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Business owners want to keep their employees safe while on the job. Despite their best efforts, they may not be able to see a workplace hazard even if its right in front of them.

Nationwide, Swiss Re Reinsurance Solutions and CompScience understand the role that Artificial Intelligence (AI) can play in helping business owners find those safety blind spots. Theyre teaming up to make CompSciences Intelligent Safety Platform available to more business owners and prevent workplace injuries.

CompSciences technology detects previously unreported workplace risks by analyzing existing workplace videos to identify and stop an accident before it happens. CompSciences computer vision models can detect more than 50 behavioral and environmental hazards. The technology has reduced their customers workers compensation claims by as much as 23%.

Nationwide looked at two years of actuarial data and saw that the technology shows promise. We found that the CompScience computer vision models, data science, and reporting tools could help potentially save lives and reduce costs, said John Lopes, SVP of Nationwide Product Expansion.This platform provides truly actionable insights into workplace risks.

We are pleased to be partnering with Nationwide and Swiss Re so that we can bring the disruptive power of computer vision and data science to help reduce losses on Workers Comp policies," explained Josh Butler, Founder and CEO, CompScience.

In this new partnership, CompScience will act as a managing general agent (MGA). Nationwide will underwrite, bind and service the workers compensation policies and Swiss Re will provide data analytics and reinsurance capacity.

"Both Nationwide and Swiss Re recognize that insurance products are ripe for innovation and we are ready to go to market now that weve proven the impact of our approach. We work tirelessly to eliminate workplace hazards and accidents so that everyone can go home safely each day, said Jacob Geyer, Chief Insurance Officer, CompScience.

"We're delighted to be part of a project that helps make workplaces safer and reduces the financial cost of insurance protection," said Sebastien Bert, Head Strategic Partnerships US,Swiss ReReinsurance Solutions. Our predictive risk models enable benchmarking against the market, monitors portfolio trends, and allows CompScience to quantify the value of its risk mitigating technology.

Link:
Artificial Intelligence Preventing Workplace Accidents Before They ... - Nationwide Newsroom

Professor Mrs. Rita Akosua Dickson Vice-Chancellor KNUST

Professor Mrs. Rita Akosua Dickson, Vice-Chancellor of the Kwame Nkrumah University of Science and Technology (KNUST) says it is imperative that Africa takes the investment in Artificial Intelligence (AI) technology and its responsible use seriously.

AI holds much promise and is seen as a game changer in transforming the digital economy.

Therefore, institutions of higher learning in the sub-Region should focus on programmes that are directed at equipping the next generation with the requisite tools to lead the digital revolution, the Vice-Chancellor advised.

Prof. Mrs. Dickson was addressing a conference dubbed: Responsible AI and Ethics A Panacea to Digital Transformation in Sub-Saharan Africa, held at the Great Hall, Kumasi.

The programme was held under the auspices of the Responsible Artificial Intelligence Lab (RAIL), KNUST, and the Responsible Artificial Intelligence Network (RAIN) Africa, which seeks to promote the responsible adaptation and use of AI in sub-Saharan Africa.

It discussed topics ranging from AI in Healthcare, AI and Human Rights, and AI Applications to the Role of Afrocentric Datasets in Promoting Responsible AI in Africa.

There were also presentations on the normative issues of AI from a business and human rights perspective, AI ethics and machine learning for identifying teenage patients at risk of gestation hypertension.

The role of Afrocentric datasets in promoting responsible AI in Africa, as well as AI ethics in finance were also looked at.

The two-day Conference comes in the wake of the varied challenges confronting the continent in developing AI such as a dearth of investment, a paucity of specialised talent and lack of access to the latest global research.

Researchers argue that these hurdles are being whittled down, albeit slowly, thanks to African ingenuity and to investments by multinational companies such as IBM Research, Google, Microsoft, and Amazon, which have all opened AI labs in Africa.

Innovative forms of trans-continental collaboration such as Deep Learning Indaba (a Zulu word for gathering), which is fostering a community of AI researchers in Africa, and Zindi, a platform that challenges African data scientists to solve the continents toughest challenges, are gaining ground.

This is buoyed by the recent influx of several globally-trained African experts in AI.

Digital development tools are the key enablers to drive economic transformation, Prof. Dickson stated, stressing the need for AI solutions to be developed and deployed responsibly.

The rights and privileges of the human person must not be trampled upon in deploying AI solutions, the Vice-Chancellor cautioned, adding that datasets based on which models were trained should not be biased.

Prof. Kwabena Biritwum Nyarko, Provost of the College of Engineering, KNUST, said the theme for the conference was relevant and timely, because AI was transforming the way we live and work and we are only beginning to scratch the surface of the potential of AI.

