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Article URL: https://www.reddit.com/r/codex/s/psWHRr9864

Comments URL: https://news.ycombinator.com/item?id=49612463

Points: 3

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Hey there, I’m Brian. I've been shipping conversational models at Tavus for the past two years. I want to tell you about our latest audio-understanding/turn-taking model: Sparrow-2! It’s a new category of model and a unique new approach to conversational flow understanding.

Earlier this year we launched Sparrow-1, (at the time) our SoTA turn-taking model. Since our Sparrow-1 launch, I’ve spent a lot of time listening to humans talking and trying to really understand how people know when to talk, when to listen, and when to wait. I’ve also been hunting down failure modes of the current SoTA models. And while Sparrow-1 is great, there are some failure patterns I see. We tried solving the problems with existing approaches, but solving one problem only created another.

Current turn-taking models (like Sparrow-1) use noise cancellation to strip background sound, leaving only basic prosody and phonetic word cues to manage turns. Non-transcribable audio such as breathing, sighs, and environmental sounds provides critical context that humans use to hold or signal turn changes. This is because these models are primarily focused on verbal activity. Treating non-verbal audio as background noise discards vital conversational data, destroying the natural flow of dialogue.

Now, with Sparrow-2, instead of modelling just the primary speaker’s verbal audio, we’ve trained a model with access to all the sounds and let it decide what matters and what does not when it comes to conversational flow. Our new model continuously streams in audio and is able to understand semantics, prosody, timing, speaker identity, sighs, breaths, back-channels, interruptions, background speech, and unintelligible audio with the goal of understanding what the agent should do given the state of the audio. Sparrow-2 recently ranked #1 on Sesame’s new Turnbench, but that’s only part of the equation when it comes to good conversation flow.

Turn-taking is good for timing, but models should provide more information downstream especially in situations where noise impacts conversational quality. Sparrow-2 fits into our larger model family and is semi-duplex. For example if the user is in a room that’s too noisy, now the bot can ask the user to move to a quieter space. It also considers the timing of sound in relation to the AI speech as well as the user’s. This helps it to disentangle normal speech from interruptions and back-channels.

Another added benefit of our approach to Sparrow-2 is to finally crack the cocktail party problem: how to have human-like conversations 1:1 in noisy environments, without losing the signals we rely upon for conversational flow. By allowing all sound to be learned, the model naturally learns to focus on what matters and transcends the rest. Noise understanding lets voice AI filter out background speech and ambient chaos, whether it's a kiosk in a noisy lobby, a shop assistant on a busy floor, or an interviewee in a cafe. When noise overwhelms the audio, the AI behaves like a human: it simply asks you to speak up, move, or swap mics.

I’d love for you guys to give it a try and let me know what you think and feel. You can try it here: https://sparrow2.tavuslabs.org/

I also wrote up some technical details about the model architecture here: https://www.tavus.io/blog/sparrow-2

Comments URL: https://news.ycombinator.com/item?id=49612459

Points: 3

# Comments: 0

Feel free to use it.

Enjoy.

Comments URL: https://news.ycombinator.com/item?id=49612451

Points: 1

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Article URL: https://link.springer.com/article/10.3758/s13423-015-0947-8

Comments URL: https://news.ycombinator.com/item?id=49612447

Points: 2

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Article URL: https://gradientflow.substack.com/p/i-think-ai-teams-are-defending-the

Comments URL: https://news.ycombinator.com/item?id=49612433

Points: 2

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Article URL: https://www.ft.com/content/2e97fb28-24cf-4ab0-89b1-454b4f97cdbc

Comments URL: https://news.ycombinator.com/item?id=49612430

Points: 4

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Article URL: https://www.spawn.co

Comments URL: https://news.ycombinator.com/item?id=49612420

Points: 1

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Article URL: https://medium.com/counterarts/museum-heists-like-cybersecurity-breaches-02fb77112688

Comments URL: https://news.ycombinator.com/item?id=49612391

Points: 2

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Location data sold by the ad industry has reportedly helped adversaries target US troops. The Pentagon has responded by switching off ad tracking on its devices - and you can do the same on yours. Read more in my article on the Hot for Security blog.

