text:
Former Denver Schools Chief Michael Bennet Running for President
By Alyson Klein — May 02, 2019 2 min read
Sen. Michael Bennet, D-Colo., the former Denver schools superintendent, is joining the very crowded Democratic campaign for the White House.
Bennet, who announced his decision on CBS Thursday, has a long record in K-12 policy. As Denver schools chief, he negotiated changes to the district's ProComp pay-for-performance plan, working with the teacher's union, and started the city's color-coded rating system for schools. Those reforms may not have quite the shine they once did: Denver teachers went on strike earlier this year, in part because of issues with ProComp.
In 2009, President Barack Obama seriously considered making Bennet his first education secretary. The job went instead to Arne Duncan, who at the time was the Chicago schools chief. Instead, then-Gov. Bill Ritter of Colorado appointed Bennet to his Senate seat when then-Sen. Ken Salazar became secretary of the Interior.
As a senator, Bennet quickly joined the education committee, where he worked on legislation that built on Obama's K-12 agenda.That work included a bill to create a new leaders for turnaround schools, and bipartisan legislation to create a new kind of teacher training program, one that wouldn't let students graduate until they demonstrated that they could actually boost student achievement. Neither of those bills made it to legislative prime-time.
Bennet also tried—but didn't succeed—in championing legislation that would have required districts to show salary equity across schools before tapping federal funding. That issue—salary comparability—is near and dear to the civil rights community, but not so appealing to the teachers' unions.
During the development of the Every Student Succeeds Act, Bennet helped champion the Education Innovation and Research program, which helps districts and states scale up and test out promising practices. And he helped author a new pilot program in the law that allows districts to try out a "weighted student funding formula" in which money is tied directly to individual students. In the pilot, vulnerable groups—like English learners—carry with them more money than others.
Want to see how Bennet's record compares to the rest of the field? Check out our cheat sheet on the candidates here.
Photo: U.S. Senator Michael Bennet, D-Colo., greets voters before U.S. Senator Bernie Sanders speaks during a 2018 rally with young voters on the campus of the University of Colorado. (David Zalubowski/AP)
Follow us on Twitter at @PoliticsK12.
Alyson Klein
Alyson Klein is an assistant editor for Education Week.


text:
\section*{Acknowledgments}
We thank Oskar van der Wal and Lucas Weber for their valuable feedback.
We thank Jack Hoeksema for providing us with the list of NPIs.
MD and JS were funded by the European Research Council under the European Unions Seventh Framework Programme (FP/20072013)/ERC Grant Agreement n. STG 716230 CoSaQ.

\section{Filtered NPIs}
\label{appendix-npis}

We here present the full list of NPIs that were used for filtering sentences from the \fullName corpus, resulting in the \noNPIName corpus.
The method for selecting these expressions is described in Section \ref{sec:exp4}.\vspace{2mm}

\noindent\textit{A damn,
any,
any longer,
any old,
anybody,
anymore,
anyone,
anything,
anything like,
anytime soon,
anywhere,
anywhere near,
as yet,
at all,
avail,
by much,
can possibly,
could possibly,
ever,
in any,
in days,
in decades,
in minutes,
in years,
just any,
just yet,
let alone,
much help,
nor,
or anything,
set foot,
squat,
such thing,
that many,
that much,
that often,
the slightest,
whatever,
whatsoever,
yet.
}\vspace{2mm}

\noindent This resulted in a reduction of 75.062 sentences out of the 3.052.726 sentences in the original \fullName corpus (2.46\%).

\section{Linguistic Background}\label{sec:background}

\paragraph{Monotonicity}

Monotonicity is a property of a linguistic environment which determines what kind of inferences relating general and particular terms are valid in that environment.
If inferences from a general to a particular term are valid, the linguistic environment is said to be \textit{downward monotone} (DM).
If inferences are valid the other way around, from a particular to a general term, the linguistic environment is said to be \mbox{\textit{upward monotone} (UM)}.

Examples of expressions inducing DM environments are negation and quantifiers like \emph{nobody, no NP}, but also specific types of adverbs and the antecedents of conditional sentences.
For instance, \Next below exemplifies that in these environments the inference from a sentence with a general term (\emph{cookies}) to that sentence with a more particular term (\emph{chocolate cookies}) is valid, but not vice versa.

