People with severe vision loss have been able to read again, thanks to a tiny wireless chip implanted in one of their eyes and a pair of high-tech glasses.

Age-related macular degeneration (AMD) is a common condition that affects the middle part of someone’s vision, often worsening over time. Its exact cause is unknown, but it occurs when light-sensitive photoreceptor cells and neurons in the centre of the retina become damaged, making it hard to recognise faces or read. Approved treatments can only slow its progression.

An advanced stage of AMD is known as geographic atrophy, but even here, people usually retain some photoreceptor cells that allow for peripheral vision and enough retinal neurons to pass visual information to the brain.

Taking advantage of this, at Stanford University in California and his colleagues have developed a device called PRIMA. It involves a small camera mounted on a pair of glasses that captures images, then projects them via infrared light to a 2-by-2-millimetre solar-powered, wireless chip implanted in the back of the eye.

The chip then converts the image information into an electrical signal that retinal neurons can pass to the brain. Infrared light is used because we can’t see in this wavelength, so the process doesn’t interfere with any existing vision. “This means patients can use both prosthetic and peripheral vision simultaneously,” says Palanker.

To put it to the test, the researchers recruited 32 people aged 60 or older who had geographic atrophy. Their vision in at least one eye was worse than 20/320, which means they could only see at 20 feet (6 metres) what a person with 20/20 vision could see at 320 feet (97.5 metres).

Free newsletter

Sign up to Health Check

Sign up to newsletter

The researchers first implanted the chip in the eyes of one of the participants, then, four to five weeks later, the volunteers began to use the glasses in their daily lives. The glasses allowed them to magnify what they were seeing by up to 12 times and to adjust the brightness and contrast.

After a year, 27 of the participants could read again, as well as perceive shapes and patterns. They could also see an additional five lines, on average, on a standard eye test chart, compared with what they could discern at the start of the study. Some could even read with the equivalent of 20/42 vision.

“When you watch them starting to read letters and then words, it’s an increasing joy on both sides. I recollect one patient telling me: ‘I thought my eyes were dead and now they are alive again’,” says team member at the University of Pittsburgh School of Medicine in Pennsylvania.

There are indications that stem cell implants or could help restore sight lost due to AMD, but these are still at early experimental stages. By giving the trial participants the ability to perceive shapes and patterns, PRIMA represents the first eye prosthesis to restore functional sight in people with the condition.

About two-thirds of the volunteers experienced short-term side effects as a result of the implant, including high pressure in the eye, but this didn’t prevent vision improvements.

“This is an exciting and significant study,” says at Imperial College London. “It gives hope for providing vision in patients for whom this was more science fiction than reality.”

The boosted vision the participants experienced is in black and white. “Our next goal is to add the software that will help resolve grey scales and enhance them for face recognition,” says Palanker. The researchers don’t expect to be providing colour vision any time soon, though.

Palanker also plans to boost PRIMA’s resolution, which is limited by the size of pixels affecting the number that can fit on the chip. He has been testing a more advanced version in rats. “This would correspond to a visual acuity of 20/80 in people, and with electronic zoom, we can go all the way to 20/20,” he says.

Will implants that meld minds with machines enhance human abilities?

Devices that let people with paralysis walk and talk are rapidly improving. Some see a future in which we alter memories and download skills – but major challenges remain

In Wilbur's distant-future vision of 1977, a transit tunnel whisked cars below the English Channel

Annalee’s week

Did you notice that the image above was created by artificial intelligence? It can be difficult to spot AI-generated images, video, audio and text at a time when technological advances are making them increasingly indistinguishable from much human-created content, leaving us open to manipulation by disinformation. But by knowing the current state of the AI technologies used to create misinformation, and the range of telltale signs that what you are looking at might be fake, you can help protect yourself from being taken in.

World leaders are concerned. According to , misinformation and disinformation may “radically disrupt electoral processes in several economies over the next two years”, while easier access to AI tools “have already enabled an explosion in falsified information and so-called ‘synthetic’ content, from sophisticated voice cloning to counterfeit websites”.

