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Robotic Automations

Google brings AI-powered editing tools, like Magic Editor, to all Google Photos users for free | TechCrunch


Google Photos is getting an AI upgrade. On Wednesday, the tech giant announced that a handful of enhanced editing features previously limited to Pixel devices and paid subscribers — including its AI-powered Magic Editor — will now make their way to all Google Photos users for free. This expansion also includes Google’s Magic Eraser, which removes unwanted items from photos; Photo Unblur, which uses machine learning to sharpen blurry photos; Portrait Light, which lets you change the light source on photos after the fact, and others.

The editing tools have historically been a selling point for Google’s high-end devices, the Pixel phones, as well as a draw for Google’s cloud storage subscription product, Google One. But with the growing number of AI-powered editing tools flooding the market, Google has decided to make its set of AI photo editing features available to more people for free.

Image Credits: Google

There are some caveats to this expansion, however.

For starters, the tools will only start rolling out on May 15 and it will take weeks for them to make it to all Google Photos users.

In addition, there are some hardware device requirements to be able to use them. On ChromeOS, for instance, the device must be a Chromebook Plus with ChromeOS version 118+ or have at least 3GB RAM. On mobile, the device must run Android 8.0 or higher or iOS 15 or higher.

The company notes that Pixel tablets will now be supported, as well.

Magic Editor is the most notable feature of the group. Introduced last year with the launch of the Pixel 8 and Pixel 8 Pro, this editing tool uses generative AI to do more complicated photo edits — like filling in gaps in a photo, repositioning the subject and other edits to the foreground or background of a photo. With Magic Editor, you can change a gray sky to blue, remove people from the background of a photo, recenter the photo subject while filling in gaps, remove other clutter and more.

Previously, these kinds of edits would require Magic Eraser and other professional editing tools, like Photoshop, to get the same effect. And those edits would be more manual, not automated via AI.

Image Credits: Google

With the expansion, Magic Editor will come to all Pixel devices, while iOS and Android users (whose phones meet the requirements) will get 10 Magic Editor saves per month. To go beyond that, they’ll still need to buy a Premium Google One plan — meaning 2TB of storage and above.

The other tools will be available to all Google Photos users, no Google One subscription is required. The full set of features that will become available includes Magic Eraser, Photo Unblur, Sky suggestions, Color pop, HDR effect for photos and videos, Portrait Blur, Portrait Light (plus the add light/balance light features in the tool), Cinematic Photos, Styles in the Collage Editor and Video Effects.

Other features like the AI-powered Best Take — which merges similar photos to create a single best shot where everyone is smiling — will continue to be available only to Pixel 8 and 8 Pro.


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Robotic Automations

Poe introduces a price-per-message revenue model for AI bot creators | TechCrunch


Bot creators now have a new way to make money with Poe, the Quora-owned AI chatbot platform. On Monday, the company introduced a revenue model that allows creators to set a per-message price for their bots so they can make money whenever a user messages them. The addition follows an October 2023 release of a revenue-sharing program that would give bot creators a cut of the earnings when their users subscribed to Poe’s premium product.

First launched by Quora in February 2023, Poe offers users the ability to sample a variety of AI chatbots, including those from ChatGPT maker OpenAI, Anthropic, Google, and others. The idea is to give consumers an easy way to toy with new AI technologies all in one place while also giving Quora a potential source of new content.

The company’s revenue models offer a new twist on the creator economy by rewarding AI enthusiasts who generate “prompt bots,” as well as developer-built server bots that integrate with Poe’s AI.

Last fall, Quora announced it would begin a revenue-sharing program with bot creators and said it would “soon” open up the option for creators to set a per-message fee on their bots. Although it’s been nearly 5 months since that announcement — hardly “soon” — the latter is now going live.

Quora CEO Adam D’Angelo explained on Monday that Poe users will only see message points for each bot, which encompasses the same points they have as either a free user or Poe subscriber. However, creators will be paid in dollars, he said.

“This pricing mechanism is important for developers with substantial model inference or API costs,” D’Angelo noted in a post on X. “Our goal is to enable a thriving ecosystem of model developers and bot creators who build on top of models, and covering these operational costs is a key part of that,” he added.

