AI/ML news, top picks, and generated innovation digests.
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The AI revolution drove frenzied investment in both private and public companies and captured the public’s imagination in 2023. Transformational consumer products like ChatGPT are powered by Large Language Models (LLMs) that excel at modeling sequences of tokens that represent words or parts of words [2]. Amazingly, structural
My founder friends constantly think about growth. They think about how to measure their business growth and how to get to the next order of magnitude scale. If they’re making $1M ARR today, they think about how to get to $10M ARR. If they have 1,000 users today, they think about how to get to 10,000 users. This made me wonder if/how people are measuring personal growth. I don’t want to use metrics like net worth or the number of followers, because that’s not what I live for. After talking with a lot of friends, I found three interesting metrics: rate of change, time to solve problems, and number of future options. Some friends told me they find this blog post mildly sociopathic. Why do I have to measure everything? Life is to be lived, not to be measured. As someone lowkey fascinated by numbers, I don’t see why measuring and living have to be mutually exclusive – measuring often helps me live better – but I see where they come from. This post is more of a thought exercise than a rigorous experiment. Rate of change I have this theory that life has a circadian rhythm. Every 3-6 years, you become a different person. You work on different problems. Your lifestyle changes. The people you hang out with are different. If you haven’t caught up with a friend in 5 years, you might no longer have anything in common. It’s not a coincidence that schools are structured into chunks of 3-6 years. Looking back, I realized that every 3-6 years, my life completely changed. From grade 3 to grad…
Trained T5 on the Pile
LangChain and Qdrant are collaborating on the launch of Qdrant Hybrid Cloud , which is designed to empower engineers and scientists globally to easily and securely develop and scale their GenAI applications. Harnessing LangChain’s robust framework, users can unlock the full potential of vector search, enabling the creation of stable and effective AI products. Qdrant Hybrid Cloud extends the same powerful functionality of Qdrant onto a Kubernetes-based architecture, enhancing LangChain’s capability to cater to users across any environment.
Diffusion models have demonstrated strong results on image synthesis in past years. Now the research community has started working on a harder task—using it for video generation. The task itself is a superset of the image case, since an image is a video of 1 frame, and it is much more challenging because: It has extra requirements on temporal consistency across frames in time, which naturally demands more world knowledge to be encoded into the model. In comparison to text or images, it is more difficult to collect large amounts of high-quality, high-dimensional video data, let along text-video pairs. 🥑 Required Pre-read: Please make sure you have read the previous blog on “What are Diffusion Models?” for image generation before continue here.
We’re excited about our collaboration with Red Hat to bring the Qdrant vector database to Red Hat OpenShift customers! With the release of Qdrant Hybrid Cloud , developers can now deploy and run the Qdrant vector database directly in their Red Hat OpenShift environment. This collaboration enables developers to scale more seamlessly, operate more consistently across hybrid cloud environments, and maintain complete control over their vector data. This is a big step forward in simplifying AI infrastructure and empowering data-driven projects, like retrieval augmented generation (RAG) use cases, advanced search scenarios, or recommendations systems.
Developers are constantly seeking new ways to enhance their AI applications with new customer experiences. At the core of this are vector databases, as they enable the efficient handling of complex, unstructured data, making it possible to power applications with semantic search, personalized recommendation systems, and intelligent Q&A platforms. However, when deploying such new AI applications, especially those handling sensitive or personal user data, privacy becomes important. DigitalOcean and Qdrant are actively addressing this with an integration that lets developers deploy a managed vector database in their existing DigitalOcean environments. With the recent launch of Qdrant Hybrid Cloud , developers can seamlessly deploy Qdrant on DigitalOcean Kubernetes (DOKS) clusters, making it easier for developers to handle vector databases without getting bogged down in the complexity of managing the underlying infrastructure.
Aleph Alpha and Qdrant are on a joint mission to empower the world’s best companies in their AI journey. The launch of Qdrant Hybrid Cloud furthers this effort by ensuring complete data sovereignty and hosting security. This latest collaboration is all about giving enterprise customers complete transparency and sovereignty to make use of AI in their own environment. By using a hybrid cloud vector database, those looking to leverage vector search for the AI applications can now ensure their proprietary and customer data is completely secure.