The terms misinformation and disinformation both refer to false or inaccurate information, but disinformation is that which is deliberately intended to deceive or mislead.

“The issue with AI-powered disinformation is the scale, speed and ease with which campaigns can be launched,” says at the University of California, Berkeley. “These attacks will no longer take state-sponsored actors or well-financed organisations – a single individual with access to some modest computing power can create massive amounts of fake content.”

He says that generative AI (see glossary, below) is “polluting the entire information ecosystem, casting everything we read, see and hear into doubt”. He says his research suggests that, in many cases, AI-generated images and audio are “nearly indistinguishable from reality”.

However, research by Farid and others reveals that there are strategies you can follow to reduce your risk of falling for social media misinformation or disinformation created by AI.

Free newsletter

Sign up to The Daily

Sign up to newsletter

How to spot fake AI images

Remember seeing a photo of Pope Francis wearing a puffer jacket? Such fake AI images have become more common as new tools based on diffusion models (see glossary, below) have allowed anyone to start churning out images from simple text prompts. One by Nicholas Dufour at Google and his colleagues found a rapid increase in the proportion of AI-generated images in fact-checked misinformation claims from early 2023 onwards.

“Nowadays, media literacy requires AI literacy,” says at Northwestern University in Illinois. In a 2024 , she and her colleagues identified five different categories of errors in AI-generated images (outlined below) and provided guidance on how people can spot these for themselves. The good news is that their research suggests people are currently about 70 per cent accurate at detecting fake AI images of people. You can use their to assess your own sleuthing skills.

5 common types of errors in AI-generated images:

1. Sociocultural implausibilities: Is the scene depicting rare, unusual or surprising behaviour for certain cultures or historical figures?
2. Anatomical implausibilities: Take a close look: are body parts like hands unusually shaped or sized? Do the eyes or mouths look strange? Have any body parts merged?
3. Stylistic artefacts: Does the image look unnatural, almost too perfect or stylistic? Does the background look odd or like it is missing something? Is the lighting strange or variable?
4. Functional implausibilities: Do any objects look bizarre or like they might not be real or work? For example, are buttons or belt buckles in weird places?
5. Violations of physics: Are shadows pointing in different directions? Are mirror reflections consistent with the world depicted within the image?

How to identify video deepfakes

AI technology known as generative adversarial networks (see glossary, below) has allowed tech-savvy individuals to create video deepfakes since 2014 – digitally manipulating existing videos of people to swap in different faces, create new facial expressions and insert new spoken audio aligned with matching lip-syncing. This has enabled a growing array of scammers, state-backed hackers and internet users to produce video deepfakes where celebrities such as Taylor Swift and ordinary people alike may find themselves unwillingly featured in non-consensual deepfake pornography, scams and political misinformation or disinformation.

The techniques for spotting AI fake images (see above) can be applied to suspect videos too. Additionally, researchers at the Massachusetts Institute of Technology and Northwestern University in Illinois have compiled for how to spot such deepfakes, but they have acknowledged that there is no fool-proof method that always works.

6 tips for spotting AI-generated video:

1. Mouth and lip movements: Are there moments when the video and audio aren’t completely synced?
2. Anatomical glitches: Does the face or body look weird or move unnaturally?
3. Face: Look for inconsistencies in face smoothness or wrinkles around the forehead and cheeks, along with facial moles.
4. Lighting: Is the lighting inconsistent? Do shadows behave as you would expect? Pay particular attention to a person’s eyes, eyebrows and glasses.
5. Hair: Does facial hair look weird or move in strange ways?
6. Blinking: Too much or too little blinking could be a sign of a deepfake.

A newer category of video deepfakes is based on diffusion models (see glossary, below) – the same AI technology behind many image generators – that can create completely AI-generated video clips based on text prompts. Companies are already testing and releasing commercial versions of AI video generators that could make it easy for anyone to do this without needing special technical knowledge. So far, the resulting videos tend to feature distorted faces or bizarre body movements.

“These AI-generated videos are probably easier for people to detect than images, because there is a lot of movement and there is a lot more opportunity for AI-generated artefacts and impossibilities,” says Kamali.

