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Innovation paves way for driverless cars, drone fleets and significantly faster broadband

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Newswise — Unparalleled speed, capacity and reliability of new fibre broadband technology, invented by UCL researchers, could provide connectivity needed for applications of the future such as driverless cars and drone fleets.

The study, published today in Nature Electronics, describes how the new telecommunications technology, called frequency referenced multiplexing, could provide more than 20 times the capacity of the best full fibre broadband networks available and 65 times the speed of typical current UK home broadband, along with a near-guaranteed connection and low latency1.

Telecommunications networks are critical to the functioning of the Internet – they are the digital equivalent of roads carrying the data that connect us to the Cloud. The best networks use fibre optic cables to transmit and receive information. For the new full fibre broadband that is rolling out throughout the UK, time division multiplexing (TDM) is the most common technology used to manage traffic, which combines the data of multiple users into one signal. Each user is assigned short time slots in which their data can be transferred in small chunks, before the data is reassembled at the destination.

The key issue with TDM is that each user’s data needs to wait for a time slot before it can be transmitted through the fibre, like cars waiting until they can drive onwards at traffic lights. With current technology, this approach has been necessary to coordinate transmission through the fibre, but this also limits the available data capacity and increases the time taken to send data through the network.

The fastest full fibre broadband services available in the UK offer upwards of one gigabit per second (Gb/s) download speed, usually with a much slower upload speed. Uptake of full fibre broadband has increased dramatically in recent years with the roll out of fibre optic connections to homes and businesses across the country, but for most UK broadband users, the final part of the line that goes into their homes remains older, slower copper wiring.

Consequently, the average broadband speed in the UK in September 2022 was just 65.3 megabits per second (Mb/s).

Demand for faster speeds and more reliable connections have also increased massively, from the rise of streaming on demand entertainment to the increase in videoconferencing use by people working from home since the Covid-19 pandemic. But certain applications of the future, such as driverless car networks, will require even higher speeds and near-guaranteed connections to operate safely and efficiently.

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In this study, researchers from UCL developed frequency-referenced multiplexing to overcome the latency and bandwidth restraints of current approaches such as TDM.

They used optical and clock frequency synchronisation, enabled by frequency comb and signal processing techniques, to provide each user with a dedicated optical channel. With this new approach, each user has the digital equivalent of their own dedicated road lane to communicate with the Cloud, with no need to wait at traffic lights. As a proof-of-concept, they set up a frequency referenced multiplexing system to provide up to 64 users with speeds of up to 4.3 Gb/s per user (or an aggregated speed of 240Gb/s for all users).

The authors hope that frequency-referenced multiplexing will be able to achieve more than 20 times the capacity and over 65 times the speed of current typical UK broadband. Because the user data is transmitted and received in parallel, this reduces the latency, power consumption, and capacity issues that arise with other approaches. This has the potential to lower the cost for future full fibre broadband, as well as increase the network availability and speed for every cloud user.

Associate Professor Zhixin Liu (UCL Electronic & Electrical Engineering), senior author of the study, said: “Some technology commentators are predicting networks of driverless cars and drone fleets in the not-too-distant future, all controlled from the Cloud. Our present telecommunications infrastructure isn’t equipped for such advancements, which necessitate guaranteed connectivity, minimal latency, synchronized clocks, and vastly improved speeds. Our research suggests that the frequency-referenced multiplexing approach can upgrade our fibre infrastructure to meet these technical demands.”

“In the short term, the technology has the potential to provide a much better home broadband service at a low infrastructure cost.”

Journal Link: Nature Electronics

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AI-generated images can exploit how your mind works − here’s why they fool you and how to spot them

Arryn Robbins discusses the challenges of recognizing AI-generated images due to human cognitive limitations and inattentional blindness, emphasizing the importance of critical thinking in a visually fast-paced online environment.

