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2021's crystal ball: 6 AI predictions that will shape a new commercial model - MedCity News

#artificialintelligence

Alan Kalton, Vice President and General Manager of Aktana Europe, is a leader in data analytics and manages all new Contextual Intelligence implementations and developments across Europe. He comes to Aktana from Cape Town, South Africa where he led a data analytics venture called BroadReach and prior was the Analytics Leader of EY in South Africa. He also held prominent executive leadership positions in data analytics at IBM, Elsevier, Cognizant, Steris, Novartis, GSK, and ZS Associates. He graduated with a BS and MSc of industrial and operations engineering from the University of Michigan. Kalton can be reached at alan.kalton@aktana.com.


This clever bot turns Reddit arguments into video game scenes

Mashable

There's plenty of drama on Reddit, but it's not often that drama gets to play out on the screen. On Sunday, 24-year-old software engineer Micah Price from Cape Town, South Africa, unveiled what can only be described as a niche-but-genius creation: a bot that takes everyday arguments on Reddit and has them play out in the style of scenes from Ace Attorney, Capcom's animated courtroom-based video game series. The end result is a gloriously dramatic affair that shines a whole new spotlight on Reddit's comment section. Price's video was shared on Reddit's r/Videos sub shortly after it went live on YouTube, and at the time of writing it's racked up over 21,000 upvotes. Price told Mashable he's always been a fan of Ace Attorney, which sees players taking on the role of defense attorneys who must carry out investigations to protect their clients (the game's episodes culminate in a courtroom trial where you have to cross-examine witnesses and present evidence to a judge).


