holy shit \(48\) wow wow yes \(8\)

\begin{equation} \frac{12}{3} - \int_{i}^{j} 8 \end{equation}

thta’s crazy.

The simplest channel-specific baseline I’d recommend is an input-gated residual belief update:

\[ q=\operatorname{LN}_x(x) \]

\begin{equation} c=\operatorname{LN}_s(s),\qquad s= \begin{cases} e_{\text{boundary}} & \text{first pass}\\ x_{\text{loop}} & \text{later passes} \end{cases} \end{equation}

\[ g=\tanh(\theta),\qquad \theta\in\mathbb{R}^{d},\quad\theta_0=0 \]

\[ q_{\text{updated}}=q+g\odot c \]

\[ x\leftarrow x+\operatorname{Attn}(q_{\text{updated}}) \]

Then keep the ordinary MLP update unchanged.

This is essentially the input-gate portion of an LSTM:

AI Safety as a Public Engagement

To what extend should we be making AI Safety as a public problem.

One-Liner

Novelty

Motivation

Notable Methods

Key Figs

• Public generally more pessimistic about AI
• Rates of adverse impact by AI decisions is reasonably low

New Concepts

Data Partnerships

Try to find independent sources of data, with contracts for how the data must be used.

Impact Horizons

Some random Instagram influencer had more accesses than our usual methods of scientific communication.