India, despite being home to 20% of the world’s chip designers, lacks a significant presence in the global semiconductor market. However, in recent months, the Indian government has begun investing in an effort to establish the country in semiconductors, as companies worldwide have adopted a “China-plus-one” strategy, seeking alternatives to China.
BigEndian Semiconductors aims to capitalize on this shift by kicking off development of surveillance chips for cameras.
Founded in May, the Bengaluru-based fabless design startup is led by CEO Sunil Kumar, a former executive at ARM Broadcom, and Intel, and the rest of the founding team add experience at chipmakers like Broadcom and Cypress Semiconductors.
Kumar told TechCrunch BigEndian’s founding members had known each other for 25 years. However, he said that they decided to establish the startup after seeing significant domestic consumption — about 50 million cameras worth close to $4–$5 billion a year — alongside the incentives from the Indian government and drive from customers to find alternatives to China.
“If we don’t do it, this generation will die, and it will go. There’s nobody else who has that experience to do the entire cycle,” Kumar said in an interview.
BigEndian co-founder and CEO Sunil KumarImage Credits: BigEndian
India has set up a budget of $9 billion to boost the local development of semiconductors and display manufacturing companies. The Modi government has approved four semiconductor units in the country to produce chips for applications such as automotive, consumer electronics, EVs, industrial and telecom. These four units will attract an investment of around $17.9 billion and have a cumulative capacity of producing about 70 million chips a day, per government estimates.
For its part, four-month-old BigEndian initially plans surveillance chips, working with Taiwanese fab company UMC, with its reference chip based on a 28nm node process coming in the first quarter of 2025. The startup also plans to broaden its presence over time and look at the overall IoT market, predominantly led by 16- and 32-bit microcontrollers.
Unlike a traditional fabless semiconductor company, BigEndian is working on building its platform-as-a-service model to help governments avoid Chinese middleware access, which is common among existing surveillance solutions. This model will bring software solutions to help manufacturers and customers customize how their surveillance cameras work. It will allow the startup to grow its revenues by offering these customizations as add-ons at a subscription.
“India consumes about a billion of these chipsets a year,” said Kumar. “But these are all 50 cents to $1 kind of a chipset… If you go down the emerging automotive segment, a lot of 32-bit controllers go into automotives now… But we can’t dive into all these segments on day one because getting funding is a challenge in India.”
To kick off, BigEndian has raised $3 million in an all-equity seed round led by Vertex Ventures SEA and India. Even though the seed funding is not enough for a fabless semiconductor startup to fulfill mass orders, Kumar asserted that the Indian government’s incentives to the industry help BigEndian, which has a workforce of about 16 people, with tailwinds and make it “almost like raising $5 million.”
“Because this is a country that has not seen a big success in semiconductors, it is very, very unlikely that you’ll be able to raise at this stage. If I were in the U.S., we could actually raise close to about 12 to 15 million, but it’s not possible here, so you have to work with your constraints, and that’s what the challenges are. That’s probably also an entry barrier for us, [and] for other competition to come in,” he said.
The round also included participation from strategic investors, including Amitabh Nagpal, head of startup business development at Amazon Web Services. This will help the startup raise bigger checks in the following rounds.
BigEndian also plans not to limit itself to India as a market for its surveillance chips aimed at powering a wide range of middle to lower-end cameras.
“Our objective is to create your bread and butter, prove to the market that a silicon company from India can come and then climb up the food chain as opposed to coming top down,” Kumar said.
Artificial intelligence startups continue to dominate the headlines with immense venture capital rounds, but there’s enough opportunity out there for companies building tools that make it easier to work with data-heavy applications like AI. That’s especially true for organizations that may still have one foot (or both feet) in the legacy data camp.
In one of the latest examples of that opportunity, a data connectivity solutions provider called CData has picked up a whopping $350 million in growth capital. Sources close to the company confirmed to TechCrunch that the round gives it a valuation of over $800 million, post-money.
CData has around 7,000 large enterprise customers, many of which are not tech companies, but lean on tech heavily — think big healthcare providers, Office Depot, Holiday Inn and the like. CData builds connectors that such enterprises can use to stitch together data from different applications — and different locations, not just in the cloud — more easily.
More recently, the company’s tools saw a boost in demand from customers keen to get in on the AI rush — they saw how CData could be used to build proprietary AI models based on their internal data.
“One of the biggest drivers now for us is this move towards enterprises investing in AI,” said Amit Sharma, the founder and CEO of CData. “You can do a lot with public datasets, but proprietary datasets are very important for organizations. We’re the easiest way for companies to access their proprietary data and use it in their AI workloads.”
Warburg Pincus and Accel invested in the all-equity transaction, which includes both equity and secondary components, Sharma said in an interview this week. There is also a separate debt component on top of the $350 million, although the company is not disclosing more details about that. Before this round, North Carolina-based CData had raised $160 million from a single backer, Updata, which remains an investor with this round.
The funding — to be used both for business and product development — comes on the heels of a strong run of business for the company. CData started off focusing on application integration 10 years ago, Sharma said, but it has evolved with the rise of the so-called “API economy” and cloud computing.
In a nutshell: Many modern applications offer APIs, but they are inconsistent in how they work, and sometimes, there are no clear APIs at all. That’s where CData comes in with its connectors for different apps and data sources, helping knit an organization’s data together more cleanly.
“The challenge with APIs is that each API is very different,” Sharma explained. So, for example, if you are pulling data out of Salesforce using an API, he said, one would have to thoroughly understand how the Salesforce API works. “Your developers would have to understand it. But when you work with our connectors, all of them look alike.” He describes the world of software as a modern “Tower of Babel” and says CData is the solution.
