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) …
Yahoo Japan Corp. and two other companies opened a website Wednesday to seek information on wanted fugitives, with artificial intelligence-generated images showing how they could look now. The website, called Tehai, was established by Yahoo Japan, digital marketing business Dentsu Digital Inc. and Party, which creates images of wanted fugitives, in cooperation with the National Police Agency. On Tehai, nine types of images are posted showing how suspects put on wanted lists long ago could look now. The images are created with AI programs that studied vast amounts of facial photo data. The AI-based images take into account how the appearances of fugitives might have changed from those in their old pictures used in conventional posters seeking information about them.
Understanding individuals' feelings are fundamental for organizations since clients can communicate their feelings and sentiments more transparently than ever before. By automatically analyzing customer feedback, from study reactions to social media discussions, brands can listen mindfully to their clients, and tailor products and services to address their issues. Sentiment analysis is a machine learning method that recognizes polarity (for example a positive or negative thought) within the text, whether a whole document, paragraph, sentence, or clause. Marketing is ending up being one of the artworks most disrupted by the digital revolution. A lot to the aversion of customary marketing proponents and maybe to the pleasure of technologists, it is presently a lot about codifying the whole knowledge chain – catching the abundance of digital data, sorting out it, applying algorithms to process it and taking care of back noteworthy decisions to different functions– all in real-time, with end to end automation, and at lightening quick speed.
Abstract: We report results of a comparison of the accuracy of crowdworkers and seven Natural Language Processing (NLP) toolkits in solving two important NLP tasks, named-entity recognition (NER) and entity-level sentiment (ELS) analysis. We here focus on a challenging dataset, 1,000 political tweets that were collected during the U.S. presidential primary election in February 2016. Each tweet refers to at least one of four presidential candidates, i.e., four named entities. The groundtruth, established by experts in political communication, has entity-level sentiment information for each candidate mentioned in the tweet. We tested several commercial and open-source tools.
Discover the future of high performance compute As business leaders seek to leverage breakthroughs in AI, HPC, and data science throughout their business, the demand for solutions that bring those capabilities to the edge continues to grow. Ahead of NVIDIA's GPU Technology Conference (GTC), Hewlett Packard Enterprise's Jeff Winterich spoke to us about the need to 'push the envelope of enterprise compute' and why HPE and NVIDIA are better together. GTC brings together developers, engineers, and innovators looking to gain a deeper understanding of how AI will transform their industry. Watch the HPE on demand sessions. Don't miss the Scheduled Session:
The development of artificial intelligence will reshape our lives in a variety of ways in the coming years. In the summer, I'd discussed why and how investors should seek out exposure in this space. A recent report from Grandview Research projected that the global artificial intelligence market would achieve a mammoth compound annual growth rate (CAGR) of 42.2% from 2020 through to 2027. Today, I want to look at two tech stocks that have already made fortunes for investors. Both have a great shot to continue this trend in the months and years ahead.
Of all the AI models in the world, OpenAI's GPT-3 has most captured the public's imagination. It can spew poems, short stories, and songs with little prompting, and has been demonstrated to fool people into thinking its outputs were written by a human. But its eloquence is more of a parlor trick, not to be confused with real intelligence. Nonetheless, researchers believe that the techniques used to create GPT-3 could contain the secret to more advanced AI. GPT-3 trained on an enormous amount of text data. What if the same methods were trained on both text and images?
Singapore will be the first country in the world to use facial verification in its national identity scheme. The biometric check will give Singaporeans secure access to both private and government services. The government's technology agency says it will be "fundamental" to the country's digital economy. It has been trialled with a bank and is now being rolled out nationwide. It not only identifies a person but ensures they are genuinely present.
Artificial intelligence has become a general-purpose technology. Not confined to futuristic applications such as self-driving vehicles, it powers the apps we use daily, from navigation with Google Maps to check deposits from our mobile banking app. It even manages the spam filters in our inbox. These are all-powerful, albeit functional roles. What's perhaps more exciting is AI's growing potential in sourcing and producing new creations and ideas, from writing news articles to discovering new drugs -- in some cases, far quicker than teams of human scientists.
Artificial intelligence (AI) and deep learning models can help advance research on neural degeneration, showing its capabilities in identifying and categorizing its forms on a model organism. Using the organism Caenorhabditis elegans or the roundworm - a 1-millimeter near-transparent nematode - researchers used deep learning to conduct a quantitative image-based analysis of neural degeneration patterns observed in the PVD neuron of the organism. Researchers from North Carolina State University have detailed their work in the journal BMC Biology, September 23. "Researchers want to study the mechanisms that drive neural degeneration, with the long-term goal of finding ways to slow or prevent the degeneration associated with age or disease," explained Adriana San Miguel in a NCSU news release. San Miguel serves as the corresponding author on the study, as well as a chemical and biomolecular assistant professor.