Maëva Ghonda is a scientist born in Kinshasa, the great capital city of the Democratic Republic of Congo (DRC). Maëva is the editor-in-chief of the IEEE Quantum Computing Newsletter, the host of the Quantum AI Series Podcast, and the chair of the Quantum AI Institute. As a research scientist, her work is centered on technological innovations -- i.e. Quantum Computing, Artificial Intelligence and Machine Learning -- to tackle challenges in Pharma and Healthcare (e.g. Maëva Ghonda's passion for quantum computing ignited while working as Joint Quantum Institute Scholar.

The world's most pampered rhino is having his mudpack applied by hand. Sudan the veteran northern white rhinoceros stands placidly and allows his personal assistant to rub dollops of wet clay into his hide, to moisturise his skin and keep insects away. 'He loves that,' says Zach, his 24-hour-a-day PA. Zach lives next door to the rhino enclosure, permanently on call in case His Lordship should require anything. And Sudan is very high maintenance – he used to have a man whose job was rubbing oil into his hooves.

It's called'Aibo,' and is promoted as using artificial intelligence to respond to people looking at it, talking to it and touching it. Japanese customers have already bought over 20,000 units, and it is expected to come to the U.S. before the holiday gift-buying season – at a price nearing US$3,000. Why would anyone pay so much for a robotic dog? My ongoing research suggests part of the attraction might be explained through humanity's longstanding connection with various forms of puppets, religious icons, and other figurines, that I collectively call'dolls.' These dolls, I argue, are embedded deep in our social and religious lives.

Recent advances in artificial intelligence (AI) speech generation and voice cloning technologies have produced naturalistic speech and accurate voice replication, yet their influence on sociotechnical systems across diverse accents and linguistic traits is not fully understood. This study evaluates two synthetic AI voice services (Speechify and ElevenLabs) through a mixed methods approach using surveys and interviews to assess technical performance and uncover how users' lived experiences influence their perceptions of accent variations in these speech technologies. Our findings reveal technical performance disparities across five regional, English-language accents and demonstrate how current speech generation technologies may inadvertently reinforce linguistic privilege and accent-based discrimination, potentially creating new forms of digital exclusion. Overall, our study highlights the need for inclusive design and regulation by providing actionable insights for developers, policymakers, and organizations to ensure equitable and socially responsible AI speech technologies.

A shower of pink petals rains down in slow motion against an ethereal backdrop of minimalist white arches, bathed in the soft focus of a cosmetics advert. The camera pulls back to reveal the petals have clustered together to form a delicate puffy armchair, standing in the centre of a temple-like space, surrounded by a dreamy landscape of fluffy pink trees. It looks like a luxury zen retreat, as conceived by Glossier. The aesthetic is eerily familiar: these are the pastel tones, tactile textures and ubiquitous arches of Instagram architecture, an amalgamation of design tropes specifically honed for likes. An ode to millennial pink, this computer-rendered scene has been finely tuned to seduce the social media algorithm, calibrated to slide into your feed like a sugary tranquilliser, promising to envelop you in its candy-floss embrace.

Africa has over 2000 languages however, these languages are not well represented in the existing Natural language processing (NLP) ecosystem. One of the challenges is the lack of useful African language datasets that can be used to solve different social and economical problems. In this article, I have compiled a list of African language datasets from across the web. These datasets can be used in numerous NLP tasks such as text classification, named entity recognition, machine translation, sentiment analysis, speech recognition, and topic modeling. This collection of datasets have been made public to give you an opportunity to use your skills and help solving different challenges.

Africa has over 2000 languages, but these languages are not well-represented in the existing Natural Language Processing ecosystem. One challenge is the lack of useful African language datasets that we can use to solve different social and economic problems. In this article, I have compiled a list of African language datasets from across the web. You can use these datasets in various NLP tasks such as text classification, named entity recognition, machine translation, sentiment analysis, speech recognition, and topic modeling. I've made this collection of datasets public to give you an opportunity to use your skills and help solve different challenges.

After more than 10 years since the release of "Robocop" the dreams of film creators are likely to become true. Of course, the humanity has not devised hi-tech cyborgs like the main character of the film. However, today complicated robots maintain public order and security in different parts of the world. Here you can get yourself acquainted with "robocops" which help police in real world. This year after annual Republican Convention the police of Cleveland allowed a robot named Griffin to patrol the streets.

The COVID-19 crisis has emphasized the need for scientific methods such as machine learning to speed up the discovery of solutions to the pandemic. Harnessing machine learning techniques requires quality data, skilled personnel and advanced compute infrastructure. In Africa, however, machine learning competencies and compute infrastructures are limited. This paper demonstrates a cross-border collaborative capacity building approach to the application of machine learning techniques in discovering answers to COVID-19 questions.

Monkeypox is a rare disease that raised concern among medical specialists following the convi-19 pandemic. It's concerning since monkeypox is difficult to diagnose early on because of symptoms that are similar to chickenpox and measles. Furthermore, because this is a rare condition, there is a knowledge gap among healthcare professionals. As a result, there is an urgent need for a novel technique to combat and anticipate the disease in the early phases of individual virus infection. Multiple CNN-based pre-trained models, including VGG-16, VGG-19, Restnet50, Inception-V3, Densnet, Xception, MobileNetV2, Alexnet, Lenet, and majority Voting, were employed in classification in this study. For this study, multiple data sets were combined, such as monkeypox vs chickenpox, monkeypox versus measles, monkeypox versus normal, and monkeypox versus all diseases. Majority voting performed 97% in monkeypox vs chickenpox, Xception achieved 79% in monkeypox against measles, MobileNetV2 scored 96% in monkeypox vs normal, and Lenet performed 80% in monkeypox versus all.