\ex.
\a. Mary did\textbf{n't} eat cookies. $\Rightarrow$\\Mary didn't eat \textit{chocolate} cookies. \label{de-inf}
\b. \textbf{Nobody} ate cookies. $\Rightarrow$\\Nobody ate \textit{chocolate} cookies.
\c. Mary \textbf{rarely} ate cookies. $\Rightarrow$\\Mary rarely ate \textit{chocolate} cookies.

Common examples of UM environments are (non-quantified) positive sentences, quantifiers such as \emph{somebody, many NP}, and other kind of adverbs.
\Next exemplifies that in these environments the inference from a sentence with a more particular term (\emph{chocolate cookies}) to the same sentence with a general term (\emph{cookies}) is valid, but not vice versa.

\ex.
\a. Mary ate \textit{chocolate} cookies.  $\Rightarrow$\\ Mary ate cookies. \label{ue-inf}
\b. \textbf{Everyone} ate \textit{chocolate} cookies. $\Rightarrow$ Everyone ate cookies.
\c. Mary \textbf{often} ate \textit{chocolate} cookies. $\Rightarrow$ Mary often ate cookies.

\paragraph{NPIs}
NPIs are expressions such as the English words \textit{any, anyone, ever}, whose acceptability depends on whether its linguistic environment is downward monotone \citep{Fauconnier, LadusawDiss, dowty1994role, kadmon1993, krifka1995, lahiri98, chierchia2006broaden, chierchia2013}.\footnote{See however \citealp{zwarts1995nonveridical, giannakidou1998polarity, barker2018negative} for different takes on NPI acceptability generalizations.}
While the conditions for NPI acceptability are complex, a good approximation is that NPIs are acceptable (or \textit{licensed}) in the syntactic scope of \textit{NPI licensors} that induce a DM environment.\footnote{An NPI occurs in the syntactic scope of a licensor if the licensor \textit{c-commands} the NPI.
An NPI licensor {c-commands} an NPI if the NPI is the licensor's sister node or one of its sister's descendants in a constituent tree \citep{reinhart1976syntactic}.
}
If we again consider the DM environment of \ref{de-inf} and the UM environment of \ref{ue-inf}, it can be seen that English \emph{any} is an NPI, as it is acceptable when inside the syntactic scope of negation (a DM expression) as in \Next[a], and not acceptable when they are in an UM environment as in \Next[b].

\ex.
\a. Mary didn't eat (any) cookies. \label{no-cookies}
\b. Mary ate (*any) cookies.

Importantly, monotonicity plays a role at the psychological level: human judgments about the monotonicity of a linguistic environment predict their judgments of NPI acceptability in that environment \citep{Chemla:2011aa, Denic-PIinferences}.
For example, how plausible someone finds the inference \ref{de-inf} predicts how acceptable they find the sentence \ref{no-cookies}.
Summing up, NPI licensing has a syntactic component (NPIs must reside in syntactic scope of a licensor) and a semantic component (NPI licensors are DM expressions), that are connected on a psychological level (monotonicity judgments predict NPI acceptability).
Our research aims to uncover whether this connection is exhibited by LMs as well.

\section{Conclusion}\label{sec:discussion}
Based on a series of experiments, we have established the following: (1) LMs categorize environments into DM and UM; (2) LMs are overall successful with NPI licensing; (3) LMs employ similar representational cues when processing NPIs and predicting monotonicity; (4) their categories of DM and UM environments can be learned independently of NPI occurrence; and (5) their connection between monotonicity and NPI processing is robust and not solely dependent on co-occurrence heuristics.
This demonstrates that LMs have quite sophisticated knowledge of NPI licensing, which may be similar to that of humans and constitutes a vital step towards better understanding the linguistic generalization capacities of LMs.

These results raise the question: what do LMs learn about the DM and UM environments which they succeed in finding?
Do they actually learn the inferential properties of those environments, or do they rely on some other property that DM environments have in common to categorize them as such?
A direction for future work would be to develop methods to probe the inferential capacities of LMs and explore how they align with the DM and UM categories they construct.

Another direction for future work would be to incorporate the recent advancements on probing-based interpretability methods in our experimental pipeline \citep{hewitt2019designing, voita2020information}.
Our DC Ranking method aligns the performance of a probe with that of the language model itself, which is related to the approaches of \citet{saphra2019understanding}, \citet{DBLP:journals/tacl/ElazarRJG21}, and  \citet{lovering2021}.
Placing our methodology more firmly in this body of work will allow for stronger conclusions to be drawn regarding the semantic knowledge of current language models.

