"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
The science of today will be the technology of tomorrow. With the same mindset, great passion, and enthusiasm towards technology I penned and tried to encapsulate the technology that shapes human life. I briefed up the introduction of machine learning, its applications with a bunch of methodologies and kept a full stop with a proper conclusion. Machine learning which is one of the finest technology which was been coined by Arthur Samuel of IBM who had developed a computer program for playing checkers in the 1950s. As the program had a very less amount of memory, Arthur initiated alpha-beta pruning.
Yeelen Knegtering, CEO & Co-founder of Klippa, is passionate about developing digital products that help people to save time on administrative hassle and spend time on the things they love. With a degree in Information Technology at the University of Groningen, he started Klippa with the idea that there had to be a better way to organize and manage receipts. Now, Klippa is a document digitization company with a focus on digitizing and automating document streams for companies.
Oracle on Tuesday rolled out the latest version of Exadata, the platform for running Oracle database. The new Exadata X9M platforms are faster and more cost effective than previous generations. The Exadata platforms include versions for on-premise, in the Oracle public cloud and for Oracle's hybrid Cloud@customer offering. The ability to run the same platform in the cloud, on premise or in a hybrid environments sets it apart from the competition, Oracle says -- as does the fast online transaction processing (OLTP) it offers. With the X9M, "we keep doing what we're really good at," Juan Loaiza, Oracle's EVP of Mission-Critical Database Technologies, said to reporters.
This is a Women in AI Podcast transcript, for this interview we have Wendy Gonzalez, CEO at Sama, speaking with us about high-quality data training and what she's getting up to in her current role. We hope you enjoy the episode. Listen to the podcast here. So today I'm joined by Wendy Gonzalez on our Women in AI podcast episode, who is the Interim CEO of Sama, and I'm really excited to speak to her today. Hi, Wendy, how are you?
Breast cancer is the second most common cancer among women in the United States; as of January 2021, there are more than 3.8 million women with a history of breast cancer in the United States. Doctors often use ultrasound, mammograms, MRI, or biopsy to find or diagnose breast cancer. In a new study, researchers from NYU and NYU Abu Dhabi (NYUAD) report that they have developed a novel artificial intelligence (AI) system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. Their findings are published in the journal Nature Communications, in a paper titled, "Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams," and was led by Farah Shamout, PhD, NYUAD assistant professor emerging scholar of computer engineering and colleagues. "Though consistently shown to detect mammographically occult cancers, breast ultrasound has been noted to have high false-positive rates, the researchers wrote. "In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images." "The AI system was developed and evaluated using the NYU Breast Ultrasound Dataset41 consisting of 5,442,907 images within 288,767 breast exams (including both screening and diagnostic exams) collected from 143,203 patients examined between 2012 and 2019 at NYU Langone Health in New York," noted the researchers. The primary goal of the AI system is to reduce the frequency of false-positive findings. It can detect cancer by assigning a probability for malignancy and highlight parts of ultrasound images that are associated with its predictions. When the researchers conducted a reader study to compare its diagnostic accuracy with board-certified breast radiologists, the system achieved higher accuracy than the ten radiologists on average. However, a hybrid model that aggregated the predictions of the AI system and radiologists achieved the best results in accurately detecting cancer in patients. "Our findings highlight the potential of AI to improve the accuracy, consistency, and efficiency of breast ultrasound diagnosis," explained Shamout. "Importantly, AI is not a replacement for the expertise of clinicians.
Machine Learning is a subset of Artificial Intelligence. Machine Learning is the study of making machines more human-like in their behavior and decisions by giving them the ability to learn and develop their own programs. The learning process is automated and improved based on the experiences of the machines throughout the process. Good quality data is fed to the machines, and different algorithms are used to build ML models to train the machines on this data. The choice of algorithm depends on the type of data at hand, and the type of activity that needs to be automated. Machine learning today has all the attention it needs.
Welcome to our September 2021 monthly digest where you can catch up with any AIhub stories you may have missed, get the low-down on recent events, and much more. In this edition we cover the release of the latest AI100 report, an award winning paper from IJCAI, some useful AI resources, and more. In this interesting article, IJCAI 2021 invited speaker Edith Elkind writes about the continuing quest to bring the theory of fair land division closer to practice. This is work that won Edith, and co-authors Erel Segal-Halevi and Warut Suksompong, a distinguished paper award at IJCAI 2021. Their article "Keep your distance: land division with separation" investigates fair land allocation under separation constraints.
Whenever a professional sector faces new technology, questions arise regarding how that technology will disrupt daily operations and the careers of those who choose that profession. And lawyers and the legal profession are no exception. Today, artificial intelligence (AI) is beginning to transform the legal profession in many ways, but in most cases it augments what humans do and frees them up to take on higher-level tasks such as advising to clients, negotiating deals and appearing in court. Artificial intelligence mimics certain operations of the human mind and is the term used when machines are able to complete tasks that typically require human intelligence. The term machine learning is when computers use rules (algorithms) to analyze data and learn patterns and glean insights from the data.
One of a new generation of microcontroller boards with hardware optimised for embedded AI at the'Edge'. No new electronic gadget is considered to be'high-tech' nowadays unless it features some form of alleged'artificial intelligence' or AI. The really sophisticated applications require staggering amounts of computer power – can anything be done with a humble microcontroller? I'll start with some basics first. One major problem encountered when trying to explain or discuss artificial intelligence is that everyone, whether layman or scientist/engineer, has a firm opinion on the subject.
Deep Learning Algorithms are extremely popular and useful in Machine Learning. Deep Learning which is a branch of Artificial Intelligence has gained an enormous amount of acceptance due to its ability to perform tasks just like the human brain. Basically, its scientific computing methods are quite popular in different industrial sectors to solve complex problems. Deep Learning is a process where algorithms train machines with the help of examples. Here Deep Learning utilizes Artificial Neural Network to perform different tasks in an advanced computational way on a large amount of data.