Voice cloning startup ElevenLabs introduced a new tool for users to generate sound effects through prompts today after announcing the project back in February.
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Biotech startup Valar Labs has built a tool that accurately predicts certain treatment outcomes, potentially saving precious time for patients.
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Poshmark, the social commerce site that lets people buy and sell new and used items to each other, launched a paid marketing tool on Thursday, giving sellers the ability to promote their entire shop at once. Poshmark’s new feature, called “Promoted Closet,” uses machine learning to automatically promote individual product listings from a seller’s entire […]
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These days every company is trying to figure out if their large language models are compliant with whatever rules they deem important, and with legal or regulatory requirements. If you’re in a regulated industry, the need is even more acute. Perhaps that’s why Patronus AI is finding early success in the marketplace. On Wednesday, the […]
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When Class founder Michael Chasen was in college, he and a buddy came up with the idea for Blackboard, an online classroom organizational tool. His original company was acquired for $1.64 billion in 2011. Chasen later developed Class, a Zoom add-on, during the pandemic to help teachers make better use of Zoom in a classroom […]
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OpenAI says it’s developing a tool to let creators better control how their content is used in AI.
Called Media Manager, the tool — once it’s released — will allow creators and content owners to identify their works to OpenAI and specify how they want those works to be included or excluded from AI research and training. The goal is to have the tool in place by 2025, OpenAI says, as the company works with creators, content owners and regulators toward a common standard.
“This will require cutting-edge machine learning research to build a first-ever tool of its kind to help us identify copyrighted text, images, audio and video across multiple sources and reflect creator preferences,” OpenAI writes in a blog post. “Over time, we plan to introduce additional choices and features.”
Microsoft has changed its policy to ban U.S. police departments from using generative AI for facial recognition through the Azure OpenAI Service, the company’s fully managed, enterprise-focused wrapper around OpenAI technologies.
Language added Wednesday to the terms of service for Azure OpenAI Service prohibits integrations with Azure OpenAI Service from being used “by or for” police departments for facial recognition in the U.S., including integrations with OpenAI’s text- and speech-analyzing models.
A separate new bullet point covers “any law enforcement globally,” and explicitly bars the use of “real-time facial recognition technology” on mobile cameras, like body cameras and dashcams, to attempt to identify a person in “uncontrolled, in-the-wild” environments.
The changes in terms come a week after Axon, a maker of tech and weapons products for military and law enforcement, announced a new product that leverages OpenAI’s GPT-4 generative text model to summarize audio from body cameras. Critics were quick to point out the potential pitfalls, like hallucinations (even the best generative AI models today invent facts) and racial biases introduced from the training data (which is especially concerning given that people of color are far more likely to be stopped by police than their white peers).
It’s unclear whether Axon was using GPT-4 via Azure OpenAI Service, and, if so, whether the updated policy was in response to Axon’s product launch. OpenAI had previously restricted the use of its models for facial recognition through its APIs. We’ve reached out to Axon, Microsoft and OpenAI and will update this post if we hear back.
The new terms leave wiggle room for Microsoft.
The complete ban on Azure OpenAI Service usage pertains only to U.S., not international, police. And it doesn’t cover facial recognition performed with stationary cameras in controlled environments, like a back office (although the terms prohibit any use of facial recognition by U.S. police).
That tracks with Microsoft’s and close partner OpenAI’s recent approach to AI-related law enforcement and defense contracts.
In January, reporting by Bloomberg revealed that OpenAI is working with the Pentagon on a number of projects including cybersecurity capabilities — a departure from the startup’s earlier ban on providing its AI to militaries. Elsewhere, Microsoft has pitched using OpenAI’s image generation tool, DALL-E, to help the Department of Defense (DoD) build software to execute military operations, per The Intercept.
Azure OpenAI Service became available in Microsoft’s Azure Government product in February, adding additional compliance and management features geared toward government agencies including law enforcement. In a blog post, Candice Ling, SVP of Microsoft’s government-focused division Microsoft Federal, pledged that Azure OpenAI Service would be “submitted for additional authorization” to the DoD for workloads supporting DoD missions.
Update: After publication, Microsoft said its original change to the terms of service contained an error, and in fact the ban applies only to facial recognition in the U.S. It is not a blanket ban on police departments using the service.