The new revenue model could spur the development of new kinds of bots, including in areas like tutoring, knowledge, assistants, analysis, storytelling, and image generation, D’Angelo believes.

The offering is currently available to U.S. bot creators only but will expand globally in the future. It joins the creator monetization program that pays up to $20 per user who subscribes to Poe thanks to a creator’s bots.

Alongside the per-message revenue model, Poe also launched an enhanced analytics dashboard that displays average earnings for creators’ bots across paywalls, subscriptions, and messages. Its insights are updated daily and will allow creators to get a better handle on how their pricing drives bot usage and revenue.




Software Development in Sri Lanka

Robotic Automations

Spotify launches personalized AI playlists that you can build using prompts | TechCrunch


Spotify already found success with its popular AI DJ feature, and now the streaming music service is bringing AI to playlist creation. The company on Monday introduced into beta a new option called AI playlists, which allows users to generate a playlist based on written prompts.

The feature will initially become available on Android and iOS devices in the U.K. and Australia and will evolve over time.

In addition to more standard playlist creation requests, like those based on genre or time frame, Spotify’s use of AI means people could ask for a wider variety of custom playlists, like “songs to serenade my cat” or “beats to battle a zombie apocalypse,” Spotify suggests. Prompts can reference all sorts of things, like places, animals, activities, movie characters, colors or emojis. The company notes that the best playlists are generated using prompts that contain a combination of genres, moods, artists and decades, however.

Spotify also leverages its understanding of users’ tastes to customize the playlists it makes with the feature.

After the playlist is generated, users can then use the AI to revise and refine the end result by issuing commands like “less upbeat” or “more pop,” for example. Users can also swipe left on any songs to remove them from the playlist.

In terms of the technology, Spotify says it’s using large language models (LLMs) to understand the user’s intent. Then, Spotify uses its personalization technology — the information it has about the listener’s history and preferences — to fulfill the prompt and create a personalized AI-generated playlist for the user.

The company uses a range of third-party tools for its AI and machine learning experiences.

TechCrunch first reported in October 2023 that Spotify was developing AI playlists, when reverse engineers Chris Messina and Alessandro Paluzzi shared screenshots of code from Spotify’s app that referred to AI playlists that were “based on your prompts.”

Spotify at the time declined to comment on the finding, saying it would not offer a statement on possible new features. However, in December 2023, the company confirmed that it was testing AI-driven playlist creation after a TikTok video of the feature surfaced showing what the Spotify user described as “Spotify’s ChatGPT.”

Image Credits: Spotify

The feature is found in the “Your Library” tab in Spotify’s app by tapping on the plus button (+) at the top right of the screen. A pop-up menu appears showing the AI Playlist as a new option alongside the existing “Playlist” and “Blend” options.

If a listener can’t think of any prompts to try, Spotify offers prompt suggestions to help people get started, like “get focused at work with instrumental electronica,” “fill in the silence with background café music,” “get pumped up with fun, upbeat, and positive songs” or “explore a niche genre like Witch House” and many others.

To save an AI playlist, tap the “Create” button to add it to the library.

The company notes the AI has guardrails around it so it will not respond to offensive prompts or those focused on current events or specific brands.

Spotify has been investing in AI technology to improve its streaming service for many months. With the launch of AI DJ, which expanded globally last year, the company used a combination of Sonantic and OpenAI technology to create an artificial version of the voice of Spotify’s head of cultural partnerships, Xavier “X” Jernigan, who introduces personalized song selections to the user. Last year, Spotify said it was investing in in-house research to better understand the latest in AI and large language models.

CEO Daniel Ek has also teased to investors other ways Spotify could leverage AI, including by summarizing podcasts, creating AI-generated audio ads and more. The company has also looked into using AI tech that would clone a podcast host’s voice for host-read ads.

Ahead of AI playlists, Spotify launched a similar feature, Niche Mixes, that allowed users to create personalized playlists using prompts, but the product did not leverage AI technology and was more limited in terms of its language understanding.