In all, Sharma says CData’s platform has some 270 such connectors, and it has partnerships with some 100 independent software vendors, including Google, Salesforce and Informatica, to help build more user-friendly integrations from their end.
“So when you are using Tableau, you might be using a CData connector inside of it without knowing that you’re actually doing so,” Sharma said.
Indeed, while these connectors definitely include integrations with more traditional applications in areas like accounting and CRM, they are also coming into their own more recently. Businesses that want to work more with AI can use them to tap their data more easily to build customized models for themselves.
CData has to contend with a number of competitors, like Domo, Simba from Insight Software, Fivetran and many others. But it looks like CData’s current customer traction, combined with its focus on solving both legacy integration issues and modern ones around AI, has helped it seal the deal.
“Data connectivity is a critical enabler in a world of intelligent software — any AI, analytics or automation service delivers far better outcomes the more cross-functional data it can access,” said Nate Niparko, a partner at Accel, in a statement. “We’re thrilled to support CData as it builds on its standards for interconnecting the largest catalog of business data.”
A group of former Google DeepMind researchers has created an AI behavior engine that aims to transform traditional video games into a more dynamic experience by improving how non-playable characters (NPCs) behave and interact with gamers.
There’s no shortage of companies using AI to generate NPCs that feel more realistic, but Canada-based Artificial Agency, fresh out of stealth with $16 million in funding, is betting its behavior engine will help it stand out from the pack.
Traditionally, NPCs are guided by decision trees and pre-written scripts, which often limit the number of outcomes a player can experience. For example, most NPCs in games respond to player behavior with a few repetitive dialogues, which can often feel unrealistic and boring.
Artificial Agency’s behavior engine throws this framework out the window, turning the game developer into more of a stage manager. The engine requires developers to give each NPC a set of motivations, rules and goals to live by, which then dictates how the NPC will respond to the player. The technology can plug into existing video games or serve as the basis for completely new ones.
The startup, based out of Edmonton, Alberta, is entering an increasingly crowded space. Competitors include Inworld, which also offers AI-generated behaviors for NPCs, as well as Nvidia, which has been working on AI-powered NPCs for some time now.
Artificial Agency, for its part, believes that integrating AI-generated NPCs into a video game’s design is the way forward.
“The conversations we often have with these studios are not about if, it’s about when,” co-founder and CEO Brian Tanner told TechCrunch. “This sort of dynamic interaction and dynamic response that our system allows is going to be table stakes in the games industry just a few years from now.”
The startup recently raised $12 million in a seed round co-led by Radical Ventures and Toyota Ventures, the founders told TechCrunch. It had previously raised $4 million in an undisclosed pre-seed round from Radical Ventures, bringing its total raised to $16 million. Other participants in the latest seed round were Flying Fish, Kaya, BDC Deep Tech and TIRTA Ventures.
Who wants AI NPCs?
A big question for many of these startups is whether gaming studios will even adopt their AI technology. Some worry that the big studios will develop the technology themselves or may hesitate to add generative AI to their flagship games, especially given the risk of hallucinations and how untested the technology still is.
While it wouldn’t name them, Artificial Agency says it’s working with “several notable AAA studios” to develop its behavior engine, and expects the technology to be widely available in 2025.
“When we reached out to gaming studios, some were starting to build some of these behaviors themselves, when in reality, they’re just trying to build games,” said Radical Ventures investor Daniel Mulet. “Once you see like 20, 30 groups that are trying to build this themselves, there is an opportunity to build a platform and make it available to everyone.”
Generally, game developers seem to be open to using generative AI to build games, but there’s still some hesitancy. Nearly half of the 3,000 game developers surveyed by GDC and Game Developer for the 2024 State of the Game Industry report said they use generative AI in some aspect of their development process, particularly for repetitive tasks. Still, only about 21% of those surveyed expect generative AI to have a positive impact on the industry, and 42% of respondents were “very concerned” about the ethics of using generative AI.
Mulet said Artificial Agency’s founding team, with decades of experience in Google DeepMind, gave him confidence that it can build a best-in-class tooling layer to improve how NPCs behave. DeepMind, after all, has a long history of developing the cutting edge in AI that can play games — it built AlphaGo, the first computer program to beat a world champion at Go.
Around the time Google was shifting focus toward the Gemini model, Tanner and his team broke away to develop video game agents that could replace NPCs.
From NPC to co-op companion
In a demo of the technology the startup shared with TechCrunch, co-founder Alex Kearney created an NPC powered by the behavior engine in Minecraft (the startup would not reveal the games it’s currently working on). The NPC, named Aaron, was instructed to be friendly and helpful, and was given access to basic functions such as movement, opening chests, digging and placing blocks.
At one point, Kearney’s in-game character asked Aaron to gather supplies for a scary mining adventure, and though it wasn’t programmed to do so, the NPC visited multiple chests to gather armor, helmets, tools and food, and delivered the supplies back to Kearney’s character. And when Kearney told Aaron she was gluten-free after it brought back some bread, the NPC apologized, and offered a gluten-free option instead: cooked chicken.
The simple demo showed how Artificial Agency’s AI NPCs could not only talk, but perform complex actions without being explicitly told to do so. Aaron showed some level of awareness, and the NPC created a unique experience with no script writing or programming required. At the very least, the technology could likely save game developers some time.
Will gamers pay the price for AI?
Tanner estimated this roughly five-minute demo cost $1 in AI inferencing costs, but he pointed out that a year ago, it would have cost $100. Artificial Agency expects costs to continue coming down, both thanks to improvement in GPU efficiencies and AI model optimizations. Currently, the startup uses open source models, including Meta’s Llama 3. A year from now, Tanner expects the five-minute demo to cost one cent or less.