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Arryn Robbins, University of Richmond

I’m more of a scroller than a poster on social media. Like many people, I wind down at the end of the day with a scroll binge, taking in videos of Italian grandmothers making pasta or baby pygmy hippos frolicking.

For a while, my feed was filled with immaculately designed tiny homes, fueling my desire for a minimalist paradise. Then, I started seeing AI-generated images; many contained obvious errors, such as staircases to nowhere or sinks within sinks. Yet, commenters rarely pointed them out, instead admiring the aesthetic.

These images were clearly AI-generated and didn’t depict reality. Did people just not notice? Not care?

As a cognitive psychologist, I’d guess “yes” and “yes.” My expertise is in how people process and use visual information. I primarily investigate how people look for objects and information visually, from the mundane searches of daily life, such as trying to find a dropped earring, to more critical searches, like those conducted by radiologists or search-and-rescue teams.

With my understanding of how people process images and notice − or don’t notice − detail, it’s not surprising to me that people aren’t tuning in to the fact that many images are AI-generated.

We’ve been here before

The struggle to detect AI-generated images mirrors past detection challenges such as spotting photoshopped images or computer-generated images in movies.

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But there’s a key difference: Photo editing and CGI require intentional design by artists, while AI images are generated by algorithms trained on datasets, often without human oversight. The lack of oversight can lead to imperfections or inconsistencies that can feel unnatural, such as the unrealistic physics or lack of consistency between frames that characterize what’s sometimes called “AI slop.”

Despite these differences, studies show people struggle to distinguish real images from synthetic ones, regardless of origin. Even when explicitly asked to identify images as real, synthetic or AI-generated, accuracy hovers near the level of chance, meaning people did only a little better than if they’d just guessed.

In everyday interactions, where you aren’t actively scrutinizing images, your ability to detect synthetic content might even be weaker.

Attention shapes what you see, what you miss

Spotting errors in AI images requires noticing small details, but the human visual system isn’t wired for that when you’re casually scrolling. Instead, while online, people take in the gist of what they’re viewing and can overlook subtle inconsistencies.

Visual attention operates like a zoom lens: You scan broadly to get an overview of your environment or phone screen, but fine details require focused effort. Human perceptual systems evolved to quickly assess environments for any threats to survival, with sensitivity to sudden changes − such as a quick-moving predator − sacrificing precision for speed of detection.

This speed-accuracy trade-off allows for rapid, efficient processing, which helped early humans survive in natural settings. But it’s a mismatch with modern tasks such as scrolling through devices, where small mistakes or unusual details in AI-generated images can easily go unnoticed.

People also miss things they aren’t actively paying attention to or looking for. Psychologists call this inattentional blindness: Focusing on one task causes you to overlook other details, even obvious ones. In the famous invisible gorilla study, participants asked to count basketball passes in a video failed to notice someone in a gorilla suit walking through the middle of the scene.

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If you’re counting how many passes the people in white make, do you even notice someone walk through in a gorilla suit?

Similarly, when your focus is on the broader content of an AI image, such as a cozy tiny home, you’re less likely to notice subtle distortions. In a way, the sixth finger in an AI image is today’s invisible gorilla − hiding in plain sight because you’re not looking for it.

Efficiency over accuracy in thinking

Our cognitive limitations go beyond visual perception. Human thinking uses two types of processing: fast, intuitive thinking based on mental shortcuts, and slower, analytical thinking that requires effort. When scrolling, our fast system likely dominates, leading us to accept images at face value.

Adding to this issue is the tendency to seek information that confirms your beliefs or reject information that goes against them. This means AI-generated images are more likely to slip by you when they align with your expectations or worldviews. If an AI-generated image of a basketball player making an impossible shot jibes with a fan’s excitement, they might accept it, even if something feels exaggerated.

While not a big deal for tiny home aesthetics, these issues become concerning when AI-generated images may be used to influence public opinion. For example, research shows that people tend to assume images are relevant to accompanying text. Even when the images provide no actual evidence, they make people more likely to accept the text’s claims as true.

