Aurelia Butler

In response to major advances in Generative AI technologies — as well as the significant questions these technologies pose in areas including intellectual property, the future of work, and even human safety — the Association for Computing Machinery’s global Technology Policy Council (ACM TPC) has issued “Principles for the Development, Deployment, and Use of Generative AI Technologies.”
Drawing on the deep technical expertise of computer scientists in the United States and Europe, the ACM TPC statement outlines eight principles intended to foster fair, accurate, and beneficial decision-making concerning generative and all other AI technologies. Four of the principles are specific to Generative AI, and an additional four principles are adapted from the TPC’s 2022 “Statement on Principles for Responsible Algorithmic Systems.”
The Introduction to the new Principles advances the core argument that “the increasing power of Generative AI systems, the speed of their evolution, broad application, and potential to cause significant or even catastrophic harm, means that great care must be taken in researching, designing, developing, deploying, and using them. Existing mechanisms and modes for avoiding such harm likely will not suffice.”
The document then sets out these eight instrumental principles, outlined here in abbreviated form:
Generative AI-Specific Principles Limits and guidance on deployment and use: In consultation with all stakeholders, law and regulation should be reviewed and applied as written or revised to limit the deployment and use of Generative AI technologies when required to minimize harm. No high-risk AI system should be allowed to operate without clear and adequate safeguards, including a “human in the loop” and clear consensus among relevant stakeholders that the system’s benefits will substantially outweigh its potential negative impacts. One approach is to define a hierarchy of risk levels, with unacceptable risk at the highest level and minimal risk at the lowest level. Ownership: Inherent aspects of how Generative AI systems are structured and function are not yet adequately accounted for in intellectual property (IP) law and regulation. Personal data control: Generative AI systems should allow a person to opt out of their data being used to train a system or facilitate its generation of information. Correctability: Providers of Generative AI systems should create and maintain public repositories where errors made by the system can be noted and, optionally, corrections made.Adapted Prior Principles Transparency: Any application or system that utilizes Generative AI should conspicuously disclose that it does so to the appropriate stakeholders. Auditability and contestability: Providers of Generative AI systems should ensure that system models, algorithms, data, and outputs can be recorded where possible (with due consideration to privacy), so that they may be audited and/or contested in appropriate cases. Limiting environmental impact: Given the large environmental impact of Generative AI models, we recommend that consensus on methodologies be developed to measure, attribute, and actively reduce such impact. Heightened security and privacy: Generative AI systems are susceptible to a broad range of new security and privacy risks, including new attack vectors and malicious data leaks, among others.”Our field needs to tread carefully with the development of Generative AI because this is a new paradigm that goes significantly beyond previous AI technology and applications,” explained Ravi Jain, Chair of the ACM Technology Policy Council’s Working Group on Generative AI and lead author of the Principles. “Whether you celebrate Generative AI as a wonderful scientific advancement or fear it, everyone agrees that we need to develop this technology responsibly. In outlining these eight instrumental principles, we’ve tried to consider a wide range of areas where Generative AI might have an impact. These include aspects that have not been covered as much in the media, including environmental considerations and the idea of creating public repositories where errors in a system can be noted and corrected.”
“These are guidelines, but we must also build a community of scientists, policymakers, and industry leaders who will work together in the public interest to understand the limits and risks of Generative AI as well as its benefits. ACM’s position as the world’s largest association for computing professionals makes us well-suited to foster that consensus and look forward to working with policy makers to craft the regulations by which Generative AI should be developed, deployed, but also controlled,” added James Hendler, Professor at Rensselaer Polytechnic Institute and Chair of ACM’s Technology Policy Council.
“Principles for the Development, Deployment, and Use of Generative AI Technologies” was jointly produced and adopted by ACM’s US Technology Policy Committee (USTPC) and Europe Technology Policy Committee (Europe TPC).
Lead authors of this document for USTPC were Ravi Jain, Jeanna Matthews, and Alejandro Saucedo. Important contributions were made by Harish Arunachalam, Brian Dean, Advait Deshpande, Simson Garfinkel, Andrew Grosso, Jim Hendler, Lorraine Kisselburgh, Srivatsa Kundurthy, Marc Rotenberg, Stuart Shapiro, and Ben Shneiderman. Assistance also was provided by Ricardo Baeza-Yates, Michel Beaudouin-Lafon, Vint Cerf, Charalampos Chelmis, Paul DeMarinis, Nicholas Diakopoulos, Janet Haven, Ravi Iyer, Carlos E. Jimenez-Gomez, Mark Pastin, Neeti Pokhriyal, Jason Schmitt, and Darryl Scriven. More