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Robotic Automations

Sundar Pichai on the challenge of innovating in a huge company and what he's excited about this year | TechCrunch


Alphabet CEO Sundar Pichai took the stage on Wednesday at a Stanford event held by the university’s business school, offering some small insights into what he thinks about running one of the world’s most valuable tech companies.

It was a notable appearance because Pichai’s been having a bit of a rough go lately. Google is widely perceived to have gotten a late start on generative AI, trailing behind Microsoft-funded OpenAI. That’s despite the fact that the company under Pichai has been focusing on AI for the better part of the last decade, and Google researchers wrote the formative paper on transformer models that really kicked off the generative AI revolution. More recently, Alphabet’s Gemini LLM was excoriated for generating bizarrely inaccurate images of historical situations, such as depicting America’s founding fathers as Black or Native American, rather than white English men, suggesting an overcorrection for certain types of bias.

The interviewer, Stanford Graduate School of Business dean Jonathan Levin, wasn’t exactly a hostile inquisitor — at the end, he revealed that the two men’s sons had once played in a middle school band together — and Pichai is deft at answering difficult questions by posing them as further questions about how he thinks, rather than with direct answers. But there were a couple nuggets of interest during the talk.

At one point, Levin asked what Pichai tried to do to keep a company of 200,000 people innovating against all the startups battling to disrupt its business. It’s obviously something Pichai worries about.

“Honestly, it’s a question which has always kept me up at night through the years,” he started. “One of the inherent characteristics of technology is you can always develop something amazing with a small team from the outside. And history has shown that. Scale doesn’t always give you … Regulators may not agree, but at least running the company, I’ve always felt you’re always susceptible to someone in a garage with a better idea. So I think, how do you as a company move fast? How do you have the culture of risk-taking? How do you incent for that? These are all things which you actually have to work at a lot. I think at least larger organizations tend to default. One of the most counterintuitive things I’ve seen is, the more successful things are, the more risk averse people become. It’s so counterintuitive. You would often find smaller companies almost make decisions which bet the company, but the bigger you are, it’s true for large university, it’s true a large company, you have a lot more to lose, or you perceive you have a lot more to lose. And so you find you don’t take as many ambitious risk-taking initiatives. So you have to consciously do that. You have to push teams to do that.” 

He didn’t offer any specific tactics that have proven successful at Google, but instead noted how difficult it is to create the proper incentives.

“One example for this is … how do you reward effort and risk-taking and good execution, and not always outcomes? It’s easy to think you should reward outcomes. But then people start gaming it, right? People take conservative things in which you will get a good outcome.”

He hearkened back to an earlier time in which Google was more willing to take weird risks, in particular pointing to the firm’s ill-fated Google Glass; it didn’t work out, but it was one of the first devices to experiment with augmented reality.

“We recently said, we went back to a notion we had in early Google of Google Labs. And so we’re setting a thing up by which it’s easier to put out something without always worrying about, you know, the full brand and the weight of building a Google product. How can you put out something in the easy way, the lighter weight way? How do you allow people to prototype more easily internally and get it out to people?”

Later, Levin asked what advances Pichai was most excited about this year.

First, he cited the multimodality of Google’s latest LLM — that is, its ability to process different kinds of inputs, such as video and text, simultaneously.

“All our AI models now already are using Gemini 1.5 Pro; that’s a 1 million context window and it’s multimodal. The ability to process huge amounts of information in any type of modality on the input side and give it on the output side, I think it’s mind blowing in a way that we haven’t fully processed.”

Second, he highlighted the ability to connect different discrete answers to provide smarter workflows. “Where today you’re using the LLMs as just an information-seeking thing, but chaining them together in a way that you can kind of tackle workflows, that’s going to be extraordinarily powerful. It could maybe make your billing system in Stanford Hospital a bit easier,” he joked.

You can watch the entire interview, along with an interview with Fed Chairman Jerome Powell that happened prior to it, on YouTube. Levin and Pichai start around 1 hour and 18 minutes in.


Software Development in Sri Lanka

Robotic Automations

Century Health, now with $2M, taps AI to give pharma access to good patient data | TechCrunch


Artificial intelligence can find hidden signals in data across healthcare, and companies like Nvidia are leaning into what this can mean. For example, it announced two dozen new AI-powered tools last week for areas including biotechnology and drug discovery. And Nvidia is not alone.