Misleading real or generated images can make false claims seem more believable and even cause people to misremember real events. AI-generated images have the power to shape opinions and spread misinformation in ways that are difficult to counter.

Beating the machine

While AI gets better at detecting AI, humans need tools to do the same. Here’s how:

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  1. Trust your gut. If something feels off, it probably is. Your brain expertly recognizes objects and faces, even under varying conditions. Perhaps you’ve experienced what psychologists call the uncanny valley and felt unease with certain humanoid faces. This experience shows people can detect anomalies, even when they can’t fully explain what’s wrong.
  2. Scan for clues. AI struggles with certain elements: hands, text, reflections, lighting inconsistencies and unnatural textures. If an image seems suspicious, take a closer look.
  3. Think critically. Sometimes, AI generates photorealistic images with impossible scenarios. If you see a political figure casually surprising baristas or a celebrity eating concrete, ask yourself: Does this make sense? If not, it’s probably fake.
  4. Check the source. Is the poster a real person? Reverse image search can help trace a picture’s origin. If the metadata is missing, it might be generated by AI.

AI-generated images are becoming harder to spot. During scrolling, the brain processes visuals quickly, not critically, making it easy to miss details that reveal a fake. As technology advances, slow down, look closer and think critically.The Conversation

Arryn Robbins, Assistant Professor of Psychology, University of Richmond

This article is republished from The Conversation under a Creative Commons license. Read the original article.


A beautiful kitchen to scroll past – but check out the clock. Tiny Homes via Facebook
AI-generated images

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How close are quantum computers to being really useful? Podcast

Quantum computers could revolutionize science by solving complex problems. However, scaling and error correction remain significant challenges before achieving practical applications.

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Gemma Ware, The Conversation

Quantum computers have the potential to solve big scientific problems that are beyond the reach of today’s most powerful supercomputers, such as discovering new antibiotics or developing new materials.

But to achieve these breakthroughs, quantum computers will need to perform better than today’s best classical computers at solving real-world problems. And they’re not quite there yet. So what is still holding quantum computing back from becoming useful?

In this episode of The Conversation Weekly podcast, we speak to quantum computing expert Daniel Lidar at the University of Southern California in the US about what problems scientists are still wrestling with when it comes to scaling up quantum computing, and how close they are to overcoming them.

https://cdn.theconversation.com/infographics/561/4fbbd099d631750693d02bac632430b71b37cd5f/site/index.html

Quantum computers harness the power of quantum mechanics, the laws that govern subatomic particles. Instead of the classical bits of information used by microchips inside traditional computers, which are either a 0 or a 1, the chips in quantum computers use qubits, which can be both 0 and 1 at the same time or anywhere in between. Daniel Lidar explains:

“Put a lot of these qubits together and all of a sudden you have a computer that can simultaneously represent many, many different possibilities …  and that is the starting point for the speed up that we can get from quantum computing.”

Faulty qubits

One of the biggest problems scientist face is how to scale up quantum computing power. Qubits are notoriously prone to errors – which means that they can quickly revert to being either a 0 or a 1, and so lose their advantage over classical computers.

Scientists have focused on trying to solve these errors through the concept of redundancy – linking strings of physical qubits together into what’s called a “logical qubit” to try and maximise the number of steps in a computation. And, little by little, they’re getting there.

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In December 2024, Google announced that its new quantum chip, Willow, had demonstrated what’s called “beyond breakeven”, when its logical qubits worked better than the constituent parts and even kept on improving as it scaled up.

Lidar says right now the development of this technology is happening very fast:

“For quantum computing to scale and to take off is going to still take some real science breakthroughs, some real engineering breakthroughs, and probably overcoming some yet unforeseen surprises before we get to the point of true quantum utility. With that caution in mind, I think it’s still very fair to say that we are going to see truly functional, practical quantum computers kicking into gear, helping us solve real-life problems, within the next decade or so.”

Listen to Lidar explain more about how quantum computers and quantum error correction works on The Conversation Weekly podcast.


