Lifeboat Foundation

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-renaiss…rlin-2022/

A detailed programme, and all the information — including logistics and hotels accommodations — are ** available on this page:**

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R&D & Innovation For U.S. Security & Resilience — 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 Security’s (DHS) operational Components and first responders across the nation.

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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 — 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.

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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. Here’s a short video about life in China:

China’s 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.

Continue reading “Residents in locked down Shanghai scream from their balconies: ‘This cannot last’” »

The goal of this virtual workshop is to discuss whether microbial pathogens may represent a causal component of Alzheimer’s 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 Alzheimer’s disease.

All comments must conform to NIA’s comments policy: https://go.usa.gov/xtqAQ

In the field of artificial intelligence, reinforcement learning is a type of machine-learning strategy that rewards desirable behaviors while penalizing those which aren’t. An agent can perceive its surroundings and act accordingly through trial and error in general with this form or presence – it’s 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 policy’s 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 agent’s 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.

Continue reading “Google AI Researchers Propose a Meta-Algorithm, Jump Start Reinforcement Learning, That Uses Prior Policies to Create a Learning Curriculum That Improves Performance” »

CSIS will host a public event on responsible AI in a global context, featuring a moderated discussion with Julie Sweet, Chair and CEO of Accenture, and Brad Smith, President and Vice Chair of the Microsoft Corporation, on the business perspective, followed by a conversation among a panel of experts on the best way forward for AI regulation. Dr. John J. Hamre, President and CEO of CSIS, will provide welcoming remarks.

Keynote Speakers:
Brad Smith, President and Vice Chair, Microsoft Corporation.
Julie Sweet, Chair and Chief Executive Officer, Accenture.

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This doesn’t mean you need to don an ushanka and start marching. You can start taking collective action by focusing on community groups and connecting with climate leaders. This will likely help you solve other issues, too, like waste disposal, recycling, and community clean-up projects in your locale.

Conclusion

Combating climate change requires all of us to reconsider our individual and collective climate responsibility. As an individual, you can do your part and let others know what you are doing. It has never been easier to connect with the world and share than it is today. You can join others in writing letters to the editor of your local newspaper, or your local and national government representatives. You can join groups like Citizens’ Climate Lobby and learn how to engage policy decision-makers. And in your daily routines, you can lead by example.