Century Health is a new startup also getting in on the action. It’s applying AI to clinical data to uncover new applications for drugs. It’s working with pharmaceutical companies and researchers, initially at Yale and UC San Diego, to identify and commercialize the next breakthrough for diseases, like Alzheimer’s, that affect tens of millions of patients.

The mission is a personal one for Century Health’s co-founder and CEO, Vish Srivastava. He watched his grandfather’s Alzheimer’s get to the point where he didn’t recognize Srivastava anymore.

“That sent me down a rabbit hole,” said Srivastava, whose background is in healthcare product development and data. “One of the biggest issues around innovation for new treatments is efficient access to good patient data. This is now only possible because of generative AI. That data sat around for decades because it takes manual effort to normalize and extract insight from it.”

That’s when he teamed up with friend Sanjay Hariharan, a data scientist and applied AI engineer, to form Century Health. They built a platform to extract that hidden data and aggregate it. Researchers and pharma companies subscribe to the platform and can then use that data on approved drugs; to expand to new drugs; or to find insights to expand access to drugs that have already been approved.

The ultimate goal is accelerating access to treatments, Srivastava said.

“Drug development is massively expensive, and on average, takes $1 billion to $2 billion to develop a new drug,” he said. “From the pharma company’s perspective, when their drug is now approved, the mission is to get it to patients as quickly as possible. For us, that also means as affordably as possible with access to good real-world data.”

Now with $2 million pre-seed funding, Century Health will run three to five pilots over the next several months. The goal is to validate the initial technology that collects the data and, most importantly, to see the impact the insights from those data sets can bring, Srivastava said.

He sees these pilots as design partnerships and a way to get feedback on the benefits of drugs, for example, which patient subpopulation might be underrepresented. In addition to the validated technology, another milestone will be to secure early revenue from the pilots, which Century Health can leverage to go after another round of venture capital.

The investment was led by 2048 Ventures with participation from LifeX, Everywhere, Alumni Ventures and a group of angel investors, including Datavant founder Travis May and Evidation founder and CEO Christine Lemke.

Alex Iskold, managing partner of 2048 Ventures, said in a statement, “At 2048 Ventures we have a strong thesis around real-time data, in healthcare and beyond. Vish and Sanjay have a vision to leverage AI and real world patient data to unlock a better feedback loop and ultimately faster and more efficient drug development and commercialization.”


Software Development in Sri Lanka

Robotic Automations

The 18 most interesting startups from YC's Demo Day show we're in an AI bubble | TechCrunch


Springtime means rain, the return of flowers and, of course, Y Combinator’s first demo day of the year. During the well-known accelerator’s first of two pitch days from the Winter 2024 cohort, a covey of TechCrunch staff tuned in, took notes, traded jokes and slowly whittled away at the dozens of presenting companies to come up with a list of early favorites.

AI was, not shockingly, the biggest theme, with 86 out of 247 companies calling themselves an AI startup, but we’re reaching bubble territory given that 187 mention AI in their pitches.

From AI-generated music and grant applications to neat new fintech applications and even some health tech work, there was something for everyone. We’re back at it Thursday for the second day of pitches. Until then, if you didn’t get to watch live, here’s a rundown of some of the best from day one.

TechCrunch’s staff favorites

Aidy

  • What it does: Uses AI to help companies find and apply for grants
  • Why it’s a fave: Landing grants isn’t easy. Max Williamson, Peter Crocker and Greg Miller know this well: They’ve worked between them at The Rockefeller Foundation and the U.S. Department of Housing and Urban Development, where grants are common currency. Finding and applying for grants involves sifting through mounds of paperwork and submitting countless forms — an expensive and time-consuming process. So why not have AI help with it? That’s the idea behind their startup Aidy, which is focused exclusively on Rural Energy for America Program grants for now. After asking a few questions, Aidy evaluates an organization’s competitiveness for grants by navigating eligibility requirements and scoring criteria, then takes a first pass at filling out any relevant forms. Aidy is clearly in the proof-of-concept stage, judging by the state of its tooling. But the concept’s an interesting one — assuming the platform’s AI doesn’t make too many mistakes.
  • Who picked it: Kyle

