Artificial Intelligence (AI), a term first coined at a Dartmouth workshop in 1956, has seen several boom and bust cycles over the last 66 years. Is the current boom different?
The most exciting advance in the field since 2017 has been the development of “Large Language Models,” giant neural networks trained on massive databases of text on the web. Still highly experimental, Large Language Models haven’t yet been deployed at scale in any consumer product — smart/voice assistants like Alexa, Siri, Cortana, or the Google Assistant are still based on earlier, more scripted approaches.
A new field of science has been emerging at the intersection of neuroscience and high-performance computing — this is the takeaway from the 2022 BrainComp conference, which took place in Cetraro, Italy from the 19th to the 22nd of September. The meeting, which featured international experts in brain mapping, machine learning, simulation, research infrastructures, neuro-derived hardware, neuroethics and more, strengthened the current collaborations in this emerging field and forged new ones.
Now in its 5th edition, BrainComp first started in 2013 and is jointly organised by the Human Brain Project and the EBRAINS digital research infrastructure, University of Calabria in Italy, the Heinrich Heine University of Düsseldorf and the Forschungszentrum Jülich in Germany. It is attended by researchers from inside and outside the Human Brain Project. This year was dedicated to the computational challenges of brain connectivity. The brain is the most complex system in the observable universe due to the tight connections between areas down to the wiring of the individual neurons: decoding this complexity through neuroscientific and computing advances benefits both fields.
Hosted by the organising committee of Katrin Amunts, Scientific Research Director of the HBP, Thomas Lippert, Leader of EBRAINS Computing Services from the Juelich Supercomputing Centre and Lucio Grandinetti from the University of Calabria, the sessions included a variety of topics over four days.
A well-known game studio is allegedly using AI voices for a video game. A clarification includes a commitment to human creativity. It’s another footnote in the debate over the value of human labor that will become more common in the future.
It’s the very debate that has erupted so vehemently around AI-generated images in recent months. Are AI images art? If so, can they be equated with human art? Are they detrimental to art? Are they even plagiarism, because the AI examines human works during training – in the inspiration phase, so to speak – and then imitates them in trace elements?
Open source is fertile ground for transformative software, especially in cutting-edge domains like artificial intelligence (AI) and machine learning. The open source ethos and collaboration tools make it easier for teams to share code and data and build on the success of others.
This article looks at 13 open source projects that are remaking the world of AI and machine learning. Some are elaborate software packages that support new algorithms. Others are more subtly transformative. All of them are worth a look.
Neurodegenerative diseases—like amyotrophic lateral sclerosis (ALS, or Lou Gehrig’s disease), Alzheimer’s, and Parkinson’s—are complicated, chronic ailments that can present with a variety of symptoms, worsen at different rates, and have many underlying genetic and environmental causes, some of which are unknown. ALS, in particular, affects voluntary muscle movement and is always fatal, but while most people survive for only a few years after diagnosis, others live with the disease for decades. Manifestations of ALS can also vary significantly; often slower disease development correlates with onset in the limbs and affecting fine motor skills, while the more serious, bulbar ALS impacts swallowing, speaking, breathing, and mobility. Therefore, understanding the progression of diseases like ALS is critical to enrollment in clinical trials, analysis of potential interventions, and discovery of root causes.
However, assessing disease evolution is far from straightforward. Current clinical studies typically assume that health declines on a downward linear trajectory on a symptom rating scale, and use these linear models to evaluate whether drugs are slowing disease progression. However, data indicate that ALS often follows nonlinear trajectories, with periods where symptoms are stable alternating with periods when they are rapidly changing. Since data can be sparse, and health assessments often rely on subjective rating metrics measured at uneven time intervals, comparisons across patient populations are difficult. These heterogenous data and progression, in turn, complicate analyses of invention effectiveness and potentially mask disease origin.
In a new paper published today in the journal Nature Ecology and Evolution, scientists have estimated the conservation status of nearly 1,900 palm species using artificial intelligence, and found more than 1,000 may be at risk of extinction.