\section{Experiments and Results}\label{sec:experiments}
In this section we describe the experimental pipeline in more detail.
A graphical overview of our experiments is depicted in Figures~\ref{fig:experimental_overview} and \ref{fig:dc_ranking_diagram}.
Each experiment description is directly followed by an \mbox{analysis} of its results.

\subsection{Experiment 1: Do LMs represent monotonicity information?} \label{sec:exp1}
In our first experiment, we test whether LMs trained on our \fullName~corpus possess a notion of monotonicity.
We train five different LMs and test how well they represent monotonicity properties of different environments by training linear \textbf{diagnostic classifiers} \citep[DCs,][]{Hupkes2018} on top of the hidden states of the LM.
To create a corpus of monotonicity sentences for training and testing the DCs, we leverage the corpus of \citet{warstadt-etal-2019-investigating}, now selecting all DM and UM sentences to build up a balanced corpus of these categories.
The nine environment classes in that corpus hence provide a broad spectrum of DM environments and their minimally different UM counterparts.

For training and testing the DCs, we consider the hidden states at the position directly before an NPI occurs (see Figure~\ref{fig:experimental_overview}).
The reason we train the DCs at this position is because only at this point we are sure that the monotonicity information should surface and be encoded \emph{linearly}.
This is due to the fact that the decoder of the LM that transforms a hidden state into a probability distribution is linear as well: if the probability of some token depends on a linguistic feature, this feature must hence be encoded linearly.
The DCs are implemented using the \texttt{diagNNose} library of \citet{jumelet-2020-diagnnose}, and trained using 10-fold cross-validation, Adam optimization \citep{DBLP:journals/corr/KingmaB14}, a learning rate of $10^{-2}$ and L1 regularization with $\lambda = 0.005$.

We train our monotonicity DCs in two separate ways.
First, we divide the entire monotonicity corpus into a 90/10 train/test split, sampled \textit{uniformly} across the different environment classes.
This allows us to examine whether DM and UM environments are linearly separable in a way that is applicable to all environment classes.
We refer to this classifier as the \textsf{All-ENV} DC.

Second, we move to a more fine-grained type of analysis.
High performance of the \textsf{All-ENV} DC namely does not provide evidence that monotonicity is encoded the same way for each environment: the set of salient hidden units used by the \textsf{All-ENV} DC for classifying monotonicity within the \textit{Adverbs} environment, for example, could be disjoint from the set of units used for the \textit{Only} environment.
To investigate this, we train a DC on the hidden states of all-but-one environment class, and test its performance on the excluded class.
This provides a measure to what extent the monotonicity representation of DM and UM environments derived from all other environment classes \textit{generalizes} to the held-out class, demonstrating stronger evidence that the model represent monotonicity in the same way across different environments.

\begin{figure*}
    \centering
    \includegraphics[width=\textwidth,clip,trim={0 4cm 0 3cm}]{figures/probe.pdf}

    \caption{
    Accuracy and standard deviation on the monotonicity diagnostic classification task, averaged over 5 seeds for each model type.
    The \textsf{All-ENV} column denotes train/test split procedure sampled uniformly over all environment class; other columns denote accuracy on one environment class that has been excluded during training.
    }\label{fig:exp1}
\end{figure*}
\begin{figure*}[ht]
    \centering
	\includegraphics[width=\textwidth,clip,trim={0 4cm 0 3cm}]{figures/warstadt.pdf}
	\vspace{-0.5cm}
	\setlength{\belowcaptionskip}{-10pt}
	\caption{
	Accuracy on the NPI acceptability task---based on whether the NPI was assigned a higher probability in the DM environment than in its UM counterpart.
	}\label{fig:npi_acceptability}\vspace{1cm}
\end{figure*}

\paragraph{Results}
The results of our first experiment are shown in the top row of Figure~\ref{fig:exp1}.
The first column contains the average accuracy for the \textsf{All-ENV} DC, and it can be seen that the diagnostic classifier succeeds in this task with high accuracy (97\%).
This indicates that the uniform split over all environment classes is linearly separable.

Next, we consider the held-out evaluation procedure for each of the nine environment classes.
It can be seen that the monotonicity signal generalizes well to five classes (\textit{adverbs, determiner negation, only, sentential negation, and embedded questions}), all with an accuracy above 90\%.
The other four classes yield a higher standard deviation, indicating that these classes are encoded less consistently across initialization seeds.
The accuracy for all held-out DCs is lower compared to the \textsf{All-ENV} DC results, indicating that the \textsf{All-ENV} DC relied partly on information unrelated to a shared notion of monotonicity.
The fact that the accuracy of these DCs is still so high, however, indicates that there is a substantial overlap between the way that monotonicity is encoded within the different environments.