Givefront

  • What it does: Serves as a banking platform for nonprofits
  • Why it’s a fave: If you’re in the nonprofit space, compliance and regulatory requirements force you to do finances a little differently. That’s where Givefront comes in. Co-founded by Ethan Sayre and Matt Tengtrakool, who previously launched a startup to help loan-takers based in Nigeria, Givefront offers banking, spend management and financial governance services for nonprofits. Specifically, Givefront provides accounts to nonprofits to store money and integrate donations, payments and reimbursements, as well as features for automatic reporting and annual regulatory filings. Givefront certainly isn’t the only nonprofit banking option out there. But it appears to be one of the first built from the ground up for this purpose — which certainly has its own appeal.
  • Who picked it: Kyle

Buster

  • What it does: Software that links databases and large language models
  • Why it’s a fave: There’s a lot of attention in the market on companies that make large language models — the bigger, the faster, the smarter; you get the idea. But when it comes to actually deploying modern AL models inside of a company, you run into data issues. For example, Skyflow, one startup I covered recently, is working to keep sensitive information out of the wrong users of LLMs. Buster was eye-catching because it appears to be working on a problem that a whole mess of companies are going to run into. Sure, new models are cool, but selling software picks and shovels during the AI gold rush is probably a darn good business model. I dig it!
  • Who picked it: Alex

Numo

  • What it does: Banking services for contractors in emerging markets
  • Why it’s a fave: Creating better payroll solutions for remote and international workers isn’t new, but Numo’s approach of focusing on contractors in emerging markets specifically stands out. It’s also smart that Numo is building a banking product on top of its payroll system so that these contractors, many of whom would be based in countries with currencies that fluctuate frequently, have a more secure place to store their earned funds.
  • Who picked it: Becca

Intercept 

  • What it does: Uses AI to help consumer packaged goods brands aggregate retail fees and dispute invalid ones
  • Why it’s a fave: Many CPG brands, especially emerging ones, have very small margins that are squeezed by numerous fees that cover shelving, packing incorrect quantities and shipping damaged products. Intercept says that spotting and flagging invalid fees could give CPG brands back an average of 15% of their revenue that would have otherwise been spent on inaccurate fees. This seems like a problem worth solving.
  • Who picked it: Becca

Nuanced Inc.

  • What it does: Helps detect deep fakes and misinformation
  • Why it’s a fave: I’m curious about any technology that seeks to find ways to parse through the inevitable rise of deep fakes and misinformation we are already encountering. Artificial intelligence is becoming more sophisticated by the hour, and we are about to enter a world where right, wrong, fact and fiction have already started to get blurry. Deep fakes are of particular concern for women, as seen by what happened to Taylor Swift — and with slow government regulation in this space, I welcome any research and technology focused on trying to address our ever-increasing cybersecurity needs.
  • Who picked it: Dom

Vectorview

  • What it does: Custom LLM evaluation
  • Why it’s a fave: One of my favorite things to read through when a new, major LLM comes to market is its benchmark stats. For example, Anthropic’s Claude 3 Opus model has a 50.4% 0-shot CoT in “Graduate level reasoning, GPQA, Diamond.” It’s super clarifying stuff. Kidding aside, it’s not. That’s why I like the idea that Vectorview is working on, namely the ability to test LLMs and AI agents for a company’s particular use case. I suspect that by having its testing tools closer to the end user than the academic side of things, Vectorview could be onto something big.
  • Who picked it: Alex

Abel

  • What it does: Uses AI to help lawyers go through legal documents quicker
  • Why it’s a fave: Abel co-founder Sean Safahi said that this eliminates the need for lawyers to choose “depth over breadth.” I think any tech that helps lawyers make more informed arguments and decisions is a good thing. Speeding up the legal process and making it more accurate seems like a solid strategy. It’s worth noting that bringing AI and automation into the legal process does add a layer of privacy risk and users of Abel will have tread carefully.
  • Who picked it: Becca

