Most people know that robots no longer sound like tinny trash cans. They sound like Siri, Alexa, and Gemini. They sound like the voices in labyrinthine customer support phone trees. And even those robot voices are being made obsolete by new AI-generated voices that can mimic every vocal nuance and tic of human speech, down to specific regional accents. And with just a few seconds of audio, AI can now clone someone’s specific voice.

This technology will replace humans in many areas. Automated customer support will save money by cutting staffing at call centers. AI agents will make calls on our behalf, conversing with others in natural language. All of that is happening, and will be commonplace soon.

But there is something fundamentally different about talking with a bot as opposed to a person. A person can be a friend. An AI cannot be a friend, despite how people might treat it or react to it. AI is at best a tool, and at worst a means of manipulation. Humans need to know whether we’re talking with a living, breathing person or a robot with an agenda set by the person who controls it. That’s why robots should sound like robots.

You can’t just label AI-generated speech. It will come in many different forms. So we need a way to recognize AI that works no matter the modality. It needs to work for long or short snippets of audio, even just a second long. It needs to work for any language, and in any cultural context. At the same time, we shouldn’t constrain the underlying system’s sophistication or language complexity.

We have a simple proposal: all talking AIs and robots should use a ring modulator. In the mid-twentieth century, before it was easy to create actual robotic-sounding speech synthetically, ring modulators were used to make actors’ voices sound robotic. Over the last few decades, we have become accustomed to robotic voices, simply because text-to-speech systems were good enough to produce intelligible speech that was not human-like in its sound. Now we can use that same technology to make robotic speech that is indistinguishable from human sound robotic again.

A ring modulator has several advantages: It is computationally simple, can be applied in real-time, does not affect the intelligibility of the voice, and—most importantly—is universally “robotic sounding” because of its historical usage for depicting robots.

Responsible AI companies that provide voice synthesis or AI voice assistants in any form should add a ring modulator of some standard frequency (say, between 30-80 Hz) and of a minimum amplitude (say, 20 percent). That’s it. People will catch on quickly.

Here are a couple of examples you can listen to for examples of what we’re suggesting. The first clip is an AI-generated “podcast” of this article made by Google’s NotebookLM featuring two AI “hosts.” Google’s NotebookLM created the podcast script and audio given only the text of this article. The next two clips feature that same podcast with the AIs’ voices modulated more and less subtly by a ring modulator:

Raw audio sample generated by Google’s NotebookLM

Audio sample with added ring modulator (30 Hz-25%)

Audio sample with added ring modulator (30 Hz-40%)

We were able to generate the audio effect with a 50-line Python script generated by Anthropic’s Claude. One of the most well-known robot voices were those of the Daleks from Doctor Who in the 1960s. Back then robot voices were difficult to synthesize, so the audio was actually an actor’s voice run through a ring modulator. It was set to around 30 Hz, as we did in our example, with different modulation depth (amplitude) depending on how strong the robotic effect is meant to be. Our expectation is that the AI industry will test and converge on a good balance of such parameters and settings, and will use better tools than a 50-line Python script, but this highlights how simple it is to achieve.

Of course there will also be nefarious uses of AI voices. Scams that use voice cloning have been getting easier every year, but they’ve been possible for many years with the right know-how. Just like we’re learning that we can no longer trust images and videos we see because they could easily have been AI-generated, we will all soon learn that someone who sounds like a family member urgently requesting money may just be a scammer using a voice-cloning tool.

We don’t expect scammers to follow our proposal: They’ll find a way no matter what. But that’s always true of security standards, and a rising tide lifts all boats. We think the bulk of the uses will be with popular voice APIs from major companies—and everyone should know that they’re talking with a robot.

This essay was written with Barath Raghavan, and originally appeared in IEEE Spectrum.

Sad news from Ubuntu founder Mark Shuttleworth today: longtime Ubuntu and Debian contributor Steve Langasek has passed away. In a touching post on the Ubuntu Discourse, Mark Shuttleworth shares: “Steve passed away at the dawn of 2025. His time was short but remarkable. He will forever remain an inspiration.” “Judging by the outpouring of feelings this week, he is equally missed and mourned by colleagues and friends across the open source landscape, in particular in Ubuntu and Debian where he was a great mind, mentor and conscience.” As a former Debian and Ubuntu release manager, and a long-term Canonical employee, […]

You're reading Long-Time Ubuntu Contributor Steve Langasek Has Passed Away, a blog post from OMG! Ubuntu. Do not reproduce elsewhere without permission.

