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) …
Figure is transforming the trillion dollar financial services industry using blockchain technology. In three short years, Figure has unveiled a series of fintech firsts using the Provenance blockchain for loan origination, equity management, private fund services, banking and payments sectors - bringing speed, efficiency and savings to both consumers and institutions. Today, Figure is one of less than a thousand companies considered a unicorn, globally. Our mission requires us to have a creative, team-oriented, and supportive environment where everyone can do their absolute best. The team is composed of driven, innovative, collaborative, and curious people who love architecting ground-breaking technologies.
We're Cruise, a self-driving service designed for the cities we love. We're building the world's most advanced, self-driving vehicles to safely connect people to the places, things, and experiences they care about. We believe self-driving vehicles will help save lives, reshape cities, give back time in transit, and restore freedom of movement for many. Cruisers have the opportunity to grow and develop while learning from leaders at the forefront of their fields. With a culture of internal mobility, there's an opportunity to thrive in a variety of disciplines.
Multinational technology corporation IBM calculated that 72% of business leaders cited fraud as a growing concern in the last year, that $44 billion will be lost worldwide due to fraud by 2024, and that a quarter of e-commerce sales transactions that were declined by artificial intelligence (AI) were false positives. AI has become the leading tool for fighting fraud, but it can still be improved upon. In the past, rule-based engines and simple predictive models were used to computationally identify the majority of fraud attempts. But these methods have not kept up with the increasingly sophisticated nature of fraud attacks today. With a proliferation of digital technologies at criminals' disposal, fraud has grown in both scale and severity over the last few decades. Large criminal organizations and even state-sponsored groups use AI-like machine learning (ML) algorithms to defraud digital businesses for millions of dollars each year.
By combining artificial intelligence (AI) and big data, organizations can see and predict upcoming trends in key sectors including business, technology, finance and healthcare. AI is the simulation of human intelligence by computers. By applying machine learning algorithms, we can make'intelligent' machines, which can employ cognitive reasoning to make decisions based on the data fed to them. Big Data, on the other hand, is a blanket term for computational strategies and techniques applied to large sets of data to mine information from them. Big data technology includes capturing and storing the data, and then analyzing data to make strategic decisions and improve business outcomes. Most companies deploy big data and AI in silos to structure their existing data sets and to develop machines which can think for themselves.
Corporations today must operate on sound intelligence that comes from every corner and crevice of their business. Information is everywhere, and the ability to extract existing data that cover all details of your operations can give you powerful insights into your people, departments, business units, and even competitors. Taking the proper steps to manage your knowledge equips you with a 360-degree view of your company, giving you a competitive edge and a clear path towards your business goals. Effective knowledge management starts with effective knowledge gathering. This begins with utilizing artificial intelligence (AI) to extract data from unstructured sources to automatically be placed in a single database and converting this data to structured information.
Network complexity is ever increasing. The introduction of 5G on top of legacy 2G, 3G and 4G networks, coupled with subscribers' increasing expectations of a mobile experience close to fiber broadband, puts tremendous pressure on the communication service providers managing day-to-day operations. Service providers also face immense financial challenges due to decreasing revenue per gigabyte and market saturation, making it critical for survival to ensure maximum return on network investment decisions. How can we leverage AI to transform our approach to network investment decisions, in order to make it faster, more granular, and able to quickly assess a variety of precise what-if scenarios that take traffic forecasts, user experience and revenue potential into consideration? A typical capacity planning exercise starts with the planning strategy phase.
Never mind having robots deliver pizza -- if MIT and QCRI researchers have their way, the automatons will make your pizza as well. They've developed a neural network, PizzaGAN (Generative Adversarial Network), that learns how to make pizza using pictures. After training on thousands of synthetic and real pizza pictures, the AI knows not only how to identify individual toppings, but how to distinguish their layers and the order in which they need to appear. From there, the system can create step-by-step guides for making pizza using only one example photo as the starting point. The result is a system that isn't perfect (it's better at ordering synethetic pizza images than real ones), but it's still reasonably accurate. The scientists found that PizzaGAN could determine the right order 88 percent of the time, albeit using pizzas with just two toppings.
Healthcare innovators are building proactive care management programs to mitigate SDOH risk by connecting high-risk members with community-based organizations to arrange food delivery, transportation to appointments, emergency housing and other services. In short, new organization- and provider-level emphasis on including SDOH along with traditional clinical diagnosis and utilization data is helping to "round out" the picture of patient populations targeted for care-management interventions. It is as if the expressed social need is now becoming recognized as the real barrier to realizing health goals – for example, completing a preventive service like breast, colorectal, prostate, cervical cancer screening or successfully controlling a chronic disease condition through medication adherence. Now, we see these social interventions occurring as a matter of daily work for accountable care organizations and physician networks participating in value-based payment arrangements with both commercial and government payers because quality, cost and patient satisfaction measures are key elements of their contract and connected to financial rewards. As an example, Genesis Physicians Group conducts individual interviews or surveys around SDOH and social needs that are highly connected to the risk of future adverse events that aren't easily incorporated into off-the-shelf predictive models.
Pioneering Artificial Intelligence (AI) technology, developed by experts at University of the West of Scotland (UWS), is capable of accurately diagnosing Covid-19 in just a few minutes. The groundbreaking programme is able to detect the virus far more quickly than a PCR test; which typically takes around 2-hours. It is hoped that the technology can eventually be used to help relieve strain on hard-pressed Accident and Emergency departments, particularly in countries where PCR tests are not readily available. The state-of-the-art technique utilizes x-ray technology, comparing scans to a database of around 3000 images, belonging to patients with Covid-19, healthy individuals and people with viral pneumonia. It then uses an AI process known as deep convolutional neural network, an algorithm typically used to analyse visual imagery, to make a diagnosis.