Researchers have been looking for ways to decompose sound into its basic ingredients for over 200 years. In the 1820s, French scientist Joseph Fourier proposed that any signal, including sounds, can be built using sufficiently many sine waves. These waves sound like whistles, each have their own frequency, level and start time, and are the basic building blocks of sound.
However, some sounds, such as the flute and a breathy human voice, may require hundreds or even thousands of sines to exactly imitate the original waveform. This comes from the fact that such sounds contain a less harmonical, more noisy structure, where all frequencies occur at once. One solution is to divide sound into two types of components, sines and noise, with a smaller number of whistling sine waves and combined with variable noises, or hisses, to complete the imitation.
Even this ‘complete’ two-component sound model has issues with the smoothing of the beginnings of sound events, such as consonants in voice or drum sounds in music. A third component, named transient, was introduced around the year 2000 to help model the sharpness of such sounds. Transients alone sound like clicks. From then on, sound has been often divided into three components: sines, noise, and transients.
The three-component model of sines, noise and transients has now been refined by researchers at Aalto University Acoustics Lab, using ideas from auditory perception, fuzzy logic, and perfect reconstruction.
Decomposition mirrors the way we hear sounds
Doctoral researcher Leonardo Fierro and professor Vesa Välimäki realized the way that people hear the different components and separate whistles, clicks, and hisses is important. If a click gets spread in time, it starts to ring and sound noisier; by contrast, focusing on very brief sounds might cause some loss of tonality.

This insight from auditory perception was coupled with fuzzy logic: at any moment, part of the sound can belong to each of the three classes of sines, transients or noise, not just one of them. With the goal of perfect reconstruction, Fierro optimized the way sound is decomposed.
In the enhanced method, sines and transients are two opposite characteristics of sound, and the sound is not allowed to belong to both classes at the same time. However, any of two opposite component types can still occur simultaneously with noise. Thus, the idea of fuzzy logic is present in a restricted way. The noise works as a fuzzy link between the sines and transients, describing all the nuances of the sound that are not captured by simple clicks and whistles. ‘It’s like finding the missing piece of a puzzle to connect those two parts that did not fit together before,’ says Fierro.
This enhanced decomposition method was compared with previous methods in a listening test. Eleven experienced listeners were individually asked to audit several short music excepts and the components extracted from them using different methods.
The new method emerged as the winning way to decompose most sounds, based on the listeners’ ratings. Only when there is a strong vibrato in a musical sound, such as in a singing voice or the violin, all decomposition methods struggle, and in these cases some previous methods are superior.
A test use case for the new decomposition method is the time-scale modification of sound, especially slowing down of music. This was tested in a preference listening test against the lab’s own previous method, which was selected as the best academic technique in a comparative study a few years ago. Again, Fierro’s new method was a clear winner.
‘The new sound decomposition method opens many exciting possibilities in sound processing,’ says professor Välimäki. ‘The slowing down of sound is currently our main interest. It is striking that for example in sports news, the slow-motion videos are always silent. The reason is probably that the sound quality in current slow-down audio tools is not good enough. We have already started developing better time-scale modification methods, which use a deep neural network to help stretch some components.’
The high-quality sound decomposition also enables novel types of music remixing techniques. One of them leads to distortion-free dynamic range compression. Namely, the transient component often contains the loudest peaks in the sound waveform, so simply reducing the level of the transient component and mixing it back with the others can limit the peak-to-peak value of audio.
Leonardo Fierro demonstrates how the “SiTraNo” app can be used to break sound into its atoms — in this case himself rapping, in this video: https://youtu.be/nZldIAYzzOs More

In a world first, a ‘biological camera’ bypasses the constraints of current DNA storage methods, harnessing living cells and their inherent biological mechanisms to encode and store data. This represents a significant breakthrough in encoding and storing images directly within DNA, creating a new model for information storage reminiscent of a digital camera.
Led by Principal Investigator Associate Professor Chueh Loo Poh from the College of Design and Engineering at the National University of Singapore, and the NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), the team’s findings, which could potentially shake up the data-storage industry, were published in Nature Communications on 3 July 2023.
A new paradigm to address global data overload
As the world continues to generate data at an unprecedented rate, data has come to be seen as the ‘currency’ of the 21st century. Estimated to be 33 ZB in 2018, it has been forecasted that the Global Datasphere will reach 175 ZB by 2025. That has sparked a quest for a storage alternative that can transcend the confines of conventional data storage and address the environmental impact of resource-intensive data centres.
It is only recently that the idea of using DNA to store other types of information, such as images and videos, has garnered attention. This is due to DNA’s exceptional storage capacity, stability, and long-standing relevance as a medium for information storage.
“We are facing an impending data overload. DNA, the key biomaterial of every living thing on Earth, stores genetic information that encodes for an array of proteins responsible for various life functions. To put it into perspective, a single gram of DNA can hold over 215,000 terabytes of data — equivalent to storing 45 million DVDs combined,” said Assoc Prof Poh.

