Computers Math

Directing light from place to the place is the backbone of our modern world. Beneath the oceans and across continents, fiber optic cables carry light that encodes everything from YouTube videos to banking transmissions — all inside strands about the size of a hair.
University of Chicago Prof. Jiwoong Park, however, wondered what would happen if you made even thinner and flatter strands — in effect, so thin that they’re actually 2D instead of 3D. What would happen to the light?
Through a series of innovative experiments, he and his team found that a sheet of glass crystal just a few atoms thick could trap and carry light. Not only that, but it was surprisingly efficient and could travel relatively long distances — up to a centimeter, which is very far in the world of light-based computing.
The research, published Aug. 10 in Science, demonstrates what are essentially 2D photonic circuits, and could open paths to new technology.
“We were utterly surprised by how powerful this super-thin crystal is; not only can it hold energy, but deliver it a thousand times further than anyone has seen in similar systems,” said lead study author Jiwoong Park, a professor and chair of chemistry and faculty member of the James Franck Institute and Pritzker School of Molecular Engineering. “The trapped light also behaved like it is traveling in a 2D space.”
Guiding light
The newly invented system is a way to guide light — known as a waveguide — that is essentially two-dimensional. In tests, the researchers found they could use extremely tiny prisms, lenses, and switches to guide the path of the light along a chip — all the ingredients for circuits and computations. More

Flat screen TVs that incorporate quantum dots are now commercially available, but it has been more difficult to create arrays of their elongated cousins, quantum rods, for commercial devices. Quantum rods can control both the polarization and color of light, to generate 3D images for virtual reality devices.
Using scaffolds made of folded DNA, MIT engineers have come up with a new way to precisely assemble arrays of quantum rods. By depositing quantum rods onto a DNA scaffold in a highly controlled way, the researchers can regulate their orientation, which is a key factor in determining the polarization of light emitted by the array. This makes it easier to add depth and dimensionality to a virtual scene.
“One of the challenges with quantum rods is: How do you align them all at the nanoscale so they’re all pointing in the same direction?” says Mark Bathe, an MIT professor of biological engineering and the senior author of the new study. “When they’re all pointing in the same direction on a 2D surface, then they all have the same properties of how they interact with light and control its polarization.”
MIT postdocs Chi Chen and Xin Luo are the lead authors of the paper, which appears today in Science Advances. Robert Macfarlane, an associate professor of materials science and engineering; Alexander Kaplan PhD ’23; and Moungi Bawendi, the Lester Wolfe Professor of Chemistry, are also authors of the study.
Nanoscale structures
Over the past 15 years, Bathe and others have led in the design and fabrication of nanoscale structures made of DNA, also known as DNA origami. DNA, a highly stable and programmable molecule, is an ideal building material for tiny structures that could be used for a variety of applications, including delivering drugs, acting as biosensors, or forming scaffolds for light-harvesting materials.
Bathe’s lab has developed computational methods that allow researchers to simply enter a target nanoscale shape they want to create, and the program will calculate the sequences of DNA that will self-assemble into the right shape. They also developed scalable fabrication methods that incorporate quantum dots into these DNA-based materials. More

In a world where annual economic losses from corrosion surpass 2.5 trillion US Dollars, the quest for corrosion-resistant alloys and protective coatings is unbroken. Artificial intelligence (AI) is playing an increasingly pivotal role in designing new alloys. Yet, the predictive power of AI models in foreseeing corrosion behaviour and suggesting optimal alloy formulas has remained elusive.
Scientists of the Max-Planck-Institut für Eisenforschung (MPIE) have now developed a machine learning model that enhances the predictive accuracy by up to 15% compared to existing frameworks. This model uncovers new, but realistic corrosion-resistant alloy compositions. Its distinct power arises from fusing both numerical and textual data. Initially developed for the critical realm of resisting pitting corrosion in high-strength alloys, this model’s versatility can be extended to all alloy properties. The researchers published their latest results in the journal Science Advances.
Merging texts and numbers
“Every alloy has unique properties concerning its corrosion resistance. These properties do not only depend on the alloy composition itself, but also on the alloy’s manufacturing process. Current machine learning models are only able to benefit from numerical data. However, processing methodologies and experimental testing protocols, which are mostly documented by textual descriptors, are crucial to explain corrosion,,” explains Kasturi Narasimha Sasidhar, lead author of the publication and former postdoctoral researcher at MPIE. The researcher team used language processing methods, akin to ChatGPT, in combination with machine learning (ML) techniques for numerical data and developed a fully automated natural language processing framework. Moreover, involving textual data into the ML framework allows to identify enhanced alloy compositions resistant to pitting corrosion. “We trained the deep-learning model with intrinsic data that contain information about corrosion properties and composition. Now the model is capable of identifying alloy compositions that are critical for corrosion-resistance even if the individual elements were not fed initially into the model,” says Michael Rohwerder, co-author of the publication and head of the group Corrosion at MPIE.
Pushing boundaries: automated data mining and image processing
In the recently devised framework, Sasidhar and his team harnessed manually gathered data as textual descriptors. Presently, their objective lies in automating the process of data mining and seamlessly integrating it into the existing framework. The incorporation of microscopy images marks another milestone, envisioning the next generation of AI frameworks that converge textual, numerical, and image-based data. More

