Listening Post

The boys talk about Thorium power, but before they get into that, they try "Sour Me," a peach sherbet, sour beer from DuClaw.

A few years ago, everybody was all excited about Thorium energy. It's abundant, safe, you can't make bombs out of it. So what's the problem? Why aren't we investing in Thorium power?

The boys discuss nuclear energy in general, Thorium power, and why this is a better path than solar or "renewable" energies.

Welcome to Finance and Fury, The Furious Friday Edition

Today – SDG9 - How infrastructure spending helps an economy -

Anyone who knows basic economic and GDP has learnt that Infrastructure spending leads to GDP growth – so the theory says – Very hard to measure benefits/gains – Direct through spending in GDP equation – flow on effects

First – estimates Research provided from McKinsey and UN – MK established the Global Infrastructure Initiative (GII) in 2012

1. MK Two reports – 2015 to 2016 – World spent $9.5trn (14% GDP) $9.6trn follow year
   1. Transport, power, water, telcom – $2.5trn, Social infrastructure, oil and gas, mining – $2.4trn, Real estate - $4.65trn
2. Spending trajectory points to a shortfall of about $350 billion a year to what we are told we need –
   1. but triples when including funds needed to meet the UN Sustainable Development Goals
   2. Report states – meeting is critical for the future of undersupplied regions such as Africa – remember Africa
   3. Emerging economies account for some 60% of that need
3. Projects – no idea – broken into categories – power, water, etc – but on what, who knows – Have programs going – but go to each and it is just another rabbit hole trying to find each individual thing
4. This ep - Focus of policy and recommendations to provide grease to the wheels of bureaucracy:
   1. Helping Financial System out through Governments
   2. Talk about the theory as justification and also the

Regulations/Legislation - Basel III, Solvency II, pension fund allocation rules

1. Basel III and Solvency II mandate classifying infrastructure as high-risk capital allocations
   1. Also - pension funds have allocation rules that specifically limit their exposure to asset classes and countries
   2. Recognizing infrastructure as an asset class with a lower risk profile – Infrastructure can be low or high risk
      1. Low upon completion and proof of profitability – In developing – high risk – many infrastructure projects don’t meet forecasts – known as ‘white elephants’
2. To avoid low return investments – governments need more oversight and analysis before a decision in made –
   1. More regulations for infrastructure works –
      1. Question: If there was a special equation to perfectly predict profitability – Gov wouldn’t be ones who have it
      2. Sadly – no tool exists – only outcome from involvement in assessment is higher costs and more delays due to layers of legislation and checklists before approval

1. Road pricing and other fees
   1. toll roads, bridges, and tunnels are increasingly common around the world.
   2. Taxes and fees - Cities including London have introduced congestion pricing on urban roads.
2. Property value capture - Governments acquire land around an infrastructure project - profit once the project is completed through lease or sale - using the resulting funds for new infrastructure investment - Spain added this to its constitution.
   1. Other methods – more traditional – increase general or specific property taxes and fees from owners/developers
3. Changes in public accounting and budgeting frameworks
   1. Treating infrastructure as an asset on a public balance sheet and depreciating it over time rather than adding the entire cost of a project to the fiscal deficit up front
      1. mirrors corporate accounting practice - helpful to Gov – gets around limits on deficits and debt.
   2. Example – 3y $6bn project is being constructed and will take 6 years – one payment upfront now and one in 3 years
      1. Current – Adds $6bn onto deficit now - although the government is actually paying money only in years one and three, it books spending of $2 billion in each of the three years. However, the roads will be operational for the ...

Back to the Future by Dolly Church Drive-ins represent nostalgia for a past in which we were hopeful about the future. The future has since been swapped out for the past as a metric of progress, and we are caught in an endless loop of short-term solutions for problems that only mount with time. You can find more essays about living with technology at reallifemag.com and follow us on Twitter at _reallifemag.

Panelists:

• Yael Niv.
  • @yael_niv
• Konrad Kording
  • @KordingLab.
  • Previous BI episodes:
    • BI 027 Ioana Marinescu & Konrad Kording: Causality in Quasi-Experiments.
    • BI 014 Konrad Kording: Regulators, Mount Up!
• Sam Gershman.
  • @gershbrain.
  • Previous BI episodes:
    • BI 095 Chris Summerfield and Sam Gershman: Neuro for AI?
    • BI 028 Sam Gershman: Free Energy Principle & Human Machines.
• Tim Behrens.
  • @behrenstim.
  • Previous BI episodes:
    • BI 035 Tim Behrens: Abstracting & Generalizing Knowledge, & Human Replay.
    • BI 024 Tim Behrens: Cognitive Maps.

This is the third in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. In this episode, the panelists discuss their experiences with stochastic processes, including Bayes, decision-making, optimal control, reinforcement learning, and causality.

The other panels:

• First panel, about model fitting, GLMs/machine learning, dimensionality reduction, and deep learning.
• Second panel, about linear systems, real neurons, and dynamic networks.
• Fourth panel, about basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization.
• Fifth panel, about “doing more with fewer parameters: Convnets, RNNs, attention & transformers, generative models (VAEs & GANs).
• Sixth panel, about advanced topics in deep learning: unsupervised & self-supervised learning, reinforcement learning, continual learning/causality.