We’re excited to share that Qdrant and Vultr are partnering to provide seamless scalability and performance for vector search workloads. With Vultr’s global footprint and customizable platform, deploying vector search workloads becomes incredibly flexible. Qdrant’s new Qdrant Hybrid Cloud offering and its Kubernetes-native design, coupled with Vultr’s straightforward virtual machine provisioning, allows for simple setup when prototyping and building next-gen AI apps. Adapting to Diverse AI Development Needs with Customization and Deployment Flexibility In the fast-paced world of AI and ML, businesses are eagerly integrating AI and generative AI to enhance their products with new features like AI assistants, develop new innovative solutions, and streamline internal workflows with AI-driven processes. Given the diverse needs of these applications, it’s clear that a one-size-fits-all approach doesn’t apply to AI development. This variability in requirements underscores the need for adaptable and customizable development environments.
Qdrant and STACKIT are thrilled to announce that developers are now able to deploy a fully managed vector database to their STACKIT environment with the introduction of Qdrant Hybrid Cloud . This is a great step forward for the German AI ecosystem as it enables developers and businesses to build cutting edge AI applications that run on German data centers with full control over their data. Vector databases are an essential component of the modern AI stack. They enable rapid and accurate retrieval of high-dimensional data, crucial for powering search, recommendation systems, and augmenting machine learning models. In the rising field of GenAI, vector databases power retrieval-augmented-generation (RAG) scenarios as they are able to enhance the output of large language models (LLMs) by injecting relevant contextual information. However, this contextual information is often rooted in confidential internal or customer-related information, which is why enterprises are in pursuit of solutions that allow them to make this data available for their AI applications without compromising data privacy, losing data control, or letting data exit the company’s secure environment.
In a move to empower the next wave of AI innovation, Qdrant and Scaleway collaborate to introduce Qdrant Hybrid Cloud , a fully managed vector database that can be deployed on existing Scaleway environments. This collaboration is set to democratize access to advanced AI capabilities, enabling developers to easily deploy and scale vector search technologies within Scaleway’s robust and developer-friendly cloud infrastructure. By focusing on the unique needs of startups and the developer community, Qdrant and Scaleway are providing access to intuitive and easy to use tools, making cutting-edge AI more accessible than ever before.
With the official release of Qdrant Hybrid Cloud , businesses running their data infrastructure on OVHcloud are now able to deploy a fully managed vector database in their existing OVHcloud environment. We are excited about this partnership, which has been established through the OVHcloud Open Trusted Cloud program, as it is based on our shared understanding of the importance of trust, control, and data privacy in the context of the emerging landscape of enterprise-grade AI applications. As part of this collaboration, we are also providing a detailed use case tutorial on building a recommendation system that demonstrates the benefits of running Qdrant Hybrid Cloud on OVHcloud.
We’re happy to announce the collaboration between LlamaIndex and Qdrant’s new Hybrid Cloud launch , aimed at empowering engineers and scientists worldwide to swiftly and securely develop and scale their GenAI applications. By leveraging LlamaIndex’s robust framework, users can maximize the potential of vector search and create stable and effective AI products. Qdrant Hybrid Cloud offers the same Qdrant functionality on a Kubernetes-based architecture, which further expands the ability of LlamaIndex to support any user on any environment.
We’re thrilled to announce the collaboration between Qdrant and Jina AI for the launch of Qdrant Hybrid Cloud , empowering users worldwide to rapidly and securely develop and scale their AI applications. By leveraging Jina AI’s top-tier large language models (LLMs), engineers and scientists can optimize their vector search efforts. Qdrant’s latest Hybrid Cloud solution, designed natively with Kubernetes, seamlessly integrates with Jina AI’s robust embedding models and APIs. This synergy streamlines both prototyping and deployment processes for AI solutions.
We’re excited to share that Qdrant and Haystack are continuing to expand their seamless integration to the new Qdrant Hybrid Cloud offering, allowing developers to deploy a managed vector database in their own environment of choice. Earlier this year, both Qdrant and Haystack, started to address their user’s growing need for production-ready retrieval-augmented-generation (RAG) deployments. The ability to build and deploy AI apps anywhere now allows for complete data sovereignty and control. This gives large enterprise customers the peace of mind they need before they expand AI functionalities throughout their operations.