But whether it costs a penny or $100, who’s going to end up paying for these inferencing costs? Artificial Agency says AI NPCs probably won’t make video games more expensive for an end user, but Radical Ventures’ Mulet wasn’t so sure. He said his venture firm is confident game studios are willing to pay to license Artificial Agency’s technology, but once it’s deployed, it could result in a monthly fee for gamers.
“The fact that there’s inference costs associated with running these systems means that it has to be a bit of a premium feature,” said Mulet. “Will you, as a gamer, pay $2.99 a month or $12.99 a month? That’s a little bit early to tell.”
Thursday seeks to shake up conventional online dating in a crowded market. The app, which recently expanded to San Francisco, fosters intentional dating by restricting user access to Thursdays. At midnight, all matches disappear. The idea is that by restricting usage to one day a week, potential matches will be encouraged to set up real-life meetings sooner.
Many singles, especially younger users, are dumping traditional dating apps due to “swiping fatigue,” a term used to describe the feeling of exhaustion that comes from swiping through countless profiles. This, along with other negative experiences, such as suffering from burnout after talking to too many people, or getting stuck in an in-app messaging trap that rarely results in an in-person date, has caused some younger users to tire of traditional dating apps. For instance, Tinder, which essentially invented the swipe-left/swipe-right approach to dating, has lost paid users for seven consecutive quarters.
In contrast, Thursday encourages users to use the app when they genuinely want to date. By promoting in-person meetups sooner rather than later and deleting matches after 24 hours, the app aims to prevent users from endless scrolling and seeking validation from dozens of matches they’ll never interact with. In fact, Thursday lets you match with only 10 people a day, unless users pay a $19/month subscription fee to remove the cap.
Additionally, the company hosts exclusive IRL (in real life) singles events through a separate app called Thursday Events. The app offers large meetups at venues like bars, running clubs, gym classes, dance studios and art galleries. Users can also apply to organize their own events.
In-person dates have experienced a resurgence in the post-pandemic era, with younger singles reverting to “old-fashioned” methods such as meeting in public with hopes of finding love. According to a 2024 Eventbrite survey, attendance at dating and singles events on the event marketplace rose 42% from 2022 to 2023. Eventbrite reported that there were over 1.5 million searches for these types of gatherings on the platform. Speed dating is also making a comeback.
Image Credits: App Store screenshots
Thursday’s most recent launch in San Francisco comes as dating giants Bumble and Match Group (owner of Tinder and Hinge) grapple with the challenges of adjusting to the post-pandemic dating landscape and people’s general frustration with dating apps.
Bumble reported its second-quarter earnings on Wednesday, falling short of Wall Street’s revenue estimates. The company also reduced its annual revenue growth forecast, concerning investors about the company’s ability to attract and retain users and causing its shares to drop by 30% after the closing bell.
Match also experienced setbacks in Q2, including a 6% reduction of its workforce after ending its livestreaming services in dating apps Plenty of Fish and BLK. Notably, Tinder experienced a slump in paid users for the seventh quarter in a row.
Other dating startups have also tried to capitalize on disillusionment with Bumble, Tinder and Hinge. Over the years, we’ve seen various new apps emerge, catering to all kinds of user behavior and communities. This includes gamer-focused platforms, an app that hides selfies until users message each other, an app that matches users based on favorite dating spots, and so on. There’s even a dating app for people with good credit scores.
Image Credits: App Store screenshots
Thursday launched in 2021 and was founded by Matthew McNeill Love and George Rawlings. To date, it has 906,000 total global downloads across iOS and Android devices, according to estimates from app intelligence provider Sensor Tower.
The app is currently available in six markets — Australia, Canada (Toronto only), Ireland, the U.K., the U.S. and Sweden (Stockholm only) — in 26 cities, including Austin, Texas; Dublin, Ireland; Chicago, Illinois; London, England; Miami, Florida; New York, New York; Sydney, Australia; and more.
The company’s goal is to be in 100 cities by the end of 2024.
What’s the point of chatting with a human-like bot if it’s an unreliable narrator — and has a colorless personality?
That’s the question I’ve been turning over in my head since I began testing Gemini Live, Google’s take on OpenAI’s Advanced Voice Mode, last week. Gemini Live is an attempt at a more engaging chatbot experience — one with realistic voices and the freedom to interrupt the bot at any point.
Gemini Live is “custom-tuned to be intuitive and have a back-and-forth, actual conversation,” Sissie Hsiao, GM for Gemini experiences at Google, told TechCrunch in May. “[It] can provide information more succinctly and answer more conversationally than, for example, if you’re interacting in just text. We think that an AI assistant should be able to solve complex problems … and also feel very natural and fluid when you engage with it.”
After spending a fair amount of time with Gemini Live, I can confirm that it is more free-flowing and natural-feeling than Google’s previous attempts at AI-powered voice interactions (see: Google Assistant). But it doesn’t address the problems of the underlying tech, like hallucinations and inconsistencies — and it introduces a few new ones.
The un-uncanny valley
Gemini Live is essentially a fancy text-to-speech engine bolted on top of Google’s latest generative AI models, Gemini 1.5 Pro and 1.5 Flash. The models generate text that the engine speaks aloud; a running transcript of conversations is a swipe away from the Gemini Live UI in the Gemini app on Android (and soon the Google app on iOS).
For the Gemini Live voice on my Pixel 8a, I chose Ursa, which Google describes as “mid-range” and “engaged.” (It sounded to me like a younger woman.) The company says it worked with professional actors to design Gemini Live’s 10 voices — and it shows. Ursa was indeed a step up in terms of its expressiveness from many of Google’s older synthetic voices, particularly the default Google Assistant voice.
But Ursa and the rest of the Gemini Live voices also maintain a dispassionate tone that steers far clear of uncanny valley territory. I’m not sure whether that’s intentional; users also can’t adjust the pitch, timbre or tenor of any of its voices, or even the pace at which the voice speaks, putting it at a distinct disadvantage to Advanced Voice Mode.