“DNA is also easy to manipulate with current molecular biology tools, can be stored in various forms at room temperature, and is so durable it can last centuries,” says Cheng Kai Lim, a graduate student working with Assoc Prof Poh.
Despite its immense potential, current research in DNA storage focuses on synthesising DNA strands outside the cells. This process is expensive and relies on complex instruments, which are also prone to errors.
To overcome this bottleneck, Assoc Prof Poh and his team turned to live cells, which contain an abundance of DNA that can act as a ‘data bank’, circumventing the need to synthesise the genetic material externally.
Through sheer ingenuity and clever engineering, the team developed ‘BacCam’ — a novel system that merges various biological and digital techniques to emulate a digital camera’s functions using biological components.
“Imagine the DNA within a cell as an undeveloped photographic film,” explained Assoc Prof Poh. “Using optogenetics — a technique that controls the activity of cells with light akin to the shutter mechanism of a camera, we managed to capture ‘images’ by imprinting light signals onto the DNA ‘film’.”
Next, using barcoding techniques akin to photo labelling, the researchers marked the captured images for unique identification. Machine-learning algorithms were employed to organise, sort, and reconstruct the stored images. These constitute the ‘biological camera’, mirroring a digital camera’s data capture, storage, and retrieval processes.
The study showcased the camera’s ability to capture and store multiple images simultaneously using different light colours. More crucially, compared to earlier methods of DNA data storage, the team’s innovative system is easily reproducible and scalable.
“As we push the boundaries of DNA data storage, there is an increasing interest in bridging the interface between biological and digital systems,” said Assoc Prof Poh.
“Our method represents a major milestone in integrating biological systems with digital devices. By harnessing the power of DNA and optogenetic circuits, we have created the first ‘living digital camera,’ which offers a cost-effective and efficient approach to DNA data storage. Our work not only explores further applications of DNA data storage but also re-engineers existing data-capture technologies into a biological framework. We hope this will lay the groundwork for continued innovation in recording and storing information.” More

Researchers from Queen Mary University of London have made groundbreaking advancements in bionics with the development of a new electric variable-stiffness artificial muscle. Published in Advanced Intelligent Systems, this innovative technology possesses self-sensing capabilities and has the potential to revolutionize soft robotics and medical applications. The artificial muscle seamlessly transitions between soft and hard states, while also sensing forces and deformations. With flexibility and stretchability similar to natural muscle, it can be integrated into intricate soft robotic systems and adapt to various shapes. By adjusting voltages, the muscle rapidly changes its stiffness and can monitor its own deformation through resistance changes. The fabrication process is simple and reliable, making it ideal for a range of applications, including aiding individuals with disabilities or patients in rehabilitation training.
In a study published recently in Advanced Intelligent Systems, researchers from Queen Mary University of London have made significant advancements in the field of bionics with the development of a new type of electric variable-stiffness artificial muscle that possesses self-sensing capabilities. This innovative technology has the potential to revolutionize soft robotics and medical applications.
Muscle contraction hardening is not only essential for enhancing strength but also enables rapid reactions in living organisms. Taking inspiration from nature, the team of researchers at QMUL’s School of Engineering and Materials Science has successfully created an artificial muscle that seamlessly transitions between soft and hard states while also possessing the remarkable ability to sense forces and deformations.
Dr. Ketao Zhang, a Lecturer at Queen Mary and the lead researcher, explains the importance of variable stiffness technology in artificial muscle-like actuators. “Empowering robots, especially those made from flexible materials, with self-sensing capabilities is a pivotal step towards true bionic intelligence,” says Dr. Zhang.
The cutting-edge artificial muscle developed by the researchers exhibits flexibility and stretchability similar to natural muscle, making it ideal for integration into intricate soft robotic systems and adapting to various geometric shapes. With the ability to withstand over 200% stretch along the length direction, this flexible actuator with a striped structure demonstrates exceptional durability.
By applying different voltages, the artificial muscle can rapidly adjust its stiffness, achieving continuous modulation with a stiffness change exceeding 30 times. Its voltage-driven nature provides a significant advantage in terms of response speed over other types of artificial muscles. Additionally, this novel technology can monitor its deformation through resistance changes, eliminating the need for additional sensor arrangements and simplifying control mechanisms while reducing costs.
The fabrication process for this self-sensing artificial muscle is simple and reliable. Carbon nanotubes are mixed with liquid silicone using ultrasonic dispersion technology and coated uniformly using a film applicator to create the thin layered cathode, which also serves as the sensing part of the artificial muscle. The anode is made directly using a soft metal mesh cut, and the actuation layer is sandwiched between the cathode and the anode. After the liquid materials cure, a complete self-sensing variable-stiffness artificial muscle is formed.
The potential applications of this flexible variable stiffness technology are vast, ranging from soft robotics to medical applications. The seamless integration with the human body opens up possibilities for aiding individuals with disabilities or patients in performing essential daily tasks. By integrating the self-sensing artificial muscle, wearable robotic devices can monitor a patient’s activities and provide resistance by adjusting stiffness levels, facilitating muscle function restoration during rehabilitation training.
“While there are still challenges to be addressed before these medical robots can be deployed in clinical settings, this research represents a crucial stride towards human-machine integration,” highlights Dr. Zhang. “It provides a blueprint for the future development of soft and wearable robots.”
The groundbreaking study conducted by researchers at Queen Mary University of London marks a significant milestone in the field of bionics. With their development of self-sensing electric artificial muscles, they have paved the way for advancements in soft robotics and medical applications. More