In their mission to support large-scale AI innovation, Airbyte and Qdrant are collaborating on the launch of Qdrant’s new offering - Qdrant Hybrid Cloud . This collaboration allows users to leverage the synergistic capabilities of both Airbyte and Qdrant within a private infrastructure. Qdrant’s new offering represents the first managed vector database that can be deployed in any environment. Businesses optimizing their data infrastructure with Airbyte are now able to host a vector database either on premise, or on a public cloud of their choice - while still reaping the benefits of a managed database product.
A brief overview and discussion on gender bias in AI
I have a data in which I have to apply a competing risk. 4 variables: Temps_Competing_Descompensacio: the time to event. Competing_Descompensacio: factor variable to identifie the event, censored, event or competing event. Grup_IQ: stratified analisis (2 groups). IPTW: the weights of the observation from a previous propensity score phase. My problem is to apply a method for competing risks with the propensity score IPTW weights. I haven't found a way to do it. The analysis without the weights was correct. Already tested and compared with a SAS sintax. Here my code from the for the crr function from the cmprsk package fit.crr $Temps_Competing_Descompensacio, fstatus = Competing_dataset$ Competing_Descompensacio, cov1 = Competing_dataset$Grup_IQ, failcode = 1, cencode = 0) The issue comes when I try to add the weights, as I do not see or find an argument to ponderate the results. I considered multiplying the time variables for the weights, but does not seem correct from methodolgy perspective, and I haven't found a solution from other libraries.
Is Attention all you need? Mamba, a novel AI model based on State Space Models (SSMs), emerges as a formidable alternative to the widely used Transformer models, addressing their inefficiency in processing long sequences.
Using /proc to find fileless malware
[ Hacker News discussion , LinkedIn discussion , Twitter thread ] Update (Feb 2026) : The full list of open source AI repos is hosted at Good AI List , updated daily. It’s balooned to 15K repos, and you can submit missing repos. You can also find some of them on my cool-llm-repos list on GitHub. Four years ago, I did an analysis of the open source ML ecosystem . Since then, the landscape has changed, so I revisited the topic. This time, I focused exclusively on the stack around foundation models. Data I searched GitHub using the keywords gpt , llm , and generative ai . If AI feels so overwhelming right now, it’s because it is. There are 118K results for gpt alone. To make my life easier, I limited my search to the repos with at least 500 stars. There were 590 results for llm , 531 for gpt , and 38 for generative ai . I also occasionally checked GitHub trending and social media for new repos. After MANY hours, I found 896 repos. Of these, 51 are tutorials (e.g. dair-ai/Prompt-Engineering-Guide ) and aggregated lists (e.g. f/awesome-chatgpt-prompts ). While these tutorials and lists are helpful, I’m more interested in software. I still include them in the final list, but the analysis is done with the 845 software repositories. It was a painful but rewarding process. It gave me a much better understanding of what people are working on, how incredibly collaborative the open source community is, and just how much China’s open source ecosystem diverges from the Western one. The Ne…
Despite the challenges faced by the media industry, the Haitian Times –a print and digital newspaper catering to Haitian immigrants in the United States– has managed to not only survive but thrive by adapting to the changing needs of its audience. Through a combination of smart financial decisions, leveraging technology like AI, and deeply understanding […] The post The Haitian Times thrives by understanding its audience, making smart financial decisions and embracing AI appeared first on LatAm Journalism Review by the Knight Center .
Despite the challenges faced by the media industry, the Haitian Times –a print and digital newspaper catering to Haitian immigrants in the United States– has managed to not only survive but thrive by adapting to the changing needs of its audience. Through a combination of smart financial decisions, leveraging technology like AI, and deeply understanding […] The post The Haitian Times thrives by understanding its audience, making smart financial decisions and embracing AI appeared first on LatAm Journalism Review by the Knight Center .
Exploring the utility of large language models in autonomous driving: Can they be trusted for self-driving cars, and what are the key challenges?
'Vec2text' can serve as a solution for accurately reverting embeddings back into text, thus highlighting the urgent need for revisiting security protocols around embedded data.
