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) …
If you've been waiting to upgrade your home with the latest gear, this weekend might be the time to do so. From robot vacuums to Instant Pots, there are a number of great sales for connected appliances and kitchen gadgets for Memorial Day this year. As you can imagine, there are quite a lot of them, so we've collected some of the best ones below. Anker's Eufy RoboVac 11S is one of our favorite budget robot vacuums thanks to its slim profile, smart features and affordable price. It doesn't have WiFi, but it does have a remote control.
An app which uses cognitive behavioural therapy techniques to help people overcome insomnia has received recommendation from the National Institute for Health and Care Excellence (NICE). Sleepio, from Big Health, uses an artificial intelligence (AI) algorithm to provide people with tailored therapy and provides a digital six-week self-help programme involving a sleep test, weekly interactive sessions with users encouraged to keep a diary about their sleeping patterns. Sleepio was rolled out in the south of England towards the end of 2018 and in 2019 was made available across London. NICE is recommending that the Sleepio app is used as cost-effective alternative to prescribed medication after is Medical Technologies Advisory Committee evaluated the platform. The committee concluded that Sleepio is more effective than conventional treatment options (sleep hygiene and medication) in reducing symptoms of insomnia in adults.
Investigators have identified characteristics of individuals with long COVID and those who are likely to have it by using machine learning techniques. The investigators, who were supported by the National Institutes of Health (NHI), analyzed a collection of electronic health records (EHR) available for COVID-19 research to help better identify who has long COVID. Investigators used the EHR data, from the National COVID Cohort Collaborative (N3C), a centralized national public database led by the NIH's National Centers for Advancing Translation Sciences, to identify more than 100,000 likely cases of long COVID, as of October 2021 and 200,000 cases as of May 2022. "It made sense to take advantage of modern data analysis tools and a unique big data resource like N3C, where many features of long COVID can be represented," Emily Pfaff, PhD, a clinical informaticist at the University of North Carolina at Chapel Hill, said in a statement. The N3C data includes information representing more than 13 million individuals nationwide and nearly 5 million positive COVID-19 cases.
Here I manually saved the column names, which are numerical and categorical, and also saved the target column. From the info function, there seem to be missing values, and we can see that location and sex should be categorical, so we have to do some data type conversion later on. Let's first visualize our target class. We see location and species seemingly for their respective locations and species (loc2 & species C, loc3 & species A). We also see there are slightly more female (1) birds than the male counterpart.
The design and optimization of new processing approaches for the development of rare earth cuprate (REBCO) high temperature superconductors is required to increase their cost-effective fabrication and promote market implementation. The exploration of a broad range of parameters enabled by these methods is the ideal scenario for a new set of high-throughput experimentation (HTE) and data-driven tools based on machine learning (ML) algorithms that are envisaged to speed up this optimization in a low-cost and efficient manner compatible with industrialization. In this work, we developed a data-driven methodology that allows us to analyze and optimize the inkjet printing (IJP) deposition process of REBCO precursor solutions. A dataset containing 231 samples was used to build ML models. Linear and tree-based (Random Forest, AdaBoost and Gradient Boosting) regression algorithms were compared, reaching performances above 87%.