That experiences leave their trace in the connectivity of the brain has been known for a while, but a pioneering study by researchers at the German Center for Neurodegenerative Diseases (DZNE) and TUD Dresden University of Technology now shows how massive these effects really are. The findings in mice provide unprecedented insights into the complexity of large-scale neural networks and brain plasticity. Moreover, they could pave the way for new brain-inspired artificial intelligence methods. The results, based on an innovative “brain-on-chip” technology, are published in the scientific journal Biosensors and Bioelectronics.
The Dresden researchers explored the question of how an enriched experience affects the brain’s circuitry. For this, they deployed a so-called neurochip with more than 4,000 electrodes to detect the electrical activity of brain cells. This innovative platform enabled registering the “firing” of thousands of neurons simultaneously. The area examined — much smaller than the size of a human fingernail — covered an entire mouse hippocampus. This brain structure, shared by humans, plays a pivotal role in learning and memory, making it a prime target for the ravages of dementias like Alzheimer’s disease. For their study, the scientists compared brain tissue from mice, which were raised differently. While one group of rodents grew up in standard cages, which did not offer any special stimuli, the others were housed in an “enriched environment” that included rearrangeable toys and maze-like plastic tubes.
“The results by far exceeded our expectations,” said Dr. Hayder Amin, lead scientist of the study. Amin, a neuroelectronics and nomputational neuroscience expert, heads a research group at DZNE. With his team, he developed the technology and analysis tools used in this study. “Simplified, one can say that the neurons of mice from the enriched environment were much more interconnected than those raised in standard housing. No matter which parameter we looked at, a richer experience literally boosted connections in the neuronal networks. These findings suggest that leading an active and varied life shapes the brain on whole new grounds.”
Unprecedented Insight into Brain Networks
Prof. Gerd Kempermann, who co-leads the study and has been working on the question of how physical and cognitive activity helps the brain to form resilience towards aging and neurodegenerative disease, attests: “All we knew in this area so far has either been taken from studies with single electrodes or imaging techniques like magnetic resonance imaging. The spatial and temporal resolution of these techniques is much coarser than our approach. Here we can literally see the circuitry at work down to the scale of single cells. We applied advanced computational tools to extract a huge amount of details about network dynamics in space and time from our recordings.”
“We have uncovered a wealth of data that illustrates the benefits of a brain shaped by rich experience. This paves the way to understand the role of plasticity and reserve formation in combating neurodegenerative diseases, especially with respect to novel preventive strategies,” Prof. Kempermann said, who, in addition to being a DZNE researcher, is also affiliated with the Center for Regenerative Therapies Dresden (CRTD) at TU Dresden. “Also, this will help provide insights into disease processes associated with neurodegeneration, such as dysfunctions of brain networks.”
Potential Regarding Brain-inspired Artificial Intelligence
“By unraveling how experiences shape the brain’s connectome and dynamics, we are not only pushing the boundaries of brain research,” states Dr. Amin. “Artificial intelligence is inspired by how the brain computes information. Thus, our tools and the insights they allow to generate could open the way for novel machine learning algorithms.” More

