Live Hong Kong Concert Highlights How Creepy Humanoid Robots Really Are






Robotics has permeated almost every aspect of our lives in manufacturing, healthcare, domestic help, and even entertainment. To accomplish this, it has evolved to have many forms, whether it’s robotic arms for sorting packages to microscopic ones that have all sorts of medical applications. And perhaps, one of its most compelling evolutions is how some robots are starting to look more like humans.

Through the years, scientists have developed a lot of humanoid robots. However, one recently started singing with supporting human musicians. In April 2026, the Hong Kong Baptist University (HKBU) unveiled a special performance with the humanoid robot Sophia, wherein she sang a trio of original songs alongside an orchestra. In a press release, HKBU shares that the performance was meant for “prompting the audience to consider questions of reality, existence, and embodiment.” That said, it isn’t the first time Sophia has dabbled in the arts. In 2019, Sophia also starred in a short film called SophiaWorld as well.

Developed by Hanson Robotics, CNBC reported that her features were inspired by the iconic film beauty, Audrey Hepburn, and the creator’s wife. But while initially unveiled over a decade ago, her (still) transparent skull and eerie facial expressions still continue to trigger a few people. Not to mention, Sophia was the poster child of how technology can turn evil when she said that she’ll destroy humans. But, what exactly causes us to be a little nervous around her?

What makes humanoid robots so creepy-looking

There’s a term for the very visceral, uncomfortable reaction we tend to experience in the presence of humanoid robots and it’s called “uncanny valley.” In general, the science behind uncanny valley has been around for half a century, wherein researchers explored how our affinity rises and falls within a spectrum of “likeness.” Many studies fundamentally attribute the fact that we (as well as other animals) are biologically wired to be cautious around “imposters” that look like us but aren’t. As of this writing, there’s no hard and fast rule for where a given likeness falls within positive or negative affinity, so it’s not exactly quantifiable how “human” something needs to be to be considered comfortable to be around them.

However, there’s still a possibility that we can still get used to our robot brethren and co-exist more meaningfully in the future. For example, if we are exposed to enough humanoid-looking robots more frequently, it’s possible that we normalize their appearances and behaviors. In recent times, body modification is growing increasingly accessible. As the appearances of human beings evolve, so do our expectations for what is considered “normal.” For example, people can add permanent horns to their head or even have futuristic tattoos that turn their skin into biological touchpads. It’s also possible that more research can help bridge the gap between the precise factors that make a robot feel more human.

New technology that might end uncanny valley

There are several components that make robots feel more human, which include gait, appearance, and ability to communicate. Technology-wise, it looks like all three are getting better each year, even if they’re done separately. Recently, the China Media Group shared a YouTube video showing humanoid robots performing impressive movements that ranged from dance, martial arts, and even backflips during its 2026 Spring Festival Gala. In the same year, other robots have proven to be formidable opponents for elite athletes from kinds of sports, including marathons and table tennis.

Around the same time, we reported how Chinese robotics company DroidUp also launched Moya, a biomimetic AI robot that was designed to be as close to a human as possible. To do this, they did everything from give her layered human-like skin, micro facial movements, and warm body temperature. While she still has a long way to go, it’s only a matter of time until technology surprises us even further, especially as developments in both appearance, movement, and artificial intelligence begin to intersect.

But until that day comes, we can all enjoy the fact that many other robotics companies are taking a different route. For now, there are a lot of little robots that you can buy from Amazon, which do everything from help you keep your plants alive, play chess or soccer with you, or teach you how to draw. While they’re not necessarily as talented as Sophia, the fact that they’re cute may help the transition.





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Another day, another politically motivated attack in the United States.

This morning’s shooting at a Dallas ICE detention facility – where a sniper killed two detainees and wounded another before taking his own life prompted me to revisit a question that’s been troubling me: Is political violence actually increasing in America, or does it just feel that way?

To explore this, I’ve conducted what I’ll call a methodological experiment.

