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) …
We know OpenTable as the restaurant reservation system, but OpenTable has revolutionized the entire restaurant industry. By compiling a comprehensive database of dates, names, places, check size and so on, OpenTable creates operational advantages for its food and beverage customers. It provides the infrastructure to manage those reservations, assign tables, recognize repeat diners and remember diner preferences. It also allows restaurants to better manage costs by staffing correctly and minimizing food waste.
The system can be deployed on a smartphone, achieves 91 percent top-10-accuracy in identifying over 215 different genetic syndromes, and has outperformed clinical experts in three separate experiments. The FDNA team's research paper, Identifying facial phenotypes of genetic disorders using deep learning, has been published in Nature Medicine. Using deep learning algorithms and brain-like neural networks, the Face3Gene app can predict congenital and neural developmental disorders in people through the detection of distinctive facial features in photos. Face2Gene builds on a technique the FDNA team introduced last January in the paper DeepGestalt -- Identifying Rare Genetic Syndromes Using Deep Learning. Researchers started by training an AI system to distinguish two conditions which cause distinct facial features -- Cornelia de Langs syndrome and Angelman syndrome -- from other, similar conditions.
To brave the unpredictability of epileptic seizures, three young tunisian entrepreneurs engineered a bracelet for users to monitor their own condition and most crucially, automatically contact their caregivers within seconds of a fit. Since their launch in 2017, Epilert continues to prevent epileptic fatalities and hopes to advance medical treatments by working closely with Tunisian doctors.
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A recent study by IBM Research, together with Sage Bionetworks, Kaiser Permanente Washington Health Research Institute, and the University of Washington School of Medicine, has uncovered how combining machine learning algorithms and assessments by radiologists could improve the overall accuracy of breast cancer screenings. Mammogram screenings, commonly used by radiologists for the early detection of breast cancer, according to IBM researcher Stefan Harrer, frequently rely on a radiologist's expertise to visually identify signs of cancer, which is not always accurate. "Through the current state of human interpretation of mammography images, two things happen: Misdiagnosis in terms of missing the cancer and also diagnosing cancer when it's not there," Harrer told ZDNet. "Both cases are highly undesirable -- you never want to miss a cancer when it's there, but also if you're diagnosing a cancer and it's not there, it creates enormous pressure on patients, on the healthcare system, that could be avoided. "That is exactly where we aim to improve things through the incorporation of AI (artificial intelligence) to decrease the rate of false positives, which is the diagnosis of cancer, and also to decrease missing the cancer when there is one." The research used more than 310,800 de-identified mammograms and clinical data from Kaiser Permanente Washington (KPWA) and the Karolinska Institute (KI) in Sweden. Of the combined datasets, KI contributed around 166,500 examinations from 6,800 women, of which 780 were cancer positive; while the remaining 144,200 examinations were provided by KPWA from 85,500 women, of which 941 were cancer positive. "We had hundreds of thousands of mammograms that were annotated.
Scientists who designed an artificially intelligent robot that helped children with autism boost their learning and social skills hope such technology could one day aid others with the developmental disorder. The study saw seven children with mild to moderate autism take home what is known as a socially assistive robot, named Kiwi, for a month. According to a statement by the University of Southern California where the team is based, the participants from the Los Angeles area were aged between three and seven years old, and played space-themed games with the robot almost daily. As Kiwi was fitted with machine-learning technology, it was able to provide unique feedback and instructions to the children based on their abilities. For instance, if the child got a question wrong Kiwi would give prompts to help them solve it, and tweak the difficulty levels to challenge the child appropriately.
Significant technological advancements and societal shifts occurred during the 2010's decade. Yet many of these developments became so quickly engrained in our daily lives that they often went relatively unnoticed, and their impact all but forgotten. Over this next decade, the 2020s, we expect similar rapid and meaningful advancements to occur. Moore's law suggests that over a 10-year period, semiconductors will advance by 32 times, bringing about mesmerizing innovation in the digital age that should not only change technology but society as well. In this piece, we review the technological advancements over the last decade and anticipate what revolutionary changes may be in store for us over the next 10 years.
If you want to master Python programming language then you can't skip projects in Python. After publishing 4 advanced python projects, DataFlair today came with another one that is the Breast Cancer Classification project in Python. To crack your next Python Interview, practice these projects thoroughly and if you face any confusion, do comment, DataFlair is always ready to help you. An intensive approach to Machine Learning, Deep Learning is inspired by the workings of the human brain and its biological neural networks. Architectures as deep neural networks, recurrent neural networks, convolutional neural networks, and deep belief networks are made of multiple layers for the data to pass through before finally producing the output.
Microsoft founder Bill Gates thinks artificial intelligence and gene therapy are the two technologies with the greatest power to change lives. In a speech Friday at the American Association for the Advancement of Science, Gates said AI can "make sense of complex biological systems," while gene-based tools have the potential to cure AIDS. The potential of AI is only just being realized now, the billionaire philanthropist said, with computational power doubling every three and a half months. Along with improvements in handling data, Gates said it's enabling "the ability to synthesize, analyze, see patterns, gain insights and make predictions across many, many more dimensions than a human can comprehend." Gates said the most exciting part of AI "is how it can help us make sense of complex biological systems and accelerate the discovery of therapeutics to improve health in the poorest countries."
The technology of today is advancing rapidly in every aspect of society both positively and negatively, making it subjective, changing our lives with high efficiency of operation, and simplifying access to various facets. While it has improved quality of life in specific ways, for the most part, it has only given an illusion of enhancing the quality of life without actually doing so. Society is dependent; instead, it works on technology. We use technology in our daily life, and demands increase exponentially due to its ease of use. Everything that exists has pros and cons.