A challenge of building AI applications is choosing which model to use. What if we don’t have to? What if we can predict the best model for any prompt? Predictive human preference aims to predict which model users might prefer for a specific query. Human preference has emerged to be both the Northstar and a powerful tool for AI model development. Human preference guides post-training techniques including RLHF and DPO . Human preference is also used to rank AI models, as used by LMSYS’s Chatbot Arena . Chatbot Arena aims to determine which model is generally preferred. I wanted to see if it’s possible to predict which model is preferred for each query . One use case of predictive human preference is model routing. For example, if we know in advance that for a prompt, users will prefer Claude Instant’s response over GPT-4, and Claude Instant is cheaper/faster than GPT-4, we can route this prompt to Claude Instant. Model routing has the potential to increase response quality while reducing costs and latency. Another use case of predictive human preference is interpretability. Mapping out a model’s performance on different prompts can help us understand this model’s strengths and weaknesses. See section Experiment results for examples. Here’s what predictive human preference for different model pairs looks like for the prompt “ What’s the best way to cluster text embeddings? ”. The predictions were generated by my toy preference predictor. The bright yellow color for the (GPT-4,…
Investing in reliability and test engineering
Have you ever trained a model you thought was good, but then it failed miserably when applied to real world data? If so, you’re in good company.
Effective developer tools
The best reason I have seen for using variational autoencoders is when dealing with sparse data. The Gaussian noise "splats" out the input distribution (see this StackExchange answer ). However, normalizing flows do the same thing, without the loss of information a VAE incurs. It feels as if VAEs are used only because everyone else is using them, and then a bunch of StackExchange posts reinforce the message that VAEs are the way to go , when they're theoretically suboptimal. I understand that normalizing flows are a little slower (at either training or inference), and more difficult to implement, but is there a theoretical reason that makes VAEs a legitimate choice?
Update June 2024: Anyscale Endpoints (Anyscale's LLM API Offering) and Private Endpoints (self-hosted LLMs) are now available as part of the Anyscale Platform. Click [here](https://console.anyscale.com/?utm_source=anyscale&utm_medium=blog&utm_campaign=blog_callout&utm_content=june2024_product_update_subheading) to get started on the Anyscale platform.
[Special thank you to Ian Kivlichan for many useful pointers (E.g. the 100+ year old Nature paper “Vox populi”) and nice feedback. 🙏 ] High-quality data is the fuel for modern data deep learning model training. Most of the task-specific labeled data comes from human annotation, such as classification task or RLHF labeling (which can be constructed as classification format) for LLM alignment training. Lots of ML techniques in the post can help with data quality, but fundamentally human data collection involves attention to details and careful execution. The community knows the value of high quality data, but somehow we have this subtle impression that “Everyone wants to do the model work, not the data work” ( Sambasivan et al. 2021 ).
Acuadata @administrador Mié, 31/01/2024 - 13:07 Las pérdidas de agua no técnicas en América Latina y el Caribe representan un desafío significativo en la gestión eficiente de los recursos hídricos. Estas pérdidas, que incluyen fugas y problemas en la distribución, contribuyen a un uso ineficiente del agua, lo que a su vez afecta la disponibilidad y la sostenibilidad de este recurso vital. Para abordar este problema, es esencial implementar estrategias integrales que combinen la tecnología avanzada con la capacitación adecuada y la gestión eficaz, con el objetivo de reducir las pérdidas de agua y garantizar un suministro sostenible para las comunidades en toda la región. Problema que se busca resolve En los países de la región de América Latina y el Caribe, se observan niveles elevados de pérdidas de agua, con aproximadamente el 38% del agua perdida antes de la facturación. En la Empresa Pública Metropolitana de Agua Potable y Saneamiento de Quito (EPMAPS), este indicador se sitúa en el 29%. EPMAPS tiene como objetivo reducir las pérdidas comerciales de agua en un 1%, mantener el nivel de multas y disminuir el número de inspecciones y personal en el equipo de monitoreo. Poblaciones que se ven afectadas por el problema Consumidores servicios de agua Respuesta actual a este problema, considerando a las instituciones relacionadas. Detectar las perdida no técnica de agua requiere una inmensa cantidad de datos, realizar este análisis de manera manual ha llevado a una baja efectivi…