\subsection{Experiment 2: Do LMs predict the licensing conditions of NPIs?}\label{sec:exp2}
In the next experiment we investigate the NPI acceptability judgments of the \fullName~LMs on the corpus of \citet{warstadt-etal-2019-investigating}.
This is done by comparing the probability of an NPI conditioned on the model's representation of a DM environment ($h^\downarrow_t$) and a UM environment ($h^\uparrow_t$), where success is defined as follows:
\[P_{\textsc{lm}}(\textsc{npi}|h^\downarrow_t) > P_{\textsc{lm}}(\textsc{npi}|h^\uparrow_t)\]
This is a common evaluation procedure in the interpretability literature \citep{Linzen2016}, and has earlier been applied in the domain of NPI licensing by \citet{Jumelet2018} and \citet{warstadt2019blimp}.
Our approach is similar to the Cloze Test of \citet{warstadt-etal-2019-investigating}, but their setup used (bi-directional) masked LMs, making it possible to directly compare the probabilities of the NPI licensor, instead of comparing the NPI probabilities.
Note that we purposefully do not base NPI acceptability on comparing full sentence probabilities: in our view this type of comparison can be distracted by token probabilities not related to the NPI itself.

We split this procedure out for each of the nine environment classes.
The example sentence of the Simple Questions environment in Table~\ref{tab:warstadt-overview}, for example, is evaluated as follows:
\[P_{\textsc{lm}}(\textit{ever}|\textit{Did the boy}) > P_{\textsc{lm}}(\textit{ever}|\textit{The boy did})\]
Using the full sentence probabilities for this comparison would require taking probabilities into account such as $P_{\textsc{lm}}(the|Did)$ and $P_{\textsc{lm}}(boy|The)$, that have no relation to NPI licensing at all.

\paragraph{Results}
We present the results for this experiment in the top row of Figure~\ref{fig:npi_acceptability}.
The \fullName models demonstrate a considerable ability at predicting NPI acceptability, with the least performing class (\mbox{\textsf{SMP-Q}}, \textit{Simple Questions}) yielding an accuracy that is still well above chance (0.72).
Compared to earlier investigations on the ability of LSTM LMs in NPI licensing, our results indicate that these models are able to obtain a more sophisticated understanding of NPIs than previously thought: both \citet{Marvin2019} and \citet{hu-etal-2020-systematic} report LSTM performance below chance on NPI acceptability tasks.
This might in part be due to the different evaluation procedure we used (conditional vs. full-sentence probability comparison).

\subsection{Experiment 3: Is the LM's knowledge of DM environments and of NPI licensing related?}\label{sec:exp3}
We have now established that our models encode a signal related to monotonicity, and are successful at predicting NPI acceptability.
In our third experiment, we assess to what extent the parameters used by the LM to predict NPIs (i.e.\ the LM's decoder embeddings for NPIs) \emph{overlap} with the information the DCs use to predict the monotonicity properties of a particular environment class.
For this we have devised a novel \textbf{DC ranking} method, that ranks the LM's decoder weights for all tokens based on their similarity with the DC weights.

\begin{figure}
    \centering
    \includegraphics[width=\columnwidth]{figures/NPI_monotonicity_overview_v6.pdf}

    \caption{
    The \textit{DC Ranking} experiment, in which we investigate whether the monotonicity DC and the LM decoder base their predictions on similar cues, by computing and ranking the cosine similarities between the DC weights and the decoder weights of each token.
    }
    \label{fig:dc_ranking_diagram}
\end{figure}

\begin{figure*}[ht]
    \centering
	\includegraphics[width=\textwidth,clip,trim={0 4cm 0 3cm}]{figures/median_rank.pdf}

	\caption{Results on the median NPI rank task.

	A low median rank indicates that the monotonicity DC uses the same representational information as the NPI decoder.
	}\label{fig:npi_rank}
\end{figure*}

We present a schematic overview of the method in Figure~\ref{fig:dc_ranking_diagram}.
The LM's decoder weight matrix can be interpreted as a collection of vectors corresponding to each token in the model's vocabulary.
The monotonicity DC is a binary classifier, so its weights are represented by a single vector.
The LM's decoder vectors are of the same dimensionality as the weight vector of the monotonicity DC, which allows us to compute the similarity between each decoder vector and the monotonicity DC.
For each of the 50.000 tokens in the LM's vocabulary, we calculate the cosine similarity between the decoder weights corresponding to that token and the DC's weights.
We then sort these similarity scores, which results in a ranking of tokens that are most similar to the DC.

