Aurelia Butler

A “biocomputer” powered by human brain cells could be developed within our lifetime, according to Johns Hopkins University researchers who expect such technology to exponentially expand the capabilities of modern computing and create novel fields of study.
The team outlines their plan for “organoid intelligence” today in the journal Frontiers in Science.
“Computing and artificial intelligence have been driving the technology revolution but they are reaching a ceiling,” said Thomas Hartung, a professor of environmental health sciences at the Johns Hopkins Bloomberg School of Public Health and Whiting School of Engineering who is spearheading the work. “Biocomputing is an enormous effort of compacting computational power and increasing its efficiency to push past our current technological limits.”
For nearly two decades scientists have used tiny organoids, lab-grown tissue resembling fully grown organs, to experiment on kidneys, lungs, and other organs without resorting to human or animal testing. More recently Hartung and colleagues at Johns Hopkins have been working with brain organoids, orbs the size of a pen dot with neurons and other features that promise to sustain basic functions like learning and remembering.
“This opens up research on how the human brain works,” Hartung said. “Because you can start manipulating the system, doing things you cannot ethically do with human brains.”
Hartung began to grow and assemble brain cells into functional organoids in 2012 using cells from human skin samples reprogrammed into an embryonic stem cell-like state. Each organoid contains about 50,000 cells, about the size of a fruit fly’s nervous system. He now envisions building a futuristic computer with such brain organoids.

Computers that run on this “biological hardware” could in the next decade begin to alleviate energy-consumption demands of supercomputing that are becoming increasingly unsustainable, Hartung said. Even though computers process calculations involving numbers and data faster than humans, brains are much smarter in making complex logical decisions, like telling a dog from a cat.
“The brain is still unmatched by modern computers,” Hartung said. “Frontier, the latest supercomputer in Kentucky, is a $600 million, 6,800-square-feet installation. Only in June of last year, it exceeded for the first time the computational capacity of a single human brain — but using a million times more energy.”
It might take decades before organoid intelligence can power a system as smart as a mouse, Hartung said. But by scaling up production of brain organoids and training them with artificial intelligence, he foresees a future where biocomputers support superior computing speed, processing power, data efficiency, and storage capabilities.
“It will take decades before we achieve the goal of something comparable to any type of computer,” Hartung said. “But if we don’t start creating funding programs for this, it will be much more difficult.”
Organoid intelligence could also revolutionize drug testing research for neurodevelopmental disorders and neurodegeneration, said Lena Smirnova, a Johns Hopkins assistant professor of environmental health and engineering who co-leads the investigations.
“We want to compare brain organoids from typically developed donors versus brain organoids from donors with autism,” Smirnova said. “The tools we are developing towards biological computing are the same tools that will allow us to understand changes in neuronal networks specific for autism, without having to use animals or to access patients, so we can understand the underlying mechanisms of why patients have these cognition issues and impairments.”
To assess the ethical implications of working with organoid intelligence, a diverse consortium of scientists, bioethicists, and members of the public have been embedded within the team.
Johns Hopkins authors included: Brian S. Caffo, David H. Gracias, Qi Huang, Itzy E. Morales Pantoja, Bohao Tang, Donald J. Zack, Cynthia A. Berlinicke, J. Lomax Boyd, Timothy DHarris, Erik C. Johnson, Jeffrey Kahn, Barton L. Paulhamus, Jesse Plotkin, Alexander S. Szalay, Joshua T. Vogelstein, and Paul F. Worley.
Other authors included: Brett J. Kagan, of Cortical Labs; Alysson R. Muotri, of the University of California San Diego; and Jens C. Schwamborn of University of Luxembourg. More

A new machine learning model can predict traffic activity in different zones of cities. To do so, a Complexity Science Hub researcher used data from a main car-sharing company in Italy as a proxy for overall city traffic. Understanding how different urban zones interact can help avoid traffic jams, for example, and enable targeted responses of policy makers — such as local expansion of public transportation.
Understanding people’s mobility patterns will be central to improving urban traffic flow. “As populations grow in urban areas, this knowledge can help policymakers design and implement effective transportation policies and inclusive urban planning,” says Simone Daniotti of the Complexity Science Hub.
For example, if the model shows that there is a nontrivial connection between two zones, i.e., that people commute from one zone to another for certain reasons, services could be provided that compensate for this interaction. If, on the flip side, the model shows that there is little activity in a particular location, policymakers could use that knowledge to invest in structures to change that.
Model Also for Other Cities Like Vienna
For this study a major car-sharing company provided the data: the location of all cars in their fleet in four Italian cities (Rome, Turin, Milan, and Florence) in 2017. The data was obtained by constantly querying the service provider’s web APIs, recording the parking location of each car, as well as the start and end timestamps. “This information allows us to identify the origin and destination of each trip,” Daniotti explains.
Daniotti used that as a proxy for all city traffic and created a model that not only allows accurate spatio-temporal forecasting in different urban areas, but also accurate anomaly detection. Anomalies such as strikes and bad weather conditions, both of which are related to traffic.