From text-generating ChatGPT to voice-activated Siri, artificial intelligence-powered tools are designed to aid our everyday life — as long as you speak a language they support. These technologies are out of reach for billions of people who don’t use English, French, Spanish or other mainstream languages, but researchers in Africa are looking to change that. In a study published August 11 in the journal Patterns, scientists draw a roadmap to develop better AI-driven tools for African languages.
“It doesn’t make sense to me that there are limited AI tools for African languages,” says first author and AI researcher Kathleen Siminyu of the Masakhane Research Foundation, a grassroots network of African scientists who aim to spur accessible AI tools for those who speak African languages. “Inclusion and representation in the advancement of language technology is not a patch you put at the end — it’s something you think about up front.”
Many of these tools rely on a field of AI called natural language processing, a technology that enables computers to understand human languages. Computers can master a language through training, where they pick up on patterns in speech and text data. However, they fail when data in a particular language is scarce, as seen in African languages. To fill the gap, the research team first identified key players involved in developing African language tools and explored their experience, motivation, focuses, and challenges. These people include writers and editors who create and curate content, as well as linguists, software engineers, and entrepreneurs who are crucial in establishing the infrastructure for language tools.
Interviews with the key players revealed four central themes to consider in designing African language tools: First, bearing the impact of colonization, Africa is a multilingual society where African language is central to people’s cultural identities and is key to societal participation in education, politics, economy, and more. Second, there is a need to support African content creation. This includes building basic tools such as dictionaries, spell checkers, and keyboards for African languages and removing financial and administrative barriers for translating government communications to multiple national languages, which includes African languages. Third, the creation of African language technologies will benefit from collaborations between linguistics and computer science. Also, there should be focus on creating tools that are human centered, which help individuals unlock greater potential. Fourth, developers should be mindful of communities and ethical practices during the collection, curation, and use of data.”There’s a growing number of organizations working in this space, and this study allows us to coordinate efforts in building impactful language tools,” says Siminyu. “The findings highlight and articulate what the priorities are, in terms of time and financial investments.”
Next, the team plans to expand the study and include more participants to understand the communities that AI language technologies may impact. They will also address barriers that may hinder people’s access to the technology. The team hopes their study could serve as a roadmap to help develop a wide range of language tools, from translation services to misinformation-catching content moderators. The findings may also pave the way to preserve indigenous African languages.
“I would love for us to live in a world where Africans can have as good quality of life and access to information and opportunities as somebody fluent in English, French, Mandarin, or other languages,” says Siminyu. More

Text-to-image (T2I) generative artificial intelligence tools are increasingly powerful and widespread tools that can create nearly any image based on just a few inputted words. T2I generative AI can create convincingly realistic photos and videos which are being used more and more for a multitude of purposes, from art to political campaigning.
However, the algorithmic models that power these tools are trained on data from humans, and can replicate human biases in the images they produce, such as biases around gender and skin tone. These biases can harm marginalized populations, reinforcing stereotypes and potentially leading to discrimination.
To address these implicit biases, Assistant Professor of Computer Science and Engineering Xin (Eric) Wang and a team of researchers from Baskin Engineering at UC Santa Cruz created a tool called the Text to Image Association Test, which provides a quantitative measurement of complex human biases embedded in T2I models, evaluating biases across dimensions such as gender, race, career, and religion. They used this tool to identify and quantify bias in the state-of-the-art generative model Stable Diffusion.
The tool is detailed in a paper for the 2023 Association for Computational Linguistics (ACL) conference, a premier computer science conference, and is available for use in a demo version.
“I think both the model owners and users care about this issue,” said Jialu Wang, a UCSC computer science and engineering Ph.D. student and the first author on the paper. “If the user is from an unprivileged group, they may not want to see just the privileged group reflected in the images they generate.”
To use the tool, a user must tell the model to produce an image for a neutral prompt, for example “child studying science.” Next, the user inputs gender specific prompts, such as “girl studying science” and “boy studying science.” Then, the tool calculates the distance between the images generated with the neutral prompt and each of the specific prompts. That difference between those two distances is a quantitative measurement of bias.
Using their tool, the research team found that the state-of-the-art generative model Stable Diffusion both replicates and amplifies human biases in the images it produces. The tool tests the association between two concepts, such as science and arts, to two attributes, such as male and female. It then gives an association score between the concept and the attribute and a value to indicate how confident the tool is in that score. More

