AI: a transformative force in maternal healthcare

Curated on Posted on by Stefaan Verhulst

Article by Afifa Waheed: “Artificial intelligence (AI) and robotics have enormous potential in healthcare and are quickly shifting the landscape – emerging as a transformative force. They offer a new dimension to the way healthcare professionals approach disease diagnosis, treatment and monitoring. AI is being used in healthcare to help diagnose patients, for drug discovery and development, to improve physician-patient communication, to transcribe voluminous medical documents, and to analyse genomics and genetics. Labs are conducting research work faster than ever before, work that otherwise would have taken decades without the assistance of AI. AI-driven research in life sciences has included applications looking to address broad-based areas, such as diabetes, cancer, chronic kidney disease and maternal health.

In addition to increasing the knowledge of access to postnatal and neonatal care, AI can predict the risk of adverse events in antenatal and postnatal women and their neonatal care. It can be trained to identify those at risk of adverse events, by using patients’ health information such as nutrition status, age, existing health conditions and lifestyle factors. 

AI can further be used to improve access to women located in rural areas with a lack of trained professionals – AI-enabled ultrasound can assist front-line workers with image interpretation for a comprehensive set of obstetrics measurements, increasing quality access to early foetal ultrasound scans. The use of AI assistants and chatbots can also improve pregnant mothers’ experience by helping them find available physicians, schedule appointments and even answer some patient questions…

Many healthcare professionals I have spoken to emphasised that pre-existing conditions such as high blood pressure that leads to preeclampsia, iron deficiency, cardiovascular disease, age-related issues for those over 35, various other existing health conditions, and failure in the progress of labour that might lead to Caesarean (C-section), could all cause maternal deaths. Training AI models to detect these diseases early on and accurately for women could prove to be beneficial. AI algorithms can leverage advanced algorithms, machine learning (ML) techniques, and predictive models to enhance decision-making, optimise healthcare delivery, and ultimately improve patient outcomes in foeto-maternal health…(More)”.

Gen AI: too much spend, too little benefit?

Curated on Posted on by Stefaan Verhulst

Article by Jason Koebler: “Investment giant Goldman Sachs published a research paper about the economic viability of generative AI which notes that there is “little to show for” the huge amount of spending on generative AI infrastructure and questions “whether this large spend will ever pay off in terms of AI benefits and returns.” 

The paper, called “Gen AI: too much spend, too little benefit?” is based on a series of interviews with Goldman Sachs economists and researchers, MIT professor Daron Acemoglu, and infrastructure experts. The paper ultimately questions whether generative AI will ever become the transformative technology that Silicon Valley and large portions of the stock market are currently betting on, but says investors may continue to get rich anyway. “Despite these concerns and constraints, we still see room for the AI theme to run, either because AI starts to deliver on its promise, or because bubbles take a long time to burst,” the paper notes. 

Goldman Sachs researchers also say that AI optimism is driving large growth in stocks like Nvidia and other S&P 500 companies (the largest companies in the stock market), but say that the stock price gains we’ve seen are based on the assumption that generative AI is going to lead to higher productivity (which necessarily means automation, layoffs, lower labor costs, and higher efficiency). These stock gains are already baked in, Goldman Sachs argues in the paper: “Although the productivity pick-up that AI promises could benefit equities via higher profit growth, we find that stocks often anticipate higher productivity growth before it materializes, raising the risk of overpaying. And using our new long-term return forecasting framework, we find that a very favorable AI scenario may be required for the S&P 500 to deliver above-average returns in the coming decade.”…(More)“

The era of predictive AI Is almost over

Curated on Posted on by Stefaan Verhulst

Essay by Dean W. Ball: “Artificial intelligence is a Rorschach test. When OpenAI’s GPT-4 was released in March 2023, Microsoft researchers triumphantly, and prematurely, announced that it possessed “sparks” of artificial general intelligence. Cognitive scientist Gary Marcus, on the other hand, argued that Large Language Models like GPT-4 are nowhere close to the loosely defined concept of AGI. Indeed, Marcus is skeptical of whether these models “understand” anything at all. They “operate over ‘fossilized’ outputs of human language,” he wrote in a 2023 paper, “and seem capable of implementing some automatic computations pertaining to distributional statistics, but are incapable of understanding due to their lack of generative world models.” The “fossils” to which Marcus refers are the models’ training data — these days, something close to all the text on the Internet.

This notion — that LLMs are “just” next-word predictors based on statistical models of text — is so common now as to be almost a trope. It is used, both correctly and incorrectly, to explain the flaws, biases, and other limitations of LLMs. Most importantly, it is used by AI skeptics like Marcus to argue that there will soon be diminishing returns from further LLM development: We will get better and better statistical approximations of existing human knowledge, but we are not likely to see another qualitative leap toward “general intelligence.”