The model could also make predictions about traffic patterns for other cities such as Vienna. “However, this would require appropriate data,” Daniotti points out.
Outperforming Other Models
While there are already many models designed to predict traffic behavior in cities, “the vast majority of prediction models on aggregated data are not fully interpretable. Even though some structure of the model connects two zones, they cannot be interpreted as an interaction” explains Daniotti. This limits understanding of the underlying mechanisms that govern citizens’ daily routines.
Since only a minimal number of constraints are considered and all parameters represent actual interactions, the new model is fully interpretable.
but What Is Prediction Without Interpretation?
“Of course it is important to make predictions,” Daniotti explains, “but you can make very accurate predictions, and if you don’t interpret the results correctly, you sometimes run the risk of drawing very wrong conclusions.”
Without knowing the reason why the model is showing a particular result, it is difficult to control for events where the model was not showing what you expected. “Inspecting the model and understanding it, helps us, and also policy makers, to not draw wrong conclusions,” Daniotti points out. More

Pet and animal-related apps are creating cybersecurity risks to their owners, new research has shown.
While being able to trace your cat and dog may be an attractive benefit to many pet owners as it can provide peace of mind, allowing a third party to track your movements may be much less attractive.
Computer scientists at Newcastle University and Royal Holloway, University of London have exposed multiple security and privacy issues by evaluating 40 popular Android apps for pets and other companion animals as well as farm animals. The results show that several of these apps are putting their users at risk by exposing their login or location details.
Password vulnerability was one of the areas exposed by the team. They identified three applications that had the user’s login details visible in plain text within non-secure HTTP traffic. This means that anyone is able to observe the internet traffic of someone using one of these apps and will be able to find out their login information. In addition to login information, two of the apps also showed user details, such as their location, that may enable someone to gain access to their devices and risk a cyber-attack.
Another area of concern identified in the study was the use of trackers. All but four of the applications were found to feature some form of tracking software. A tracker gathers information on the person using the application, on how they use it, or on the smartphone being used.

The scientists also warn that the apps perform very poorly in terms of notifying the user of their privacy policy. Their analysis shows that 21 of the apps are tracking the user in some way before the user even has a chance to consent to this, violating current data protection regulations.
The study was led by Newcastle University and the Royal Holloway, University of London and presented at the 2022 IEEE European Symposium on Security and Privacy Workshops conference.
Scott Harper is a PhD student at Newcastle University’s School of Computing and the lead author of the study. He said: “Pet tech such as smart collars and GPS trackers for your cat or dog, is a rapidly growing industry and it brings with it new security, privacy, and safety risks to the pet owners.
“While owners might use these apps for peace of mind about the health of their dog or where their cat is, they may not be happy to find out about the risks the apps hold for their own cybersecurity.
“We would urge anyone using these apps to take the time to ensure they are using a unique password, check the settings and ensure that they consider how much data they are sharing or willing to share.”
Pet tech, the technology in the pet care industry to improve the health, wellbeing, and overall quality of life of pets, is growing rapidly and includes a wide range of products, including GPS trackers, automatic feeders and pet cameras.

Examples of pet tech include wearable devices that monitor a pet’s activity levels, heart rate, and sleep patterns, as well as smart feeding systems that dispense food on a set schedule or in response to the animal’s behaviour. There are also apps and platforms that allow owners to track and manage their pets’ health records and connect with veterinary professionals.
Co-author, Dr Maryam Mehrnezhad, from the Department of Information Security at Royal Holloway, University of London, added: “We are using modern technologies to improve several aspects of our lives. However, some of these (often) cheap technologies come at the price of our privacy, security, and safety. Animal technologies can create complex risks and harms that are not easy to recognise and address. In this interdisciplinary project, we are working on solutions to mitigate such risks an allow the animal owners to use such technologies without risk or fear.”
A second study by the research team included a survey of almost 600 participants from the UK, USA and Germany. The researchers asked questions about the technologies used, incidents that have occurred or participants believe may occur, and the methods used by participants to protect their online security and privacy and whether they apply these to their pet tech.
Published in the journal Proceedings of the 12th International Conference on the Internet of Things, the findings show that participants do believe that a range of attacks may occur targeting their pet tech. Despite this, they take few precautions to protect themselves and their pets from the possible risks and harms of these technologies.
Co-author, Dr Matt Leach, Director of Comparative Biology Centre, Newcastle University added: “We would urge those developing these technologies to increase the security of these devices and applications to reduce risk of their personal information or location being shared.”
The researchers urge those who are using pet tech, to ensure they are using a unique password only for that app, check the settings and consider what data they are sharing. Users should be cautious about any new IoT devices they bring into their home. They should download apps associated with animal tech from known app stores and constantly check the permissions of such apps and revoke any unnecessary permission from them. Guides such as Mozilla’s `*Privacy Not Included’ project are available to help inform consumers on the potential security and privacy risks. More