⚙️ AI cameras were used to detect the emotions of UK train passengers

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Good morning. Today’s main story is a little longer than usual — it pertains to the release of a lengthy selection of documents concerning the way AI is being applied to CCTV cameras in U.K. train stations. Read on for the full story.

On a semi-related note, The Deep View is hiring. If you’re interested in joining our team, send an application here.

In today’s newsletter: 

AI for Good: How AI is helping people with ALS communicate

Impaired speech, known also as dysarthria, is a common occurrence in people living with ALS. The subsequent challenges they face in communication have begun to give way to a technological solution, one achieved through artificial intelligence. 

The details: In 2019, Google’s Project Euphonia partnered with the ALS Therapy Development Institute (ALS TDI) to improve the ability of computers to understand impaired speech. 

You can watch Project Euphonia in action here. 

Voice Preservation: A component of the project — and something that has been advancing since 2016 — involves voice preservation, where an AI model can create a synthesis of your voice before you lose it. 

Why it matters: Loss of communication, as one study says, can increase social isolation, reducing quality of life. It also makes it much harder for people to ask for help.

The challenges to a simple plan for safe AI deployment

As powerful as today’s AI applications might seem, limitations (& hallucinations) are baked into their architecture. Self-driving cars, for example, struggle to detect obstructions, objects and pedestrians, and in general, cannot be adequately trained to respond to edge cases. 

Large language models (LLMs), meanwhile, routinely provide incorrect information. Whether this be through the false summarization of an article or faulty responses to specific queries, models’ propensity to make stuff up makes them hard to trust. 

Here’s one solution: In a new paper, Tim Rudner and Helen Toner argue that a key way to overcome the problem involves building models that “know what they don’t know.” 

The problem is that it’s “impossible to know in advance what kinds of unexpected situations — what unknown unknowns — a system deployed in the messy real world may encounter.”

Why it matters: This has been an active area of research for several years now. And there are certain approaches — Deterministic Methods, Model Ensembling, Conformal Prediction and Bayesian Inference — which, though they have many drawbacks, act as a partial solution to the problem. The authors argue that continuing research into these areas will “play a crucial role” in ensuring the safe, explainable and reliable deployment of generative AI. 

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Apple’s ChatGPT integration won’t come until 2025

Apple, according to Bloomberg’s Mark Gurman, is taking a “staggered” approach to the launch of its coming AI-enabled operating system. 

The details: While IOS 18 will be packed with AI features at launch, many more significant features likely won’t become available for a while yet. 

Apple did not respond to a request for comment regarding the rollout. 

The background: Apple unveiled its coming AI integration — dubbed Apple Intelligence — at its WWDC conference last week. Investors rejoiced at the announcement, cheering the stock onto new heights as Apple finally boarded the genAI train. Apple is once again the largest company by market cap in the world. 

But it also set off major backlash for a long list of unanswered questions regarding security, data privacy and user choice (which we covered here).

Why it matters: This slower approach could certainly give Apple time to adequately address user concerns, something that could improve the adoption of its new software. 

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AI cameras are quietly being used to detect the emotions of UK train passengers

Over the past two years, Network Rail has been trialing the use of AI-enabled “smart” CCTV cameras across eight major train stations in the United Kingdom. 

The purpose behind these trials — first reported in February by James O’Malley — was broadly to increase safety and security at train stations across the country. The software being applied to cameras throughout each test station was designed to recognize objects and activities; if someone fell onto the tracks, or entered a restricted area, or got into a fight with another passenger, station employees would receive an instant notification so they could step in. 

The full story: A new set of documents — obtained by Big Brother Watch and first reported by Wired — detail the full scope of the trial. 

Another document details feedback on the trial. In one section, the document explores a “passenger demographics” use case, which “produces a statistical analysis of age range and male/female demographics. The use case is also able to analyze for emotion in people (e.g., happy, sad and angry).”

According to the document, there are two possible benefits to this: 

This use case, according to the document, will “probably” be used sparingly and “is viewed with more caution.” 

Network Rail did not respond to my request for comment, but told Wired in a statement that it takes security “extremely seriously” and works closely with the police “to ensure that we’re taking proportionate action, and we always comply with the relevant legislation regarding the use of surveillance technologies.”

It is not clear if these systems are currently in use, or at what scale.

Why this isn’t great

The technical problem: On a technical level, this kind of technology is just not that good at properly detecting emotions in humans. Part of this is rooted in models’ inherent lack of understanding. The other part revolves around the simple fact that human emotions are really complex. 

The ethical problem: The ethical considerations of using this kind of tech are numerous (this paper breaks down 50 of them). 

Ella Jakubowska, a senior policy advisor at EDRi, told the MIT tech review last year that emotion recognition is “about social control; about who watches and who gets watched; about where we see a concentration of power.”

My view: When I first explored the ethics of AI, this was the kind of issue that I encountered. Not an out-of-control superintelligence, but a concentration of power. This specific instance might be well-meaning, but it establishes a dangerous precedent and points us toward a potentially dark path. The term “Orwellian,” though perhaps overused, comes to mind here. 

As Nell Watson, an AI researcher and ethicist, told me a year ago: “There isn't a synchronicity between the ability for people to make decisions about AI systems, what those systems are doing, how they're interpreting them and what kinds of impressions these systems are making.”

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That's a wrap for now! We hope you enjoyed today’s newsletter :)

We appreciate your continued support! We'll catch you in the next edition 👋

-Ian Krietzberg, Editor-in-Chief, The Deep View