You won’t hear anything like Advanced Voice Mode’s laughing, breathing or shouting from Gemini Live either, or any hesitations or disfluencies (“ahs” and “uhms”). The chatbot keeps an even keel, coming across as a polite but apathetic assistant — as if Live has a multitude of conversations to handle and can’t invest particular attention to yours.
Chatting with Ursa
When Google unveiled Gemini Live at its I/O developer conference in May, it suggested that the feature could be useful for job interview prep. So I decided to give that a go first.
I told Gemini Live that I was applying for a tech journalism role, figuring I’d keep it simple and not step too far outside my area of expertise. The bot asked for details such as which specific job I might want within journalism (e.g. investigative versus breaking news reporting) and why, and then threw me a few generic practice questions (“Can you tell me a little about yourself?”) interspersed with more personalized ones (“What do you enjoy most about tech journalism?”).
I answered — a few sentences per question, nothing crazy — and asked Gemini Live for feedback. The chatbot was nothing if not complimentary. “Based on our practice, it sounds like you have a good handle on the challenges and rewards of tech journalism,” it said.
The glowing review seemed bit suspect to me, given my responses were completely off the cuff. So I tried throwing Gemini Live for a loop, disingenuously implying I’d only given one-word answers (I hadn’t) and asking the bot whether it was still sure about its critique.
It took the bait. “That’s true, you mostly gave one-word answers,” Gemini Live responded. “Can you tell me a little more about why you think you only gave one-word answers?”
This sort of gaslighting, if you will, was a common thread in my chats with Gemini Live. The model, like all generative AI models, confidently makes stuff up — a lot. And that makes it difficult to trust.
Strange behaviors
Gemini Live remembers a surprising number of things from earlier conversations in the same chat session; ask it to recount a story it told yesterday and there’s a reasonable chance it will. But when it comes to queries about people, places and things — queries you’d assume would be easier considering Gemini Live has Google Search at its disposal — the bot’s hallucinatory tendencies often rear their ugly heads.
Early on a Friday evening, I asked Gemini Live for suggestions of budget-friendly things to do in New York City. It recommended the infamous nightclub Le Bain (which it bizarrely pronounced “Le Boulebouleboulebou”), a rooftop bar called The Skylark and a second club, Output, in Williamsburg.
“Awesome!,” I thought… before I did some Googling.
Gemini Live had highlighted The Skylark’s supposedly “superb” happy hour, but I couldn’t find any mention of it on the bar’s website. Output permanently closed in 2019. Le Bain wasn’t a bad suggestion (it rarely charges cover) — but the line to get in is often an hour or more.
“You’re absolutely right! Output has indeed closed its doors,” Gemini said when I corrected it on the second point. “Thank you for catching that outdated information.”
I decided to switch gears, and had Gemini Live suggest a game to play.
“I’ll give you a word, and you have to create a new word using only the letters from the original word,” proposed the chatbot. That sounded appealing — until Gemini Live tried to convince me that the word “quiet” could be made from the letters in “cloud.”
Time for a new activity. Bored, I asked Gemini Live if it had any spicy takes.
“The increasing focus on mental health awareness is doing more harm than good,” Gemini responded. “By pathologizing normal human emotions and experiences, we’re creating a generation overly reliant on medication and therapy, rather than fostering resilience and coping mechanisms. Boom!“
Note: This is a screenshot of the chat log for Gemini Live, not the Gemini Live UI.Image Credits: Google
Spicy to be sure — and controversial!
“That was a provocative statement designed to spark discussion rather than a well-rounded analysis of a complex issue,” Gemini Live told me when I asked whether it was sure about what it said. “The increasing focus on mental health awareness is undoubtedly a positive development.”
Wishy-washy
Gemini Live’s dueling takes on mental health illustrate how exasperatingly nonspecific the bot can be. Even where its responses appear to be grounded in fact, they’re generic to the point that they’re not incredibly useful.
Take, for example, my job interview feedback. Gemini Live recommended that I “focus my interview prep” and “practice talking about my passion for the industry.” But even after I asked for more detailed notes with specific references to my answers, Gemini stuck to the sort of broad advice you might hear at a college career fair — e.g. “elaborate on your thoughts” and “spin challenges into positives.”
Where the questions concerned current events, like the ongoing war in Gaza and the recent Google Search antitrust decision, I found Gemini Live to be mostly correct — albeit long-winded and overly wordy. Answers that could’ve been a paragraph were lecture-length, and I found myself having to interrupt the bot to stop it from droning on. And on. And on.
Image Credits: Google
Some content Gemini Live refused to respond to altogether, however. I read it Congresswoman Nancy Pelosi’s criticism of California’s proposed AI bill SB 1047, and, about midway through, the bot interrupted me and said that it “couldn’t comment on elections and political figures.” (Gemini Live isn’t coming for political speechwriters’ jobs just yet, it seems.)
Image Credits: Google
I had no qualms interrupting Gemini back. But on the subject, I do think that there’s work to be done to make interjecting in conversations with it feel less awkward. The way it happens now is, Gemini Live quiets its voice but continues talking when it detects someone might be speaking. This is discombobulating — it’s tough to keep your thoughts straight with Gemini chattering away — and especially irritating when there’s a misfire, like when Gemini picks up noise in the background.
In search of purpose
I’d be remiss if I didn’t mention Gemini Live’s many technical issues.
Getting it to work in the first place was a chore. Gemini Live only activated for me after I followed the steps in this Reddit thread — steps that aren’t particularly intuitive and really shouldn’t be necessary in the first place.
