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) …
Seven years ago, my student and I at Penn State built a bot to write a Wikipedia article on Bengali Nobel laureate Rabindranath Tagore's play "Chitra." First, it culled information about "Chitra" from the internet. Then it looked at existing Wikipedia entries to learn the structure for a standard Wikipedia article. Finally, it summarised the information it had retrieved from the internet to write and publish the first version of the entry. However, our bot did not "know" anything about "Chitra" or Tagore. It did not generate fundamentally new ideas or sentences.
Image matting is an essential technique to estimate the foreground objects in images and videos for editing and composition. The conventional deep learning approach takes the input image and associated trimap to get the alpha matte using convolution neural networks. But since the real-world input images for matting are mostly of very high resolution, such approaches efficiency suffers in real-world matting applications due to hardware limitations. To address the issue mentioned above, HD-Matt, the first deep learning-based image matting approach for high-resolution image inputs, is proposed by a group of researchers from UIUC (University of Illinois, Urbana Champaign), Adobe Research, and the University of Oregon. HD-Matt works on the'divide-and-conquer' principle.
Compute'experts' misuse our language because computers do not have the ability to function as these so-called'experts' maintain. In his interesting article, 'Forget About Coding, The Job Of The Future Is Philosophy', Luca Rossi defined intelligence as follows: 'The ability to solve complex problems rapidly and efficiently.' However, Rossi was mistaken because intelligence is a faculty, not an ability. Nor is intelligence your'disfaculty' you call your'intellect' because the intellect only contains your disability of intellectualizing. I have coined the word, 'disfaculty', to describe the intellect.
Time series forecasting is an important area of machine learning. It is important because there are so many prediction problems that involve a time component. However, while the time component adds additional information, it also makes time series problems more difficult to handle compared to many other prediction tasks. Time series data, as the name indicates, differ from other types of data in the sense that the temporal aspect is important. On a positive note, this gives us additional information that can be used when building our machine learning model -- that not only the input features contain useful information, but also the changes in input/output over time.
Background: Malaria is still a major global health burden, with more than 3.2 billion people in 91 countries remaining at risk of the disease. Accurately distinguishing malaria from other diseases, especially uncomplicated malaria (UM) from non-malarial infections (nMI) remains a challenge. Furthermore, the success of rapid diagnostic tests (RDT) is threatened by Pfhrp2/3 deletions and decreased sensitivity at low parasitemia. Analysis of haematological indices can be used to support identification of possible malaria cases for further diagnosis, especially in travelers returning from endemic areas. As a new application for precision medicine, we aimed to evaluate machine learning (ML) approaches that can accurately classify nMI, UM and severe malaria (SM) using haematological parameters.
Corn, coffee, chocolate, even wine are a few of the foods that stand to be massively disrupted by the effects of climate change, population growth and water scarcity -- if they haven't already. A recent study found the yields of the world's top ten crops have begun to decrease, a drop that is disproportionately affecting food-insecure countries. The situation stands to worsen. Researchers project that the global population will increase by 3 billion in 2050. To feed these additional global residents, agricultural production must increase by 50 percent, says Dr. Ranga Raju Vatsavai, an associate professor in computer science at North Carolina State University and the associate director of the Center for Geospatial Analytics.
New Delhi, September 10, 2020: As part of its ongoing efforts to promote skilling as a national priority, NASSCOM FutureSkills and Microsoft have joined hands to launch a nation-wide AI skilling initiative. The initiative aims to skill 1 million students in AI by 2021. The collaboration will provide Microsoft's AI, machine learning and data science expertise to students through easy to consume modules including live demos, hands on workshops and assignments. These introductory sessions on AI will be available for undergraduate students at no cost and will cover the basics of data science, machine learning models on Azure, and understanding of cognitive services to build intelligent solutions. The partnership with NASSCOM FutureSkills is an extension of Microsoft's global skilling initiative to help 25 million people worldwide acquire new digital skills, needed to thrive in a digital economy.
In October of 2019 Crunchbase raised $30M in Series C financing from OMERS Ventures. Crunchbase is charging forward, focusing more deeply on the analysis of business signals for both private and public companies. Here at the Engineering Team, we have been working on the interesting challenge of detecting these high value business signals from various sources, such as Tweets and news articles. Some examples of important signals include funding rounds, acquisitions, and key leadership hires. Finding these signals the moment they are announced empowers our customers to make well-informed business decisions.
For many of us, artificial intelligence seems like a distant future where robots will take over our jobs. In reality, AI is already everywhere and many Shopify apps have turned to AI and specifically machine learning to help you work with your data in several different ways. There are currently less than 100 Shopify apps that employ the use of AI for different types of tasks you might need help with from product recommendations to SEO, video creation, and email marketing. We've had an in-depth look at all AI-driven Shopify apps to select 8 of the best ones, each for a different need. Disclaimer: Remember that not all of these artificial intelligence apps will be compatible with your store's settings and theme.