If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Summary: Combining new wearable technology and artificial intelligence, researchers are better able to track motion and monitor the progression of movement disorders. A multi-disciplinary team of researchers has developed a way to monitor the progression of movement disorders using motion capture technology and AI. In two ground-breaking studies, published in Nature Medicine, a cross-disciplinary team of AI and clinical researchers have shown that by combining human movement data gathered from wearable tech with a powerful new medical AI technology they are able to identify clear movement patterns, predict future disease progression and significantly increase the efficiency of clinical trials in two very different rare disorders, Duchenne muscular dystrophy (DMD) and Friedreich's ataxia (FA). DMD and FA are rare, degenerative, genetic diseases that affect movement and eventually lead to paralysis. There are currently no cures for either disease, but researchers hope that these results will significantly speed up the search for new treatments.
There is some incredible emerging tech on the horizon for 2023, but there are also some dangerous and worrying advances that should be on your radar. This emerging tech could have huge implications for the human race. After all, we applaud scientific progress, but it's important for us to monitor how some of these technologies are being used. Some breakthroughs can easily be abused or used in dangerous or scary ways. Let's take a look at the scariest tech trends everyone should know about today.
Scientists from the University of California San Diego School of Medicine and Rady Children's Institute for Genomic Medicine have developed a method for identifying mosaic mutations using deep learning. The process involves training a model to analyze large amounts of genomic data and recognize patterns associated with mosaic mutations. The researchers hope that this approach will help increase our understanding of the genetic basis of disease and lead to the development of more effective treatments. Genetic mutations can lead to a wide range of disorders that are often difficult to treat or understand. One type of mutation, called mosaic mutations, is particularly challenging to identify because it only affects a small percentage of cells.
Genetic mutations cause hundreds of unsolved and untreatable disorders. Among them, DNA mutations in a small percentage of cells, called mosaic mutations, are extremely difficult to detect because they exist in a tiny percentage of the cells. Current DNA mutation software detectors, while scanning the 3 billion bases of the human genome, are not well suited to discern mosaic mutations hiding among normal DNA sequences. Often medical geneticists must review DNA sequences by eye to try to identify or confirm mosaic mutations--a time-consuming endeavor fraught with the possibility of error. Writing in the January 2, 2023, issue of Nature Biotechnology, researchers from the University of California San Diego School of Medicine and Rady Children's Institute for Genomic Medicine describe a method for teaching a computer how to spot mosaic mutations using an artificial intelligence approach termed "deep learning."
In April 2016, Waseem Qasim, a professor of cell and gene therapy, was captivated by a new scientific paper that described a revolutionary way to manipulate DNA: base editing. The paper, published by David Liu's lab at the Broad Institute of MIT and Harvard, described a version of Crispr gene editing that allowed for more precise changes than ever before. "It seemed like science fiction had arrived," says Qasim, who teaches at University College London. The genetic code of every living thing is made up of a string composed of four chemical bases: A, C, G, and T. These pair up to form the double helix structure of DNA.
Machine learning and other artificial intelligence tools are already improving the detection of relatively common conditions, such as breast cancer through mammography. Benjamin Solomon, M.D., NHGRI clinical director and senior clinician in the NHGRI Medical Genetics Branch, wants to know if we can find a way to use these tools at surface level -- to diagnose genetic conditions that affect the skin. Genetic disorders are often rare and notoriously difficult to diagnose. On average, it takes between five and 10 years from the onset of symptoms to pinpoint the exact genetic cause of a rare condition. The long and arduous diagnostic journey often delays treatment, and it typically ends up being costly and isolating.
We aimed to investigate the association between multi-modality features and epilepsy drug treatment outcomes and propose a machine learning model to predict epilepsy drug treatment outcomes with multi-modality features. This retrospective study consecutively enrolled 103 epilepsy children with rare TSC. Multi-modality data were used to characterize risk factors for epilepsy drug treatment outcome of TSC, including clinical data, TSC1, and TSC2 genes test results, magnetic resonance imaging (MRI), computerized tomography (CT), and electroencephalogram (EEG). Three common feature selection methods and six common machine learning models were used to find the best combination of feature selection and machine learning model for epilepsy drug treatment outcomes prediction with multi-modality features for TSC clinical application. The analysis of variance based on selected 35 features combined with multilayer perceptron (MLP) model achieved the best area-under-curve score (AUC) of 0.812 (±0.005).
Abstract: In this paper, we propose a personalized seizure detection and classification framework that quickly adapts to a specific patient from limited seizure samples. We achieve this by combining two novel paradigms that have recently seen much success in a wide variety of real-world applications: graph neural networks (GNN), and meta-learning. We train a Meta-GNN based classifier that learns a global model from a set of training patients such that this global model can eventually be adapted to a new unseen patient using very limited samples. We apply our approach on the TUSZ-dataset, one of the largest and publicly available benchmark datasets for epilepsy. We show that our method outperforms the baselines by reaching 82.7% on accuracy and 82.08% on F1 score after only 20 iterations on new unseen patients.
If you've ever wondered if machines could become conscious and self-aware, then this article is for you. We'll explore what it means to be human, how AI could become conscious, and why we should be worried about the possibility of an intelligence explosion. Consciousness is the state of being aware, where you think and feel. It's not just having a brain or being able to understand language. For example, if you have a stroke, your ability to think and feel can be affected by it -- but your consciousness will still remain intact even though you no longer have control over your body parts such as speech or movement. We can see this in people who suffer from epilepsy: their brains are functioning normally but they're not able to speak clearly or control their actions because their motor cortex has been damaged by an epileptic seizure (1).
Abstract: We propose a computationally efficient algorithm for seizure detection. Instead of using a purely data-driven approach, we develop a hybrid model-based/data-driven method, combining convolutional neural networks with factor graph inference. On the CHB-MIT dataset, we demonstrate that the proposed method can generalize well in a 6 fold leave-4-patientout evaluation. Moreover, it is shown that our algorithm can achieve as much as 5% absolute improvement in performance compared to previous data-driven methods. Abstract: Documentation of epileptic seizures plays an essential role in planning medical therapy. Solutions for automated epileptic seizure detection can help improve the current problem of incomplete and erroneous manual documentation of epileptic seizures.