COVID-19 testing: One size does not fit all

Science

Tests for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were developed within days of the release of the virus genome ([ 1 ][1]). Multiple countries have been successful at controlling SARS-CoV-2 transmission by investing in large-scale testing capacity ([ 2 ][2]). Most testing has focused on quantitative polymerase chain reaction (qPCR) assays, which are capable of detecting minute amounts of viral RNA. Although powerful, these molecular tools cannot be scaled to meet demands for more extensive public health testing. To combat COVID-19, the “one-size-fits-all” approach that has dominated and confused decision-making with regard to testing and the evaluation of tests is unsuitable: Diagnostics, screening, and surveillance serve different purposes, demand distinct strategies, and require separate approval mechanisms. By supporting the innovation, approval, manufacturing, and distribution of simpler and cheaper screening and surveillance tools, it will be possible to more effectively limit the spread of COVID-19 and respond to future pandemics. Many types of tests are available for COVID-19 for clinical and public health use (see the figure). Testing can be performed in a central laboratory, at the point of care (POC), or in the community at the workplace, school, or home. COVID-19 testing begins with specimen collection. For medical use, a nasopharyngeal swab collected by a health care professional has been used for detection of virus infections. Demands on testing throughput for COVID-19, however, have driven new collection approaches, including saliva and less invasive nasal swabs. COVID-19 tests include molecular tests such as qPCR, isothermal amplification, and CRISPR, as well as antigen tests that detect SARS-CoV-2 proteins directly. Although rapid antigen tests have lower analytical sensitivity (i.e., require greater amounts of virus material to turn positive) than qPCR-based tests, their ability to detect infectious individuals with culturable virus is as high as for qPCR ([ 3 ][3]). Specificity (i.e., correctly identifying those not infected with SARS-CoV-2) of antigen tests achieves comparable results to molecular tests ([ 4 ][4]). Diagnostic testing for COVID-19 focuses on accurately identifying patients who are infected with SARS-CoV-2 to establish the presence or absence of disease and is performed on symptomatic patients or asymptomatic individuals who are at high risk of infection. This type of testing requires assays that are highly sensitive, so as to not miss COVID-19 patients (false negatives), and specific, so as to not wrongly diagnose SARS-CoV-2–negative individuals as having COVID-19 (false positives). These tests are typically performed by centralized high-complexity laboratories with specialized equipment using qPCR assays, with results that can be reported within 12 to 48 hours. Major bottlenecks in testing, however, have led to turnaround times exceeding 5 to 10 days in some regions, making such tests useless to prevent transmission. POC diagnostic testing at medical facilities can be qPCR assays, isothermal amplification, or antigen-based ([ 4 ][4]). These POC tests often require instruments that run a limited number of tests and can return results in under an hour. The need for an instrument limits the number of tests that can be performed and where they can be used. However, newer antigen tests are becoming available that do not require instruments or skilled operators, potentially allowing for much more distributed POC testing. Surveillance testing of populations can be used both as a tool for understanding historical exposures and as a measure of ongoing community transmission. For the former, serological testing of individuals for the presence of SARS-CoV-2–specific antibodies is used to identify those previously infected. For the latter, surveillance testing can be an effective way to monitor real-time SARS-CoV-2 spread in communities. One promising method is wastewater surveillance, which has been used to assess community transmission of poliovirus ([ 5 ][5]) and has shown potential for COVID-19 ([ 6 ][6]). qPCR testing of wastewater is used to detect SARS-CoV-2, and frequency dynamics of viral genetic material indicate COVID-19 infections in a community. Surveillance can also be performed from swab or saliva samples taken directly from individuals, and, in populations with low COVID-19 prevalence, pooling can be used to increase capacity and lower cost. For surveillance testing, the goal is not identification of every case but rather the collection of data from representative samples that accurately measure prevalence and serve to inform public health policy and resource allocation. Because the focus is on extrapolations to the population and not the individual, tests with known deviations from 100% sensitivity and specificity are still appropriate when the variance can be statistically corrected ([ 7 ][7]). To be most effective, results should include reported qPCR cycle thresholds, which is an estimate of viral load ([ 7 ][7]), to model epidemic trajectory and allow for real-time evaluation of mitigation programs ([ 8 ][8]), including once vaccination programs have begun. Screening testing of asymptomatic individuals to detect people who are likely infectious has been critically underused yet is one of the most promising tools to combat the COVID-19 pandemic ([ 9 ][9]). Infection with SARS-CoV-2 does not lead to symptoms in ∼20 to 40% of cases, and symptomatic disease is preceded by a presymptomatic incubation period ([ 10 ][10]). However, asymptomatic and presymptomatic cases are key contributors to virus spread, complicating our ability to break transmission chains ([ 10 ][10]). Entry screening to detect infectious individuals before accessing facilities (e.g., nursing homes, restaurants, and airports), along with symptom screening