According to the Provost, AI was making significant strides in various fields and expected to transform many industries in the coming years.

Due to that, the challenges of AI in data privacy, bias and ethical concerns must be addressed, he said.

He said the College of Engineering was committed to ensuring the success of the RAIN and RAIL activities, noting that that was clearly demonstrated by the KNUST College of Engineering hosting the first RAIL and RAIN Conference.

Prof. Jerry John Kponyo, Principal Investigator and Scientific Director, RAIL and RAIN Cofounder, RAIN Africa, said the RAIL and RAIN Conference was the fruit of five years of collaboration between the Faculty of Electrical and Computer Engineering, College of Engineering KNUST, and the Institute of Ethics in Artificial Intelligence (IEAI), Technical University of Munich (TUM), Germany.

Like the biblical mustard seed, what began as a collaboration between two institutions to serve as a voice of advocacy for the responsible use of Artificial Intelligence has grown to become a robust network of at least thirteen universities and organizations in the sub-Region, he said.

Through the experience drawn from working in RAIN, the KNUST team, through funding from the International Development Research Centre (IDRC) and German Agency for International Cooperation (GIZ), set up a Responsible Artificial Intelligence Lab (RAIL) to serve as a vehicle for building capacity in the responsible use of AI in the sub-Region, Prof. Kponyo said.

According to him, RAIL had satellite labs in Senegal and Cape Verde and supporting labs in Germany.

Source: GNA

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Artificial Intelligence vital in transforming Africas digital economy ... - Ghana Business News

Andrea Jalandoni knows all too well the challenges of archaeological work. As a senior research fellow at the Center for Social and Cultural Research at Griffith University in Queensland, Australia, Jalandoni has dodged crocodiles, scaled limestone cliffs and sailed traditional canoes in shark-infested waters, all to study significant sites in the Pacific, Southeast Asia and Australia. One of her biggest challenges is a modern one: analysing the exponential amounts of raw data, such as photos and tracings, collected at the sites.

Manual identification takes too much time, money and specialist knowledge, Jalandoni says. She set her trowel down years ago in favour of more advanced technologies. Her toolkit now includes multiple drones and advanced imaging techniques to record sites and discover things not apparent to the naked eye. But to make sense of all the data, she needed to make use of one more cutting-edge tool: artificial intelligence (AI).

Jalandoni teamed up with Nayyar Zaidi, senior lecturer in computer science at Deakin University in Victoria, Australia. Together they tested machine learning, a subset of AI, to automate image detection to aid in rock art research. Jalandoni used a dataset of photos from the Kakadu National Park in Australias Northern Territory and worked closely with the regions First Nations elders. Some findings from this research were published last August by the Journal of Archaeological Science.

Kakadu National Park, a Unesco world heritage site, contains some of the most well-known examples of painted rock art. The works are created from pigments made of iron-stained clays and iron-rich ores that were mixed with water and applied using tools made of human hair, reeds, feathers and chewed sticks. Some of the paintings in this region date back 20,000 years, making them among the oldest art in recorded history. Despite its world-renowned status for rock art, only a fraction of the works in the park have been studied.

For First Nations people, rock art is an essential aspect of contemporary Indigenous cultures that connects them directly to ancestors and ancestral beings, cultural stories and landscapes, Jalandoni says. Rock art is not just data, it is part of Indigenous heritage and contributes to Indigenous wellbeing.

An example of artificial intelligence extracting a figure from a rock art photo Courtesy Andrea Jalandoni

For the AI study, the researchers tested a machine learning model to detect rock art from hundreds of photos, some of which showed painted rock art images and others with bare rock surfaces. The system found the art with a high degree of accuracy of 89%, suggesting it may be invaluable for assessing large collections of images from heritage sites around the world.

Image detection is just the beginning. The potential to automate many steps in rock art research, coupled with more sophisticated analysis, will speed up the pace of discovery, Jalandoni says. Trained systems are expected to be able to classify images, extract motifs and find relationships among the different elements. All this will lead to deeper knowledge and understanding of the images, stories and traditions of the past.

Eventually, AI systems may be able to be trained on more complex tasks such as identifying the works of individual artists or virtually restoring lost or degraded works.

This is important because time is of the essence for many ancient forms of art and storytelling. In areas where numerous rock art sites exist, much of it is often unidentified, unrecorded and unresearched, Jalandoni says. And with climate change, extreme weather events, natural disasters, encroaching development and human mismanagement, this inherently finite form of art and culture will continue to become more vulnerable and more rare.

Jannie Loubser, a rock art specialist and a cultural resource management archaeologist from conservation group Stratum Unlimited, sees another important use for AI in conservation and preservation. Trained systems will help monitor imperceptible changes to surfaces or conditions at rock art sites. But, he adds, ground truthingstanding face-to-face with the workwill always be important for understanding a site.