Microsoft has changed its policy to ban U.S. police departments from using generative AI through the Azure OpenAI Service, the company’s fully managed, enterprise-focused wrapper around OpenAI technologies.
Language added Wednesday to the terms of service for Azure OpenAI Service prohibits integrations with Azure OpenAI Service from being used “by or for” police departments in the U.S., including integrations with OpenAI’s text- and speech-analyzing models.
A separate new bullet point covers “any law enforcement globally,” and explicitly bars the use of “real-time facial recognition technology” on mobile cameras, like body cameras and dashcams, to attempt to identify a person in “uncontrolled, in-the-wild” environments.
The changes in terms come a week after Axon, a maker of tech and weapons products for military and law enforcement, announced a new product that leverages OpenAI’s GPT-4 generative text model to summarize audio from body cameras. Critics were quick to point out the potential pitfalls, like hallucinations (even the best generative AI models today invent facts) and racial biases introduced from the training data (which is especially concerning given that people of color are far more likely to be stopped by police than their white peers).
It’s unclear whether Axon was using GPT-4 via Azure OpenAI Service, and, if so, whether the updated policy was in response to Axon’s product launch. OpenAI had previously restricted the use of its models for facial recognition through its APIs. We’ve reached out to Axon, Microsoft and OpenAI and will update this post if we hear back.
The new terms leave wiggle room for Microsoft.
The complete ban on Azure OpenAI Service usage pertains only to U.S., not international, police. And it doesn’t cover facial recognition performed with stationary cameras in controlled environments, like a back office (although the terms prohibit any use of facial recognition by U.S. police).
That tracks with Microsoft’s and close partner OpenAI’s recent approach to AI-related law enforcement and defense contracts.
In January, reporting by Bloomberg revealed that OpenAI is working with the Pentagon on a number of projects including cybersecurity capabilities — a departure from the startup’s earlier ban on providing its AI to militaries. Elsewhere, Microsoft has pitched using OpenAI’s image generation tool, DALL-E, to help the Department of Defense (DoD) build software to execute military operations, per The Intercept.
Azure OpenAI Service became available in Microsoft’s Azure Government product in February, adding additional compliance and management features geared toward government agencies including law enforcement. In a blog post, Candice Ling, SVP of Microsoft’s government-focused division Microsoft Federal, pledged that Azure OpenAI Service would be “submitted for additional authorization” to the DoD for workloads supporting DoD missions.
Microsoft and OpenAI did not immediately return requests for comment.
IT operations personnel have a lot going on, and when an incident occurs that brings down a key system, time is always going to be against them. Over the years, companies have looked for an edge in getting up faster with playbooks designed to find answers to common problems, and postmortems to keep them from repeating, but not every problem is easily solved, and there is so much data and so many possible points of failure.
It’s actually a perfect problem for generative AI to solve, and AIOps startup BigPanda announced a new generative AI tool today called Biggy to help solve some of these issues faster. Biggy is designed to look across a wide variety of IT-related data to learn how the company operates and compare it to the problem scenario and other similar scenarios and suggest a solution.
BigPanda has been using AI since the early days of the company and deliberately designed two separate systems: one for the data layer and another for the AI. This in a way prepared them for this shift to generative AI based on large language models. “The AI engine before Gen AI was building a lot of other types of AI, but it was feeding off of the same data engine that will be feeding what we’re doing with Biggy, and what we’re doing with generative and conversational AI,” BigPanda CEO Assaf Resnick told TechCrunch.
Like most generative AI tools, this one makes a prompt box available where users can ask questions and interact with the bot. In this case, the underlying models have been trained on data inside the customer company, as well as on publicly available data on a particular piece of hardware or software, and are tuned to deal with the kinds of problems IT deals with on a regular basis.
“The out-of-the box LLMs have been trained on a huge amount of data, and they’re really good actually as generalists in all of the operational fields we look at — infrastructure, network, application development, everything there. And they actually know all the hardware very well,” Jason Walker, chief innovation officer at BigPanda, said. “So if you ask it about a certain HP blade server with this error code, it’s pretty good at putting that together, and we use that for a lot of the event traffic.” Of course, it has to be more than that or a human engineer could simply look this up in Google Search.
