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) …
IT leaders and business executives around the world recognize the strategic importance of operationalizing AI, yet surprisingly few have moved beyond experimentation. A recent Capgemini survey finds that only 13% of companies have moved beyond proofs of concept (POC) to scaling AI across the enterprise. The struggle to operationalize AI is painful because it represents lost time and resources and unrealized potential. Articles abound full of suggestions, frameworks and manifestos, shared with the intent of closing the gap between AI concept and enterprise delivery (including one proposal to eliminate the POC altogether). Many of these are smart and worthwhile.
Neo4j, the leader in graph technology, announced the latest version of Neo4j for Graph Data Science, a breakthrough that democratizes advanced graph-based machine learning (ML) techniques by leveraging deep learning and graph convolutional neural networks. Until now, few companies outside of Google and Facebook have had the AI foresight and resources to leverage graph embeddings. This powerful and innovative technique calculates the shape of the surrounding network for each piece of data inside of a graph, enabling far better machine learning predictions. Neo4j for Graph Data Science version 1.4 democratizes these innovations to upend the way enterprises make predictions in diverse scenarios from fraud detection to tracking customer or patient journey, to drug discovery and knowledge graph completion. Neo4j for Graph Data Science version 1.4 is the first and only graph-native machine learning functionality commercially available for enterprises.
In a world of Self-driving vehicles, traffic lights would become a thing of the past. But as long as humans are driving alongside, self-driving vehicles have to follow the rules made by humans. One of these rules is following the traffic lights. The autonomous-vehicles have to detect and recognize the traffic lights to avoid accidents and mess on the street. Here is my recent contribution to the Tessellate Imaging Monk Object detection library.
The most obvious explanation for this gap is AI systems' expense, but there are a number of other concerns that keep banks from jumping aboard the AI bandwagon. AI systems often do not operate in real time, with 45.6 per cent of fraud specialists citing this as a concern -- a significant obstacle for pay-ments that need to be processed instantly. A lack of transparency is a problem as well, according to 42.8 per cent of specialists. A human analyst could definitely provide justification for rejection of any given transaction, as opposed to many AI systems, whose reasonings may much more nebulous.
Global Climate Change Data – This dataset includes information from the Climate Change Knowledge Portal and World Development indicators. Daily Sea Ice Extent Data – From The National Snow and Ice Data Center, this climate change dataset has information on the Earth's cryosphere, and includes glacier, ice, snow and frozen ground data. The Climate Change Knowledge Portal – This portal from World Bank Group is an easy-to-navigate platform where you can view climate change data visualizations based on historical data and projections. SGMA Climate Change Resources – From the California Natural Resources Agency, the SGMA Climate Change Resources Dataset includes data on changes in precipitation and bodies of water within the state of California. Harvard Dataset of Climate Change Tweet IDs – Collected between September 2017 and May 2019, the Climate Change Tweet IDs Dataset contains the IDs from over 39 million tweets about climate change.
Personalised nutrition start-up myAir has unveiled its nutritional solution for better management of stress. The company developed a series of plant-based nutrition bars with a personalised edge. Each formulation contains a botanical blend designed to deliver a specific stress-relief effect. The herbal extract blends are based on profiling machine learning technology, and are customised to the consumer's stress profile and cognitive needs.
NVIDIA has recently announced project Maxine, a cloud-native streaming video AI platform for applications like video calls. Using AI, the project perceives important features of a face, sends changes of those features, and re-animates faces based on such points. The AI also allows you to reorient your face so that you'll be making eye contact with each person on the call individually. You can turn the tool on and become an alien or get a stylized face. What is more, Maxine allows users to remove background noise, see better in low light, replace the background, and more.
A University of Michigan-led research team has uncovered a neural network that enables Drosophila melanogaster fruit flies to convert external stimuli of varying intensities into a "yes or no" decision about when to act. The research, described in Current Biology, helps to decode the biological mechanism that the fruit fly nervous system uses to convert a gradient of sensory information into a binary behavioral response. The findings offer up new insights that may be relevant to how such decisions work in other species, and could possibly even be applied to help artificial intelligence machines learn to categorize information. Senior study author Bing Ye, PhD, a faculty member at the University of Michigan Life Science Institute (LSI), believes the mechanism uncovered could have far-reaching applications. "There is a dominant idea in our field that these decisions are made by the accumulation of evidence, which takes time," Ye said.
One of the biggest trends in photography is the increasing presence of tools that leverage AI technologies to make everything from subject selection to full-scale edits more powerful and to enable new creative avenues. Is that necessarily a good thing, though? This thought-provoking video tackles the topic and offers an experienced landscape photographer's thoughts on the
Artificial intelligence makes teaching more efficient. There is constant discussion of using artificial intelligence and learning analytics to support teaching. New digital methods, platforms and tools are being introduced more and more, and the opportunities created by the development of artificial intelligence are to be harnessed to enhance teaching and provide students with increasingly individualized teaching. Jiri Lallimo (Project Manager, Teacher Services), Ville Kivimäki (Expert, Dean's Unit, School of Engineering), Thomas Bergström (Expert, IT Services) and Juha Martikainen (Systems Specialist, IT Services) from Aalto University have been studying the issue. The key is to listen to the end users The use of artificial intelligence in teaching and learning is still at a fairly early stage, but technology is constantly evolving, and new opportunities are being discovered.