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) …
Researchers at the Roslin Institute, which cloned Dolly the Sheep, used gene-editing to protect pigs from a respiratory condition. They have now proven the pigs do not become ill when infected with the virus. The breakthrough raises fears over'Frankenstein food', with the team stating last year that they could produce GM bacon, sausages and pork within a decade. But researchers say it will still be'several years before we're eating bacon sandwiches' from the genetically modified pigs. Scientists have successfully created super pigs resistant to a killer virus which costs the worldwide farming industry billions per year.
With the enough data, the company thinks it can predict when a patient will die with up to 95 per cent accuracy. In May, Google scientists published the account of a woman who came to hospital with late stage breast cancer and fluid building in her lungs. After the hospital equipment and computers took the woman's vital signs, it estimated that she had a 9.3 per cent chance of dying during her stay at the hospital. Then it was Google's turn. Its neural network, a type of artificial intelligence that can analyse huge reams of data and automatically learn and improve, was fed 175,639 data points on the woman including past health records and her current vital signs.
Autism spectrum disorder (ASD) is a developmental disorder which is currently only diagnosed through behavioral testing. Impaired folate‐dependent one carbon metabolism (FOCM) and transsulfuration (TS) pathways have been implicated in ASD, and recently a study involving multivariate analysis based upon Fisher Discriminant Analysis returned very promising results for predicting an ASD diagnosis. This article takes another step toward the goal of developing a biochemical diagnostic for ASD by comparing five classification algorithms on existing data of FOCM/TS metabolites, and also validating the classification results with new data from an ASD cohort. The comparison results indicate a high sensitivity and specificity for the original data set and up to a 88% correct classification of the ASD cohort at an expected 5% misclassification rate for typically‐developing controls. These results form the foundation for the development of a biochemical test for ASD which promises to aid diagnosis of ASD and provide biochemical understanding of the disease, applicable to at least a subset of the ASD population.
Artificial intelligence tools are helping to reveal the genetic components of autism. For geneticists, autism is a vexing challenge. Inheritance patterns suggest it has a strong genetic component. But variants in scores of genes known to play some role in autism can explain only about 20% of all cases. Finding other variants that might contribute requires looking for clues in data on the 25,000 other human genes and their surrounding DNA--an overwhelming task for human investigators.
Epileptic seizures strike with little warning and nearly one third of people living with epilepsy are resistant to treatment that controls these attacks. More than 65 million people worldwide are living with epilepsy. For more information see the IDTechEx reports on digital health 2018 and wearable technology. Now researchers at the University of Sydney have used advanced artificial intelligence and machine learning to develop a generalised method to predict when seizures will strike that will not require surgical implants. Dr Omid Kavehei from the Faculty of Engineering and IT and the University of Sydney Nano Institute said: "We are on track to develop an affordable, portable and non-surgical device that will give reliable prediction of seizures for people living with treatment-resistant epilepsy."
Parents can now download an iPhone app to screen their children for autism. The tool uses the phone's camera to track the child's facial movement as they watch short clips on the screen. Specially-designed coding software detects tell-tale movements in the child's face that are signs of the disorder, which could takes weeks of sessions for trained medical professionals to spot. The app is expected to reignite controversy between the United States Preventive Services Task Force and the American Academy of Pediatrics, which disagree on screening. The USPSTF warns against widespread screening of children for autism, after a review found it leads to more false positives and hydochondria.
Seizure prediction has attracted growing attention as one of the most challenging predictive data analysis efforts to improve the life of patients with drug-resistant epilepsy and tonic seizures. Many outstanding studies have reported great results in providing sensible indirect (warning systems) or direct (interactive neural stimulation) control over refractory seizures, some of which achieved high performance. However, to achieve high sensitivity and a low false prediction rate, many of these studies relied on handcraft feature extraction and/or tailored feature extraction, which is performed for each patient independently. This approach, however, is not generalizable, and requires significant modifications for each new patient within a new dataset. In this article, we apply convolutional neural networks to different intracranial and scalp electroencephalogram (EEG) datasets and propose a generalized retrospective and patient-specific seizure prediction method.
Artificial intelligence is better than doctors at spotting skin cancer, a study has shown. A Google algorithm devised to recognise unusual moles not only picked up more cancers but also ruled out more benign lesions. Experts are increasingly optimistic that within a few years machine learning will automate many diagnoses, helping doctors to become more efficient and reducing mistakes. Last week Theresa May said AI that harnessed genetic information and medical records to spot disease early was crucial to Britain's future prosperity. The latest study, led by Professor Holger Haenssle, of the University of Heidelberg, is one of the first in which AI convincingly comes out on top against specialists in spotting cancer.
Artificial Intelligence, or AI, is empowering people with physical disabilities, allowing them to take charge of their own lives but it's also having a surprising impact on people with neuro-diverse conditions like autism. It's easy to generalise about people on the autism spectrum; they like consistency, take things literally and like routine. They are built to provide consistency. They don't (yet) understand sarcasm and they like logic, a lot. But it's important to remember that although people on the autism spectrum will share certain difficulties, everyone's experience of the condition will be very different.
When Ariana Anderson had her first child, she was as clueless as any new parent about how to interpret her infant's cries. Every wail, every sob sounded an urgent alarm to her postpartum brain. But by the time Anderson's third kid came along, the UCLA computational neuropsychologist realized she had become fluent in baby. Her ear had learned which sounds meant "feed me!" which ones were "change me!" and which ones signaled something more serious: pain. Anderson wondered if she could train an algorithm to do the same thing.