Small satellite technologies have enhanced the potential and feasibility of geodesic missions, through simplification of design and decreased costs allowing for more frequent launches. On-satellite data acquisition systems can benefit from the implementation of machine learning (ML), for better performance and greater efficiency on tasks such as image processing or feature extraction. This work presents convolutional autoencoders for implementation on the payload of small satellites, designed to achieve dual functionality of data compression for more efficient off-satellite transmission, and at-source anomaly detection to inform satellite data-taking. This capability is demonstrated for a use case of disaster monitoring using aerial image datasets of the African continent, offering avenues for both novel ML-based approaches in small satellite applications along with the expansion of space technology and artificial intelligence in Africa.

Algorithms for neural architecture search (NAS) seek to automate the design of high-performing neural architectures for a given dataset.

Singapore's Prime Minister Lawrence Wong said jobs for citizens will be the government's top priority as the city-state faces risks from rising global trade barriers and artificial intelligence. The impact of the U.S.-China rivalry, President Donald Trump's tariff war and threats to workers from new technologies were the key challenges Wong highlighted in his annual National Day Rally speech on Sunday, marking the country's 60th year. "This next chapter opens in a more troubled and turbulent world," Wong said.

We introduce the goal of learning a context-agnostic classifier, i.e., a classifier whose predictions are invariant under perturbations of the context.

Desination will be a red barn on the right 1. Continued on next page 18 Use Case Example rewrite Rewrite the following text to be more light-hearted: -- {very formal text} -- chat The following is a conversation with an AI assistant.

In total we have 1200 comparisons, and each comparison is rated by 3 raters. In total we have 2400 comparisons, and each comparison is rated by 3 raters. These were: 1. Coherence: Is the system's generation aligned in meaning and topic with the prompt? We sampled 100 prompts randomly from the corpus, and then evaluated 19 different algorithms. HITs was 2.2K, and the total number of ratings was 6.6K.