Welcome to the first episode of New voices in AI! You can find David on Twitter @davlanade and find out more about Masakhane here. The music used is'Wholesome' by Kevin MacLeod, Licensed under Creative Commons Daly: Hello and welcome to new voices in AI, this a new series from AIhub where we celebrate the voices PhD students, early career researchers, and those with a new perspective on AI. And without further ado, let's begin. First up, a big welcome to our very first guest on "New voices in AI" and if you could introduce yourself, who are you? Adelani: Thank you very much for having me. So, Masakhane is this grassroots organization, whose mission is to strengthen and spur NLP research in African languages, by Africans for Africans, so, and currently the organization we are majorly operating on Slack we already have over 1000 Members. Of course, not everyone is active but we have more than 100 or close to 100 active members as well, yeah. So how did, how did you get into AI?

Apart from its distinct epidemiology, the natural behavior, treatment, and prognosis are different from other head and neck cancers. With the growing trend of artificial intelligence (AI), especially deep learning (DL), in head and neck cancer care, we sought to explore the unique clinical application and implementation direction of AI in the management of NPC. Methods: The search protocol was performed to collect publications using AI, machine learning (ML) and DL in NPC management from PubMed, Scopus and Embase. The articles were filtered using inclusion and exclusion criteria, and the quality of the papers was assessed. Data were extracted from the finalized articles.

Analysis For campaigners hoping that 2022 could be the year that deepfaked imagery falls within a stricter legal purview, the early indicators are unpromising. Last Thursday the European Parliament ratified amendments to the Digital Services Act (DSA, due to take effect in 2023), in regards to the dissemination of deepfakes. The modifications address deepfakes across two sections, each directly related to online advertising: amendment 1709 pertaining to Article 30, and a related amendment to article 63. 'Where a very large online platform becomes aware that a piece of content is a generated or manipulated image, audio or video content that appreciably resembles existing persons, objects, places or other entities or events and falsely appears to a person to be authentic or truthful (deep fakes), the provider shall label the content in a way that informs that the content is inauthentic and that is clearly visible for the recipient of the services.' The second adds text to the existing article 63, which is itself mainly concerned with increasing the transparency of large advertising platforms. 'In addition, very large online platforms should label any known deep fake videos, audio or other files.'

Microsoft continues to make significant investments in deep learning, computer vision, and AI. The Microsoft Maps Team has been leveraging that investment to identify map features at scale and produce high-quality building footprint data sets with the overall goal to add to the OpenStreetMap and MissingMaps humanitarian efforts. As of this post, the following locations are available and Microsoft offers access to this data under the Open Data Commons Open Database License (ODbL). Country/Region Million buildings United States of America 129.6 Nigeria and Kenya 50.5 South America 44.5 Uganda and Tanzania 17.9 Canada 11.8 Australia 11.3 As you might expect, the vintage of the footprints depends on the collection date of the underlying imagery. Bing Maps Imagery is a composite of multiple sources with different capture dates (ranging 2012 to 2021).

Language models increasingly rely on massive web dumps for diverse text data. However, these sources are rife with undesirable content. As such, resources like Wikipedia, books, and newswire often serve as anchors for automatically selecting web text most suitable for language modeling, a process typically referred to as quality filtering. Using a new dataset of U.S. high school newspaper articles -- written by students from across the country -- we investigate whose language is preferred by the quality filter used for GPT-3. We find that newspapers from larger schools, located in wealthier, educated, and urban ZIP codes are more likely to be classified as high quality. We then demonstrate that the filter's measurement of quality is unaligned with other sensible metrics, such as factuality or literary acclaim. We argue that privileging any corpus as high quality entails a language ideology, and more care is needed to construct training corpora for language models, with better transparency and justification for the inclusion or exclusion of various texts.

Since proposed in 1965, Lotfi Zadeh's idea of "fuzzy logic" has penetrated every science and engineering field. Physix is a "fuzzy metric system" to measure people's opinion and emotional response more accurately. AI lacks a logic that that can interpret the subtleties of thought, emotion and communication. The continua provide simple, unbiased metrics of personal experience and belief for any given topic; every opinion and feeling can be seen relative to others'. A shared visualization of perspectives will defuse antagonism and reduce racism leaving a positive impact on our society.

Did you miss a session from the Future of Work Summit? This article was contributed by Frederik Bussler, consultant and analyst. Around one in four American adults are underbanked, meaning they are underserved by traditional finance, and rely on high-fee alternative financial systems. For underbanked Americans, getting a loan or a credit card can range between being either difficult or next to impossible. For those who do have a credit score, it's often not a very high one.

AI has the potential to generate meaningful returns for the enterprise. Responding to a 2018 PricewaterhouseCoopers survey, 54% of business executives say that their adoption of AI within the workplace has led to a boost in productivity. A separate 2019 McKinsey report found that 44% of firms using AI achieved a reduction in business costs in departments where AI is implemented. But barriers stand in the way of deployment, including a lack of production-grade data and expensive tools and development processes. Among the top challenges enterprises face in adopting AI is an absence of in-house talent.

According to the World Health Organization (WHO), the world is going hungry. WHO data shows that in 2018, the most recent year for which data is available, 820 million people lacked enough food to eat, an increase of nine million people over the year before. Hunger kills plenty of people worldwide. It also impacts those who survive, causing serious childhood development issues like stunting, where children are too short for their age, and wasting, where they're too thin for their age. The explosion in our planet's population is a major factor in there not being enough food to go around.

The Arm Compute Library is a collection of low-level machine learning functions optimized for Cortex-A CPU and Mali GPU architectures.