Rather than relying on traditional datasets, I’ve used ChatGPT and Claude to construct a synthetic index of political violence in the US since 1945. Let me be absolutely clear: this isn’t conventional data. It’s data generated through language models, with all the limitations that implies.

The Methodology (and Its Limitations)

Here’s what I did: I asked both ChatGPT and Claude to generate lists of politically motivated violent incidents since 1945, then had them score each incident’s severity on a scale where 50 represents a “normal” level.

The models assessed both casualties and symbolic significance, and I used them to cross-check each other’s work. I then quality-checked the output myself and categorised perpetrators by political affiliation where this was clearly established.

This approach is, admittedly, unorthodox. Language models are trained on existing texts and may reflect biases in their training data. They might overweight highly publicised events or recent incidents that featured prominently in their training corpus.

The “data” we’re looking at is essentially a structured synthesis of what these models have absorbed about American political violence.

Yet there’s something intriguing here. These models have processed vast amounts of information about political violence – news reports, academic studies, government documents. Their output might capture patterns that traditional datasets miss, though it might also amplify certain narratives or blind spots.

What the Synthetic Data Reveal

With those caveats firmly in mind, the patterns that emerge from this exercise are concerning. The model-generated index shows a clear upward trend in political violence over the past decade.

Looking at the breakdown by perpetrator ideology (where clearly established), the data suggest that right-wing extremist groups have been responsible for the majority of incidents in recent years, though we cannot draw conclusions about today’s attack whilst investigations are ongoing.

The synthetic data align with some empirical observations. Princeton’s Bridging Divides Initiative recorded over 600 incidents of threats and harassment against local officials in 2024 – a 74% increase from 2022. The University of Maryland found that in the first half of 2025, 35% of violent events targeted U.S. government personnel or facilities – more than twice the rate in 2024.

The Charlie Kirk Assassination and Recent Patterns

The September assassination of conservative activist Charlie Kirk marked a particularly dark moment.

The incident followed numerous recent acts of political violence, including the murder of Minnesota Democratic state Rep. Melissa Hortman and her husband, and two assassination attempts on President Trump in 2024.

What the synthetic data reveal is not just increased frequency but a shift in patterns. While overall levels of physical political violence remained low in 2024 compared to years prior, acts of vigilante violence grew as a proportion of all reported incidents.

We’re seeing less organised group violence and more lone-wolf attacks – a pattern that’s harder to predict and prevent.

The Epistemological Challenge

When we use language models to generate “data” about social phenomena, what exactly are we measuring? We’re essentially extracting structured information from the collective corpus of human writing about these events. It’s aggregating distributed information, but through an AI intermediary rather than traditional data collection methods.

This raises fascinating questions.

The models suggest that right-wing extremist violence has been responsible for a fairly large majority of U.S. domestic terrorism deaths since 2001. But how much of this reflects actual patterns versus the way these events are covered and discussed in the sources the models were trained on?

The synthetic data are, in a sense, a mirror of our collective discourse about political violence. They reflect not just what happened, but how we’ve talked about what happened. That’s both a limitation and, potentially, a feature – understanding the narrative landscape around political violence might be as important as counting incidents.

An Experimental Tool

I’ve built an interactive app (using the AI coding tool Lovable) based on this language model-generated violence index.

Users can explore the synthetic data, examine patterns across different time periods and perpetrator groups, and understand the methodology behind it. Think of it as an experiment in using AI to structure historical information rather than a definitive dataset.

The value isn’t in treating this as gospel truth, but in what it reveals about how these events are recorded, remembered, and synthesised in our collective digital memory.

When language models trained on our civilisation’s text output show rising political violence, it tells us something – even if that something is as much about narrative as about underlying reality.

This morning’s tragedy in Dallas reminds us that behind every data point – whether traditionally collected or AI-generated – there are real victims and real consequences. Understanding the patterns, however imperfectly, is the first step toward addressing them.

Try the tool here.





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