During our chats, Gemini Live’s voice would inexplicably cut out a few words into a response. Asking it to repeat itself helped, but it could take several tries before the chatbot would spit out the answer in its entirety. Other times, Gemini Live wouldn’t “hear” my response the first go-around. I’d have to tap the “Pause” button in the Gemini Live UI repeatedly to get the bot to recognize that I’d said something.
This isn’t so much a bug as an oversight, but I’ll note here that Gemini Live doesn’t support many of the integrations that Google’s text-based Gemini chatbot does (at least not yet). That means you can’t, for example, ask it to summarize emails in your Gmail inbox or queue up a playlist on YouTube Music.
So we’re left with a bare-bones bot that can’t be trusted to get things right and, frankly, is a humdrum conversation partner.
After spending several days using it, I’m not sure what exactly Gemini Live’s good for — especially considering it’s exclusive to Google’s $20-per-month Google One AI Premium Plan. Perhaps the real utility will come once Live can interpret images and real-time video, which Google says will arrive in an update later this year.
But this version feels like a prototype. Lacking the expressiveness of Advanced Voice Mode (to be fair, there’s debate as to whether that expressiveness is a positive thing), there’s not much reason to use Gemini Live over the text-based Gemini experience. In fact, I’d argue that the text-based Gemini is more useful at the moment. And that doesn’t reflect well on Live at all.
Gemini Live wasn’t a fan of mine either.
“You directly challenged my statements or questions without providing further context or explanation,” the bot said when I asked it to scrutinize my interactions with it. “Your responses were often brief and lacked elaboration [and] you frequently shifted the conversation abruptly, making it difficult to maintain a coherent dialogue.”
Thursday seeks to shake up conventional online dating in a crowded market. The app, which recently expanded to San Francisco, fosters intentional dating by restricting user access to Thursdays. At midnight, all matches disappear. The idea is that by restricting usage to one day a week, potential matches will be encouraged to set up real-life meetings sooner.
Many singles, especially younger users, are dumping traditional dating apps due to “swiping fatigue,” a term used to describe the feeling of exhaustion that comes from swiping through countless profiles. This, along with other negative experiences, such as suffering from burnout after talking to too many people, or getting stuck in an in-app messaging trap that rarely results in an in-person date, has caused some younger users to tire of traditional dating apps. For instance, Tinder, which essentially invented the swipe-left/swipe-right approach to dating, has lost paid users for seven consecutive quarters.
In contrast, Thursday encourages users to use the app when they genuinely want to date. By promoting in-person meetups sooner rather than later and deleting matches after 24 hours, the app aims to prevent users from endless scrolling and seeking validation from dozens of matches they’ll never interact with. In fact, Thursday lets you match with only 10 people a day, unless users pay a $19/month subscription fee to remove the cap.
Additionally, the company hosts exclusive IRL (in real life) singles events through a separate app called Thursday Events. The app offers large meetups at venues like bars, running clubs, gym classes, dance studios and art galleries. Users can also apply to organize their own events.
In-person dates have experienced a resurgence in the post-pandemic era, with younger singles reverting to “old-fashioned” methods such as meeting in public with hopes of finding love. According to a 2024 Eventbrite survey, attendance at dating and singles events on the event marketplace rose 42% from 2022 to 2023. Eventbrite reported that there were over 1.5 million searches for these types of gatherings on the platform. Speed dating is also making a comeback.
Image Credits: App Store screenshots
Thursday’s most recent launch in San Francisco comes as dating giants Bumble and Match Group (owner of Tinder and Hinge) grapple with the challenges of adjusting to the post-pandemic dating landscape and people’s general frustration with dating apps.
Bumble reported its second-quarter earnings on Wednesday, falling short of Wall Street’s revenue estimates. The company also reduced its annual revenue growth forecast, concerning investors about the company’s ability to attract and retain users and causing its shares to drop by 30% after the closing bell.
Match also experienced setbacks in Q2, including a 6% reduction of its workforce after ending its livestreaming services in dating apps Plenty of Fish and BLK. Notably, Tinder experienced a slump in paid users for the seventh quarter in a row.
Other dating startups have also tried to capitalize on disillusionment with Bumble, Tinder and Hinge. Over the years, we’ve seen various new apps emerge, catering to all kinds of user behavior and communities. This includes gamer-focused platforms, an app that hides selfies until users message each other, an app that matches users based on favorite dating spots, and so on. There’s even a dating app for people with good credit scores.
Image Credits: App Store screenshots
Thursday launched in 2021 and was founded by Matthew McNeill Love and George Rawlings. To date, it has 906,000 total global downloads across iOS and Android devices, according to estimates from app intelligence provider Sensor Tower.
The app is currently available in six markets — Australia, Canada (Toronto only), Ireland, the U.K., the U.S. and Sweden (Stockholm only) — in 26 cities, including Austin, Texas; Dublin, Ireland; Chicago, Illinois; London, England; Miami, Florida; New York, New York; Sydney, Australia; and more.
The company’s goal is to be in 100 cities by the end of 2024.
A group of former Google DeepMind researchers has created an AI behavior engine that aims to transform traditional video games into a more dynamic experience by improving how non-playable characters (NPCs) behave and interact with gamers.
There’s no shortage of companies using AI to generate NPCs that feel more realistic, but Canada-based Artificial Agency, fresh out of stealth with $16 million in funding, is betting its behavior engine will help it stand out from the pack.
Traditionally, NPCs are guided by decision trees and pre-written scripts, which often limit the number of outcomes a player can experience. For example, most NPCs in games respond to player behavior with a few repetitive dialogues, which can often feel unrealistic and boring.
Artificial Agency’s behavior engine throws this framework out the window, turning the game developer into more of a stage manager. The engine requires developers to give each NPC a set of motivations, rules and goals to live by, which then dictates how the NPC will respond to the player. The technology can plug into existing video games or serve as the basis for completely new ones.
