Archive for the ‘policy’ category: Page 6

May 13, 2022

Kathryn Coulter Mitchell — R&D For US Security & Resilience — Science & Technology Directorate — DHS

Posted by in categories: biotech/medical, cybercrime/malcode, government, policy, science

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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May 13, 2022

A Generalist Agent

Posted by in categories: policy, robotics/AI

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.

Apr 26, 2022

Olivia Zetter — Head of Government Affairs and AI Strategy — National Resilience, Inc.

Posted by in categories: biotech/medical, government, military, policy, robotics/AI, terrorism

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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Apr 18, 2022

Residents in locked down Shanghai scream from their balconies: ‘This cannot last’

Posted by in categories: biotech/medical, finance, food, policy

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.

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Apr 12, 2022

Infectious Etiology of Alzheimer’s Disease Workshop — Day 1

Posted by in categories: biotech/medical, neuroscience, policy

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

Apr 11, 2022

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

Posted by in categories: information science, policy, robotics/AI

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.

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Apr 10, 2022

Responsible AI in a Global Context

Posted by in categories: business, economics, governance, policy, robotics/AI, security

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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Mar 29, 2022

Taking Definitive Action on Your Sustainability Goals

Posted by in categories: climatology, government, policy, sustainability

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.


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.

Mar 24, 2022

A Talk Sponsored

Posted by in categories: nuclear energy, policy

It’s a big ask to tell countries with very little access to electricity to accept the same level of responsibility as electricity-rich nations in striving to achieve the net-zero 2050 emissions target set by the United Nations. And nuclear energy has to be in the mix.

Is the IPCC goal of getting to net-zero by 2050 aspirational or legitimate? A Foreign Policy Review panel tackles the question.

Mar 20, 2022

AI and Human Enhancement: Americans’ Openness Is Tempered by a Range of Concerns

Posted by in categories: economics, information science, policy, robotics/AI, surveillance, transportation

Developments in artificial intelligence and human enhancement technologies have the potential to remake American society in the coming decades. A new Pew Research Center survey finds that Americans see promise in the ways these technologies could improve daily life and human abilities. Yet public views are also defined by the context of how these technologies would be used, what constraints would be in place and who would stand to benefit – or lose – if these advances become widespread.

Fundamentally, caution runs through public views of artificial intelligence (AI) and human enhancement applications, often centered around concerns about autonomy, unintended consequences and the amount of change these developments might mean for humans and society. People think economic disparities might worsen as some advances emerge and that technologies, like facial recognition software, could lead to more surveillance of Black or Hispanic Americans.

This survey looks at a broad arc of scientific and technological developments – some in use now, some still emerging. It concentrates on public views about six developments that are widely discussed among futurists, ethicists and policy advocates. Three are part of the burgeoning array of AI applications: the use of facial recognition technology by police, the use of algorithms by social media companies to find false information on their sites and the development of driverless passenger vehicles.

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