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) …
Children on the autism spectrum often suffer from other medical conditions. As many as one-fifth of those diagnosed with the neurodevelopmental disorder, which affects communication and behavior, have epilepsy, for example, according to research on the subject. Probably Genetic, which recently graduated from the startup accelerator Y Combinator, wants to test the DNA of children with autism to provide them early diagnoses of more than 15 severe genetic diseases that are often grouped under the initial autism diagnosis. Using machine learning and direct-to-consumer DNA tests, Probably Genetic hopes to provide families of children on the spectrum with more complete and correct diagnoses and a path to appropriate treatment and therapy. "There is really low awareness still in the medical community for a lot of these diseases," Probably Genetic co-founder and chief executive officer Lukas Lange tells TechCrunch.
Published today in the peer-reviewed journal Radiology, an IBM Research team created a new artificial intelligence (AI) model that can predict breast cancer malignancy and identify normal digital mammography exams as accurately as radiologists. Mammography, a low-dose x-ray procedure to image breasts, is considered the best breast cancer screening test available according to the American Cancer Society. However, mammograms are not always accurate. According to a U.S. 10-year study published in the New England Journal of Medicine, 23.8 percent of study participants had at least one false positive mammogram where breast cancer was not actually present. Furthermore, the American Cancer Society estimates that one in five screening mammograms are false-negatives that fail to detect existing breast cancer.
Breast cancer is the second leading cancer-related cause of death among women in the US. Early detection, through routine annual screening mammography, is the best first line of defense against breast cancer. However, these screening mammograms require expert radiologists (i.e. A radiologist can spend up to 10 hours a day working through these mammograms, in the process experiencing both eye-strain and mental fatigue. Modern computer vision models, built principally on Convolutional Neural Networks (CNNs), have seen incredible progress in recent years.
F. Allen et al., "Predicting the mutations generated by repair of Cas9-induced double-strand breaks," Nat Biotechnol, 37:64–72, 2019. During gene editing with CRISPR technology, the Cas9 scissors that cut DNA home in on the right spot to snip with the help of guide RNA. The way the genetic material is stitched back together afterward isn't terribly precise, though; in fact, scientists have long thought that without a template, the process is random. However, "there's been anecdotal evidence that cells don't repair DNA randomly," geneticist Richard Sherwood of Brigham and Women's Hospital tells The Scientist. A 2016 paper also suggested patterns in the repairs.
The current availability of ever-increasing computational power, highly developed pattern recognition algorithms and advanced image processing software working at very high speeds has led to the emergence of computer-based systems that are trained to perform complex tasks in bioinformatics, medical imaging and medical robotics. Accessibility to'big data' enables the'cognitive' computer to scan billions of bits of unstructured information, extract the relevant information and recognize complex patterns with increasing confidence. Computer-based decision-support systems based on machine learning (ML) have the potential to revolutionize medicine by performing complex tasks that are currently assigned to specialists to improve diagnostic accuracy, increase efficiency of throughputs, improve clinical workflow, decrease human resource costs and improve treatment choices. These characteristics could be especially helpful in the management of prostate cancer, with growing applications in diagnostic imaging, surgical interventions, skills training and assessment, digital pathology and genomics. Medicine must adapt to this changing world, and urologists, oncologists, radiologists and pathologists, as high-volume users of imaging and pathology, need to understand this burgeoning science and acknowledge that the development of highly accurate AI-based decision-support applications of ML will require collaboration between data scientists, computer researchers and engineers.
Artificial Intelligence (AI) has greatly impacted nearly every industry. It has brought innovation in machine learning and transformed various sectors such as education, finance, and healthcare. Artificial Intelligence aims to create machines as intelligent as humans. In recent years, there has been a number of activities carried out with the help of Artificial Intelligence such as speech recognition, distance learning and problem-solving. In medical applications, the usage of this technology has become substantial.
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Artificial intelligence is enabling many scientific breakthroughs, especially in fields of study that generate high volumes of complex data such as neuroscience. As impossible as it may seem, neuroscientists are making strides in decoding neural activity into speech using artificial neural networks. Yesterday, the neuroscience team of Gopala K. Anumanchipalli, Josh Chartier, and Edward F. Chang of University of California San Francisco (UCSF) published in Nature their study using artificial intelligence and a state-of-the-art brain-machine interface to produce synthetic speech from brain recordings. The concept is relatively straightforward--record the brain activity and audio of participants while they are reading aloud in order to create a system that decodes brain signals for vocal tract movements, then synthesize speech from the decoded movements. The execution of the concept required sophisticated finessing of cutting-edge AI techniques and tools.
Electrodes on the brain have been used to translate brainwaves into words spoken by a computer – which could be useful in the future to help people who have lost the ability to speak. When you speak, your brain sends signals from the motor cortex to the muscles in your jaw, lips and larynx to coordinate their movement and produce a sound. "The brain translates the thoughts of what you want to say into movements of the vocal tract, and that's what we're trying to decode," says Edward Chang at the University of California San Francisco (UCSF). He and his colleagues created a two-step process to decode those thoughts using an array of electrodes surgically placed onto the part of the brain that controls movement, and a computer simulation of a vocal tract to reproduce the sounds of speech. In their study, they worked with five participants who had electrodes on the surface of their motor cortex as a part of their treatment for epilepsy.