The Global Exploration Strategy is a blueprint for understanding space policy today.
15 years ago, the Global Exploration Strategy set out the space policy agenda on course to put humans back on the Moon as soon as possible.
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The United Kingdom has confirmed what everybody who ugly cried during âMy Octopus Teacherâ already knew: Octopuses are sentient â capable, that is, of perceiving things like pain and pleasure.
The country is adding an amendment to its Animal Welfare Sentience Bill to recognize creatures such as octopus, crabs, squids, and lobsters along with âall other decapod crustaceans and cephalopod molluscsâ as sentient creatures, according to a press release from the UK Department for Environment, Food & Rural Affairs. The bill aims to ensure animal sentience is taken into account when developing government policy, and as such could inform debates around animal rights and dietary choices.
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TOKYO, May 24 (Reuters) â President Joe Biden on Tuesday said there was no change to a U.S. policy of âstrategic ambiguityâ on Taiwan, a day after he angered China by saying he would be willing to use force to defend the democratic island.
The issue of Taiwan loomed over a meeting in Tokyo of leaders of the Quad grouping of the United States, Japan, Australia and India, who stressed their determination to ensure a free and open Indo-Pacific region in the face of an increasingly assertive China â though Japanese Prime Minister Fumio Kishida said the group was not aimed at any one country.
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The Festival will take place, from 7 to 9 July 2022, at the Archenhold Observatory in Berlin (Germany).
You are welcome to join the Festival in presence, sizing an excellent opportunity to visit the historic Archenhold Observatory and the beautiful city of Berlin. However, the Festival will be an hybrid conference, therefore virtual attendees are welcome as well.
Register here for free: https://spacerenaissance.space/register-to-the-space-renaissâŠrlin-2022/
A detailed programme, and all the information â including logistics and hotels accommodations â are ** available on this page:**
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R&D & Innovation For U.S. Security & Resilience â Kathryn Coulter Mitchell, Acting Under Secretary for Science and Technology, DHS Science and Technology Directorate, Department of Homeland Security.
Kathryn Coulter Mitchell (https://www.dhs.gov/person/kathryn-coulter-mitchell), is Acting Under Secretary for Science and Technology (S&T), at the U.S. Department of Homeland Security, where as the science advisor to the Homeland Security Secretary, she heads the research, development, innovation and testing and evaluation activities in support of the Department of Homeland Securityâs (DHS) operational Components and first responders across the nation.
Inspired by progress in large-scale language modelling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens.
During the training phase of Gato, data from different tasks and modalities are serialised into a flat sequence of tokens, batched, and processed by a transformer neural network similar to a large language model. The loss is masked so that Gato only predicts action and text targets.
When deploying Gato, a prompt, such as a demonstration, is tokenised, forming the initial sequence. Next, the environment yields the first observation, which is also tokenised and appended to the sequence. Gato samples the action vector autoregressively, one token at a time.
Making the future of medicine possible by rethinking how medicines are made â olivia zetter, head of government affairs & AI strategy, resilience.
Olivia Zetter is Head of Government Affairs and AI Strategy at National Resilience, Inc. (https://resilience.com/) a first-of-its-kind manufacturing and technology company dedicated to broadening access to complex medicines and protecting bio-pharmaceutical supply chains against disruption.
For those not paying attention, Tesla has been unable to build cars in China for a few weeks as China shuts down due to a zero Covid policy. Hereâs a short video about life in China:
Chinaâs financial hub Shanghai has started easing its lockdown in some areas on Monday, despite reporting a record high of more than 25,000 new Covid-19 infections, as authorities sought to get the city moving again after more than two weeks.
The goal of this virtual workshop is to discuss whether microbial pathogens may represent a causal component of Alzheimerâs disease, review knowledge gaps, and establish scientific priorities to address these gaps. The workshop discussed gaps in current knowledge and explored new opportunities for research in the areas intersecting infectious organisms and Alzheimerâs disease.
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In the field of artificial intelligence, reinforcement learning is a type of machine-learning strategy that rewards desirable behaviors while penalizing those which arenât. An agent can perceive its surroundings and act accordingly through trial and error in general with this form or presence â itâs kind of like getting feedback on what works for you. However, learning rules from scratch in contexts with complex exploration problems is a big challenge in RL. Because the agent does not receive any intermediate incentives, it cannot determine how close it is to complete the goal. As a result, exploring the space at random becomes necessary until the door opens. Given the length of the task and the level of precision required, this is highly unlikely.
Exploring the state space randomly with preliminary information should be avoided while performing this activity. This prior knowledge aids the agent in determining which states of the environment are desirable and should be investigated further. Offline data collected by human demonstrations, programmed policies, or other RL agents could be used to train a policy and then initiate a new RL policy. This would include copying the pre-trained policyâs neural network to the new RL policy in the scenario where we utilize neural networks to describe the procedures. This process transforms the new RL policy into a pre-trained one. However, as seen below, naively initializing a new RL policy like this frequently fails, especially for value-based RL approaches.
Google AI researchers have developed a meta-algorithm to leverage pre-existing policy to initialize any RL algorithm. The researchers utilize two procedures to learn tasks in Jump-Start Reinforcement Learning (JSRL): a guide policy and an exploration policy. The exploration policy is an RL policy trained online using the agentâs new experiences in the environment. In contrast, the guide policy is any pre-existing policy that is not modified during online training. JSRL produces a learning curriculum by incorporating the guide policy, followed by the self-improving exploration policy, yielding results comparable to or better than competitive IL+RL approaches.
