You have almost as many cells in your brain as there are stars in the Milky Way – roughly 200 billion – all of which came from a small pool of fast-dividing stem cells. In order for the brain to develop correctly as a foetus grows in the womb, not only is the number of cells important – they must also be the right types of cells in the right places. To find out more about how this happens, researchers developed a computer algorithm that can identify individual cells in living tissue (each cell is highlighted by a white spot in this image) and track what happens as they divide and specialise. Using fruit fly larvae as a simple model for humans, the researchers were able to continuously monitor developing larval brains for up to 24 hours. The results provide unprecedented insights into how complex organs like brains are built from single cells.
Written by Kat Arney
Image from work by Martin Hailstone and colleagues
Department of Biochemistry, University of Oxford, Oxford
Image originally published with a Creative Commons Attribution 4.0 International (CC BY 4.0)
Published in eLife, May 2020
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One of the factors that make controlling a pandemic like the COVID-19 virus so challenging is the colossal feat of finding out who, out of the billions of people on the planet, is infected. Individually testing each person in a city takes a long time, and most people don’t even...
Surgery is the single most effective treatment for breast cancer diagnosed at an early stage, before it has spread through the body. If the tumour is small enough, patients will be offered a ‘lumpectomy’ (breast conserving surgery) rather than removing the whole breast. In order to cut out the tumour and a safe margin of healthy tissue around it, which may be harbouring rogue cancer cells, it’s essential that the surgeon knows exactly where it is within the breast. This is usually done through an MRI scan, with the patient lying face down inside the scanner. However, surgery obviously has to be done with the patient lying face up. Researchers are using computer algorithms to analyse and compare MRI scans taken in the face down (top row) or face up position (bottom row), enabling surgeons to predict the location of a tumour more accurately and ensure they’ve removed it all.
Written by Kat Arney
Image from work by Chuan-Bing Wang, Sangwook Lee, Namkug Kim and BeomSeok Ko, and colleagues
University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
Image originally published with a Creative Commons Attribution 4.0 International (CC BY 4.0)
Published in Scientific Reports, March 2020
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Wanting to know more about the dark world inside our cells, microscopists are usually torn by a choice – do they aim for a detailed 3D picture or capture a quick, but less-detailed video? Super-resolved structured illumination microscopy (SR-SIM), for example, captures high-resolution pictures of the tiniest aspects of a living cell, but these images require processing which usually limits how fast pictures can be snapped. Here though, a new technique uses algorithms to offload the processing work to a separate graphics processing unit – an example of GPU-acceleration captures this image of a living bone cancer cell in a fraction of a second, with its nucleus highlighted in blue, mitochondria (green) and cytoskeleton (pink). Using SR-SIM to take videos has huge potential – from recording the speedy movements of tiny microbes in living cells to allowing researchers to quickly 'screen' samples based on tell-tale signs of health and disease.
Written by John Ankers
Image by W. Hübner, Bielefeld University
Biomolecular Photonics, Faculty of Physics, Bielefeld University, Bielefeld, Germany
Image copyright held by the original authors
Research published in Nature Communications, September 2019
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Microscopy is enjoying huge leaps in recent technology and technique, bringing tiny life into sharp focus and allowing its processes to be studied like never before. Yet for the best results, techniques like super-resolution microscopy require optimisation – juggling mechanical settings of lenses and lasers with software for capturing images. Even then, scientists struggle with a question – how do you know if the image you’ve taken is the best it can be? Maybe just one more tweak in the right direction would reveal even more detail. In this artificially-coloured human cell, we see the results of a new computer algorithm. Designed to estimate an image’s resolution, it helps to spot when microscope settings could be improved further (left), guiding scientists towards a clearer, optimised image (right). Released as open-source software, such approaches will help researchers squeeze more information from their experiments, and value from their expensive microscopes.
Written by John Ankers
Image from work by A. Descloux, K. S. Grußmayer and A. Radenovic, ÉPFL
École Polytechnique Fédérale de Lausanne, Laboratory of Nanoscale Biology, Lausanne, Switzerland
Image copyright held by the original authors
Research published in Nature Methods, September 2019
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The brain’s processing power easily surpasses the most advanced supercomputer, yet information technology can still help us to understand our nervous systems. Mapping out connections of this developing fruit fly’s nervous system, the sophisticated computer algorithms of a new software called BigStitcher work alongside two microscopy techniques: expansion microscopy, to swell and inflate the tiny tissues under the lens, and light sheet microscopy to take thousands of virtual 'slices' through the cells. Each coloured area is a huge 3D image (carrying many gigabytes of data) virtually reassembled into a mosaic ‘map’ of the whole nervous system. The next challenge is interpreting the information – perhaps calling on algorithms again to help follow the snaking paths of each individual nerve cell or neuron – and deciphering clues about common neuronal patterns found in ‘higher’ organisms like humans.