Energizados @administrador Mié, 31/01/2024 - 13:04 Para una sociedad que depende altamente de la disponibilidad, eficiencia y confiabilidad de la electricidad, se hace indispensable para las empresas eléctricas del sector tener una buena administración de la producción y distribución de energía. Uno de los grandes problemas que preocupa son las pérdidas eléctricas. En el transporte de energía, las pérdidas eléctricas son la diferencia entre la electricidad que ingresa a la red y la que es entregada para el consumo final, y son reflejo del nivel de eficiencia de la infraestructura en transmisión y distribución. El concepto de pérdidas eléctricas incluye también la electricidad entregada pero no facturada, que se traduce directamente en pérdidas financieras y sirve como indicador del desempeño operacional de las empresas eléctricas. Problema que se busca resolve El problema que se busca resolver a partir de las pérdidas eléctricas en la red de distribución de energía es la ineficiencia en la entrega y uso de la electricidad. Estas pérdidas pueden deberse al robo de energía, y resultan en la disipación de energía valiosa y en costos adicionales para las empresas de servicios públicos y los consumidores. La reducción de las pérdidas eléctricas no solo contribuye a un suministro más confiable y económico de energía, sino que también tiene un impacto positivo en la sostenibilidad ambiental al reducir la necesidad de generar energía adicional para compensar estas pérdidas. Poblacio…
ViaSegura @administrador Mié, 31/01/2024 - 12:46 Uno de los mecanismos para salvar vidas en siniestros viales es la detección temprana de las fallas en la infraestructura vial que potencialmente pueden ocasionar las catástrofes. Problema que se busca resolve Actualmente, 1.35 millones de personas mueren y 50 millones son heridas en siniestros de tránsito por año en el mundo. Reducir la siniestralidad en las vías e incrementar la seguridad es posible. Los traumatismos causados por el tráfico vehicular son la principal causa de muerte a nivel global de niños y adultos jóvenes entre 5 y 29 años. Las muertes por accidentes de tránsito son una crisis de salud pública que puede evitarse y que afecta particularmente a los países de ingresos medio y bajo.Las lesiones graves o fatales derivadas de incidentes de tráfico son resultado del diseño y mantenimiento de la infraestructura vial. La seguridad en las carreteras es un enfoque clave del Plan Global para la Segunda Década de Acción para la Seguridad Vial 2021-2030 , que insta a los gobiernos y socios a implementar el Enfoque de Sistemas Seguros para reducir a la mitad las muertes y lesiones viales para el año 2030. Poblaciones que se ven afectadas por el problema Peatones, conductores y pasajeros de vehículos, entidades de logística, concesionarios, y autoridades viales. Respuesta actual a este problema, considerando a las instituciones relacionadas. El análisis de la seguridad vial de las carreteras se por parte de personal espec…
Pavimenta2 @administrador Mié, 31/01/2024 - 12:40 Pavimenta2 permite la detección de defectos en el pavimento de autopistas y carreteras, así como la categorización de señales de tráfico verticales y horizontales. La herramienta automatiza el análisis de las condiciones del pavimento utilizando inteligencia artificial (IA) y visión por computadora a partir de videos capturados por una cámara estándar montada en un vehículo. Esto permite a las autoridades de transporte evaluar eficazmente el inventario de carreteras, cruzar datos de accidentes y tiempos de viaje, y estimar los costos de mantenimiento con recursos limitados. La herramienta acorta un proceso que podría llevar varios años a unas pocas semanas de recopilación de videos y horas de procesamiento de imágenes. Problema que se busca resolve Los defectos en el pavimento representan más que una simple incomodidad; constituyen un riesgo significativo para los conductores, dando lugar a colisiones y fatalidades. Históricamente, la identificación de los defectos en las carreteras ha sido un proceso manual, caracterizado por su naturaleza que consume mucho tiempo y sus costos elevados. Analizar manualmente una red de carreteras de 10,000 km demanda aproximadamente 78 semanas y un presupuesto que supera los $3,000,000. Ante semejantes complejidades, los países de América Latina y el Caribe han estado realizando análisis parciales y poco frecuentes de sus redes viales. Poblaciones que se ven afectadas por el problema Autorida…
