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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

Other members of the task force include Gabrielle Bray, a fourth-year student who chairs the Honor Committee; T. Kenny Fountain, an associate professor of English and director of Writing Across the Curriculum in the College and Graduate School of Arts & Sciences; Briana Morrison, an associate professor of computer science in the School of Engineering and Applied Science; Reza Mousavi, an assistant professor of commerce in the McIntire School of Commerce; and Michael Palmer, director of the Center for Teaching Excellence.

The task force will hold a series of virtual town hall meetings, organized by school. Faculty and students may attend any session. Participants may register for each session on the task force website. Each session is limited to 300 participants. If there is enough demand, more sessions may be added.

The scheduled sessions are: Arts & Sciences (natural sciences), March 27, 3 to 4:30 p.m.; Architecture, Batten, Education and Human Development, and Nursing, March 29, noon to 1:30 p.m.; Engineering and Applied Sciences and Data Science, April 10, 3 to 4:30 p.m.; Arts & Sciences (social sciences), April 11, 12:30 to 2 p.m.; Arts & Sciences (arts and humanities) and Professional and Continuing Education, April 12, noon to 1:30 p.m.; and Darden, Law and McIntire, April 14, 2 to 3:30 p.m.

The link to its questionnaire is available on the task force website.

Both faculty and students are invited to these online town halls, Heny said. We will provide some information and then ask them critical questions that get them to engage, and they will record their responses in a form we will use as a source of data.

We want to learn how students and faculty are actually using this technology in courses, Pennock said. We hear anecdotal evidence from the faculty members who are closest to us, but we really want to understand how our students using it to study as well as to complete assignments. Theres a real opportunity for faculty to make their classes better, to be able to get more work done in the same amount of time.

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Task Force To Convene Conversations on Artificial Intelligence's ... - UVA Today

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Artificial Intelligence (AI) has been in the news in recent months with many questioning whether it will replace humans in the workforce in the future. Many people globally have started using AI for tasks such as writing emails, article summaries, cover letters, etc. AI is also being used in the field of medicine to search medical data and uncover insights to help improve health outcomes and patient experiences.

Cancer- a disease in which some of the bodys cells grow uncontrollably and spread to other parts of the body- continues to plague countries. And among all types of cancer, breast cancer is the most common type of canceroccurring in women globally. Several factors including genetics, lifestyle, and the environment have contributed to the rise in the prevalence of breast cancer among women.

Proper screening for early diagnosis and treatment is an essential factor when combating the disease.

According to a report published in the PubMed Central (PMC) journal in October last year, faster and more accurate results are some of the benefits of AI methods in breast cancer screening.

Breast cancer is more effective to treat if diagnosed early and the effectiveness of treatment in the later stages is poor. The report in the PMC titled- "Artificial Intelligence in Breast Cancer Screening and Diagnosis" says that the incorporation of AI into screening methods is a relatively new and emerging field thatshows a lot of promise in the early detection of breast cancer, thus resulting in a better prognosis of the condition.

"Human intelligence has always triumphed over every other form of intelligence on this planet. The defining feature of human intelligence is the ability to use previous knowledge, adapt to new conditions, and identify meaning in patterns. The success of AI lies in the capacity to reproduce the same abilities," it adds.

Incorporating AI into the screening methods such as the examination of biopsy slides enhances the treatment success rate. Machine learning and deep learning are some of the important aspects of AI which are required in breast cancer imaging.

Machine learning is used to store a large dataset, which is later used to train prediction models and interpret generalisations. On the other hand, deep learning- the newest branch of machine learning- works by establishing a system of artificial neural networks that can classify and recognise images, as per the report.

Regarding breast cancer treatment, the use of AI for early detection by making use of data obtained by radiomics and biopsy slides is done. This is backed by a global effort to manufacture learning algorithms for understanding mammograms by reducing the number of false positives as an outcome.

"AI has increased the odds of identifying metastatic breast cancer in whole slide images of lymph node biopsy. Because people's risk factors and predispositions differ, AI algorithms operate differently in different populations," the report further says.

AI seems a very helpful tool when it comes to treating cancer. It has shown impressive outcomes and there is a possibility that it can change every method of treatment which is used presently. However, there are some challenges.

The report, published in the PMC journal in October last year, says that a concerning question is where can one draw the line between AI and human intelligence. "AI is based on data collected from populations. Therefore, a disparity is sure to rise when it comes to the development of data from people belonging to different socio-economic conditions," it adds and points out that cancer is one particular disease that has indices that vary across different races.

Studies relating to the efficiency of AI have certain set outcomes that can be used to assess their standards and credibility. And for AI machines to be accepted, people must be able to independently replicate and produce the machine like any other scientific finding. This implies a common code must be available to all, and it is only possible if data is shared with everyone equally.

AI models used for managing cancer are centred on image data, and the report says the problem with this aspect is the underutilisation of patient histories saved as electronic health records in hospitals.

"Easy-to-access databases and user-friendly software must be incorporated into the software systems of hospitals worldwide, which is a difficult task at the moment."

One of the biggest challenges is building trust among doctors to make their decisions with the help of AI, and adequate training must be provided to doctors on how to use this technology.

Another challenge is that there are a lot of ethical risks to consider while using AI methods which include data confidentiality, privacy violation, the autonomy of patients, and consent. But the report said that many measures are taken to prevent any violation of confidentiality and legislation to keep a check on malpractices.

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