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) …
Nestlé Purina Petcare is launching the Petivity Smart Litterbox System, consisting of a smartphone app and litterbox monitor that captures and transforms behavioral data into actionable insights that help owners proactively care for their cat. The smart monitor helps owners provide a new standard of care for their cats by using artificial intelligence to learn each cat's unique litterbox patterns and identify subtle but meaningful changes in weight, frequency, waste type and elimination schedule. "The Petivity Smart Litterbox System allows cat owners to get personalized insights to their cat's litterbox usage," said Dr. Avi Shaprut, DVM, Purina veterinarian. "While the device is not intended to diagnose, treat, mitigate or cure any conditions, it can help detect changes that can be early signs of health conditions like diabetes, kidney disease, hyperthyroidism, urinary tract infections and obesity, allowing cat owners to proactively seek out veterinary care earlier and unlock better outcomes." Using artificial intelligence developed by a team of Purina pet and data experts, the Petivity Smart Litterbox System detects meaningful changes that indicate health conditions that may require a veterinarian's attention or diagnosis.
Oceans cover 71% of our planet and contain a volume of plastic that has become so high that "in just a few years, we might end up with a pound of plastic for every three pounds of fish in the sea" (1). Every year, more than 8 millions metric tons of plastics enter our oceans on top of an estimated 150 million metric tons already circulating through them (1). The Pacific Garbage Patch has become a poignant icon of ocean pollution across media outlets, environmental campaigns, and social media platforms. We've all seen the images of birds nesting in piles of garbage, sea turtles with plastic straws up their noses, and fish entangled in netting that are displayed across the internet. But even more horrifying is that "approximately half of all plastic pollution is submerged below the ocean surface, much of it in the form of microplastics so small that we may never be able to clean them up completely" (2).
Open source is fertile ground for transformative software, especially in cutting-edge domains like artificial intelligence (AI) and machine learning. The open source ethos and collaboration tools make it easier for teams to share code and data and build on the success of others. This article looks at 13 open source projects that are remaking the world of AI and machine learning. Some are elaborate software packages that support new algorithms. Others are more subtly transformative.
Dask is a powerful open-source Python parallel computing framework. Dask scales Python programs from single-core local workstations to huge distributed cloud clusters. Dask provides a familiar user experience by replicating the APIs of other PyData ecosystem programs like Pandas, Scikit-learn, and NumPy. It also offers low-level APIs that allow programmers to execute bespoke algorithms concurrently.
Machine learning (ML) is helping companies remain competitive. In fact, many companies' core business today is based on machine learning and image/speech recognition. Google, for example, uses machine learning in image recognition for Google Photos and speech recognition for Google Home and Google Assistant. Millions of people talk to Siri, Apple's virtual assistant. The company extended the application of its virtual assistant through HomePod, a smart home device.
Activating mutations in KRAS occur in 32% of lung adenocarcinomas (LUAD). Despite leading to aggressive disease and resistance to therapy in preclinical studies, the KRAS mutation does not predict patient outcome or response to treatment, presumably due to additional events modulating RAS pathways. To obtain a broader measure of RAS pathway activation, we developed RAS84, a transcriptional signature optimised to capture RAS oncogenic activity in LUAD. We report evidence of RAS pathway oncogenic activation in 84% of LUAD, including 65% KRAS wild-type tumours, falling into four groups characterised by coincident alteration of STK11/LKB1, TP53 or CDKN2A, suggesting that the classifications developed when considering only KRAS mutant tumours have significance in a broader cohort of patients. Critically, high RAS activity patient groups show adverse clinical outcome and reduced response to chemotherapy. Patient stratification using oncogenic RAS transcriptional activity instead of genetic alterations could ultimately assist in clinical decision-making. Mutations in RAS oncogenes and related pathways are frequent in lung cancers. Here, the authors derive a RAS gene expression signature and a machine learning classifier to predict drug response and clinical outcomes in lung adenocarcinoma and other solid tumours, with improved performance over KRAS mutations alone.
Our model requires capturing an additional background image and produces state-of-the-art matting results at 4K 30fps and HD 60fps on an Nvidia RTX 2080 TI GPU. Disclaimer: The video conversion script in this repo is not meant be real-time. Our research's main contribution is the neural architecture for high resolution refinement and the new matting datasets. The inference_speed_test.py script allows you to measure the tensor throughput of our model, which should achieve real-time.
Here's is our 2023 edition of 50 Top Global Digital Influencers to Follow on Twitter at the end of 2022 & in 2023 [ranked by our home made algorithm]. This list is of course non exhaustive. Hope you'll enjoy this list (the ranking here is in fact not important, the important things are the ideas, knowledge, and insights shared by the experts, KOLs & tech influencers there). We analyzed on stochastic samples the keywords mentioned at the beginning of the article (the fields of digital transformation) on Internet and Twitter between November 2022 and the end of September 2022. This analysis was done using a home made NLP algorithm written by our team, mainly in Prolog (yes, there are still people who use Prolog;)). The main structure of the algorithm is a decision tree and so the AI here is "symbolic" because Prolog is a programming language based on mathematical logic.
Before we can discuss how this law applies, let's start with what the law actually says. This is a fairly broad bill, and one that requires a bit of unpacking before understanding the responsibilities of the employers. Unfortunately, the law, as written, does not give much guidance for employers. Let's attempt to unpack what the bill does provide. Before discussing what this means for employers, let's look at some definitions: The Bias Audit must include testing of the AI Tool to assess the tool's disparate impact on persons of any Component 1 Category.
The Papermill Alarm looks for similarities to text found in bogus papers.Credit: Raimund Koch/Getty A software tool that analyses the titles and abstracts of scientific papers and detects text similar to that found in bogus articles is gaining interest from publishers. The tool, called the Papermill Alarm, was developed by Adam Day, who is director of scholarly data-services company Clear Skies in London, UK. Day says he ran all the titles listed in citation database PubMed through the system, and found that 1% of currently listed papers contain text very similar to that of articles produced by paper mills -- companies or individuals that fabricate scientific manuscripts to order. The Papermill Alarm does not say definitively whether an article is fabricated, but flags those that are worthy of further investigation. Day says his analysis is not intended to estimate the scale of paper-milling among PubMed entries, because it can recognize only papers that are similar to those from known paper mills.