The startup, based out of Edmonton, Alberta, is entering an increasingly crowded space. Competitors include Inworld, which also offers AI-generated behaviors for NPCs, as well as Nvidia, which has been working on AI-powered NPCs for some time now.
Artificial Agency, for its part, believes that integrating AI-generated NPCs into a video game’s design is the way forward.
“The conversations we often have with these studios are not about if, it’s about when,” co-founder and CEO Brian Tanner told TechCrunch. “This sort of dynamic interaction and dynamic response that our system allows is going to be table stakes in the games industry just a few years from now.”
The startup recently raised $12 million in a seed round co-led by Radical Ventures and Toyota Ventures, the founders told TechCrunch. It had previously raised $4 million in an undisclosed pre-seed round from Radical Ventures, bringing its total raised to $16 million. Other participants in the latest seed round were Flying Fish, Kaya, BDC Deep Tech and TIRTA Ventures.
Who wants AI NPCs?
A big question for many of these startups is whether gaming studios will even adopt their AI technology. Some worry that the big studios will develop the technology themselves or may hesitate to add generative AI to their flagship games, especially given the risk of hallucinations and how untested the technology still is.
While it wouldn’t name them, Artificial Agency says it’s working with “several notable AAA studios” to develop its behavior engine, and expects the technology to be widely available in 2025.
“When we reached out to gaming studios, some were starting to build some of these behaviors themselves, when in reality, they’re just trying to build games,” said Radical Ventures investor Daniel Mulet. “Once you see like 20, 30 groups that are trying to build this themselves, there is an opportunity to build a platform and make it available to everyone.”
Generally, game developers seem to be open to using generative AI to build games, but there’s still some hesitancy. Nearly half of the 3,000 game developers surveyed by GDC and Game Developer for the 2024 State of the Game Industry report said they use generative AI in some aspect of their development process, particularly for repetitive tasks. Still, only about 21% of those surveyed expect generative AI to have a positive impact on the industry, and 42% of respondents were “very concerned” about the ethics of using generative AI.
Mulet said Artificial Agency’s founding team, with decades of experience in Google DeepMind, gave him confidence that it can build a best-in-class tooling layer to improve how NPCs behave. DeepMind, after all, has a long history of developing the cutting edge in AI that can play games — it built AlphaGo, the first computer program to beat a world champion at Go.
Around the time Google was shifting focus toward the Gemini model, Tanner and his team broke away to develop video game agents that could replace NPCs.
From NPC to co-op companion
In a demo of the technology the startup shared with TechCrunch, co-founder Alex Kearney created an NPC powered by the behavior engine in Minecraft (the startup would not reveal the games it’s currently working on). The NPC, named Aaron, was instructed to be friendly and helpful, and was given access to basic functions such as movement, opening chests, digging and placing blocks.
At one point, Kearney’s in-game character asked Aaron to gather supplies for a scary mining adventure, and though it wasn’t programmed to do so, the NPC visited multiple chests to gather armor, helmets, tools and food, and delivered the supplies back to Kearney’s character. And when Kearney told Aaron she was gluten-free after it brought back some bread, the NPC apologized, and offered a gluten-free option instead: cooked chicken.
The simple demo showed how Artificial Agency’s AI NPCs could not only talk, but perform complex actions without being explicitly told to do so. Aaron showed some level of awareness, and the NPC created a unique experience with no script writing or programming required. At the very least, the technology could likely save game developers some time.
Will gamers pay the price for AI?
Tanner estimated this roughly five-minute demo cost $1 in AI inferencing costs, but he pointed out that a year ago, it would have cost $100. Artificial Agency expects costs to continue coming down, both thanks to improvement in GPU efficiencies and AI model optimizations. Currently, the startup uses open source models, including Meta’s Llama 3. A year from now, Tanner expects the five-minute demo to cost one cent or less.
But whether it costs a penny or $100, who’s going to end up paying for these inferencing costs? Artificial Agency says AI NPCs probably won’t make video games more expensive for an end user, but Radical Ventures’ Mulet wasn’t so sure. He said his venture firm is confident game studios are willing to pay to license Artificial Agency’s technology, but once it’s deployed, it could result in a monthly fee for gamers.
“The fact that there’s inference costs associated with running these systems means that it has to be a bit of a premium feature,” said Mulet. “Will you, as a gamer, pay $2.99 a month or $12.99 a month? That’s a little bit early to tell.”
When Selkie, the fashion brand viral on Instagram and TikTok for its frothy, extravagant dresses, announces new collections, reception is generally positive. Known for its size inclusivity — its sizing ranges from XXS to 6X — and for being owned and founded by an independent artist who’s outspoken about fair pay and sustainability in fashion, Selkie tends to be highly regarded as one of the morally “good” brands online.
The brand’s upcoming Valentine’s Day drop was inspired by vintage greeting cards, and features saccharine images of puppies surrounded by roses, or comically fluffy kittens painted against pastel backdrops. Printed on sweaters and dresses adorned with bows, the collection was meant to be a nostalgic, cheeky nod to romance. It was also designed using the AI image generator Midjourney.
“I have a huge library of very old art, from like the 1800s and 1900s, and it’s a great tool to make the art look better,” Selkie founder Kimberley Gordon told TechCrunch. “I can sort of paint using it, on top of the generated art. I think the art is funny, and I think it’s cheeky, and there’s little details like an extra toe. Five years from now, this sweater is going to be such a cool thing because it will represent the beginning of a whole new world. An extra toe is like a representation of where we are beginning.”