NIST’s second draft of its “SP 800-63-4“—its digital identify guidelines—finally contains some really good rules about passwords:

The following requirements apply to passwords:

1. lVerifiers and CSPs SHALL require passwords to be a minimum of eight characters in length and SHOULD require passwords to be a minimum of 15 characters in length.
2. Verifiers and CSPs SHOULD permit a maximum password length of at least 64 characters.
3. Verifiers and CSPs SHOULD accept all printing ASCII [RFC20] characters and the space character in passwords.
4. Verifiers and CSPs SHOULD accept Unicode [ISO/ISC 10646] characters in passwords. Each Unicode code point SHALL be counted as a signgle character when evaluating password length.
5. Verifiers and CSPs SHALL NOT impose other composition rules (e.g., requiring mixtures of different character types) for passwords.
6. Verifiers and CSPs SHALL NOT require users to change passwords periodically. However, verifiers SHALL force a change if there is evidence of compromise of the authenticator.
7. Verifiers and CSPs SHALL NOT permit the subscriber to store a hint that is accessible to an unauthenticated claimant.
8. Verifiers and CSPs SHALL NOT prompt subscribers to use knowledge-based authentication (KBA) (e.g., “What was the name of your first pet?”) or security questions when choosing passwords.
9. Verifiers SHALL verify the entire submitted password (i.e., not truncate it).

Hooray.

News article.Shashdot thread.

Data scientists love debating which skills are essential for success in the field. It makes sense: in a rapidly changing ecosystem that adopts new and powerful technologies all the time, job requirements and toolkits never stop evolving.

Statistics seem to be one major outlier, though. Data professionals of all stripes seem to agree that a solid foundation in stats and math will make your life easier regardless of your role, and can open up opportunities that would otherwise remain beyond reach.

To help you on your learning journey, we’re sharing a few of our favorite recent posts that focus on statistics for data science and machine learning. They go from the basics all the way to more specialized use cases, but they’re all accessible, beginner-friendly, and emphasize practical applications over lofty theory. Let’s dive in!

• Stats novice? Not for long! If you’re tackling stats for the first time in your professional life—and especially if your memories of high school math inspire more dread than joy—you’re bound to appreciate Chi Nguyen’s simple explanations of basic concepts.
• A structured approach to learning statistics. Looking for a thorough, step-by-step resource for learning stats? Adrienne Kline recently launched an excellent Statistics Bootcamp that unpacks the math behind all the data science libraries practitioners use daily. (If you’ve already discovered the first installment, linked above, parts two and three are already out!)
• Making sense of occasionally confusing terms. For his debut TDS article, Ajay Halthor shared a lucid explanation of likelihood, and focused on the role it plays in machine learning, as well as its sometimes hard-to-grasp connection to probability, an equally crucial concept.

• Putting your statistical know-how to good use. There’s always a gap between theoretical knowledge and its effective application. Mintao Wei’s recent contribution does a great job bridging it, as it walks us through the process of selecting the right statistical tests for a range of A/B testing metrics.
• The inner workings of a powerful algorithm, explained. The bootstrap, says Christian Leschinski, “is an algorithm that allows you to determine the distribution of a test statistic without doing any theory.” It’s also one that’s been “widely overlooked.” Harnessing his deep knowledge as a statistician, Christian guides us through the magic behind the boostrap, and shows how it can help practitioners in their analyses.
• Why it’s crucial to connect statistics to business outcomes. Cassie Kozyrkov identifies the challenges data professionals face when they bring their stats and math knowledge to work projects, and stresses the importance of data budgeting, a topic college classes rarely cover. (If you’d like to read more of Cassie’s insights—and you should!—don’t miss our brand-new Q&A with her, which touches on data career paths, the value data analysts bring to companies, and much more.)

All stats-ed out, are we? We hope not, but just in case—here are some non-statistics-related reading recommendations we think you’ll enjoy.

• In her first TDS post, Sara Tähtinen offered a useful, thorough breakdown of the EU’s Artificial Intelligence Act, covering its major implications for practitioners, and sharing her own views on this landmark legislative effort.
• Gadi Singer’s new article continues to refine the concept of cognitive AI, and lays out four properties that will be crucial for its emergence: a world model, a theory of mind, continual learning, and late-binding context.
• If you’re looking to read about a project that’s both fun and thought-provoking, Egon Ferri’s exploration of AI-generated art fits the bill (it also features Pikachus in the style of Picasso, van Gogh, and many other artists you might recognize).
• A perennial problem for data teams both big and small is addressing ad-hoc requests from other stakeholders. Olivia Tanuwidjaja’s new guide will help you keep things under control and focus on the projects that matter.

Your support means so much to us — thank you for reading our authors’ work; a special shoutout goes to all of you who’ve recently become Medium members.

Until the next Variable,

TDS Editors

Stats for Data Science, from the Ground Up was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.