AI, computer vision and machine learning systems proved that machines are better and faster than humans analyzing big data. Today, organizations have large datasets of patient data and insights about diseases through techniques like Genome Wide Association Studies (GWAS). Using AI, healthcare providers can analyze and interpret the available patient data more precisely for early diagnosis and better treatment. Today, it is possible to say whether a person has the chance to get cancer from a selfie using computer vision and machine learning to detect increased bilirubin levels in a person's sclera, the white part of the eye. As the interest in AI in the healthcare industry continues to grow, there are numerous current AI applications, and more use cases will emerge in the future.
This is an Inside Science story. Artificial intelligence can design computer microchips that perform at least as well as those designed by human experts, devising such blueprints thousands of times faster. This new research from Google is already helping with the design of microchips for the company's next generation of AI computer systems. The process of designing the physical layout of a chip's parts, known as floor planning, is key to a device's ultimate performance. This complex task often requires months of intense efforts from experts, and despite five decades of research, no automated floorplanning technique has reached human-level performance until now.
Artificial intelligence has for the first time predicted the reproductive behaviour of Yellowtail Kingfish by tracking their movements as part of new research revealed on #WorldOceanDay. The new study published in Movement Ecology used machine learning algorithms to identify and distinguish between behaviours including courtship, feeding, escape, chafing, and swimming to showcase how technology can offer greater understanding of marine life. The results revealed spawning behaviour of Yellowtail Kingfish within the Neptune Islands Group Marine Park and Thorny Passage Marine Park in South Australia. Researchers tagged captive Kingfish and filmed their behaviour in tanks to identify the acceleration signatures and applied artificial intelligence to identify behaviour in free-ranging fish. Flinders University PhD student, Thomas Clarke, in the College of Science & Engineering, says it's the first study to use machine learning to identify spawning behaviours in wild Kingfish and demonstrates how artificial intelligence can be used to better understand reproductive patterns.
Using drones and artificial intelligence to monitor large colonies of seabirds can be as effective as traditional on-the-ground methods, while reducing costs, labor and the risk of human error, a new study finds. Scientists at Duke University and the Wildlife Conservation Society (WCS) used a deep-learning algorithm--a form of artificial intelligence--to analyze more than 10,000 drone images of mixed colonies of seabirds in the Falkland Islands off Argentina's coast. The Falklands, also known as the Malvinas, are home to the world's largest colonies of black-browed albatrosses (Thalassarche melanophris) and second-largest colonies of southern rockhopper penguins (Eudyptes c. chrysocome). Hundreds of thousands of birds breed on the islands in densely interspersed groups. The deep-learning algorithm correctly identified and counted the albatrosses with 97% accuracy and the penguins with 87%.
An advanced artificial intelligence technique known as deep learning can predict major adverse cardiac events more accurately than current standard imaging protocols, according to research presented at the Society of Nuclear Medicine and Molecular Imaging 2021 Annual Meeting. Utilizing data from a registry of more than 20,000 patients, researchers developed a novel deep learning network that has the potential to provide patients with an individualized prediction of their annualized risk for adverse events such as heart attack or death. Deep learning is a subset of artificial intelligence that mimics the workings of the human brain to process data. Deep learning algorithms use multiple layers of "neurons," or non-linear processing units, to learn representations and identify latent features relevant to a specific task, making sense of multiple types of data. It can be used for tasks such as predicting cardiovascular disease and segmenting lungs, among others.
You are free to share this article under the Attribution 4.0 International license. An artificial intelligence algorithm can improve diagnoses, treatments, and our overall understanding of sleep disorders, researchers report. "The algorithm is extraordinarily precise. We completed various tests in which its performance rivaled that of the best doctors in the field, worldwide," says Mathias Perslev, a PhD in the computer science department at the University of Copenhagen and lead author of the study in the journal npj Digital Medicine. Today's sleep disorder examinations typically begin with admittance to a sleep clinic.
In order to develop artificial general intelligence (AGI), the sort of all-encompassing AI that we see in science fiction, we might need to merely sit back and let a simple algorithm develop on its own. Reinforcement learning, a kind of gamified AI architecture in which an algorithm "learns" to complete a task by seeking out preprogrammed rewards, could theoretically grow and learn so much that it breaks the theoretical barrier to AGI without any new technological developments, according to research published by the Google-owned DeepMind last month in the journal Artificial Intelligence and spotted by VentureBeat. While reinforcement learning is often overhyped within the AI field, it's interesting to consider that engineers could have already built all the tech needed for AGI and now simply need to let it loose and watch it grow. The kind of artificial intelligence that we encounter every day of our lives, whether it's machine learning or reinforcement learning, is narrow AI: an algorithm designed to accomplish a very specific task like predicting your Google search, spotting objects in a video feed, or mastering a video game. By contrast, AGI -- sometimes called human-level AI intelligence -- would be more along the lines of C-3PO from "Star Wars," in the sense that it could understand context, subtext, and social cues.
In the wake of COVID-19, all kinds of technological processes have been considered in an attempt to make up for supply chain complications and labor challenges. In this unstable environment, artificial intelligence has been instrumental in streamlining shipping logistics to accommodate the new normal, and the implications of this tech are widespread. AI itself has had a heyday in modern industry. One study found the use of AI in business processes has jumped 25 percent year-over-year as companies of all kinds integrate smarter computing processes to increase efficiency. The power of this tech across shipping and supply chains, in particular, is transforming the industry in the form of data analytics, connected monitoring systems, and automated processes.
Many marketers are already integrating artificial intelligence (AI) into their everyday strategies. However, new research suggests that they want to do so in order to boost creativity and personalize the customer experience. Persado recently published the results of the "AI and Creativity Survey," and statistics showed that 48% of respondents are already using AI to improve the performance of their marketing efforts. Furthermore, 67% say that they have seen a 10% to 70% boost in revenue as a result of utilizing AI technologies. In general, 85% of marketers say that they would still like to personalize content and create more individual experiences through digital channels, and AI could help with this.
In just a few years, your visit to the psychiatrist's office could look very different – at least according to Daniel Barron. Your doctor could benefit by having computers analyze recorded interactions with you, including subtle changes in your behavior and in the way you talk. "I think, without question, having access to quantitative data about our conversations, about facial expressions and intonations, would provide another dimension to the clinical interaction that's not detected right now," said Barron, a psychiatrist based in Seattle and author of the new book Reading Our Minds: The Rise of Big Data Psychiatry. Barron and other doctors believe that the use of artificial intelligence (AI) will grow rapidly in psychiatry and therapy, including facial recognition and text analysis software, which will supplement clinicians' efforts to spot mental illnesses earlier and improve treatments for patients. But the technologies first need to be shown to be effective, and some experts are wary of bias and other ethical issues as well.