and temperature checks, can be beneficial, particularly in high-risk facilities such as skilled nursing facilities. When used strategically, entry-screening measures can be effective at suppressing transmission. Entry screening requires testing that provides rapid results—ideally within 15 min—to be most effective. The required sensitivity and specificity of entry-screening tests are, like all tests, context dependent. Entry-screening tests for a nursing home, for example, must be highly sensitive because the consequences of bringing SARS-CoV-2 into a nursing home can be devastating. Such tests must also be highly specific because the consequences of grouping a false-positive person with COVID-19–positive individuals could be deadly. Conversely, because children have substantially reduced mortality from COVID-19, entry screening into schools might require greater compromise that balances resources and sensitivity to test as many individuals as possible with a need to minimize disruptive false positives. Key to use of tests for entrance screening is that a negative test alone should not be considered sufficient to enter—that should be based on satisfying other requirements, including masks and physical distancing. Conversely, a positive test should be sufficient to bar entry in most settings. Public health screening is potentially the most powerful form of COVID-19 testing, aimed at outbreak suppression through maximizing detection of infectious individuals. This type of screening entails frequent serial testing of large fractions of the population, through self-administered at-home rapid tests, or in the community at high-contact settings, such as schools and workplaces ([ 9 ][9]). Public health screening can achieve herd effects by stopping onward spread through detection of asymptomatic or presymptomatic cases (fig. S1). Notably, not every transmission chain needs to be severed to achieve herd effects. Mathematical models that incorporate relevant variation in viral loads and test accuracy suggest that with frequent testing of a large fraction of a population, a sufficient number of cases could be detected to create herd effects ([ 11 ][11]). For example, Slovakia undertook public health screening to address COVID-19 ([ 12 ][12]): During a 2-week period, ∼80% of the population was screened using rapid antigen tests. With 50,000 cases identified, combined with other public health measures, it reduced incidence by 82% within 2 weeks ([ 12 ][12]). An important feature of large-scale public health screening is that centrally controlled reporting and contact tracing programs are not essential to induce herd effects as they are for surveillance testing. In a robust public health screening program, sufficient numbers of people are routinely testing themselves, such that contact tracing is subsumed by the screening program ([ 11 ][11]). Similar to home pregnancy tests, screening tests should be easy to obtain and administer, fast, and cheap. Like diagnostic tests, these tests must produce very low false-positive rates. If a screening test does not achieve high-enough specificity (e.g., >99.9%), screening programs can be paired with secondary confirmatory testing. Unlike diagnostic tests, however, the sensitivity of screening tests should not be determined based on their ability to diagnose patients but rather by their ability to accurately identify people who are most at risk of transmitting SARS-CoV-2. Such individuals tend to have higher viral loads ([ 13 ][13]), which makes the virus easier to detect ([ 14 ][14]). A focus on identifying infectious people means that frequency and abundance of tests should be prioritized above achieving high analytical sensitivity ([ 11 ][11]). Indeed, loss in sensitivity of individual tests, within reason, can be compensated for by frequency of testing and wider dissemination of tests ([ 9 ][9]). In addition, public health messaging should ensure appropriate expectations of screening, particularly around sensitivity and specificity so that false negatives and false positives do not erode public trust. ![Figure][15] COVID-19 testing strategies Testing for SARS-CoV-2 can be for personal or population health. Collection can be from symptomatic or asymptomatic individuals, as well as from wastewater and swabs of surfaces. The tests may be performed in central laboratories, at the POC, or using rapid tests. Attributes of tests differ according to application. GRAPHIC: KELLIE HOLOSKI/ SCIENCE Tests for public health screening require rapid, decentralized solutions that can be scaled for frequent screening of large numbers of asymptomatic individuals. Lateral-flow antigen tests and upcoming paper-based synthetic biology and CRISPR-based assays fit these needs and could be scaled to tens of millions of daily tests ([ 9 ][9]). These tests are simple and cheap, can be self-administered, and do not require machines to run and return results. The Abbott BinaxNOW rapid antigen test, which recently received an Emergency Use Authorization (EUA) in the United States as a diagnostic device, also comes with a smartphone app, allowing self-reporting of COVID-19 status that could be used instead of centralized reporting by public health agencies. Critically, despite being shown to be highly effective at detecting infectious individuals ([ 14 ][14]), very few of these tests are currently approved for screening of asymptomatic individuals, substantially limiting their utility. If such tests were made available direct to consumer (priced to allow equitable access) or produced and provided free of charge by governments, individuals could obtain their COVID-19 status at their own choosing and without complex medical decisions. Testing is a central pillar of clinical and public health response to global health emergencies, including the COVID-19 pandemic. Nearly all testing modalities have a role, and the one-size-fits-all