Jalandoni concurs that there is nothing like the in-person study of works created by artists thousands or tens of thousands of years ago and trying to understand and acknowledge the story being told. But she sees great potential in combining her new and old tools to explore and document difficult-to-reach sites.

Martin Puchner, author of Culture: The Story of Us, From Cave Art to K-Pop (2023), sees a poetic resonance in the use of AI, the most contemporary of tools, to reveal the past.

Even as we are moving into the future we are also discovering more about the past, sometimes through accidents when someone discovers the cave, but also, of course, through new technologies, Puchner says.

Link:
Artificial intelligence is helping researchers identify and analyse ... - Art Newspaper

By Kavli Institute for the Physics and Mathematics of the UniverseMarch 24, 2023

Figure 1. A schematic illustration of the first stars supernovae and observed spectra of extremely metal-poor stars. Ejecta from the supernovae enrich pristine hydrogen and helium gas with heavy elements in the universe (cyan, green, and purple objects surrounded by clouds of ejected material). If the first stars are born as a multiple stellar system rather than as an isolated single star, elements ejected by the supernovae are mixed together and incorporated into the next generation of stars. The characteristic chemical abundances in such a mechanism are preserved in the atmosphere of the long-lived low-mass stars observed in our Milky Way Galaxy. The team invented the machine learning algorithm to distinguish whether the observed stars were formed out of ejecta of a single (small red stars) or multiple (small blue stars) previous supernovae, based on measured elemental abundances from the spectra of the stars. Credit: Kavli IPMU

By using machine learning and state-of-the-art supernova nucleosynthesis, a team of researchers has found the majority of observed second-generation stars in the universe were enriched by multiple supernovae, reports a new study in The Astrophysical Journal.

Nuclear astrophysics research has shown elements including and heavier than carbon in the Universe are produced in stars. But the first stars, stars born soon after the Big Bang, did not contain such heavy elements, which astronomers call metals. The next generation of stars contained only a small amount of heavy elements produced by the first stars. To understand the universe in its infancy, it requires researchers to study these metal-poor stars.

Luckily, these second-generation metal-poor stars are observed in our Milky Way Galaxy, and have been studied by a team of Affiliate Members of the Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU) to close in on the physical properties of the first stars in the universe.

Figure 2. Carbon vs. iron abundance of extremely metal-poor (EMP) stars. The color bar shows the probability for mono-enrichment from our machine learning algorithm. Stars above the dashed lines (at [C/Fe] = 0.7) are called carbon-enhanced metal-poor (CEMP) stars and most of them are mono-enriched. Credit: Hartwig et al.

The teams results give the first quantitative constraint based on observations on the multiplicity of the first stars.

Figure 3. (from left) Visiting Senior Scientist Kenichi Nomoto, Visiting Associate Scientist Miho Ishigaki, Kavli IPMU Visiting Associate Scientist Tilman Hartwig, Visiting Senior Scientist Chiaki Kobayashi, and Visiting Senior Scientist Nozomu Tominaga. Credit: Kavli IPMU, Nozomu Tominaga

Multiplicity of the first stars were only predicted from numerical simulations so far, and there was no way to observationally examine the theoretical prediction until now, said lead author Hartwig. Our result suggests that most first stars formed in small clusters so that multiple of their supernovae can contribute to the metal enrichment of the early interstellar medium, he said.

Our new algorithm provides an excellent tool to interpret the big data we will have in the next decade from ongoing and future astronomical surveys across the world, said Kobayashi, also a Leverhulme Research Fellow.

At the moment, the available data of old stars are the tip of the iceberg within the solar neighborhood. The Prime Focus Spectrograph, a cutting-edge multi-object spectrograph on the Subaru Telescope developed by the international collaboration led by Kavli IPMU, is the best instrument to discover ancient stars in the outer regions of the Milky Way far beyond the solar neighborhood, said Ishigaki.

The new algorithm invented in this study opens the door to making the most of diverse chemical fingerprints in metal-poor stars discovered by the Prime Focus Spectrograph.

The theory of the first stars tells us that the first stars should be more massive than the Sun. The natural expectation was that the first star was born in a gas cloud containing a mass a million times more than the Sun. However, our new finding strongly suggests that the first stars were not born alone, but instead formed as a part of a star cluster or a binary or multiple star system. This also means that we can expect gravitational waves from the first binary stars soon after the Big Bang, which could be detected in future missions in space or on the Moon, said Kobayashi.

Hartwig has made the code developed in this study publicly available at https://gitlab.com/thartwig/emu-c.