Written by John Ankers
Image from Janelia / MDC
Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
Image copyright held by the original authors
Research published in Nature Methods, August 2019
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Artificial intelligence can now emulate human behaviors – soon it will be dangerously good
by Ana Santos Rutschman
Is this face just an assembly of computer bits? PHOTOCREO Michal Bednarek/Shutterstock.com
When artificial intelligence systems start getting creative, they can create great things – and scary ones. Take, for instance, an AI program that let web users compose music along with a virtual Johann Sebastian Bach by entering notes into a program that generates Bach-like harmonies to match them.
Run by Google, the app drew great praise for being groundbreaking and fun to play with. It also attracted criticism, and raised concerns about AI’s dangers.
My study of how emerging technologies affect people’s lives has taught me that the problems go beyond the admittedly large concern about whether algorithms can really create music or art in general. Some complaints seemed small, but really weren’t, like observations that Google’s AI was breaking basic rules of music composition.
In fact, efforts to have computers mimic the behavior of actual people can be confusing and potentially harmful.
Impersonation technologies
Google’s program analyzed the notes in 306 of Bach’s musical works, finding relationships between the melody and the notes that provided the harmony. Because Bach followed strict rules of composition, the program was effectively learning those rules, so it could apply them when users provided their own notes.
The Google Doodle team explains the Bach program.
The Bach app itself is new, but the underlying technology is not. Algorithms trained to recognize patterns and make probabilistic decisions have existed for a long time. Some of these algorithms are so complex that people don’t always understand how they make decisions or produce a particular outcome.
AI systems are not perfect – many of them rely on data that aren’t representative of the whole population, or that are influenced by human biases. It’s not entirely clear who might be legally responsible when an AI system makes an error or causes a problem.
Now, though, artificial intelligence technologies are getting advanced enough to be able to approximate individuals’ writing or speaking style, and even facial expressions. This isn’t always bad: A fairly simple AI gave Stephen Hawking the ability to communicate more efficiently with others by predicting the words he would use the most.
More complex programs that mimic human voices assist people with disabilities – but can also be used to deceive listeners. For example, the makers of Lyrebird, a voice-mimicking program, have released a simulated conversation between Barack Obama, Donald Trump and Hillary Clinton. It may sound real, but that exchange never happened.
From good to bad
In February 2019, nonprofit company OpenAI created a program that generates text that is virtually indistinguishable from text written by people. It can “write” a speech in the style of John F. Kennedy, J.R.R. Tolkien in “The Lord of the Rings” or a student writing a school assignment about the U.S. Civil War.
The text generated by OpenAI’s software is so believable that the company has chosen not to release the program itself.
Similar technologies can simulate photos and videos. In early 2018, for instance, actor and filmmaker Jordan Peele created a video that appeared to show former U.S. President Barack Obama saying things Obama never actually said to warn the public about the dangers posed by these technologies.
Be careful what videos you believe.
In early 2019, a fake nude photo of U.S. Rep. Alexandria Ocasio-Cortez circulated online. Fabricated videos, often called “deepfakes,” are expected to be increasingly used in election campaigns.
Members of Congress have started to look into this issue ahead of the 2020 election. The U.S. Defense Department is teaching the public how to spot doctored videos and audio. News organizations like Reuters are beginning to train journalists to spot deepfakes.
But, in my view, an even bigger concern remains: Users might not be able to learn fast enough to distinguish fake content as AI technology becomes more sophisticated. For instance, as the public is beginning to become aware of deepfakes, AI is already being used for even more advanced deceptions. There are now programs that can generate fake faces and fake digital fingerprints, effectively creating the information needed to fabricate an entire person – at least in corporate or government records.
Machines keep learning
At the moment, there are enough potential errors in these technologies to give people a chance of detecting digital fabrications. Google’s Bach composer made some mistakes an expert could detect. For example, when I tried it, the program allowed me to enter parallel fifths, a music interval that Bach studiously avoided. The app also broke musical rules of counterpoint by harmonizing melodies in the wrong key. Similarly, OpenAI’s text-generating program occasionally wrote phrases like “fires happening under water” that made no sense in their contexts.
As developers work on their creations, these mistakes will become rarer. Effectively, AI technologies will evolve and learn. The improved performance has the potential to bring many social benefits – including better health care, as AI programs help democratize the practice of medicine.
Giving researchers and companies freedom to explore, in order to seek these positive achievements from AI systems, means opening up the risk of developing more advanced ways to create deception and other social problems. Severely limiting AI research could curb that progress. But giving beneficial technologies room to grow comes at no small cost – and the potential for misuse, whether to make inaccurate “Bach-like” music or to deceive millions, is likely to grow in ways people can’t yet anticipate.
About The Author:
Ana Santos Rutschman is an Assistant Professor of Law at Saint Louis University
This article is republished from The Conversation under a Creative Commons license.