The international team of researchers from the Royal Botanic Gardens, Kew, the University of Zurich, and the University of Amsterdam, combined existing data from the International Union for Conservation of Nature (IUCN) Red List with novel machine learning techniques to paint a clearer picture of how palms may be threatened. Although popular and well represented on the Red List, the threat to some 70% of these plants has remained unclear until now.
The IUCN Red List of Threatened Species is widely considered to be a gold standard for evaluating the conservation status of animal, plant, and fungal species. But there are gaps in the Red List that need to be addressed, as not all species have been listed and many of the assessments are in need of an update. Conservation efforts are further complicated by inadequate funding, the sheer amount of time needed to manually assess a species, and public perception favoring certain vertebrate species over plants and fungi.
Daegu Gyeongbuk Institute of Science & Technology (DGIST, President Yang Kook) Professor Hongsoo Choi’s team of the Department of Robotics and Mechatronics Engineering collaborated with Professor Sung-Won Kim’s team at Seoul St. Mary’s Hospital, Catholic University of Korea, and Professor Bradley J. Nelson’s team at ETH Zurich to develop a technology that produces more than 100 microrobots per minute that can be disintegrated in the body.
Microrobots aiming at minimal invasive targeted precision therapy can be manufactured in various ways. Among them, ultra-fine 3D printing technology called two-photon polymerization method, a method that triggers polymerization by intersecting two lasers in synthetic resin, is the most used. This technology can produce a structure with nanometer-level precision. However, a disadvantage exists in that producing one microrobot is time consuming because voxels, the pixels realized by 3D printing, must be cured successively. In addition, the magnetic nanoparticles contained in the robot can block the light path during the two-photon polymerization process. This process result may not be uniform when using magnetic nanoparticles with high concentration.
To overcome the limitations of the existing microrobot manufacturing method, DGIST Professor Hongsoo Choi’s research team developed a method to create microrobots at a high speed of 100 per minute by flowing a mixture of magnetic nanoparticles and gelatin methacrylate, which is biodegradable and can be cured by light, into the microfluidic chip. This is more than 10,000 times faster than using the existing two-photon polymerization method to manufacture microrobots.
The joint research team of Professor Choi Hongsoo at Robotics Engineering, DGIST, a senior researcher Jinyoung Kim from DGIST-ETH Microrobotics Research Center, and the research team of Professor Sung Won Kim at Seoul St. Mary’s Hospital of the Catholic University, made a breakthrough for the improvement of the therapeutic efficacy and safety in stem cell-based treatments.
The team developed a magnetically powered human nuclear transfer stem cells (hNTSC)-based microrobot and a method of minimally invasive delivery of therapeutic agents into the brain via the intranasal pathway. And they also accomplished transplanting the developed stem cell-based microrobot into brain tissue through the intranasal pathway that bypasses the blood-brain barrier. The proposed method is superior in efficacy and safety compared to the conventional surgical method and is expected to bring new possibilities of treating various intractable neurological diseases such as Alzheimer’s disease, Parkinson’s disease, and brain tumors, in the future.
The limitation of stem cell therapy is the difficulty in delivering an exact amount of stem cells to an accurate targeted location deep in the body where the treatment is with high risk. Another limitation is that both efficacy and safety of the treatment are low owing to a large amount of the therapeutic agent loss during delivery, while the cost of the treatment is high. In particular, when delivering stem cells into the brain through blood, the efficiency of cell delivery may decrease owing to the “blood-brain barrier,” which is a unique and specific component of the cerebrovascular network.
One day they shall make nano bots out of graphine.
A team of researchers affiliated with several institutions in South Korea has created microrobots that are able to serve as bridge builders between rat nerve cell networks. In their paper published in the journal Science Advances, the group describes how their microrobots were constructed and how well they served as a bridge builder between neural networks.
Scientists have taken many approaches to study of the brain. One way is to try to grow a brain from nerve cells. Prior work has shown that it is possible to grow a network of neural cells on a glass plate Such a network is, of course, 2-D. In this new effort, the researchers have taken a step toward the creation of a 3D neural network by devising a way to connect 2-D neural networks using microrobots.
