Africa faces significant challenges in healthcare delivery due to limited infrastructure and access to advanced medical technologies. This study explores the use of federated learning to overcome these barriers, focusing on perinatal health. We trained a fetal plane classifier using perinatal data from five African countries: Algeria, Ghana, Egypt, Malawi, and Uganda, along with data from Spanish hospitals. To incorporate the lack of computational resources in the analysis, we considered a heterogeneous set of devices, including a Raspberry Pi and several laptops, for model training. We demonstrate comparative performance between a centralized and a federated model, despite the compute limitations, and a significant improvement in model generalizability when compared to models trained only locally. These results show the potential for a future implementation at a large scale of a federated learning platform to bridge the accessibility gap and improve model generalizability with very little requirements.

Recent AI research has significantly reduced the barriers to apply AI, but the process of setting up the necessary tools and frameworks can still be a challenge. While AI-as-a-Service platforms have emerged to simplify the training and deployment of AI models, they still fall short of achieving true democratization of AI. In this paper, we aim to address this gap by comparing several popular AI-as-a-Service platforms and identifying the key requirements for a platform that can achieve true democratization of AI. Our analysis highlights the need for self-hosting options, high scalability, and openness. To address these requirements, we propose our approach: the "Open Space for Machine Learning" platform. Our platform is built on cutting-edge technologies such as Kubernetes, Kubeflow Pipelines, and Ludwig, enabling us to overcome the challenges of democratizing AI. We argue that our approach is more comprehensive and effective in meeting the requirements of democratizing AI than existing AI-as-a-Service platforms.

Stable Diffusion disrupted the deep learning scene when it was released in August, advancing the field of text-to-image models because of its ability to generate photo-realistic images given any text input. And unlike other models, Stable Diffusion makes its source code available, further advancing the democratization of AI. Stable Diffusion was created by Stability AI, in collaboration with EleutherAI and LAION. Stability AI is on a mission to design and implement solutions using collective intelligence and augmented technology and has developer communities with over 20,000 members who are building AI for the future. In this fireside chat, Emad Mostaque, CEO of Stability AI, will discuss emerging trends for open source AI infrastructure, the importance of data for real-world applications of AI, and predictions on the development of "text-to-everything" in artificial intelligence.

When you think about AI, you don't typically think about agriculture. But imagine how much easier farmers' lives would be if they could use computer vision to track livestock or detect pests in their fields. Just one problem: How can an enterprise leverage AI if they don't already have a team of data scientists? This is a pressing question not only in agriculture but also in a wide range of industrial businesses, such as manufacturing and logistics. After all, data scientists are in short supply! In this podcast, we explore how companies can deploy computer vision with their existing staff--no expensive hiring or extensive training required. We explain how to democratize AI so non-experts can use it, the possibilities that come from making AI more accessible, and unexpected ways AI transforms a range of industries. Our guests this episode are Elizabeth Spears, Co-Founder and Chief Product Officer for Plainsight, a machine learning lifecycle management provider for AIoT platforms, and Bridget Martin, Director of Industrial AI & Analytics of the Internet of Things Group at Intel . In her current role, Elizabeth works on innovating Plainsight's end-to-end, no-code computer vision platform. She spends most of her time focusing on products offered by Plainsight, particularly thinking of what new products to build, what order to build them in, and why they are needed. Bridget focuses on building up the knowledge and understanding that occur during the process of adopting AI, especially in an industrial space.

AI applications are starting to appear in almost all aspects of our everyday lives, from healthcare and finance to entertainment and environmental protection. But a large number of AI applications never make it from concept to implementation, and an even larger amount never even get started. How do we enable more data scientists and developers to quickly create the path from data to insights with the data and compute resources available to them? The key to traversing this path and enabling AI everywhere is software that both unlocks hardware performance and maximizes developer productivity. Dr. Wei Li is the Vice President and General Manager of AI & Analytics at Intel.

Socially disadvantaged communities have often raised legitimate concerns about being over-policed and under-protected. Now, the rise of AI algorithms driving a myriad of "predictive policing" attempts has threatened to exacerbate the problem. The use of automated algorithms in policing does not do away with inequity; biases might be introduced through how such machines are trained. The black-box nature of state-of-the-art AI algorithms that do not consider the underlying social mechanics of crime, fosters little confidence that such schemes can ultimately thwart crime in any meaningful manner. To make things worse, AI algorithms are demonstrably an effective force-multiplier for the state, manifesting an evermore intrusive control and surveillance apparatus to monitor all aspects of our lives.

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The start of the Democratizing AI Newsletter which focuses in the first edition on "Artificial Intelligence a Key Exponential Technology in the Smart Technology Era" coincides with the launch of BiCstreet's "AI World Series" Live event, which kicks off both virtually and in-person (limited) from 10 March 2022, where this theme, amongst others, will be discussed in more detail over a 10-week AI World Series programme. The event is an excellent opportunity for companies, startups, governments, organisations and white collar professionals all over the world, to understand why Artificial Intelligence is critical towards strategic growth for any department or genre. See the 10 Weekly Program here: https://www.BiCstreet.com)). We live in tremendously exciting times where we already experience the disruptive and far-reaching impact of a smart technology revolution that seems to be on track to comprehensively change how we live, work, play, interact, and relate to one another.

Enterprise deployment of artificial intelligence (AI) is positioned for tremendous growth. Artificial intelligence is set to change the business world by improving predictive analytics, sales forecasting, customer needs, process automation and security systems. IBM's Global AI Adoption Index revealed that a third of those surveyed will be investing in AI skills and solutions over the next 12 months. The latter group might include people in leadership, sales, finance, human resources and operations. This is where AI will shine, empowering business teams to make AI-driven decisions.

Gradsflow can automatically train Deep Learning Models for different tasks on your laptop or to a remote cluster directly from your laptop. It provides a powerful and easy-to-extend Model Training API that can be used to train almost any PyTorch model. You can use the AutoTask feature to automatically build and train models without writing any Machine Learning code across various areas including Vision and Text. Currently, it supports Image Classification, Text Classification, Sentiment Analysis and Text Summarization. Just dump the data and you are ready to train the model.