Back in 2022, the tech community was buzzing over image-generating AI models, such as Midjourney, Stable Diffusion, and OpenAI's DALL-E 2, which could follow simple word prompts to depict fantasylands or whimsical chairs made of avocados. But artists saw this technological wonder as a new kind of theft. They felt the models were effectively stealing and replacing their work. Ben Zhao, a computer security researcher at the University of Chicago, was listening. He and his colleagues have built arguably the most prominent weapons in an artist's arsenal against nonconsensual AI scraping: two tools called Glaze and Nightshade that add barely perceptible perturbations to an image's pixels so that machine-learning models cannot read them properly.

Hunger crises are critical global issues affecting millions, particularly in low-income and developing countries. This research investigates how machine learning can be utilized to predict and inform decisions regarding famine and hunger crises. By leveraging a diverse set of variables (natural, economic, and conflict-related), three machine learning models (Linear Regression, XGBoost, and RandomForestRegressor) were employed to predict food consumption scores, a key indicator of household nutrition. The RandomForestRegressor emerged as the most accurate model, with an average prediction error of 10.6%, though accuracy varied significantly across countries, ranging from 2% to over 30%. Notably, economic indicators were consistently the most significant predictors of average household nutrition, while no single feature dominated across all regions, underscoring the necessity for comprehensive data collection and tailored, country-specific models. These findings highlight the potential of machine learning, particularly Random Forests, to enhance famine prediction, suggesting that continued research and improved data gathering are essential for more effective global hunger forecasting.

Russia fired its biggest barrage of loitering munitions to date at Ukraine overnight as Kyiv prepares to commemorate victims of the 1930s famine orchestrated by Soviet leader Josef Stalin to force Ukrainian peasants onto collective farms. Ukraine's air defense said it shot down 71 of 75 Shahed-131/136 drones aimed mainly toward the capital region and launched from two directions within Russia. The overnight attack caused electricity to be shut off to hundreds of people in Kyiv and the surrounding region, the provider said Saturday morning as temperatures dipped to around freezing. The air alert in Kyiv lasted for about six hours. Falling debris from a drone set a fire in a kindergarten, while some apartment buildings were damaged and five people were wounded, said Mayor Vitali Klitschko.

There are three components to a search problem. The first is the finite discrete search space, Ω, which is the set of elements to be examined. Next is the target set, T, which is a nonempty subset of the search space that we are trying to find. Finally, we have an external information resource, F, which provides an evaluation of elements of the search space. Typically, there is a tight relationship between the target set and the external information resource, as the resource is expected to lead to or describe the target set in some way, such as the target set being elements which meet a certain threshold under the external information resource. Within the framework, we have an iterative algorithm which seeks to find elements of the target set, shown in Figure 1. The algorithm is a black-box that has access to a search history and produces a probability distribution over the search space. At each step, the algorithm samples over the search space using the probability distribution, evaluates that element using the information resource, adds the result to the search history, and determines the next probability distribution. The abstraction of finding the next probability distribution as a black-box algorithm allows the search framework to work with all types of search problems.

Building on the view of machine learning as search, we demonstrate the necessity of bias in learning, quantifying the role of bias (measured relative to a collection of possible datasets, or more generally, information resources) in increasing the probability of success. For a given degree of bias towards a fixed target, we show that the proportion of favorable information resources is strictly bounded from above. Furthermore, we demonstrate that bias is a conserved quantity, such that no algorithm can be favorably biased towards many distinct targets simultaneously. Thus bias encodes trade-offs. The probability of success for a task can also be measured geometrically, as the angle of agreement between what holds for the actual task and what is assumed by the algorithm, represented in its bias. Lastly, finding a favorably biasing distribution over a fixed set of information resources is provably difficult, unless the set of resources itself is already favorable with respect to the given task and algorithm.

Traditionally famine is classified in five stages, from "minimal" food insecurity through "crisis" to "famine". Modelling of last year's famine in Somalia suggested intervening before stage five could reduce aid costs by 30 per cent. But the real saving, Mr Kim said, would be in preventing the permanent developmental damage done to children by malnutrition, which leaves them with "fewer neural connections" and less ability to participate in the workforce. Research shows that children born during a famine earn around 13 per cent less over their lifetime. Mr Kim said: "One of the things that has been shown in Ethiopia is that if you think a famine is coming, if you do something really simple like double the amount of cash transfers that poor people get, you can actually stop the famine from going forward. "We have not yet had a system like Artemis that tells us about potential famines that early in the process... our hope is that by working closely with it we'll be able to detect potential famines early enough that we can start investing in things."

The usual people are teaming up with the usual people to try to harness artificial intelligence (AI). That is, Google, Amazon and Microsoft are tying up with the UN, the World Bank and the Red Cross to try to use algorithms to predict famine. Every conference this year contains a dead human genius reincarnated as software system or a robot. Yes, there is a lot of hype, but there is real worth in AI and Machine Learning. Read our counseling on how to avoid adopting "black box" approach.

At this week's United Nations General Assembly, the World Bank, the United Nations, and the Red Cross teamed up with tech giants Amazon, Microsoft, and Google to announce an unlikely new tool to stop famine before it starts: artificial intelligence. The Famine Action Mechanism (FAM), as they're calling it, is the first global tool dedicated to preventing future famines -- no small news in a world where one in nine people don't have enough food. Building off of previous famine-prediction strategies, the tool will combine satellite data of things like rainfall and crop health with social media and news reports of more human factors, like violence or changing food prices. It will also establish a fund that will be automatically dispersed to a food crisis as soon as it meets certain criteria, speeding up the often-lengthy process for funding famine relief. For a famine to be declared in a country or region, three criteria have to be met: At least one in five households has an extreme lack of food; over 30 percent of children under five have acute malnutrition; and two out of 10,000 people die each day.

WASHINGTON – Tech giants Microsoft, Amazon and Google are joining forces with international organizations to help identify and head off famines in developing nations using data analysis and artificial intelligence, a new initiative unveiled Sunday. Rather than waiting to respond to a famine after many lives already have been lost, the tech firms "will use the predictive power of data to trigger funding" to take action before it becomes a crisis, the World Bank and United Nations announced in a joint statement. "The fact that millions of people -- many of them children -- still suffer from severe malnutrition and famine in the 21st century is a global tragedy," World Bank Group President Jim Yong Kim said in a statement. "We are forming an unprecedented global coalition to say, 'no more.' " Last year more than 20 million people faced famine conditions in Nigeria, Somalia, South Sudan and Yemen, while 124 million people currently live in crisis levels of food insecurity, requiring urgent humanitarian assistance for their survival, the agencies said. Over half of them live in areas affected by conflict.

Despite being a slow-moving disaster, famine is notoriously difficult to predict. The reason for this, experts say, is that severe food shortages are hardly ever about food supply alone. A famine might be triggered by drought or some other climatic interference in crop production, but other powerful forces usually bring the scourge to full bloom: food price inflation, political instability, military conflict and even too much rain. "The root cause of famine is extremely complex," said Franck Bousquet, senior director of the World Bank Fragility, Conflict, and Violence Group (FCV). "Usually, the poorest and most vulnerable are the most affected and the least able to cope with shocks that other populations can absorb. Out of the last 10 major famines, nine have resulted from conflict and war."