Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. This database is also available through the UW CS ftp server: ftp ftp.cs.wisc.edu Also can be found on UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Breast The mean, standard error and "worst" or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features.
It is no coincidence that companies are investing in AI at unprecedented levels at a time when they are under tremendous pressure to innovate. The artificial intelligence models developed by data scientists give enterprises new insights, enable new and more efficient ways of working, and help identify opportunities to reduce costs and introduce profitable new products and services. The possibilities for AI use grow almost daily, so it's important not to limit innovation. Unfortunately, many organizations do just that by tethering themselves to proprietary tools and solutions. This can handcuff data scientists and IT as new innovations become available, and results in higher costs than an open environment that supports best-of-breed AI model development and management.
Computer scientists have created an AI called BAYOU that is able to write its own software code, Though there have been attempts in the past at creating software that can write its own code, programmers generally needed to write as much or more code to tell the program what kind of applications they want it to code as they would write if they just coded the app itself. The AI studies all the code posted on GitHub and uses that to write its own code. Using a process called neural sketch learning, the AI reads all the code and then associates an "intent" behind each. Now when a human asks BAYOU to create an app, BAYOU associates the intent it learned from codes on Github to the user's request and begins writing the app it thinks the user wants. As reported by Futurism, BAYOU is a deep learning tool that basically works like a search engine for coding: tell it what sort of program you want to create with a couple of keywords, and it will spit out java code that will do what you're looking for, based on its best guess.
I can trace it back to when I watched a video of America's Got Talent. It started with singers, but soon it moved on to other categories, including illusionists. That was enough to tell Facebook's algorithms that I had to be interested in magic and that it should show me more of what it deduced I wanted to see. Now I have to be careful, because if I click on any of that content, it will reinforce the algorithm's notion that I must really be interested in card tricks, and pretty soon that's all Facebook will ever show me. Even if it was all just a passing curiosity.
Artificial Intelligence is no new concept. The phrase was first coined by John McCarthy in 1956, when he invited a group of researchers to discuss the notion of'thinking machines' during a conference at Dartmouth College. Since then, it has been a point of fascination for scientists, academics, software developers, and moviemakers alike. Fast-forward to today where you'll find lots of examples hiding in plain sight. From digital assistants like Amazon's Alexa or Apple's Siri, who use AI to learn from user interactions, to automated email responses and search engines predicting what you're looking for.
This series defines that environment & provides a framework to align current efforts with a 2.0 Future. What are the 2.0 Underwriting Requirements? How are new data sources, machine learning and AI, and RPA automation being used to address them? How does that change digital transformation efforts. One of InsurTech's top influencers, author, speaker and consultant in connected insurance, innovation, transformation and leadership.
Iowa State University researchers are growing two kinds of corn plants. If you drive past the many fields near the university's campus in Ames, you can see row after row of the first. But the second exists in a location that hasn't been completely explored yet: cyberspace. The researchers, part of the AI Institute for Resilient Agriculture, are using photos, sensor data and artificial intelligence to create "digital twins" of corn plants that, through analysis, can lead to a better understanding of their real-life counterparts. They hope the resulting software and techniques will lead to better management, improved breeding, and ultimately, smarter crops.
AI models, applications and systems are not impervious to cyber-attacks. So, organizations must make efforts to protect their AI infrastructure from such threats. A secure AI infrastructure bodes well for the future of your organization's association with intelligent technology. Due to its obvious list of benefits, the dependence of all types of businesses on AI has increased greatly in the last decade or so. Unfortunately, the heavy reliance on AI also becomes a weakness for businesses, especially when you consider the possibility of cyber-attacks that can affect their AI infrastructure.
U.S. Secretary of Commerce Gina Raimondo announced Wednesday that the Commerce Department has established a high-level committee to advise the president and other federal agencies on a range of issues related to artificial intelligence (AI). Working with the National AI Initiative Office (NAIIO) in the White House Office of Science and Technology Policy (OSTP), the department is now seeking to recruit top-level candidates to serve on the committee. A formal notice describing the National Artificial Intelligence Advisory Committee (NAIAC) and the call for nominations for the committee and its Subcommittee on Artificial Intelligence and Law Enforcement appear in the Federal Register published today. With AI already changing how society addresses economic competitiveness, national security challenges, and equitable opportunities, the National Institute of Standards and Technology (NIST) and its researchers are dedicated to ensuring AI technologies are developed and used in a trustworthy and responsible manner that allows for accuracy, security, explainability and interpretability, reliability, privacy, safety, and the mitigation of bias. Trustworthy data, standards, and integration of machine learning and AI in applications are critical for the successful deployment of new technologies and the identification and mitigation of sources of algorithmic bias.
Artificial intelligence (AI) has virtually unlimited applications that are part of our everyday life. It offers countless solutions across all industries. Artificial intelligence is a major market player in the business world. AI plays a key role in data analysis, marketing, finance, business, advertising, medicine, technology, science and engineering where machines are learning from stimuli and reacting in ways more human than ever before. Artificial intelligence has several advantages and disadvantages, so it's important to know how to use it to maximize its potential within your organization.