As researchers make major advances in medical care, they are also discovering that the efficacy of these treatments can be enhanced by individualized approaches. Therefore, clinicians increasingly need methods that can both continuously monitor physiological signals and then personalize responsive delivery of therapeutics.
Need for safe, flexible bioelectronic devices
Implanted bioelectronic devices are playing a critical role in these treatments, but there are a number of challenges that have stalled their widespread adoption. These devices require specialized components for signal acquisition, processing, data transmission, and powering. Up to now, achieving these capabilities in an implanted device has entailed using numerous rigid and non-biocompatible components that can lead to tissue disruption and patient discomfort. Ideally, these devices need to be biocompatible, flexible, and stable in the long term in the body. They also must be fast and sensitive enough to record rapid, low-amplitude biosignals, while still being able to transmit data for external analysis.
Columbia researchers invent first stand-alone, flexible, fully organic bioelectronic device
Columbia Engineering researchers announced today that they have developed the first stand-alone, conformable, fully organic bioelectronic device that can not only acquire and transmit neurophysiologic brain signals, but can also provide power for device operation. This device, about 100 times smaller than a human hair, is based on an organic transistor architecture that incorporates a vertical channel and a miniaturized water conduit demonstrating long-term stability, high electrical performance, and low-voltage operation to prevent biological tissue damage. The findings are outlined in a new study, published today in Nature Materials.
Both researchers and clinicians knew there was a need for transistors that concurrently pose all of these features: low voltage of operation, biocompatibility, performance stability, conformability for in vivo operation; and high electrical performance, including fast temporal response, high transconductance, and crosstalk-free operation. Silicon-based transistors are the most established technologies, but they are not a perfect solution because they are hard, rigid, and unable to establish a very efficient ion interface with the body. ]
The team addressed these issues by introducing a scalable, self-contained, sub-micron IGT (internal-ion-gated organic electrochemical transistor) architecture, the vIGT. They incorporated a vertical channel arrangement that augments the intrinsic speed of the IGT architecture by optimizing channel geometry and permitting a high density arrangement of transistors next to each other — , 155,000of them per centimeter square.

Scalable vGITs are the fastest electrochemical transistors
The vIGTs are composed of biocompatible, commercially available materials that do not require encapsulation in biological environments and are not impaired by exposure to water or ions. The composite material of the channel can be reproducibly manufactured in large quantities and is solution-processible, making it more accessible to a broad range of fabrication processes. They are flexible and compatible with integration into a wide variety of conformable plastic substrates and have long-term stability, low inter-transistor crosstalk, and high-density integration capacity, allowing fabrication of efficient integrated circuits.
“Organic electronics are not known for their high performance and reliability,” said the study’s leader Dion Khodagholy, associate professor of electrical engineering. “But with our new vGIT architecture, we were able to incorporate a vertical channel that has its own supply of ions. This self-sufficiency of ions made the transistor to be particularly fast — in fact, they are currently the fastest electrochemical transistors.”
To push the speed of operation even further, the team used advanced nanofabrication techniques to miniaturize and densify these transistors at submicro-meter scales. Fabrication took place in the cleanroom of the Columbia Nano Initiative.
Collaborating with CUIMC clinicians
To develop the architecture, the researchers first needed to understand the challenges involved with diagnosis and treatment of patients with neurological disorders like epilepsy, as well as the methodologies currently used. They worked with colleagues at the Department of Neurology at Columbia University Irving Medical Center, in particular, with Jennifer Gelinas, assistant professor of neurology, electrical and biomedical engineering and director of the Epilepsy and Cognition Lab.

The combination of high-speed, flexibility. and low-voltage operation enables the transistors to not only be used for neural signal recording but also for data transmission as well as powering the device, leading to a fully conformable implant. The researchers used this feature to demonstrate fully soft and confirmable implants capable of recording and transmitting high resolution neural activity from both outside, on the surface of the brain, as well as inside, deep within the brain.
“This work will potentially open a wide range of translational opportunities and make medical implants accessible to a large patient demographic who are traditionally not qualified for implantable devices due to the complexity and high risks of such procedures,” said Gelinas.
“It’s amazing to think that our research and devices could help physicians with better diagnostics and could have a positive impact on patients’ quality of life,” added the study’s lead author Claudia Cea, who recently completed her PhD and will be a postdoctoral fellow at MIT this fall.
Next steps
The researchers plan next to join forces with neurosurgeons at CUIMC to validate the capabilities of vIGT-based implants in operating rooms. The team expects to develop soft and safe implants that can detect and identify various pathological brain waves caused by neurological disorders. More