The application of convolutional autoencoder deep learning to imaging data for planetary science and astrobiological use is briefly reviewed and explored with a focus on the need to understand algorithmic rationale, process, and results when machine learning is utilized. Successful autoencoders train to build a model that captures the features of data in a dimensionally reduced form (the latent representation) that can then be used to recreate the original input. One application is the reconstruction of incomplete or noisy data. Here a baseline, lightweight convolutional autoencoder is used to examine the utility for planetary image reconstruction or inpainting in situations where there is destructive random noise (i.e., either luminance noise with zero returned data in some image pixels, or color noise with random additive levels across pixel channels). It is shown that, in certain use cases, multi-color image reconstruction can be usefully applied even with extensive random destructive noise with 90% areal coverage and higher. This capability is discussed in the context of intentional masking to reduce data bandwidth, or situations with low-illumination levels and other factors that obscure image data (e.g., sensor degradation or atmospheric conditions). It is further suggested that for some scientific use cases the model latent space and representations have more utility than large raw imaging datasets.

AI and ML enable rapid processing of large datasets, and offer advanced feature extraction and pattern recognition capabilities that deliver meaningful insights, enhancing human analysts' ability to identify correlations within complex, multi - variable datasets. This is especially needed for astrobiology, where m odels must distinguish complex biotic patterns fro m intricate abiotic backgrounds. As data volume outpaces the capacity for timely data analysis, AI and ML become essential for data processing. They could also prove invaluable for the complex data analysis that will accompany flight instruments ' advancements. ML has been widely applied in image processing of large datasets in astrophysics and Earth observation ( e.g., crater identification [2 - 4], sample targeting [5]). Similar techniques that share methodology but are improved for onboard computational rest rictions could be leveraged for astrobiology missions to identify key features [6].

A new study involving University of Oxford researchers has found that artificial intelligence could accelerate the search for extraterrestrial life by showing the most promising places to look. The findings have been published in Nature Astronomy. In the search for life beyond Earth, researchers have few opportunities to collect samples from Mars or elsewhere. This makes it critical that these missions target locations that have the best chance of harbouring life. In this new study, researchers demonstrated that artificial intelligence (AI) and machine learning methods can support this by identifying hidden patterns within geographical data that could indicate the presence of life.

Research team leader Moeto Nagai explains: "We wanted to apply AI to the detection of single cells. As I had been performing mostly experiment-based research, the use of experimental data for AI research seemed to me to be a significant obstacle. However, the participation of graduate student Tanmay Debnath, the lead author of our study, who has experience in the research and development of AI technology, meant that we could rapidly make use of AI and ultimately led to the success of our development." The single-cell isolation and detection developed by this research can also be used to automatically monitor the activities of single cells. This research achieves accurate and highly reliable automated cell detection, while reducing human labor. Future applications for single-cell analysis include medical engineering applications in a wide range of areas such as cancer diagnosis, immune response, and drug discovery screening, which will contribute to the discovery of new treatment methods.

Exoplanet detection opens the door to the discovery of new habitable worlds and helps us understand how planets were formed. With the objective of finding earth-like habitable planets, NASA launched Kepler space telescope and its follow up mission K2. The advancement of observation capabilities has increased the range of fresh data available for research, and manually handling them is both time-consuming and difficult. Machine learning and deep learning techniques can greatly assist in lowering human efforts to process the vast array of data produced by the modern instruments of these exoplanet programs in an economical and unbiased manner. However, care should be taken to detect all the exoplanets precisely while simultaneously minimizing the misclassification of non-exoplanet stars.

Let me tell you what it's like to be an astrobiologist. I painted a white picket fence this summer. It was a task I'd set myself without realizing what a long-winded and frustrating process it would be. But eventually that endless scraping, priming, painting, and maneuvering settled into something therapeutic, even meditative. I'd paint the apex--dab, dab--run down the narrow sides, coat the smooth front, shuffle along, repeat. All the while acutely aware of being surrounded by the churn of summer in the northern hemisphere of a living planet.

We design and train a neural network (NN) model to efficiently predict the infrared spectra of interstellar polycyclic aromatic hydrocarbons (PAHs) with a computational cost many orders of magnitude lower than what a first-principles calculation would demand. The input to the NN is based on the Morgan fingerprints extracted from the skeletal formulas of the molecules and does not require precise geometrical information such as interatomic distances. The model shows excellent predictive skill for out-of-sample inputs, making it suitable for improving the mixture models currently used for understanding the chemical composition and evolution of the interstellar medium. We also identify the constraints to its applicability caused by the limited diversity of the training data and estimate the prediction errors using a ensemble of NNs trained on subsets of the data. The power of these topological descriptors is demonstrated by the limited effect of including detailed geometrical information in the form of Coulomb matrix eigenvalues.

Anonymous claims Nasa is about to announce it has found alien life. The truth is a little more complicated – but no less wondrous. An account affiliated with the hacking and activism collective has released a viral video claiming Nasa is "on the verge" of detailing contact with extraterrestrial species. It takes much of its evidence from the work the space agency is doing to explore space and look for alien worlds across the universe. And while such claims might overestimate just how quickly the discovery will emerge, they are based in the truth.

Some things in life cannot be offset by a mere net gain in intelligence. The last few years have seen the widespread recognition that sophisticated AI is under development. Bill Gates, Stephen Hawking, and others warn of the rise of "superintelligent" machines: AIs that outthink the smartest humans in every domain, including common sense reasoning and social skills. Superintelligence could destroy us, they caution. In contrast, Ray Kurzweil, a Google director of engineering, depicts a technological utopia bringing about the end of disease, poverty and resource scarcity.