Accelerating development velocity
Build an end-to-end payment flow with Next.js Server Actions
Alerta de sepsis hospitalaria Laura @administrador Jue, 25/01/2024 - 13:04 Se esta implementando una solución de inteligencia oficial en el Hospital Villa El Salvador para la identificación de sepsis utilizando algoritmos desarrollados por la empresa Laura. Problema que se busca resolve Detección temprana de sepsis hospitalaria Poblaciones que se ven afectadas por el problema Población internada en el hospital Villa El Salvador de Lima. Propuesta para solucionar dicho problema usando IA Detección temprana de sepsis con base en información de historia clínica electrónica. Avances/resultados Se está llevando acabo la implementación. Metas Finalización del piloto e inicio de análisis de datos. Principales retos en la implementación Participación del equipo del hospital dado el limitado numero de personal con el que cuentan. Principales retos de la IA identificados Acceso a información debidamente estructurada. Sector Salud País Perú Contacto fairlac@iadb.org Entidad Ejecutora MINSA Estado Uso y monitoreo
Protección trayectorias educativas @administrador Jue, 25/01/2024 - 13:00 El sistema educativo uruguayo presenta importantes desafíos para lograr trayectorias continuas, completas y exitosas de los jóvenes. A pesar de haber alcanzado la cobertura universal en primaria, un importante porcentaje de estudiantes uruguayos repite los primeros grados (13,4% en 1º grado y 6,9% en segundo grado). Estas altas tasas de repitencia generan rezago, y al llegar al 6º grado el 29% de los alumnos tienen sobreedad. Estos problemas en la educación primaria tienen impacto en la educación media (EM), donde Uruguay enfrenta tres retos: (i) la integración, retención y egreso de los jóvenes en el sistema; (ii) la calidad en términos de aprendizajes y desarrollo de competencias: y (iii) la equidad. Respecto al primer reto, aun cuando la EM es obligatoria la asistencia escolar disminuye a partir del ingreso a este nivel. En 2013 la tasa neta de matrícula (TNM) en la educación media básica (EMB) fue de 76,2%. A la dificultad que presenta el sistema para incorporar a los jóvenes se le suma el hecho de que el 25% de los estudiantes uruguayos de EMB tienen dos o más años de rezago escolar. Como resultado, la tasa de egreso de la EMB es solo del 57%. Todos los indicadores de la EMB ponen a Uruguay en comparación desfavorable respecto de países como Chile (88%) y Ecuador (73%). La situación se agrava en la educación media superior (EMS), donde se evidencian las dificultades del sistema para retener a los…
Predicción de Abandono y Reprobación Escolar @administrador Jue, 25/01/2024 - 12:55 El proyecto de construcción de algoritmos predictivos de deserción y fracaso escolar tiene como objetivo la modelación de algoritmos que predicen el riesgo de deserción y reprobación escolar para el nivel de secundaria. Problema que se busca resolve Reducir abandono escolar y tasas de repitencia. Poblaciones que se ven afectadas por el problema Estudiantes de 10-12 año (ensino medio) de Redes Estaduales Públicas Respuesta actual a este problema, considerando a las instituciones relacionadas. Acciones inorgánicas con escaso respaldo en información actualizada, para docentes, directores y niveles regionales de gestión. Propuesta para solucionar dicho problema usando IA Elaborar un sistema de alerta temprano para detectar abandono y reprobación – piloto con escuelas de secundaria de la Secretaria Estadual de Educação do Espírito Santo (Brasil), con apoyo del Instituto Unibanco. ¿Qué consideraciones de seguridad, leyes nacionales o estándares se tienen que tener en cuenta para utilizar cada fuente de información? Ley de protección de datos personales. Avances/resultados Convenios de uso de datos firmados: inicio de las tareas técnicas de desarrollo de algoritmos de predicción. Metas Concluir algoritmos de predicción. Elección de próxima red educativa. Principales retos en la implementación Definición de protocolos de intervención pedagógicos a partir de los riesgos identificados. Gobernanza. Prin…