The startup founded by Palo Alto Networks’ Nir Zuk has raised $290 million to build an AI-native security platform for highly regulated organizations that cannot rely on the public cloud.

The post Cylake Raises $245 Million Ahead of Cybersecurity Platform Beta appeared first on SecurityWeek.

Affecting the SAP kernel code, the flaw allows unauthenticated, remote attackers to run arbitrary commands, recover secrets, and modify data.

The post SAP Patches Critical Extended Passport Processing Vulnerability appeared first on SecurityWeek.

Members of the troubled outsourced scheme are still waiting for pensions nine months after Capita took over its management

High energy costs and inability to manufacture hardware and frontier models limit the UK’s advanced tech defence and security capabilities

The scammers purchased fleets of sports cars, flew on private jets, hired security guards and rented mansions in Miami and the Hamptons.

The post Party’s Over for Crypto Scammers Who Went on a Spending Spree After a $240 Million Bitcoin Theft appeared first on SecurityWeek.

Grindr has reportedly agreed to pay £26 million (around $35 million) to settle a UK privacy lawsuit alleging that it shared sensitive user data, including some users’ HIV status, with advertisers.

The claim was brought by London law firm Austen Hays on behalf of roughly 12,000 UK Grindr users. It alleges that the dating app breached privacy and data-protection laws during a period ending in early 2020.

The claimants allege that Grindr shared personal and highly sensitive information with advertising companies without consent. According to Austen Hays, the shared data may have included ethnicity, HIV status, the date of a user’s last HIV test, and whether they used pre-exposure prophylaxis (PrEP).

At the time of the alleged data sharing practices, Grindr was owned and controlled by the Chinese gaming company Beijing Kunlun Tech. Grindr was sold to US owners in 2020.

According to a US regulatory filing, Grindr will make two payments of £13 million: one by December 31, 2026, and the second by March 31, 2027.

In its SEC filing, Grindr said that the settlement is not an admission of liability and, while it disputes the allegations, it:

recognizes and acknowledges the distress and loss of trust expressed by some of its UK users regarding that pre-2020 period.

The UK settlement follows a separate enforcement case in Norway. The country’s Data Protection Authority found that Grindr had shared users’ personal data with advertising partners for behavioral advertising without a valid legal basis.

These cases illustrate a crucial privacy point: information does not need to be explicitly labeled as medical information or information about sexual orientation to expose intimate details about someone. Advertising identifiers, IP addresses, locations, device information, and confirmation that a person uses a particular app can be combined to identify them or draw sensitive conclusions about their life.

Many free apps rely on advertising SDKs, analytics providers, and other third parties to make money. These integrations can receive identifiers and event data that help target or measure advertising, but they can also create extensive trails of user behavior.

How to protect your privacy on dating apps

Grindr says it has overhauled its privacy program since 2020 and remains committed to user control and responsible data practices. Even so, dating apps can hold unusually personal information about their users.

To limit what you reveal:

• Review the app’s privacy settings and turn off optional personalized advertising where available.
• Limit your profile to details you’re comfortable sharing with potential matches.
• Avoid linking a dating profile to public social-media accounts unless you want identities to be easily connected.
• Revoke location permissions when you’re not actively using the app, or choose “while using the app” rather than continuous access where your operating system offers it.
• Keep the app, your operating system, and your security software updated.
• Watch for romance scams and extortion attempts, particularly requests to move the conversation off the app, send money, share intimate photos, or reveal identifying information.

If you’re unsure whether a message may be part of a scam, you can check it with Malwarebytes Scam Guard, which can help you assess the conversation and decide what to do next.

Scammers don’t need to hack you. They just need you to click once. 

Malwarebytes Identity Theft Protection catches suspicious activity before it becomes a problem.

Mobile compliance now requires governance over how sensitive data is accessed across managed and personal devices. Here are practical steps for sustainable enterprise compliance.

William Webster, the biometrics and surveillance camera commissioner for England and Wales, reflects on the state of facial recognition in the UK

Article URL: https://github.com/jakubro/dbprint

Comments URL: https://news.ycombinator.com/item?id=49609457

Points: 1

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