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 are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Today is a big day for AI announcements from Microsoft, both from this week's Build conference and beyond. But one common theme bubbles over consistently: For AI to become more useful for business applications, it needs to be easier, simpler, more explainable, more accessible and, most of all, responsible. Responsible AI is actually at the heart of a lot of today's Build news, John Montgomery, corporate vice president of Azure AI, told VentureBeat. Most notable is Azure Machine Learning's preview of a responsible AI dashboard, which brings together capabilities in use over the past 18 months, such as data explorer, model interpretability, error analysis, counterfactual and causal inference analysis, into a single view.
Ever since Ada Lovelace, a polymath often considered the first computer programmer, proposed in 1843 using holes punched into cards to solve mathematical equations on a never-built mechanical computer, software developers have been translating their solutions to problems into step-by-step instructions that computers can understand. Today, AI-powered software development tools are allowing people to build software solutions using the same language that they use when they talk to other people. These AI-powered tools translate natural language into the programming languages that computers understand. "That allows you, as a developer, to have an intent to accomplish something in your head that you can express in natural language and this technology translates it into code that achieves the intent you have," Scott said. "That's a fundamentally different way of thinking about development than we've had since the beginning of software."
Fragility hip fracture increases morbidity and mortality in older adult patients, especially within the first year. Identification of patients at high risk of death facilitates modification of associated perioperative factors that can reduce mortality. Various machine learning algorithms have been developed and are widely used in healthcare research, particularly for mortality prediction. This study aimed to develop and internally validate 7 machine learning models to predict 1-year mortality after fragility hip fracture. This retrospective study included patients with fragility hip fractures from a single center (Siriraj Hospital, Bangkok, Thailand) from July 2016 to October 2018. A total of 492 patients were enrolled. They were randomly categorized into a training group (344 cases, 70%) or a testing group (148 cases, 30%). Various machine learning techniques were used: the Gradient Boosting Classifier (GB), Random Forests Classifier (RF), Artificial Neural Network Classifier (ANN), Logistic Regression Classifier (LR), Naive Bayes Classifier (NB), Support Vector Machine Classifier (SVM), and K-Nearest Neighbors Classifier (KNN). All models were internally validated by evaluating their performance and the area under a receiver operating characteristic curve (AUC). For the testing dataset, the accuracies were GB model = 0.93, RF model = 0.95, ANN model = 0.94, LR model = 0.91, NB model = 0.89, SVM model = 0.90, and KNN model = 0.90. All models achieved high AUCs that ranged between 0.81 and 0.99. The RF model also provided a negative predictive value of 0.96, a positive predictive value of 0.93, a specificity of 0.99, and a sensitivity of 0.68. Our machine learning approach facilitated the successful development of an accurate model to predict 1-year mortality after fragility hip fracture. Several machine learning algorithms (eg, Gradient Boosting and Random Forest) had the potential to provide high predictive performance based on the clinical parameters of each patient. The web application is available at www.hipprediction.com . External validation in a larger group of patients or in different hospital settings is warranted to evaluate the clinical utility of this tool. Thai Clinical Trials Registry (22 February 2021; reg. no. TCTR20210222003 ).
The Pentagon has tapped artificial intelligence ethics and research expert Diane Staheli to lead the Responsible AI (RAI) Division of its new Chief Digital and AI Office (CDAO), FedScoop confirmed on Tuesday. In this role, Staheli will help steer the Defense Department's development and application of policies, practices, standards and metrics for buying and building AI that is trustworthy and accountable. She enters the position nearly nine months after DOD's first AI ethics lead exited the Joint Artificial Intelligence Center (JAIC), and in the midst of a broad restructuring of the Pentagon's main AI-associated components under the CDAO. "[Staheli] has significant experience in military-oriented research and development environments, and is a contributing member of the Office of the Director of National Intelligence AI Assurance working group," Sarah Flaherty, CDAO's public affairs officer, told FedScoop. Advanced computer-driven systems use AI to perform tasks that generally require some human intelligence.
In our increasingly digitized world, enterprise software applications can better serve your business and your customers. By connecting every department within a company, enterprise systems allow companies to improve productivity and efficiency. These systems are generally created with specific goals in mind and serve many users at the same time, typically over a computer network instead of an end-user application. From payment processing and online shopping to automated billing, interactive product catalogs, business process management, content management, security, and more, the services such software systems provide are built to satisfy the needs of businesses, schools, clubs, charities, and government organizations alike. These systems are frequently enhanced to meet the changing needs and opportunities of the particular entity for which they were written.
You have heard that AI can be useful in various industries to do tasks. AI is a group of many different technologies working together to enable machines to sense, act and learn with human-like levels of intelligence. Maybe that's why it seems the definition of artificial intelligence is different. Meanwhile, technologies like machine learning and natural language processing are all parts of artificial intelligence. Each one is revolving along its own path.
Feed these programs any text you like and they'll generate remarkably accurate pictures that match that description. They can match a range of styles, from oil paintings to CGI renders and even photographs, and -- though it sounds cliched -- in many ways the only limit is your imagination. To date, the leader in the field has been DALL-E, a program created by commercial AI lab OpenAI (and updated just back in April). Yesterday, though, Google announced its own take on the genre, Imagen, and it just unseated DALL-E in the quality of its output. The best way to understand the amazing capability of these models is to simply look over some of the images they can generate. There's some generated by Imagen above, and even more below (you can see more examples at Google's dedicated landing page).
Google has a new text-to-image AI that the company says beats the competition. Called Imagen, the program takes in text -- for example, "a photo of a Persian cat wearing a cowboy hat and red shirt playing a guitar on a beach" -- and outputs a result. Imagen can produce images that are photorealistic or an artistic rendering. Google's website for Imagen let's people people select text to change the resulting image. Imagen follows other text-to-image generators such as DALL-E, VQ-GAN CLIP and Latent Diffusion Models.
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.
Elastic is a free and open search company that powers enterprise search, observability, and security solutions built on one technology stack that can be deployed anywhere. From finding documents to monitoring infrastructure to hunting for threats, Elastic makes data usable in real-time and at scale. Thousands of organizations worldwide, including Barclays, Cisco, eBay, Fairfax, ING, Goldman Sachs, Microsoft, The Mayo Clinic, NASA, The New York Times, Wikipedia, and Verizon, use Elastic to power mission-critical systems. Founded in 2012, Elastic is a distributed company with Elasticians around the globe. The Machine Learning team is responsible for developing and integrating statistical tools and machine learning models in ElasticSearch and Kibana.