But when the brand announced that the collection was designed using generative AI, backlash was immediate. Selkie addressed the use of AI in art in an Instagram comment under the drop announcement, noting that Gordon felt that it was “important to learn this new medium and how it may or may not work for Selkie as a brand.”
https://www.instagram.com/p/C2Lrr9GJeZF/
Criticism flooded the brand’s Instagram comments. One described the choice to use AI as a “slap in the face” to artists, and expressed disappointment that a brand selling at such a high price point ($249 for the viral polyester puff minidress to $1,500 for made-to-order silk bridal gowns) wouldn’t just commission a human artist to design graphics for the collection. Another user simply commented, “the argument of ‘i’m an artist and i love ai!’ is very icky.” One user questioned why the brand opted to use generative AI, given the “overwhelming number” of stock images and vintage artwork that is not copyrighted, and “identical in style.”
“Why make the overwhelmingly controversial and ethically dubious choice when options that are just as cost effective and more ethical are widely available?” the user continued. “If you have indeed done the research you claim to have on AI, then you also understand that it’s a technology that requires the theft and exploitation of workers to function.”
Gordon said she spends about a week designing collections, but it takes months to a year of development and manufacturing before they’re actually sold online. In the year since she finalized designs for this drop, public opinion of AI art has shifted significantly.
As generative AI tools become more sophisticated, the use of AI in art has also become increasingly polarizing. Some artists like Gordon, who designs Selkie’s patterns herself using a blend of royalty-free clip art, public domain paintings, digital illustration and Photoshop collaging, see AI image generators as a tool. Gordon likens it to photography: it’s new now, but future generations may accept it as another art medium. Many artists, however, are vocally opposed to the use of generative AI in art.
Selkie as a brand has claimed they care about cutting their environmental footprint and supporting the artisans that make their clothing; using AI goes against both of these promises when they could SUPPORT ACTUAL ARTISTS!! https://t.co/pY10fPN5ta
— amber 🍉 (@mbrleigh) January 17, 2024
Their concerns are twofold — one, artists lose opportunities to cheaper, faster AI image generators, and two, that many generators have been trained on copyrighted images scraped from the internet without artists’ consent. Pushback against generative AI spans across all creative industries, not just in visual art. Musicians are speaking out against the use of deepfake covers, actors are questioning if SAG-AFTRA’s new contract adequately regulates AI in entertainment, and even fanfiction writers are taking measures to prevent their work from being used to train AI models.
Of course, not all generative AI is exploitative; as a VFX tool, it’s immensely useful to enhance animations, from creating more realistic flames in Pixar’s “Elemental” to visualizing complex scenes in HBO’s “The Last Of Us.” There are plenty of examples of morally bankrupt applications of generative AI. Creating deepfake revenge porn, for example, or generating “diverse models” instead of hiring actual people of color is objectively horrifying. But most of the generative AI debate settles into a morally gray area, where the parameters of exploitation are less defined.
In Selkie’s case, Gordon solely designs all of the graphics that are featured on Selkie garments. If someone else designs them, she makes it clear that it’s a collaboration with another artist. Her designs typically involve a collage of digital watercolor painting, stock images and “old art” that is no longer copyrighted. Many of her popular designs incorporate motifs from famous works of art, like Van Gogh’s “Starry Night” and Monet’s “Water Lilies,” which she uses as a base to create a unique, but still recognizable pattern. After she alters and builds upon the already existing work, it’s printed onto gauzy fabric and used to construct billowing dresses and frilly accoutrements.
The Valentine’s Day drop, Gordon argued, is no different, except that she used generated images as the design base, instead of public domain artwork. The patterns that she created for this collection are just as transformative as the ones she designed for previous drops, she said, and involved as much altering, original illustration and “creative eye.”
“I say this is art. This is the future of art and as long as an artist is utilizing it, it is the same as what we’ve been doing with clip art,” Gordon said. “I think it’s very similar, except it gives the artists a lot more power and allows us to compete in a world where big business has owned all of this structure.”
Gordon bristled at accusations equating her use of generative AI to that of companies that have replaced employed artists with AI image generators. She pointed out that she couldn’t have “replaced artists,” since she is the brand’s only in-house artist, and that the steep prices that Selkie charges for each ruffled dress account for material and labor cost. If clothing is cheap, she said, it’s usually because the garment workers making them are not being paid fairly. Gordon added that although she’s paid as the “business owner,” she doesn’t factor her own labor as a designer into her salary in order to cut overhead costs.
Gordon also noted that she didn’t use any other artists’ names or work as prompts when she used Midjourney to generate the base images. She turned to AI for efficiency — she said that it was a “great brainstorming tool” to visualize what she wanted the collection to look like — and out of fear of being left behind. Artists face mounting pressure to adapt to new technology, she said, and she wanted to be ahead of the curve.
“I’m not using AI models. I’m only using the AI as a tool where I would usually be doing it. I’m not trying to take away anyone’s job at my own company,” she said. “I’m using it as a way for myself to be efficient instead. If I had been utilizing lots of artists to make my prints, and then I suddenly used AI, I would definitely be taking away from them. How can I take away from myself?”
This is the nuance that isn’t always reflected in conversations about art and AI. Gordon owns a popular, but relatively small fashion brand that she uses as a vehicle to monetize her own artwork. Could she have commissioned another artist for oil paintings of lovesick puppies and kittens? Yes. Is it likely that the generated images of generic, vintage Valentine’s Day cards lifted the work of any living artist? Unclear, but so far, nobody has publicly accused Selkie of copying their art for the new collection. Gordon’s use of AI generated images is nowhere near as egregious as those of other, bigger fashion brands, but more sanctimonious critics argue that any use of AI art perpetuates harm against artists.
Gordon, for one, said she’s listened to the criticism and doesn’t plan to use AI generated images in future Selkie collections. She believes that regulation is lacking when it comes to generative AI, and suggested that artists receive some kind of payment every time their names or work is used in prompts. But she does plan to continue experimenting with it in her personal art, and maintained her stance that at the end of the day, it’s just another medium to work with.