It combines this knowledge with what it is able to cull internally across a range of data types. “BigPanda ingests the customer’s operational and contextual data from observability, change, CDMB (the file that stores configuration information) and topology along with historical data and human, institutional context — and normalizes the data into key-value pairs, or tags,” Walker said. That’s a lot of technical jargon, but basically it means it looks at system-level information, organizational data and human interactions to deliver a response to help engineers solve the problem.
When a user enters a prompt, it looks across all the data to generate an answer that will hopefully point the engineers in the right direction to fix the problem. They acknowledge that it’s not always perfect because no generative AI is, but they let the user know when there is a lower degree of certainty that the answer is correct.
“For areas where we think we don’t have as much certainty, then we tell them that this is our best information, but a human should take a look at this,” Resnick said. For other areas where there is more certainty, they may introduce automation, working with a tool like Red Hat Ansible to solve the issue without human interaction, he said.
The data ingestion part isn’t always going to be trivial for customers, and this is a first step toward providing an AI assistant that can help IT get at the root of problems and solve them faster. No AI is foolproof, but having an interactive AI tool should be an improvement over current, more time-consuming manual approaches to IT systems troubleshooting.
Sanlo, a fintech startup that helps gaming companies manage finances, announced Wednesday the closed beta launch of its webshop tool, giving select game developers and studios a plug-in-play solution that works alongside their existing tech stacks. Gaming companies can join the waitlist starting today.
With Google and Apple charging a 30% fee for in-app purchases (IAPs), it’s more challenging than ever for small- to mid-size gaming companies to run profitable businesses. Gaming giant Epic has complained about Apple’s revenue cut for years now, accusing it of being predatory toward smaller businesses.
As a result, many mobile game developers are no longer relying on app stores for monetization and are turning to external webshops, a rising trend in gaming where companies can run stores on their own websites for a much lower fee (around 4-10%). Plus, webshops are believed to boost revenue since players buy directly from the gaming company, as opposed to app stores taking a portion of the sales. In fact, Sanlo said developers can earn up to 25% additional revenue with a webshop.
“A workshop is one of those super tactical steps that actually proved to show that you can implement revenue from,” Sanlo co-founder and CEO Olya Caliujnaia told TechCrunch. “The reason being that it’s usually your most engaged, loyal players who go to the webshop and they get special offers that allow them to do better in the game.”
With Sanlo’s new webshop tool, game developers get a range of promotional mechanics like exclusive digital items, bundle packs, discounted offers, and loyalty programs to incentivize more players to try the game. Developers can also access player data so they can monitor profiles and purchase activity in order to target individual users with compelling offers.
Companies can test and set pricing “with no price caps,” according to Sanlo. Earnings from webshop sales are deposited into the developer’s account once a week.
One downside about webstores is that Apple and Google don’t let mobile games advertise them in-app. Sanlo offers marketing tools as a solution to this issue, such as in-game prompts to promote the webshop, sending emails to returning visitors, and ROAS (Return on Ad Spend) attribution tracking.
Sanlo has onboarded an undisclosed number of gaming companies to its webshop platform, including Fusebox Games, the developer behind mobile titles inspired by “Love Island” IP.
“The biggest attraction for me was the plug-and-play nature of the Sanlo tool in addition to the hands-on service they provide,” Terry Lee, COO at Fusebox, told us. “We are a small company without the internal resources to cover all the bases when it comes to supporting a whole new technical capability.”
Sanlo plans to officially launch the new product to all developers this summer.
Caliujnaia and William Liu (CTO) founded Sanlo in 2020. The company’s team touts having previous experience at Sony PlayStation, Electronic Arts, Visa, Facebook, Capital One, Earnest, SigFig, and more.
To date, the company has raised $13.5 million in total funding, and is backed by Initial Capital, Portage Ventures, XYZ Venture Capital, London Venture Partners, Index Ventures, and Konvoy.
Webstore solutions have existed for years now, from more established companies like Xsolla to newer entrants like Appcharge. Popular games leveraging webshops include Clash of Clans, Marvel Strike Force, Game of Thrones: Conquest, and Star Trek Fleet Command.
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