How to identify AI bots

Social media accounts controlled by computer bots have become common on many social media and messaging platforms. A growing number of these bots have also been taking advantage of generative AI technologies such as large language models (see glossary, below) since 2022. These make it both easy and cheap to churn out AI-written content through thousands of bots that is grammatically correct and convincingly customised to different situations.

It has become much easier “to customise these large language models for specific audiences with specific messages”, says at the University of Notre Dame in Indiana.

Brenner and his colleagues have found in their research that volunteers could only distinguish AI-powered bots from humans – despite the participants being told they were potentially interacting with bots. You can test your own bot detection skills .

Some strategies can help identify less sophisticated AI bots, says Brenner.

5 ways to determine whether a social media account is an AI bot:

1. Emojis and hashtags: Excessive use of these can be a sign.
2. Uncommon phrasing, word choices or analogies: Unusual wording could indicate an AI bot.
3. Repetition and structure: Bots may use repeated wording that follows similar or rigid forms and they may overuse certain slang terms.
4. Ask questions: These can reveal a bot’s lack of knowledge about a topic – particularly when it comes to local places and situations.
5. Assume the worst: If a social media account isn’t a personal contact and their identity hasn’t been clearly validated or verified, it could well be an AI bot.

How to detect audio cloning and speech deepfakes

Voice cloning (see glossary, below) AI tools have made it easy to generate new spoken audio that can mimic practically anyone. This has led to the rise of audio deepfake scams that clone the voices of family members, company executives and political leaders such as US President Joe Biden. These can be much more difficult to identify compared with AI-generated videos or images.

“Voice cloning is particularly challenging to distinguish between real and fake because there aren’t visual components to support our brains in making that decision,” says , co-founder of SocialProof Security, a white-hat hacking organisation.

Detecting such AI audio deepfakes can be especially tricky when they are used in video and phone calls. But there are some common-sense steps you can follow to distinguish authentic humans from AI-generated voices.

The truth about social media and screen time’s impact on young people

There are many scary claims about excess time on digital devices for children and teenagers. Here’s a guide to the real risks - and what to do about them

4 steps for recognising if audio has been cloned or faked using AI:

1. Public figures: If the audio clip is of an elected official or celebrity, check if what they are saying is consistent with what has already been publicly reported or shared about their views and behaviour.
2. Look for inconsistencies: Compare the audio clip with previously authenticated video or audio clips that feature the same person’s voice. Are there any inconsistencies in the sound of their voice or their speech mannerisms?
3. Awkward silences: If you are listening to a phone call or voicemail and the speaker is taking unusually long pauses while speaking, they may be using AI-powered voice cloning technology.
4. Weird and wordy: Any robotic speech patterns or an unusually verbose manner of speaking could indicate that someone is using a combination of voice cloning to mimic a person’s voice and a large language model to generate the exact wording.

The technology will only get better

As it stands, there are no consistent rules that can always distinguish AI-generated content from authentic human content. AI models capable of generating text, images, video and audio will almost certainly continue to improve and they can often quickly produce authentic-seeming content without any obvious artefacts or mistakes. “Be politely paranoid and realise that AI has been manipulating and fabricating pictures, videos and audio fast – we’re talking completed in 30 seconds or less,” says Tobac. “This makes it easy for malicious individuals who are looking to trick folks to turn around AI-generated disinformation quickly, hitting social media within minutes of breaking news.”

The future of AI: The 5 possible scenarios, from utopia to extinction

How will the rise of artificial intelligence ultimately pan out for society? We sketch the most likely outcomes, including a world where AIs solve all our problems and another in which they wipe us out

While it is important to hone your eye for AI-generated false information and learn to ask more questions of what you read, see and hear, ultimately this won’t be enough to stop harm and the responsibility to detect fakes can’t fall fully on individuals. Farid is among researchers who say that government regulators must hold to account the largest tech companies – along with start-ups backed by prominent Silicon Valley investors – that have developed many of the tools that are flooding the internet with fake AI-generated content. “Technology is not neutral,” says Farid. “This line that the technology sector has sold us that somehow they don’t have to absorb liability where every other industry does, I simply reject it.”