Asignación centralizada de alumnos (Perú) @administrador Jue, 25/01/2024 - 12:45 Alertas de riesgo 3 años. Se usa ML para predecir riesgo Problema que se busca resolve El problema descrito es que los usuarios interactúan con plataformas centralizadas de búsqueda y elección, como los sistemas de selección de escuelas, con información limitada y creencias sesgadas. Esto conduce a ineficiencias y, en ocasiones, a desigualdades en las asignaciones finales. También hay evidencia de que buscar opciones como escuelas es costoso y que los solicitantes enumeran demasiadas pocas opciones debido a creencias sesgadas sobre las probabilidades de asignación. Poblaciones que se ven afectadas por el problema Los estudiantes de Tacna que ingresan al sistema educativo entre pre-K y grado 1 (3-6 años) Respuesta actual a este problema, considerando a las instituciones relacionadas. El artículo propone colaborar con los Ministerios de Educación para enviar "boletines informativos" a los solicitantes durante la fase piloto de plataformas centralizadas de elección de escuelas. Estos boletines, dados a un grupo seleccionado al azar, proporcionan información sobre las opciones de escuelas disponibles para mejorar la toma de decisiones y las asignaciones. Propuesta para solucionar dicho problema usando IA El envío de estas tarjetas permite que los usuarios cuenten con más información en el momento de la elección de plataforma digital Avances/resultados Los estudiantes agregan más y mejores escuelas e…
Asignación centralizada de docentes (Ecuador) @administrador Jue, 25/01/2024 - 12:22 Quiero ser maestro Problema que se busca resolve El proyecto busca resolver las ineficiencias en el mercado laboral de docentes, causadas por una falta de información. Examina cómo las preferencias de los profesores por escuelas más cercanas a su hogar, ubicadas en áreas urbanas, con mejor infraestructura o con estudiantes de mayor ventaja socioeconómica, resultan en una alta demanda para algunas escuelas y vacantes en otras. Poblaciones que se ven afectadas por el problema Los maestros que participan del concurso Quiero Ser Maestro en Ecuador Respuesta actual a este problema, considerando a las instituciones relacionadas. El estudio pone a prueba una intervención con el fin de mejorar la asignación de empleos y la tasa de plazas ocupadas, proporcionando información más precisa a los docentes. Propuesta para solucionar dicho problema usando IA Específicamente, para informar mejor a los candidatos a docentes que participaron en el proceso de selección de Ecuador en 2021, estos últimos recibieron un informe personalizado a través de WhatsApp y correo electrónico que contenía un resumen de su solicitud. Para los candidatos cuyo riesgo estimado de no ser asignados era "alto" (por encima de un nivel de corte definido), el informe también incluía una advertencia de riesgo de no asignación y una lista de escuelas recomendadas donde tenían mayores posibilidades de asegurar una posición. Evaluamos el…
Egyptian prop-tech startup Partment is working to redefine second-home ownership and real estate investment via its “invest and experience” co-ownership platform. Founded in 2022, Partment offers users the chance to part-invest in meticulously curated properties, enabling personalised and diversified real estate portfolios. Through fractional ownership, co-owners access a set number of nights for personal use [...]
Core DAO is excited to introduce the African Innovation Fund, a groundbreaking initiative to provide resources and networks to support local Web3 builders and projects across the African continent. This new fund is set to foster innovation, sustainability, accessibility and growth within the African blockchain ecosystem. If you’re building interesting web3 projects in Africa and [...]
The United Nations Development Programme (UNDP) has launched the “timbuktoo” initiative together with African countries, which it said is positioned to be “the world’s largest financing facility”, bringing catalytic and commercial capital together to support Africa’s startup ecosystem. President Paul Kagame of Rwanda, President Nana Akufo-Addo of Ghana, African Continental Free Trade Area secretariat secretary [...]
Africa Tech Summit Nairobi has announced the 10 African tech ventures that will showcase their solutions to an audience of industry experts, investors and fellow innovators on February 14-15. Africa Tech Summit Nairobi is a leading African tech event providing insight and networking with the African tech ecosystem, bringing together tech leaders, MNOs, banks, international [...]
Egyptian digital healthcare startup Yodawy, which has pioneered a pharmacy benefit management platform in the MENA region using technology, expert pharmacists, and state-of-the-art logistics, has raised US$10 million in extra funding, taking its total raised capital to US$34.5 million. Founded in 2018, Yodawy enables its partners – insurance companies, medical providers, pharmacies, and pharmaceutical/FMCG companies [...]
Disrupt Africa has released the third – and last – episode of its three-part podcast series zeroing in on the state of Africa’s ed-tech space, looking at trends, opportunities and challenges within the vital sector. Disrupt Podcast has released a number of focused series in the last couple of years, including ones on venture capital, [...]