What will it Take for Security to be Taken very Seriously?

Good considerable piece by Bruce Schneier, aiming at the  Policy Makers.  Intro: 

What Will It Take?

What will it take for policy makers to take cybersecurity seriously? Not minimal-change seriously. Not here-and-there seriously. But really seriously. What will it take for policy makers to take cybersecurity seriously enough to enact substantive legislative changes that would address the problems? It’s not enough for the average person to be afraid of cyberattacks. They need to know that there are engineering fixes—and that’s something we can provide. ...  (much more) 

Thinking Industrial Policy

 Brought to my attention for comment,  somewhat dense, but scannable:

FIRST: A Note on "Industrial Policy”…

Stephen S. Cohen & J. Bradford DeLong

September, 2022

The not-quite-surprise passage of the CHIPS Act and the surprise passage of the IRA have brought the idea that the United States should consciously pursue  “industrial policy” back to the front burner of politics, and of political economy.

In some ways, however, “industrial policy” is a poisonous term in the discourse of U.S. politics. In the 1980s Democratic economic-policy stalwart Charles Schultze engaged in a full-throated campaign against the idea that the U.S. could run a successful “industrial policy”—“picking winners” in the rhetorical dismissive. Follower nations like Japan attempting to catch up to the U.S. that had succeeded in insulating economic bureaucracies from interest-group rent-seeking might be able to, he argued. But the United States would, to the extent that it embarked on industrial policy, further entrench dissipative rent-seeking interests. Allowing “industrial policy” into the rhetorical room would provide them with yet another set of plausible excuses that the legislators they influence could use for channeling resources in directions that had neither a valid social-welfare nor a valid economic-growth rationale for government protection and assistance.

So let us not call it “industrial policy”. Let us call it “pragmatism” instead. For it is a fact that, from Hamilton through Eisenhower and a bit longer, the American government’s attitude toward the use of public power and public funds for economic development was highly, highly pragmatic—and successful.

Looking back at the economic history of the United States, there is a pattern by which again and again the U.S. economy has been redesigned. The shifts of the economy toward new growth directions have sometimes been the emergent outcomes of innumerable individual actions guided by local price signals. But at other times, and perhaps at more times, they have not. New directions have, instead, been the results of purposeful decisions, taken by government backed by powerful and often broad political forces, guided by their vision of how the economy ought to change. And once the public sector and its allies have launched a new economic space, it has then been expanded and transformed in unimaginable ways by entrepreneurial activity and energy surging into those new directions.

Cities Using Digital Twins Examine SimCity for Policymakers: Metaverse

 Note considerable detail in the simulation here.

Cities Using Digital Twins Like SimCity for Policymakers

By Bloomberg CityLab, April 6, 2022

A collage of 3-D reality mesh images of Singapore

Said Cityzenith CEO Michael Jansen, “I think digital twins will deliver on the promise that open data failed to do. Without the digital twin calculator to make that data make sense, open data is a waste of money, to be honest.”

Cities like Orlando, FL, and Singapore are using digital twins to generate virtual models of themselves, in order to simulate the effects of potential new policies or infrastructure projects that can inform real-world decision-making.

For example, the Orlando Economic Partnership and gaming company Unity have developed a three-dimensional (3D) model of the region that the city can show to potential investors as it attempts to expand as a technology hub.

Meanwhile, the Virtual Singapore model incorporates over 3 million street-level and 160,000 aerial images, plus billions of 3D data points, exceeding 100 terabytes of raw data.

Singapore Land Authority's Victor Khoo said the model differentiates between individual elements, making it easier to test their responses to different conditions in various simulations.

From Bloomberg CityLab

View Full Article

Digital Twins for Virtual Cities

An approach I had not seen, but we had used a simplistic SimCity like model for thinking about interaction in groups.