Soundry AI, Sonauto

  • What they do: AI-powered music generation
  • Why they’re faves: Soundry AI’s technology could be incredibly useful to create music that sits neatly in the background. Muzak, elevator tunes, corporate learning soundtracks, whatever they play in loud restaurants that you can never quite make out, but might be a song that you know. It’s a big market, and I can see companies tuning their own mixes to get the right vibe. Then there’s Sonauto, a startup that wants to help you make hits. I am more skeptical here, mostly because the music I love the most takes a lot of humans working super hard to push the boundaries of what music can be. The latest Tesseract record is a good example. Goddamn, what an incredible piece of art. That said, I am open to being wrong here, and that the robots will eventually write better progressive metal and pop and experimental jazz than we humble meatsacks can. I love music, I love tech, so I presume that I am going to eventually love their union. (Though I also have copyright worries here regarding source material, I must add as I am no fun.)
  • Who picked it: Alex

Starlight Charging

  • What it does: EV chargers and management software for apartments, condos and commercial buildings
  • Why it’s a fave: Most EV charging happens at home, unless you live in a multifamily building, where infrastructure can be scant and forcing drivers to find power elsewhere. That’s not only a headache for drivers, it’s unrealized revenue for building owners. Starlight Charging centralizes key parts of the infrastructure to keep costs down. “Since our installation costs are so low, we can actually offer our solution for no upfront cost and still make money,” founder Andrew Kouri said. “Our payback period is less than one year. The company seems to be sweating the small stuff, too, offering its own charging equipment that adheres to the Plug & Charge standard for payments and comes with a removable cable that’s easy to swap in case of damage or vandalism. That should help with maintenance, something that’s tripped up many other EV charging networks.
  • Who picked it: Tim

Eggnog.ai

  • What it does: Online video creation and hosting for AI-generated clips
  • Why it’s a fave: I muted the Demo Day stream to give this a try — you can check out my creation here — because one thing I am constantly bummed out by is the dearth of new sci-fi films for me to watch late at night. We need more! So, video creation tools that lean on user prompts are super interesting to me. Mix in the fact that AI-generated stuff might not find a permanent home on mainstream video platforms (brand safety, copyright concerns, the list goes on), Eggnog could be onto something. Still, while my little video clip was neat, it is about as close to a feature film as my doodles are to the best animated series out there.
  • Who picked it: Alex

Pump

  • What it does: Bundles small businesses so they can save on AWS
  • Why it’s a fave: This is a great approach to help small and emerging companies get the cloud services they need without having to spend a significant portion of their capital on software. Pump’s decision to monetize through AWS, not the small companies themselves, is smart and makes it much more likely it could generate strong traction. It’s easy to get excited about a company called the “Costco of cloud compute.”
  • Who picked it: Becca

Pico

  • What it does: Seeks to organize screenshots
  • Why it’s a fave: It’s a favorite because I have, like, 13,000 photos on my phone, most of which are screenshots. And when I need to find a screenshot, I’m stuck searching through the abyss of my phone’s library. Having something that helps group these photos could be a lifesaver that allows me to attend to the important tasks, like sending out timely memes to the group chat. The founder billed this as Pinterest for screenshots, which also grabbed me as I am an avid Pinterest user. Anything that makes photo grouping and sharing easier and fun is a product I’m bound to use.
  • Who picked it: Dom

TrueClaim

  • What it does: Uses AI to help self-funded companies save 7% on health insurance
  • Why it’s a fave: Health insurance costs are skyrocketing. Large corporations can “eat” the fees, but absorbing the high cost is much harder for small and medium-sized businesses. SMBs are often forced to pass a large part of what they pay to their employees. Seven percent may not feel like a lot, but since health insurance can cost thousands of dollars a year, the savings could be meaningful for a small business or startup.
  • Who picked it: Marina

Manifold Freight

  • What it does: Aggregates spot freight
  • Why it’s a fave: The founders’ discovered demand for spot freight technology building a similar solution at Convoy and noted it was the only profitable part of the shuttered company that was snapped up by Flexport. Manifold Freight is focusing on companies that have 50 or more trucks, which means they are targeting a customer base that other freight software is overlooking. Plus, targeting larger carriers means their customers likely have more funds to spend on new technology.
  • Who picked it: Becca