approach to testing by many Western countries has failed. Many lower- and middle-income countries—including Senegal, Vietnam, and Ghana—have fared far better in their COVID-19 response, often using strong testing programs. The focus on diagnostic tests and the use of preexisting authorization pathways focused on qPCR-based clinical diagnostics not only slows the development and deployment of new surveillance and screening tests but also confuses the picture of what metrics effective public health tools should achieve. Testing to diagnose a patient with COVID-19 is fundamentally different from testing a person to prevent onward transmission. Regulatory pathways should be modified to incorporate these differences so that public health and screening tests are appropriately evaluated. It is necessary to be innovative and produce, distribute, and continuously improve the tests that exist to save lives and gain control of the COVID-19 pandemic. [science.sciencemag.org/content/371/6525/126/suppl/DC1][16] 1. [↵][17]1. V. M. Corman et al ., Euro. Surveill. 25, 2000045 (2020). [OpenUrl][18][CrossRef][19][PubMed][20] 2. [↵][21]1. M. G. Baker et al ., N. Engl. J. Med. 383, e56 (2020). [OpenUrl][22][CrossRef][23][PubMed][24] 3. [↵][25]1. A. Pekosz et al ., medRxiv 10.1101/2020.10.02.20205708 (2020). 4. [↵][26]1. R. Weissleder et al ., Sci. Transl. Med. 12, abc1931 (2020). [OpenUrl][27][CrossRef][28] 5. [↵][29]1. H. Asghar et al ., J. Infect. Dis. 210, S294 (2014). [OpenUrl][30][CrossRef][31][PubMed][32] 6. [↵][33]1. A. Nemudryi et al ., Cell Rep. Med. 1, 100098 (2020). [OpenUrl][34][CrossRef][35][PubMed][36] 7. [↵][37]1. R. Kahn et al ., medRxiv 10.1101/2020.05.02.20088765 (2020). 8. [↵][38]1. J. A. Hay et al ., medRxiv 10.1101/2020.10.08.20204222 (2020). 9. [↵][39]1. M. J. Mina et al ., N. Engl. J. Med. 383, e120 (2020). [OpenUrl][40][PubMed][41] 10. [↵][42]1. X. He et al ., Nat. Med. 26, 672 (2020). [OpenUrl][43][CrossRef][44][PubMed][41] 11. [↵][45]1. D. B. Larremore et al ., Sci. Adv. 10.1126/sciadv.abd5393 (2020). 12. [↵][46]1. M. Pavelka et al ., “The effectiveness of population-wide, rapid antigen test based screening in reducing SARS-CoV-2 infection prevalence in Slovakia,” CMMID Repository, 11 November 2020; . 13. [↵][47]1. E. A. Meyerowitz et al ., Ann. Intern. Med. 10.7326/M20-5008 (2020). 14. [↵][48]1. V. M. Corman et al ., medRxiv 10.1101/2020.11.12.20230292 (2020). [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-7 [8]: #ref-8 [9]: #ref-9 [10]: #ref-10 [11]: #ref-11 [12]: #ref-12 [13]: #ref-13 [14]: #ref-14 [15]: pending:yes [16]: http://science.sciencemag.org/content/371/6525/126/suppl/DC1 [17]: #xref-ref-1-1 "View reference 1 in text" [18]: {openurl}?query=rft.jtitle%253DEuro.%2BSurveill.%26rft.volume%253D25%26rft.spage%253D2000045%26rft_id%253Dinfo%253Adoi%252F10.2807%252F1560-107917.ES.2020.25.3.2000045%26rft_id%253Dinfo%253Apmid%252F31992387%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [19]: /lookup/external-ref?access_num=10.2807/1560-107917.ES.2020.25.3.2000045&link_type=DOI [20]: /lookup/external-ref?access_num=31992387&link_type=MED&atom=%2Fsci%2F371%2F6525%2F126.atom [21]: #xref-ref-2-1 "View reference 2 in text" [22]: {openurl}?query=rft.jtitle%253DN.%2BEngl.%2BJ.%2BMed.%26rft.volume%253D383%26rft.spage%253De56%26rft_id%253Dinfo%253Adoi%252F10.1056%252FNEJMc2025203%26rft_id%253Dinfo%253Apmid%252F32767891%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [23]: /lookup/external-ref?access_num=10.1056/NEJMc2025203&link_type=DOI [24]: /lookup/external-ref?access_num=32767891&link_type=MED&atom=%2Fsci%2F371%2F6525%2F126.atom [25]: #xref-ref-3-1 "View reference 3 in text" [26]: #xref-ref-4-1 "View reference 4 in text" [27]: {openurl}?query=rft.jtitle%253DSci.%2BTransl.%2BMed.%26rft.volume%253D12%26rft.spage%253Dabc1931%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscitranslmed.abc1931%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [28]: /lookup/external-ref?access_num=10.1126/scitranslmed.abc1931&link_type=DOI [29]: #xref-ref-5-1 "View reference 5 in text" [30]: {openurl}?query=rft.jtitle%253DJ.%2BInfect.%2BDis.%26rft_id%253Dinfo%253Adoi%252F10.1093%252Finfdis%252Fjiu384%26rft_id%253Dinfo%253Apmid%252F25316848%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [31]: /lookup/external-ref?access_num=10.1093/infdis/jiu384&link_type=DOI [32]: /lookup/external-ref?access_num=25316848&link_type=MED&atom=%2Fsci%2F371%2F6525%2F126.atom [33]: #xref-ref-6-1 "View reference 6 in text" [34]: {openurl}?query=rft.jtitle%253DCell%2BRep.%2BMed.%26rft.volume%253D1%26rft.spage%253D100098%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.xcrm.2020.100098%26rft_id%253Dinfo%253Apmid%252F32904687%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [35]: /lookup/external-ref?access_num=10.1016/j.xcrm.2020.100098&link_type=DOI [36]: /lookup/external-ref?access_num=32904687&link_type=MED&atom=%2Fsci%2F371%2F6525%2F126.atom [37]: #xref-ref-7-1 "View reference 7 in text" [38]: #xref-ref-8-1 "View reference 8 in text" [39]: #xref-ref-9-1 "View reference 9 in text" [40]: {openurl}?query=rft.jtitle%253DN.%2BEngl.%2BJ.%2BMed.%26rft.volume%253D383%26rft.spage%253De120%26rft_id%253Dinfo%253Apmid%252Fhttp%253A%252F%252Fwww.n%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [41]: /lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fsci%2F371%2F6525%2F126.atom [42]: #xref-ref-10-1 "View reference 10 in text" [43]: {openurl}?query=rft.jtitle%253DNat.%2BMed.%26rft.volume%253D26%26rft.spage%253D672%26rft_id%253Dinfo%253Adoi%252F10.7326%252FM20-3012%26rft_id%253Dinfo%253Apmid%252Fhttp%253A%252F%252Fwww.n%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [44]: /lookup/external-ref?access_num=10.7326/M20-3012&link_type=DOI [45]: #xref-ref-11-1 "View reference 11 in text" [46]: #xref-ref-12-1 "View reference 12 in text" [47]: #xref-ref-13-1 "View reference 13 in text" [48]: #xref-ref-14-1 "View reference 14 in text"