Reference: Machine Learning Detects Multiplicity of the First Stars in Stellar Archaeology Data by Tilman Hartwig, Miho N. Ishigaki, Chiaki Kobayashi, Nozomu Tominaga and Kenichi Nomoto, 22 March 2023, The Astrophysical Journal.DOI: 10.3847/1538-4357/acbcc6

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State-of-the-Art Artificial Intelligence Sheds New Light on the ... - SciTechDaily

(THE CONVERSATION) What comes to mind when you think of Wikipedia?

Maybe you think of clicking link after link to learn about a topic, followed by another topic and then another. Or maybe youve heard a teacher or librarian tell you that what you read on Wikipedia isnt reliable.

Asresearchandinstruction librarians, we know people have concerns about using Wikipedia in academic work. And yet, in interacting with undergraduate and graduate students doing various kinds of research, we also see how Wikipedia can be an important source for background information, topic development and locating further information.

What exactly is Wikipedia?

Wikipedia, whichlaunched in 2001is a free online encyclopedia run by the nonprofit Wikimedia Foundation and written collaboratively by its users.

There are 10 rulesandfive pillarsfor contributing to the site. The five pillars establish Wikipedia as a free online encyclopedia, with articles that are accurate and cite reliable sources, and editors called Wikipedians who avoid bias and treat one another with respect.

Policies and guidelinesbuild upon the five pillars by establishing best practices for writing and editing on Wikipedia. Common issues that go against the guidelines, for example, includepaid editingandvandalism, which refers to editing an article in an intentionally malicious, offensive or libelous way.

Here are what we see as the main pros and cons to college students using Wikipedia as a source of information in their research and assignments, though anyone can consider these tips when using Wikipedia.

Wikipedias strengths

1. Basic information on virtually any topic

In addition to being free and readily available, Wikipedias standardizedarticle layoutand hyperlinks to other articles enable readers to quickly track down the basics on their topic the who, what, when, where and why.

In our experience, many students come to the library with a chosen topic for example, voting rights during Reconstruction but little knowledge about it. Before searching for the scholarly articles and books typically needed to complete their assignment, students benefit from knowing keywords and concepts related to their topic. This ensures they can try a variety of words and phrases in the catalog and databases as part of their search strategy.

2. Notes and references encourage readers to go deeper

TheWiki rabbit holeis a real browsing behavior of endlessly hopping from topic to topic, which is a testament to the sites easy navigation. Students can find valuable information such as important scholars on the topic by scrolling to the Notes and References sections of the Wikipedia page. Here they can find out who authored the various sources used in the article, as well as the citation information needed to locate additional books and articles.

3. Students can be editors

Students can write content, share information and properly cite scholarly sources on Wikipedia by becoming an editor. Quick-acting editors can become the first to add changes to an articleas events unfold. Those of us with access to scholarly sources, both in print and online through libraries,can expand Wikipedias contentby sharing information that might otherwise be behind a paywall.

Wikipedia edit-a-thonsare events at which people gather to edit articles on topics of interest or that might otherwise be ignored. American universities have hosted edit-a-thons onBlack artists,womens historyanddiverse artists in Appalachia.

Someprofessors assign Wikipedia editingas an alternative to the traditional research paper. This practice engages students in digital literacy and teaches themhow societal knowledge is constructed and shared.

Wikipedias drawbacks

1. Systemic and gender bias

The crowdsourced nature of Wikipedia can lead to the exclusion of some voices and topics. Although anyone can edit, not everyone does.

On the issue ofgender bias, Wikipedia acknowledges that most contributors are male, few biographies are about women, and topics of interest to women receive less coverage. This dynamic can be observed in other areas of underrepresentation, especially race and ethnicity. Nearly90% of U.S. Wikipedia editors identify as white, which leads to missing topics, perspectives and sources.

2. Citation requirements can exclude important sources

Wikipedia requires that information included in an article waspublished by a reliable source. While this is often an important element to confirm something is true or correct, it can be limiting for topics that have not received coverage in newspapers or scholarly journals. For some topics, such as Indigenous peoples of Canada, anoral historymay be an important source, but it could not be cited in a Wikipedia article.

3. Not all cited sources are open-access

Some sources may be behind paywalls, and since citationsdrive traffic and revenue, academic publishers have a vested interest in their publications being cited, whether or not they are freely available. However, college students can use their schools library to get full text access to the sources they discover in Wikipedia articles.

4. Articles change frequently

While timely updates are an advantage of Wikipedia, the impermanence of articles can make them difficult to rely on for information. Students can keep track of the date they find a piece of information on Wikipedia as it might not be the same when they return. The Talk page of a Wikipedia entry provides a discussion of changes to the article, and theInternet Archive Wayback Machinecan be used to view previous versions.

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Is Wikipedia a good source? 2 college librarians explain when to use the online encyclopedia and when to avoid it - KRQE News 13