Shepherd

  • What it does: Personalized teaching assistant that combines human tutors with AI
  • Why it’s a fave: I liked this because unlike other learning assistants, Shepherd works with academic institutions. This means the startup is not only authorized to tutor students, it also knows exactly what material needs to be learned. Shepherd also claims that it can help plan and manage students’ time. I would have liked to have had this when I was in college. It wasn’t always clear which learning task would be most challenging, and that ate up a lot of valuable time. Some of the countless hours I wasted learning to write code and get the program to work could have been better allocated to calculus, which wasn’t easy either.
  • Who picked it: Marina

Senso

  • What it does: AI-powered knowledge base for customer support in regulated industries, starting with credit units
  • Why it’s a fave: I hate being stuck on customer support calls. A conversation can seem to last forever as an agent puts you on repeated multi-minute holds to help figure out regulations or whatever other problems I’m trying to solve. If customer support specialists can quickly find an answer to an arcane regulation issue, it could save customers and banks (or insurance agencies) time and money.
  • Who picked it: Marina


Software Development in Sri Lanka

Robotic Automations

The AI world needs more data transparency and web3 startup Space and Time says it can help | TechCrunch


As AI proliferates and things on the internet are easier to manipulate, there’s a need more than ever to make sure data and brands are verifiable, said Scott Dykstra, CTO and co-founder of Space and Time, on TechCrunch’s Chain Reaction podcast.


“Not to get too cryptographically religious here, but we saw that during the FTX collapse,” Dykstra said. “We had an organization that had some brand trust, like I had my personal life savings in FTX. I trusted them as a brand.”

But the now-defunct crypto exchange FTX was manipulating its books internally and misleading investors. Dykstra sees that as akin to making a query to a database for financial records, but manipulating it inside their own database.

And this transcends beyond FTX, into other industries, too. “There’s an incentive for financial institutions to want to manipulate their records … so we see it all the time and it becomes more problematic,” Dykstra said.

But what is the best solution to this? Dykstra thinks the answer is through verification of data and zero-knowledge proofs (ZK proofs), which are cryptographic actions used to prove something about a piece of information — without revealing the origin data itself.

“It has a lot to do with whether there’s an incentive for bad actors to want to manipulate things,” Dykstra said. Anytime there’s a higher incentive, where people would want to manipulate data, prices, the books, finances or more, ZK proofs can be used to verify and retrieve the data.

At a high level, ZK proofs work by having two parties, the prover and the verifier, that confirm a statement is true without conveying any information more than whether it’s correct. For example, if I wanted to know whether someone’s credit score was above 700, if there’s one in place, a ZK proof — prover — can confirm that to the verifier, without actually disclosing the exact number.

Space and Time aims to be that verifiable computing layer for web3 by indexing data both off-chain and on-chain, but Dykstra sees it expanding beyond the industry and into others. As it stands, the startup has indexed from major blockchains like Ethereum, Bitcoin, Polygon, Sui, Avalanche, Sei and Aptos and is adding support for more chains to power the future of AI and blockchain technology.

Dykstra’s most recent concern is that AI data isn’t really verifiable. “I’m pretty concerned that we’re not really efficiently ever going to be able to verify that an LLM was executed correctly.”

There are teams today that are working on solving that issue by building ZK proofs for machine learning or large language models (LLMs), but it can take years to try and create that, Dykstra said. This means that the model operator can tamper with the system or LLM to do things that are problematic.

There needs to be a “decentralized, but globally, always available database” that can be created through blockchains, Dykstra said. “Everyone needs to access it, it can’t be a monopoly.”

For example, in a hypothetical scenario, Dykstra said OpenAI itself can’t be the proprietor of a database of a journal, for which journalists are creating content. Instead, it has to be something that’s owned by the community and operated by the community in a way that’s readily available and uncensorable. “It has to be decentralized, it’s going to have to be on-chain, there’s no way around it,” Dykstra said.

This story was inspired by an episode of TechCrunch’s podcast Chain Reaction. Subscribe to Chain Reaction on Apple Podcasts, Spotify or your favorite pod platform to hear more stories and tips from the entrepreneurs building today’s most innovative companies.

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