science, found evidence of heavily ionized argon (meaning argon atoms that had become electrically charged) in the center of the exploded material. Researchers think the most likely explanation for the changed argon is ionizing radiation from a neutron star.

“To create these ions that we observed in the ejecta, it was clear that there had to be a source of high-energy radiation in the center of the SN 1987A remnant,” Fransson said in a statement. “Only a few scenarios are likely, and all of these involve a newly born neutron star.”

Solving this mystery may help scientists better understand how stellar corpses evolve over time.





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NASA Spacecraft Spots Dramatic View Of New Impact Crater On Mars

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There’s a fresh crater on Mars, a reminder of our still-dynamic solar system.

NASA‘s Mars Reconnaissance Orbiter, a spacecraft orbiting Mars since 2006, uses an extremely powerful camera to observe the Martian surface. The team running the aptly named High Resolution Imaging Experiment, or HIRISE camera, recently released a detailed image of this impact crater.

“A Small, Very Recent Impact Crater,” they succinctly posted online. “That’s it. That’s the whole caption.”

It’s not that small. Maybe small compared to the Martian behemoths. The image above is 1 kilometer (0.6 miles) across, while the zoomed-out view below shows a Martian scene 5 km (3.1 miles) wide.

It’s unclear when such a recent object, likely an asteroid, crashed into Mars, leaving a sizable dent in the equatorial region of the Red Planet. But you can see markings from ejecta strewn around the impact basin.

The “very recent” impact crater spotted in the equatorial region of Mars.
Credit: NASA / JPL-Caltech / UArizona

Mars is absolutely covered in craters. NASA estimates there are over a quarter-million impact craters about the size of Arizona’s famous Barringer Crater, which is some 4,000 feet across. And there are over 43,000 Martian craters larger than three miles wide.

The Red Planet is much closer to our solar system’s asteroid belt, a region teeming with millions of asteroids. When they do collide with Mars, the Martian atmosphere is just 1 percent the volume of Earth’s, meaning these space rocks are less likely to heat up and disintegrate. What’s more, Mars isn’t nearly geologically dead — marsquakes frequently occur there — but it’s not nearly as active as Earth, a water-blanketed planet teeming with erupting volcanoes. On Mars today, there’s no geologic activity or volcanism to wash away, or cover up, new craters.

(Meanwhile, Earth has just around 120 known impact craters. That’s because over hundreds of millions of years, different parts of Earth’s surface have both been covered in lava or recycled as the giant plates that compose Earth’s crust, tectonic plates, continually move rock below and back up to the surface.)

As for us Earthlings, significant strikes from asteroids are rare:

– Every single day, about 100 tons of dust and sand-sized particles fall through Earth’s atmosphere and promptly burn up.

– Every year, on average, an “automobile-sized asteroid” plummets through our sky and explodes, explains NASA.

– Impacts by objects around 460 feet in diameter occur every 10,000 to 20,000 years.

– And a “dinosaur-killing” impact from a rock perhaps a half-mile across or larger happens on 100-million-year timescales.

So there’s no reason to live in fear — but it’s reasonable to have a healthy level of respect for the big space rocks out there. After all, with the asteroid deflection technology being created and tested today, we might be able to nudge a menacing asteroid off its course, should one ever barrel toward our humble blue planet.



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Miranda Bogen is creating solutions to help govern AI

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To give AI-focused women academics and others their well-deserved — and overdue — time in the spotlight, TechCrunch is launching a series of interviews focusing on remarkable women who’ve contributed to the AI revolution. We’ll publish several pieces throughout the year as the AI boom continues, highlighting key work that often goes unrecognized. Read more profiles here.

Miranda Bogen is the founding director of the Center of Democracy and Technology’s AI Governance Lab, where she works to help create solutions that can effectively regulate and govern AI systems. She helped guide responsible AI strategies at Meta and previously worked as a senior policy analyst at the organization Uptown, which seeks to use tech to advance equity and justice.

Briefly, how did you get your start in AI? What attracted you to the field?

I was drawn to work on machine learning and AI by seeing the way these technologies were colliding with fundamental conversations about society — values, rights, and which communities get left behind. My early work exploring the intersection of AI and civil rights reinforced for me that AI systems are far more than technical artifacts; they are systems that both shape and are shaped by their interaction with people, bureaucracies, and policies. I’ve always been adept at translating between technical and non-technical contexts, and I was energized by the opportunity to help break through the appearance of technical complexity to help communities with different kinds of expertise shape the way AI is built from the ground up.

What work are you most proud of (in the AI field)?

When I first started working in this space, many folks still needed to be convinced AI systems could result in discriminatory impact for marginalized populations, let alone that anything needed to be done about those harms. While there is still too wide a gap between the status quo and a future where biases and other harms are tackled systematically, I’m gratified that the research my collaborators and I conducted on discrimination in personalized online advertising and my work within the industry on algorithmic fairness helped lead to meaningful changes to Meta’s ad delivery system and progress toward reducing disparities in access to important economic opportunities.

How do you navigate the challenges of the male-dominated tech industry and, by extension, the male-dominated AI industry?

I’ve been lucky to work with phenomenal colleagues and teams who have been generous with both opportunities and sincere support, and we tried to bring that energy into any room we found ourselves in. In my most recent career transition, I was delighted that nearly all of my options involved working on teams or within organizations led by phenomenal women, and I hope the field continues to lift up the voices of those who haven’t traditionally been centered in technology-oriented conversations.

What advice would you give to women seeking to enter the AI field?

The same advice I give to anyone who asks: find supportive managers, advisors, and teams who energize and inspire you, who value your opinion and perspective, and who put themselves on the line to stand up for you and your work.

What are some of the most pressing issues facing AI as it evolves?

The impacts and harms AI systems are already having on people are well-known at this point, and one of the biggest pressing challenges is moving beyond describing the problem to developing robust approaches for systematically addressing those harms and incentivizing their adoption. We launched the AI Governance Lab at CDT to drive progress in both directions.

What are some issues AI users should be aware of?

For the most part, AI systems are still missing seat belts, airbags, and traffic signs, so proceed with caution before using them for consequential tasks.

What is the best way to responsibly build AI?

The best way to responsibly build AI is with humility. Consider how the success of the AI system you are working on has been defined, who that definition serves, and what context may be missing. Think about for whom the system might fail and what will happen if it does. And build systems not just with the people who will use them but with the communities who will be subject to them.

How can investors better push for responsible AI?

Investors need to create room for technology builders to move more deliberately before rushing half-baked technologies to market. Intense competitive pressure to release the newest, biggest, and shiniest new AI models is leading to concerning underinvestment in responsible practices. While uninhibited innovation sings a tempting siren song, it is a mirage that will leave everyone worse off.

AI is not magic; it’s just a mirror that is being held up to society. If we want it to reflect something different, we’ve got work to do.



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