Legal restrictions placed on the amount of time young people in China can play video games may be less effective than originally thought, a new study has revealed.
To investigate the effectiveness of the policy, a team of researchers led by the University of York, analysed over 7 billion hours of playtime data from tens of thousands of games, with data drawn from over two billion accounts from players in China, where legal restrictions on playtime for young people have been in place since 2019.
The research team, however, did not find evidence of a decrease in heavy play of games after these restrictions were put in place.
The video games industry has witnessed a surge in popularity, and as many as 4 billion people are now estimated to engage in gaming worldwide each year.
Many countries across the globe have expressed concerns about the number of hours young people spend playing video games and the potential impact of this on wellbeing. In response to these concerns, in 2019 China restricted playtime for people under 18.
China is one of the first countries to explore legal means of restricting gameplay for young people with the aim of limiting the potential risks of gaming to wellbeing, and the policy was assumed to be effective, with some bodies suggesting that it had resolved issues relating to disordered gaming.
Dr David Zendle, from the University of York’s Department of Computer Science, said: “Policymakers around the world have been discussing how to understand the impact of video gameplay, particularly on young people, for some time now, and how to ensure a healthy relationship with games. The UK government, for example, has recently issued guidelines for high quality research into gaming and wellbeing to inform future decision making. More

Developing new materials requires significant time and labor, but some chemists are now hopeful that artificial intelligence (AI) could one day shoulder much of this burden. In a new study in the Journal of the American Chemical Society, a team prompted a popular AI model, ChatGPT, to perform one particularly time-consuming task: searching scientific literature. With that data, they built a second tool, a model to predict experimental results.
Reports from previous studies offer a vast trove of information that chemists need, but finding and parsing the most relevant details can be laborious. For example, those interested in designing highly porous, crystalline metal-organic frameworks (MOFs) — which have potential applications in areas such as clean energy — must sort through hundreds of scientific papers describing a variety of experimental conditions. Researchers have previously attempted to coax AI to take over this task; however, the language processing models they used required significant technical expertise, and applying them to new topics meant changing the program. Omar Yaghi and colleagues wanted to see if the next generation of language models, which includes ChatGPT, could offer a more accessible, flexible way to extract information.
To analyze text from scientific papers, the team gave ChatGPT prompts, or instructions, guiding it through three processes intended to identify and summarize the experimental information the manuscripts contained. The researchers carefully constructed these prompts to minimize the model’s tendency to make up responses, a phenomenon known as hallucination, and to ensure the best responses possible.
When tested on 228 papers describing MOF syntheses, this system extracted more than 26,000 factors relevant for making roughly 800 of these compounds. With these data, the team trained a separate AI model to predict the crystalline state of MOFs based on these conditions. And finally, to make the data more user friendly, they built a chatbot to answer questions about it. The team notes that, unlike previous AI-based efforts, this one does not require expertise in coding. What’s more, scientists can shift its focus simply by adjusting the narrative language in the prompts. This new system, which they dub the “ChatGPT Chemistry Assistant,” could also be useful in other fields of chemistry, according to the researchers. More

Researchers are developing a way to incorporate one of the most human of characteristics — uncertainty — into machine learning systems.
Human error and uncertainty are concepts that many artificial intelligence systems fail to grasp, particularly in systems where a human provides feedback to a machine learning model. Many of these systems are programmed to assume that humans are always certain and correct, but real-world decision-making includes occasional mistakes and uncertainty.
Researchers from the University of Cambridge, along with The Alan Turing Institute, Princeton, and Google DeepMind, have been attempting to bridge the gap between human behaviour and machine learning, so that uncertainty can be more fully accounted for in AI applications where humans and machines are working together. This could help reduce risk and improve trust and reliability of these applications, especially where safety is critical, such as medical diagnosis.
The team adapted a well-known image classification dataset so that humans could provide feedback and indicate their level of uncertainty when labelling a particular image. The researchers found that training with uncertain labels can improve these systems’ performance in handling uncertain feedback, although humans also cause the overall performance of these hybrid systems to drop. Their results will be reported at the AAAI/ACM Conference on Artificial Intelligence, Ethics and Society (AIES 2023) in Montréal.
‘Human-in-the-loop’ machine learning systems — a type of AI system that enables human feedback — are often framed as a promising way to reduce risks in settings where automated models cannot be relied upon to make decisions alone. But what if the humans are unsure?
“Uncertainty is central in how humans reason about the world but many AI models fail to take this into account,” said first author Katherine Collins from Cambridge’s Department of Engineering. “A lot of developers are working to address model uncertainty, but less work has been done on addressing uncertainty from the person’s point of view.”
We are constantly making decisions based on the balance of probabilities, often without really thinking about it. Most of the time — for example, if we wave at someone who looks just like a friend but turns out to be a total stranger — there’s no harm if we get things wrong. However, in certain applications, uncertainty comes with real safety risks. More