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?

As more and more workforces adopt hybrid and work-from-anywhere models, using the right combination of technologies to create effective meetings can not only enhance productivity, but also promote well-being. Using a variety of tech solutions that improve scheduling and time management, communication, and collaboration is key to achieving positive work-life balance. In a world of constant technological innovation, it can be difficult to determine what's most helpful to supporting a workforce's performance. As we navigate new work models such as hybrid and work-from-anywhere, this technology must also factor in the importance of protecting employee personal time. Meetings can have a major impact on the length of a workday, which affects work-life balance.

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.

What we really need is not a Turing test or a Lovelace test, but a Terminator test. If we create an all-powerful artificial intelligence, we cannot assume it will be friendly. We cannot guarantee anything about the AI's behavior due to something known as Rice's theorem. Rice's theorem states that all non-trivial semantic properties of programs are undecidable. Benevolence is certainly a non-trivial semantic property of programs, which means we cannot guarantee benevolent AIs.

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.

Plain Complex is a Finnish startup company founded in 2021, although the initial product development started already three years ago. As an anesthesiologist Sasu Liuhanen repeatedly witnessed the challenges of planning nurses’ shifts in his daily work; the process was slow, required a lot of manual work and the quality of the rosters left all too often a lot to desire.To develop a solution to the problem, Sasu Liuhanen, an experienced anesthesiologist and software developer, Tuomo Peltola, a seasoned professional in health-tech sales and marketing, and Stefano Campadello, a professional in business development and information technology founded a company and are now commercializing its artificial intelligence-based roster planning software. 150k from Finnish angel investors  The first angel investment round of the company was completed with four renowned angel investors. Ali Omar (FiBAN business angel of the year 2019), Reima Linnanvirta (chair of the board, FiBAN), Henry Nilert (founder of IoBox, FiBAN angel investor), and Pekka Ylitalo (Dimerent) made a 150 000 € seed investment into the company.  - Roster planning affects a great number of nurses and their families. Hence, the quality of the rosters has an immense effect on employees’ work-life balance and their well-being. Artificial intelligence is a true game-changer and enables finding optimal shifts for each employee, says Ali Omar.  - Well-being employees are the focus of Plain Complex, but at the same time, an organization can achieve significant cost savings brought by the uniform quality and fairness of the rosters. Using artificial intelligence is a true win-win, says FiBAN’s chair of the board, Reima Linnanvirta.  Artificial intelligence improves well-being at work and brings costs savings to healthcare  An ongoing pilot in a large Finnish hospital has already proven that artificial intelligence can plan rosters where employees can combine shift work and personal life in a way that has not been possible before. Transforming the previously long and tedious planning process from days or even weeks into a few minutes opens up new and unseen opportunities. - A good roster needs to comply with all applicable laws, collective agreements, organizational requirements, criteria for ergonomic design, and the employees’ personal wishes and preferences. Such a puzzle is often extremely difficult to solve and frequently it is the employees’ wishes that need to give in. Artificial intelligence can change all this and solve the puzzle in a way that everyone wins. A plan that takes all the aforementioned aspects into account is ready in minutes, says Sasu Liuhanen, CEO and co-founder of the company.  More information: Sasu LiuhanenCEO, Co-Founder, Plain Complex040-516 6467sasu.liuhanen@plaincomplex.complaincomplex.com / linkedin.com/company/plaincomplex Antti ViitanenDeal Flow Manager, FiBAN+358 45 2565 220antti@fiban.orgfiban.org

Artificial intelligence is transforming our world. The software that enables computers to do things that once required human perception and judgment depends largely on hardware made possible by Jensen Huang. In 2003, amid great skepticism, Huang directed his company Nvidia to adapt chips designed to paint graphics on computer screens, known as graphics processing units or GPUs, to perform other, more general-purpose computing tasks. The resulting advancements--and powerful chips--laid a foundation that could accommodate much bigger neural networks, the programs behind much of today's AI. In the process, he has helped enable a revolution that allows phones to answer questions out loud, farms to spray weeds but not crops, doctors to predict the properties of new drugs--with more wonders to come.

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.