A Clever Strategy to Distribute Covid Aid--With Satellite Data

WIRED

When the novel coronavirus reached Togo in March, its leaders, like those of many countries, responded with stay-at-home orders to suppress contagion and an economic assistance program to replace lost income. But the way Togo targeted and delivered that aid was in some ways more tech-centric than many larger and richer countries. No one got a paper check in the mail. Instead, Togo's government quickly assembled a system to support its poorest people with mobile cash payments--a technology more established in Africa than in the rich nations supposedly at the forefront of mobile technology. The most recent payments, funded by nonprofit GiveDirectly, were targeted with help from machine learning algorithms, which seek signs of poverty in satellite photos, and cellphone data.


Graph integration of structured, semistructured and unstructured data for data journalism

arXiv.org Artificial Intelligence

Such a query can be answered currently at a high human effort cost, by inspecting e.g., a JSON list of Assemblée elected officials (available from NosDeputes.fr) and manually connecting the names with those found in a national registry of companies. This considerable effort may still miss connections that could be found if one added information about politicians' and business people's spouses, information sometimes available in public knowledge bases such as DBPedia, or journalists' notes. No single query language can be used on such heterogeneous data; instead, we study methods to query the corpus by specifying some keywords and asking for all the connections that exist, in one or across several data sources, between these keywords. This problem has been studied under the name of keyword search over structured data, in particular for relational databases [49, 27], XML documents [24, 33], RDF graphs [30, 16]. However, most of these works assumed one single source of data, in which connections among nodes are clearly identified. When authors considered several data sources [31], they still assumed that one query answer comes from a single data source. In contrast, the ConnectionLens system [10] answers keyword search queries over arbitrary combinations of datasets and heterogeneous data models, independently produced by actors unaware of each other's existence.


Artificial Intelligence's Power, and Risks, Explored in New Report - Market Brief

#artificialintelligence

Picture this: a small group of middle school students are learning about ancient Egypt, so they strap on a virtual reality headset and, with the assistance of an artificial intelligence tour guide, begin to explore the Pyramids of Giza. The teacher, also journeying to one of the oldest known civilizations via a VR headset, has assigned students to gather information to write short essays. During the tour, the AI guide fields questions from students and points them to specific artifacts and discuss what they see. In preparing the AI-powered lesson on Egypt, the teacher beforehand would have worked with the AI program to craft a lesson plan that not only dives deep into the subject, but figures out how to keep the group moving through the virtual field trip and how to create more equal participation during the discussion. In that scenario, the AI listens, observes and interacts naturally to enhance a group learning experience, and to make a teacher's job easier.


A taxonomy of explainable (XAI) AI models

#artificialintelligence

Vaishak Belle (University of Edinburgh & Alan Turing Institute) and Ioannis Papantonis (University of Edinburgh) which presents a taxonomy of explainable AI (XAI). XAI is a complex subject and as far as I can see, I have not yet seen a taxonomy of XAI. Model-agnostic Explainability Approaches are designed to be flexible and do not depend on the intrinsic architecture of a model(such as Random forest). These approaches solely relate the inputs to the outputs. Model agnistic approaches could be explanation by simplification, explanation by feature relevance or explanation by visualizations.


The problems AI has today go back centuries – MIT Technology Review

#artificialintelligence

In March of 2015, protests broke out at the University of Cape Town in South Africa over the campus statue of British colonialist Cecil Rhodes. Rhodes, a mining magnate who had gifted the land on which the university was built, had committed genocide against Africans and laid the foundations for apartheid. Under the rallying banner of "Rhodes Must Fall," students demanded that the statue be removed. Their protests sparked a global movement to eradicate the colonial legacies that endure in education. The events also provoked Shakir Mohamed, a South African AI researcher at DeepMind, to reflect on what colonial legacies might exist in his research as well.


Artificial Intelligence and Start-Ups in Low- and Middle-Income Countries: Progress, Promise and Perils

#artificialintelligence

Around the world, artificial intelligence (AI) is automating functions and making new services possible with breakthroughs in low-cost computing power, cloud computing services, growth in big data and advancements in machine learning and related processes. This webinar discussed the current use of AI in low- and middle-income countries (LMICs), along with trends and challenges in business models, barriers to innovation and AI's ethical and responsible use towards achieving the sustainable development goals. This study examines the current use of AI in low- and middle-income countries (LMICs) across Sub-Saharan Africa, North Africa and South and Southeast Asia. The report mapped a sample of 450 start-ups by sector in alignment with the UN Sustainable ...


Artificial Intelligence's Power, and Risks, Explored in New Report

#artificialintelligence

In preparing the the AI-powered lesson on Egypt, the teacher beforehand would have worked with the AI program to craft a lesson plan that not only …