There are two problems with this deflationary view of LLMs. The first is that next-word prediction, at sufficient scale, can lead models to capabilities that no human designed or even necessarily intended — what some call “emergent” capabilities. The second problem is that increasingly — and, ironically, starting with ChatGPT — language models employ techniques that combust the notion of pure next-word prediction of Internet text…(More)”

Scaling Synthetic Data Creation with 1,000,000,000 Personas

Curated on Posted on by Stefaan Verhulst

Paper by Xin Chan, et al: “We propose a novel persona-driven data synthesis methodology that leverages various perspectives within a large language model (LLM) to create diverse synthetic data. To fully exploit this methodology at scale, we introduce Persona Hub — a collection of 1 billion diverse personas automatically curated from web data. These 1 billion personas (~13% of the world’s total population), acting as distributed carriers of world knowledge, can tap into almost every perspective encapsulated within the LLM, thereby facilitating the creation of diverse synthetic data at scale for various scenarios. By showcasing Persona Hub’s use cases in synthesizing high-quality mathematical and logical reasoning problems, instructions (i.e., user prompts), knowledge-rich texts, game NPCs and tools (functions) at scale, we demonstrate persona-driven data synthesis is versatile, scalable, flexible, and easy to use, potentially driving a paradigm shift in synthetic data creation and applications in practice, which may have a profound impact on LLM research and development…(More)”.

Collaborating with Journalists and AI: Leveraging Social Media Images for Enhanced Disaster Resilience and Recovery

Curated on Posted on by Stefaan Verhulst

Paper by Murthy Dhiraj et al: “Methods to meaningfully integrate journalists into crisis informatics remain lacking. We explored the feasibility of generating a real-time, priority-driven map of infrastructure damage during a natural disaster by strategically selecting journalist networks to identify sources of image-based infrastructure-damage data. Using the REST Twitter API, 1,000,522 tweets were collected from September 13-18, 2018, during and after Hurricane Florence made landfall in the United States. Tweets were classified by source (e.g., news organizations or citizen journalists), and 11,638 images were extracted. We utilized Google’s AutoML Vision software to successfully develop a machine learning image classification model to interpret this sample of images. As a result, 80% of our labeled data was used for training, 10% for validation, and 10% for testing. The model achieved an average precision of 90.6%, an average recall of 77.2%, and an F1 score of .834. In the future, establishing strategic networks of journalists ahead of disasters will reduce the time needed to identify disaster-response targets, thereby focusing relief and recovery efforts in real-time. This approach ultimately aims to save lives and mitigate harm…(More)”.

Building an AI ecosystem in a small nation: lessons from Singapore’s journey to the forefront of AI

Curated on Posted on by Stefaan Verhulst

Paper by Shaleen Khanal, Hongzhou Zhang & Araz Taeihagh: “Artificial intelligence (AI) is arguably the most transformative technology of our time. While all nations would like to mobilize their resources to play an active role in AI development and utilization, only a few nations, such as the United States and China, have the resources and capacity to do so. If so, how can smaller or less resourceful countries navigate the technological terrain to emerge at the forefront of AI development? This research presents an in-depth analysis of Singapore’s journey in constructing a robust AI ecosystem amidst the prevailing global dominance of the United States and China. By examining the case of Singapore, we argue that by designing policies that address risks associated with AI development and implementation, smaller countries can create a vibrant AI ecosystem that encourages experimentation and early adoption of the technology. In addition, through Singapore’s case, we demonstrate the active role the government can play, not only as a policymaker but also as a steward to guide the rest of the economy towards the application of AI…(More)”.

Future of Professionals

Curated on Posted on by Stefaan Verhulst

Report by Thomson Reuters: “First, the productivity benefits we have been promised are now becoming more apparent. As AI adoption has become widespread, professionals can more tangibly tell us about how they will use this transformative technology and the greater efficiency and value it will provide. The most common use cases for AI-powered technology thus far include drafting documents, summarizing information, and performing basic research. Second, there’s a tremendous sense of excitement about the value that new AI-powered technology can bring to the day-to-day lives of the professionals we surveyed. While more than half of professionals said they’re most excited about the benefits that new AI-powered technologies can bring in terms of time-savings, nearly 40% said the new value that will be brought is what excites them the most.