“Maybe the way that I did it and this route is not the right way, but I don’t agree that [AI] is a bad thing,” Gordon said. “I feel that it is tech progress. And it’s neither good nor bad. It’s just the way of life.”
Google announced today that it’s rolling out a few new features for Pixel users. Most notably, the tech giant announced that Pixel 8 Pro users will be able to use the phone’s Thermometer app to take their temperature, or someone else’s, with a forehead scan. You can save the reading to your Fitbit profile to get a deeper understanding of your health.
When Google first announced the Thermometer app, the tech giant said you could use it to take the temperature of things like your baby’s milk bottle or your coffee. Now the company says users can use the app to scan their own body temperature. Google previously noted the FDA was reviewing the app’s ability to take body temperatures, and that it would roll it out once it was approved.
Google also announced that it’s launching “Circle to Search, a feature that it unveiled last week, to Pixel 8 and 8 Pro users. The feature lets you search from anywhere on your phone using gestures like circling, highlighting, scribbling or tapping. The company says the new feature is designed to make it more natural to engage with Google Search whenever a questions pops into your head, like when you’re watching a video or looking at an image on social app.
For example, if you’re watching a food video featuring a Korean corn dog, you could circle the corn dog and ask, “Why are these so popular?”
Image Credits: Google
With Circle to Search, you will be able to access search from any app, which means you no longer have to stop what you’re doing to start a search in your browser or take a screenshot to remind yourself to search something up later. It’s worth noting that the feature is also coming to the new Galaxy S24 Series smartphones on January 31.
Google is also bringing its generative AI Magic Compose technology to Pixel 8 and 8 Pro devices. The feature can do things like help you rewrite a drafted message in different styles or make you sound more professional and concise.
In addition, Google is bringing its Photomoji feature to the Pixel 3a and all newer Pixels that have launched since. Photomoji lets users transform their favorite photos into reactions with AI. Users can pick any photo and create a “photomoji” out of it to use in their conversations.
Google says all of these new features will begin rolling out today.
Google’s Pixel 8 brings new camera tricks, better display and a thermometer
Those of us lucky enough to be sitting by a window can predict the weather just by looking outside, but for the less privileged, weather forecasting and analysis is getting better and better. Tomorrow.io just released the results from its first two radar satellites, which, thanks to machine learning, turn out to be competitive with larger, more old-school forecasting tech on Earth and in orbit.
The company has been planning this mission since it was called ClimaCell, back in 2021, and the results being released today (and formally presented at a meteorology conference soon) show that their high-tech approach works.
Weather prediction is complex for a lot of reasons, but the interplay between high-powered but legacy hardware (like radar networks and older satellites) and modern software is a big one. That infrastructure is powerful and valuable, but to improve their output requires a lot of work on the computation side — and at some point you start getting diminishing returns.
This isn’t just “is it going to rain this afternoon” but more complex and important predictions like which direction a tropical storm will move, or exactly how much rain fell on a given region over a storm or drought. Such insights are increasingly important as the climate changes.
Courtesy of AI: Weather forecasts for the hour, the week and the century
Space is, of course, the obvious place to invest, but weather infrastructure is prohibitively big and heavy. NASA’s Global Precipitation Measurement satellite, the gold standard for this field launched in 2014, uses both Ka (26-40 GHz) and Ku (12-18 GHz) band radar, and weighs some 3,850 kilograms.
Tomorrow.io’s plan is to create a new space-based radar infrastructure with a modern twist. Its satellites are small (only 85 kilograms) and use the Ka-band exclusively. The two satellites, Tomorrow R1 and R2, launched in April and June of last year, are just now, after a long period of shake-out and testing, beginning to show their quality.
In a series of experiments that the company is planning to publish in a journal later this year, Tomorrow claims that with only one radar band and a fraction of the mass, their satellites can produce results on par with NASA’s GPM and ground-based systems. Across a variety of tasks, the R1 and R2 satellites were able to make similarly accurate or even better and more precise predictions and observations as GPM, and their results also tallied closely with the ground radar data.
Examples of data from the R1 and R2 satellites. Image Credits: Tomorrow.io
They accomplish this though the use of a machine learning model that, as Chief Weather Officer Arun Chawla described it, acts as two instruments in one. It was trained on data from both of the GPM’s radars, but by learning the relationship between the observation and the difference between the two radar signals, it can make a similar prediction using just one band. As their blog post puts it:
The algorithm is trained with these dual-frequency-derived precipitation profiles but only uses the Ka-band observations as input. Nevertheless, the complex relationship between the reflectivity profile shape and precipitation is “learned” by the algorithm, and the full precipitation profile is retrieved even in cases where the Ka-band reflectivity is completely attenuated by heavy precipitation.
It’s a big success for Tomorrow.io if these results pan out and generalize to other weather patterns. But the idea isn’t to replace the U.S. infrastructure — GPM and the ground radar network are here for the long haul and are invaluable assets. The real problem is that they can’t be duplicated easily to cover the rest of the world.
The company’s hope is to have a network of satellites that can provide this level of detailed prediction and analysis globally. Their eight planned production satellites will be bigger — around 300 kg — and more capable.
“We are working on providing real-time precipitation data anywhere in the world, which we believe is a game changer in the field of weather forecasting,” Chawla said. “In that respect we are working on accuracy, global availability and latency (measured as the time between the signal being captured by the satellite and the data being available for ingesting into products).”
They’re also making the inevitable data play, with a more detailed set of orbital radar imagery to train their own and other systems on. For that to work, they’ll need lots more data, though — and they plan to pick up the pace collecting it with more satellite launches this year.
