A chance meeting with Jake Taylor, National Institute of Standards and Technology's (NIST) new lead for the AI Safety Institute Consortium,a led me to wonder about the interoperability of machine learning (ML) and large language model (LLM) systems. I am persuaded that these powerful technologies will be widely used, and we will likely want or even need for them to interwork. Looking at today's LLMs, one is struck by their glib ability to generate text (among other modalities). Some of these systems have been outfitted with special-purpose application programming interfaces (APIs). For example, if the LLM discovers a need to respond to a mathematical computation, it might use a specialized interface to deliver the problem to MATLABb to be processed and return a result.

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The energy output a photo voltaic(PV) panel is a function of solar irradiation and weather parameters like temperature and wind speed etc. A general measure for solar irradiation called Global Horizontal Irradiance (GHI), customarily reported in Watt/meter$^2$, is a generic indicator for this intermittent energy resource. An accurate prediction of GHI is necessary for reliable grid integration of the renewable as well as for power market trading. While some machine learning techniques are well introduced along with the traditional time-series forecasting techniques, deep-learning techniques remains less explored for the task at hand. In this paper we give deep learning models suitable for sequence to sequence prediction of GHI. The deep learning models are reported for short-term forecasting $\{1-24\}$ hour along with the state-of-the art techniques like Gradient Boosted Regression Trees(GBRT) and Feed Forward Neural Networks(FFNN). We have checked that spatio-temporal features like wind direction, wind speed and GHI of neighboring location improves the prediction accuracy of the deep learning models significantly. Among the various sequence-to-sequence encoder-decoder models LSTM performed superior, handling short-comings of the state-of-the-art techniques.

Machine learning, in which computers learn new skills by looking for patterns in training data, is the basis of most recent advances in artificial intelligence, from voice-recognition systems to self-parking cars. It's also the technique that autonomous robots typically use to build models of their environments. That type of model-building gets complicated, however, in cases in which clusters of robots work as teams. The robots may have gathered information that, collectively, would produce a good model but which, individually, is almost useless. If constraints on power, communication, or computation mean that the robots can't pool their data at one location, how can they collectively build a model? At the Uncertainty in Artificial Intelligence conference in July, researchers from MIT's Laboratory for Information and Decision Systems will answer that question.