This episode of The Conversation Weekly was written and produced by Gemma Ware with assistance from Katie Flood and Mend Mariwany. Sound design was by Michelle Macklem, and theme music by Neeta Sarl.

Clips in this episode from Google Quantum AI and 10 Hours Channel.

You can find us on Instagram at theconversationdotcom or via e-mail. You can also subscribe to The Conversation’s free daily e-mail here.

Listen to The Conversation Weekly via any of the apps listed above, download it directly via our RSS feed or find out how else to listen here.

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Gemma Ware, Host, The Conversation Weekly Podcast, The Conversation

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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AI gives nonprogrammers a boost in writing computer code

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AI coding handles the hard parts for nonprogrammers. Andriy/Moment via Getty Images

Leo Porter, University of California, San Diego and Daniel Zingaro, University of Toronto

What do you think there are more of: professional computer programmers or computer users who do a little programming?

It’s the second group. There are millions of so-called end-user programmers. They’re not going into a career as a professional programmer or computer scientist. They’re going into business, teaching, law, or any number of professions – and they just need a little programming to be more efficient. The days of programmers being confined to software development companies are long gone.

If you’ve written formulas in Excel, filtered your email based on rules, modded a game, written a script in Photoshop, used R to analyze some data, or automated a repetitive work process, you’re an end-user programmer.

As educators who teach programming, we want to help students in fields other than computer science achieve their goals. But learning how to program well enough to write finished programs can be hard to accomplish in a single course because there is so much to learn about the programming language itself. Artificial intelligence can help.

Lost in the weeds

Learning the syntax of a programming language – for example, where to place colons and where indentation is required – takes a lot of time for many students. Spending time at the level of syntax is a waste for students who simply want to use coding to help solve problems rather than learn the skill of programming.

As a result, we feel our existing classes haven’t served these students well. Indeed, many students end up barely able to write small functions – short, discrete pieces of code – let alone write a full program that can help make their lives better.

a teacher speaks to students in a classroom with a large screen displaying computer code
Learning a programming language can be difficult for those who are not computer science students. LordHenriVoton/E+ via Getty Images

Tools built on large language models such as GitHub Copilot may allow us to change these outcomes. These tools have already changed how professionals program, and we believe we can use them to help future end-user programmers write software that is meaningful to them.

These AIs almost always write syntactically correct code and can often write small functions based on prompts in plain English. Because students can use these tools to handle some of the lower-level details of programming, it frees them to focus on bigger-picture questions that are at the heart of writing software programs. Numerous universities now offer programming courses that use Copilot.

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At the University of California, San Diego, we’ve created an introductory programming course primarily for those who are not computer science students that incorporates Copilot. In this course, students learn how to program with Copilot as their AI assistant, following the curriculum from our book. In our course, students learn high-level skills such as decomposing large tasks into smaller tasks, testing code to ensure its correctness, and reading and fixing buggy code.

Freed to solve problems

In this course, we’ve been giving students large, open-ended projects and couldn’t be happier with what they have created.

For example, in a project where students had to find and analyze online datasets, we had a neuroscience major create a data visualization tool that illustrated how age and other factors affected stroke risk. Or, for example, in another project, students were able to integrate their personal art into a collage, after applying filters that they had created using the programming language Python. These projects were well beyond the scope of what we could ask students to do before the advent of large language model AIs.

Given the rhetoric about how AI is ruining education by writing papers for students and doing their homework, you might be surprised to hear educators like us talking about its benefits. AI, like any other tool people have created, can be helpful in some circumstances and unhelpful in others.

In our introductory programming course with a majority of students who are not computer science majors, we see firsthand how AI can empower students in specific ways – and promises to expand the ranks of end-user programmers.

Leo Porter, Teaching Professor of Computer Science and Engineering, University of California, San Diego and Daniel Zingaro, Associate Professor of Mathematical and Computational Sciences, University of Toronto

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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