 Cities Using Digital Twins Like SimCity for Policymakers

Bloomberg CityLab, Linda Poon, April 5, 2022

Cities like Orlando, FL, and Singapore are using digital twins to generate virtual models of themselves, in order to simulate the effects of potential new policies or infrastructure projects that can inform real-world decision-making. For example, the Orlando Economic Partnership and gaming company Unity have developed a three-dimensional (3D) model of the region that the city can show to potential investors as it attempts to expand as a technology hub. Meanwhile, the Virtual Singapore model incorporates over 3 million street-level and 160,000 aerial images, plus billions of 3D data points, exceeding 100 terabytes of raw data. Singapore Land Authority's Victor Khoo said the model differentiates between individual elements, making it easier to test its responses to different conditions in various simulations.  ... '

AI Development and Policymaking

Obvious, and exists well beyond AI into all decision oriented computation.    Must be done as part of a process understanding model of how decisions are made and implemented, under regulation and compliance.  So ultimately complex. 

Bridging The Gap Between AI Policymakers and AI Developers

By Analytics India Magazine, March 5, 2021in CACM

The current gap in policymakers' tech knowledge and technologists' ethics knowledge needs to be bridged to ensure AI's sustainable development.

To encourage more AI professionals to participate in public policy, the governments should encourage or invest in universities to introduce subjects on ethics, policy, and social sciences in AI and data science courses. 

Only by familiarizing themselves with AI through qualified technologists, policymakers can draft sensible regulation that strikes the right balance between developing ethical AI and maximizing its potential.

From Analytics India Magazine

Reinforcement Learning and Entropy

 Have recently been looking at Reinforcement Learning methods.   And this as a form of simulation-optimization for 'Twin' style models that need training.  Berkeley BAIR  makes some points about entropy (disorder) in a recent  article.   (Technical)  Consideringe the application. See the full article, linked to below,  for sufficient detail.

Maximum Entropy RL (Provably) Solves Some Robust RL Problems  By Ben Eysenbach    Mar 10, 2021    Berkeley BAIR  AI

Nearly all real-world applications of reinforcement learning involve some degree of shift between the training environment and the testing environment. However, prior work has observed that even small shifts in the environment cause most RL algorithms to perform markedly worse. As we aim to scale reinforcement learning algorithms and apply them in the real world, it is increasingly important to learn policies that are robust to changes in the environment.

Robust reinforcement learning maximizes reward on an adversarially-chosen environment.

Broadly, prior approaches to handling distribution shift in RL aim to maximize performance in either the average case or the worst case. The first set of approaches, such as domain randomization, train a policy on a distribution of environments, and optimize the average performance of the policy on these environments. While these methods have been successfully applied to a number of areas (e.g., self-driving cars, robot locomotion and manipulation), their success rests critically on the design of the distribution of environments. Moreover, policies that do well on average are not guaranteed to get high reward on every environment. The policy that gets the highest reward on average might get very low reward on a small fraction of environments. The second set of approaches, typically referred to as robust RL, focus on the worst-case scenarios. The aim is to find a policy that gets high reward on every environment within some set. Robust RL can equivalently be viewed as a two-player game between the policy and an environment adversary. The policy tries to get high reward, while the environment adversary tries to tweak the dynamics and reward function of the environment so that the policy gets lower reward. One important property of the robust approach is that, unlike domain randomization, it is invariant to the ratio of easy and hard tasks. Whereas robust RL always evaluates a policy on the most challenging tasks, domain randomization will predict that the policy is better if it is evaluated on a distribution of environments with more easy tasks.

Prior work has suggested a number of algorithms for solving robust RL problems. Generally, these algorithms all follow the same recipe: take an existing RL algorithm and add some additional machinery on top to make it robust. For example, robust value iteration uses Q-learning as the base RL algorithm, and modifies the Bellman update by solving a convex optimization problem in the inner loop of each Bellman backup. Similarly, Pinto ‘17 uses TRPO as the base RL algorithm and periodically updates the environment based on the behavior of the current policy. These prior approaches are often difficult to implement and, even once implemented correctly, they requiring tuning of many additional hyperparameters. Might there be a simpler approach, an approach that does not require additional hyperparameters and additional lines of code to debug? ... "