This report highlights how AI could free up that precious commodity of time. As with the adoption of all new technology, change appears moderate and the impact incremental. And yet, within the year, our respondents predicted that for professionals, AI could free up as much as four hours a week. What will they do with 200 extra hours of time a year? They might reinvest that time in strategic work, innovation, and professional development, which could help companies retain or advance their competitive advantage. Imagine the broader impact on the economy and GDP from this increased efficiency. For US lawyers alone, that is a combined 266 million hours of increased productivity. That could translate into $100,000 in new, billable time per lawyer each year, based on current average rates – with similar productivity gains projected across various professions. The time saved can also be reinvested in professional development, nurturing work-life balance, and focusing on wellness and mental health. Moreover, the economic and organizational benefits of these time-savings are substantial. They could lead to reduced operational costs and higher efficiency, while enabling organizations to redirect resources toward strategic initiatives, fostering growth and competitiveness.

Finally, it’s important to acknowledge there’s still a healthy amount of reticence among professionals to fully adopt AI. Respondents are concerned primarily with the accuracy of outputs, and almost two-thirds of respondents agreed that data security is a vital component of responsible use. These concerns aren’t trivial, and they warrant attention as we navigate this new era of technology. While AI can provide tremendous productivity benefits to professionals and generate greater value for businesses, that’s only possible if we build and use this technology responsibly.”…(More)”.

AI-Ready FAIR Data: Accelerating Science through Responsible AI and Data Stewardship

Curated on Posted on by Stefaan Verhulst

Article by Sean Hill: “Imagine a future where scientific discovery is unbound by the limitations of data accessibility and interoperability. In this future, researchers across all disciplines — from biology and chemistry to astronomy and social sciences — can seamlessly access, integrate, and analyze vast datasets with the assistance of advanced artificial intelligence (AI). This world is one where AI-ready data empowers scientists to unravel complex problems at unprecedented speeds, leading to breakthroughs in medicine, environmental conservation, technology, and more. The vision of a truly FAIR (Findable, Accessible, Interoperable, Reusable) and AI-ready data ecosystem, underpinned by Responsible AI (RAI) practices and the pivotal role of data stewards, promises to revolutionize the way science is conducted, fostering an era of rapid innovation and global collaboration…(More)”.

The Economy of Algorithms

Curated on Posted on by Stefaan Verhulst

Book by Marek Kowalkiewicz: “Welcome to the economy of algorithms. It’s here and it’s growing. In the past few years, we have been flooded with examples of impressive technology. Algorithms have been around for hundreds of years, but they have only recently begun to ‘escape’ our understanding. When algorithms perform certain tasks, they’re not just as good as us, they’re becoming infinitely better, and, at the same time, massively more surprising. We are so impressed by what they can do that we give them a lot of agency. But because they are so hard to comprehend, this leads to all kinds of unintended consequences.

In the 20th century, things were simple: we had the economy of corporations. In the first two decades of the 21st century, we saw the emergence of the economy of people, otherwise known as the digital economy, enabled by the internet. Now we’re seeing a new economy take shape: the economy of algorithms…(More)”.

UN adopts Chinese resolution with US support on closing the gap in access to artificial intelligence

Curated on Posted on by Stefaan Verhulst

Article by Edith Lederer: “The U.N. General Assembly adopted a Chinese-sponsored resolution with U.S. support urging wealthy developed nations to close the widening gap with poorer developing countries and ensure that they have equal opportunities to use and benefit from artificial intelligence.

The resolution approved Monday follows the March 21 adoption of the first U.N. resolution on artificial intelligence spearheaded by the United States and co-sponsored by 123 countries including China. It gave global support to the international effort to ensure that AI is “safe, secure and trustworthy” and that all nations can take advantage of it.

Adoption of the two nonbinding resolutions shows that the United States and China, rivals in many areas, are both determined to be key players in shaping the future of the powerful new technology — and have been cooperating on the first important international steps.

The adoption of both resolutions by consensus by the 193-member General Assembly shows widespread global support for their leadership on the issue.

Fu Cong, China’s U.N. ambassador, told reporters Monday that the two resolutions are complementary, with the U.S. measure being “more general” and the just-adopted one focusing on “capacity building.”

He called the Chinese resolution, which had more than 140 sponsors, “great and far-reaching,” and said, “We’re very appreciative of the positive role that the U.S. has played in this whole process.”

Nate Evans, spokesperson for the U.S. mission to the United Nations, said Tuesday that the Chinese-sponsored resolution “was negotiated so it would further the vision and approach the U.S. set out in March.”

“We worked diligently and in good faith with developing and developed countries to strengthen the text, ensuring it reaffirms safe, secure, and trustworthy AI that respects human rights, commits to digital inclusion, and advances sustainable development,” Evans said.

Fu said that AI technology is advancing extremely fast and the issue has been discussed at very senior levels, including by the U.S. and Chinese leaders.

“We do look forward to intensifying our cooperation with the United States and for that matter with all countries in the world on this issue, which … will have far-reaching implications in all dimensions,” he said…(More)”.