Currently, the mechanics of robotics does not allow for such dexterous movements, however companies such as Boston Dynamics are working towards improving this. Sentiment Analysis (also known as opinion mining or emotion AI) is a sub-field of NLP (Natural Language Processing) that tries to identify and extract opinions within a given text across blogs, reviews, social media, forums, news, etc. With CIMON, this is now in the early days of reality. A variety of factors are driving space organisations towards increased automation for space. Dr. Wanda Curlee: That’s quite interesting, because I know NASA has had its problems in the past, and I’m sure they learned from it. Whilst this would not negate the need for on the ground medical expertise, the ability to make early warning predictions, before symptoms grow and develop would allow for quicker preventative measures and would represent a significant breakthrough in ensuring the health and safety of astronauts on long-duration missions. This leads to improve satellite coordination and operations as fleets of small RoboSat constellations, bring flexible to operations, including relative positioning, communication, and end-of-life management, e.g. For instance, in the case of conducting an experiment, CIMON can aid astronauts by answering questions such as: what kind of tool do I need to use? Perception requires building understanding of the environment based on the sensor inputs to provide situational awareness for space robotic agents, explorers and assistants. Along the same rationale as using autonomous robots for exploring hazardous planetary environments, Robonauts can be deployed to carry out these tasks, significantly reducing the current risk to astronaut safety today. Therefore an AI command computer onboard the probe is necessary to operate the probe in navigation to deal with course corrections and communications. It is still “early days” for most insurers in Canada when it comes to employing artificial intelligence (AI), but exploration and use of the techniques is … AI can even be used to analyse the data gathered onboard the spacecraft to determine the most useful and significant data to transmit back to Earth. AI can therefore find underutilised portions of the electromagnetic spectrum without human intervention. RoboSats have been proposed for space exploration missions. Artificial Intelligence is not a new phenomena. Whilst autonomous drones provides an example of the state of existing technology on Earth, there are significant limitations to deploying this technology for deep space exploration missions. AI will have a significant impact that touches many different aspects of human and robotic exploration missions. The manual decision making is taken at the sequence level which are the low level commands that actually get communicated to the spacecraft. AI robotics has the potential to create a new labour force in low-value industries (agriculture and factories) or harsh/dangerous environments (oil rigs, mines). A number of space agencies are looking into building a base or settlement on the Moon to support cislunar and deep space activities. Machine learning often necessitates an “expert”, in many cases team of expert scientists, to create “feature extractors” which enable the model to learn. Future AI robots will need to improve beyond basic rover movements to incorporate new forms of mobility, including: walking, flying, climbing, rappelling, tunnelling, swimming, and sailing. Artificial Intelligence is an evolving field, whereby new forms of deep learning are showing great promise, yet lack significant scalability and cannot be relied upon currently. With a single objective function, such as to score the highest amount of points in the Go example, or traverse 100m across open terrain to point B in a space exploration context, an AI powered machine, using reinforcement learning, can become competent through its only learning and adaptation. Most of it may sound hypothetical, but it will prove to be a lot of help to astronauts. The AI would be trained with the millions of the moon’s images and then use a neural network to create a virtual moon’s map. The key benefit to emphasise is an ability to handle unforeseen and unpredictable scenarios with some autonomy onboard the probe. In the future, this could lead to probes that are sent into deep space, powered by deep learning algorithms, that can perform autonomous science experiments, by collecting and analysing data, forming their own hypotheses and sending results back to Earth. As per the World Economic Forum, Artificial Intelligence automation will replace more than 75 million jobs by 2022. Could the same computer algorithms that teach autonomous cars to drive safely help identify nearby asteroids or discover life in the universe? D, Rogers, A, 2014. AI powered healthcare in the form of doctor assisted recommendations is ready to become a huge breakthrough in the medical industry, with AI models able to make very accurate and reliable diagnoses through pattern recognition and NLP techniques (though patient consent, data privacy and data security all represent major blocks in the use of AI in the public health system). Advanced navigation, such as autonomous rendezvous and docking requires improved guidance, navigation and control (GNC) algorithms, along with improved docking and capture mechanisms and interfaces for multi-agent coordination. It is possible to categorise AI into four major areas: (1) a foundational layer which encompasses traditional methods such as statistics and econometrics, complexity theory, and game theory; (2) a behavioral layer which serves as the operational processes such as process automation, machine translation, and collaborative and adaptive systems; (3) a sensory layer which provides information to the model in the form of language, audio, and vision; (4) a cognitive layer which provides the “intelligence”, including machine learning (deep learning), reasoning, and knowledge representation. Deep learning models are able to find their own features of interest which is a huge advantage in the area of scientific discovery where humans do not know what to look for and have incomplete information. Although astronauts are trained physically and psychologically to deal with extreme space situations, living in a confined space with no gravity could sometimes be stressful and could hamper their decision-making processes. AI builds upon this deep history and current developments in space robotics to offer promising augmentation capabilities to achieve complete autonomy, allowing for greater perception (vision) and dexterity which enables robots to make their own decisions. Artificial Intelligence enables local decision-making which sets future “intelligent” probes apart from their counterparts. Due to the complex nature of space exploration, there exists a lag between technology readiness on Earth, in typical industries such as retail and food where AI can be more quickly implemented and tested, such as marketing, demand modelling, and supply chain logistics, and for space. However, the process of scheduling the DSN is highly time-consuming and complex. 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