As we are interested in finding the connection of the monotonicity DC and the LM's NPI processing in general, we compute the \textbf{median rank} over a set of 11 NPIs.\footnote{We consider the following 11 single-token NPI expressions: \textit{dared, any, anybody, anymore, anyone, anything, anywhere, ever, nor, whatsoever,} and \textit{yet}.
These are all the single-token NPIs taken from a list of NPIs that is described in \S\ref{sec:exp4}.}
A low median NPI rank indicates that the LM uses the same cues for NPI prediction as the monotonicity DC, demonstrating a clear connection between NPI licensing and monotonicity.

Contrary to Experiment~1, we no longer make use of the hold-one-out training procedure, that gave insights to what extent a general monotonicity signal generalizes to a held-out environment class.
Instead, we train a separate diagnostic classifier for each environment class using a train/test split made up of DM and UM environments within that class.
This results in a classifier that represents the class-specific decision boundary between minimal pairs of DM and UM items and allows us to investigate to what extent these decision boundaries align with the weights of the LM decoder.
Next to the environment-specific DCs we also report the DC ranking outcome for the \textsf{All-ENV} DC that has been trained on all environments.

\paragraph{Results}
The results of this experiment are presented in the top row of Figure~\ref{fig:npi_rank}.
The first column (\textsf{All-ENV}) contains the result for the DC trained on all environment classes, and the median NPI rank of 9 demonstrates that the monotonicity DC aligns very closely with the NPI decoder weights of the LMs.
This median rank should be interpreted within the context of the model vocabulary size: it can range upwards to 50.000, so a rank that is close to 0 signifies a tight connection between the probing task and the tokens of interest.

Moving on to the environment-specific results, it can be seen that the results vary considerably between the environment classes.
The worst scoring class is again that of Simple Questions.
This makes sense, as the licensing conditions for question constructions do not depend on the presence of a specific licensing token such as \textit{not}, but on the overall structure of the whole sentence.
The other environment classes lead to scores far closer to 0, indicating that for these classes monotonicity classification is closely aligned to NPI processing.

Interestingly, the median rank of the \textsf{All-ENV} DC is lower than the ranks of all other DCs.
This shows that the model has aligned its representation of NPIs to an aggregate of the monotonicity representations in the different environment classes.
This allows the model to flexibly deal with NPIs in a wide range of licensing environments.

\subsection{Experiment 4: Are NPIs important for learning monotonicity information?}\label{sec:exp4}
With Experiment 3 we established that NPI processing and monotonicity are related in our LMs.
Now, we investigate to what extent their representations are entangled during training.
More specifically, we investigate if the signal from the presence of NPIs is indispensable for the LM to develop a notion of monotonicity, or if instead the success in categorizing monotonicity environments can be learned independently of NPIs.

We address this question by testing whether LMs can still classify the monotonicity properties of environments when they are completely deprived of NPIs during training.
To do so, we train new language models on a modified corpus that does not contain any NPIs at all.
To arrive at this corpus, we remove all sentences that contain at least one NPI expression from the \fullName corpus.
We identify these expressions based on a comprehensive list of NPI expressions in English collected by \citet{hoeksema2012natural} and the list of NPIs in English compiled by \citet{israel2011grammar}.
From this list, we manually removed expressions that have both NPI and non-NPI uses (e.g.\ \textit{a thing, a bit}).
The 40 NPI expressions that resulted from this procedure can be found in Appendix~\ref{appendix-npis}.
We train 5 models on this corpus and refer to them by the name \noNPIName.

In this experiment, we run the monotonicity probing procedure of Experiment~1 on the \noNPIName models.
We posit that if the notion of monotonicity can be learned independently of NPIs, there should be no significant drop in performance compared to the results of the \fullName LMs.

\paragraph{Results}
We report the results of this experiment in the bottom row of Figure~\ref{fig:exp1}.
Again it can be noted that the \textsf{All-ENV} DC, trained and tested uniformly over all environment classes, obtains a high accuracy on the task (0.95).
Furthermore, none of the held-out environment DCs lead to significant drops in performance compared to the \fullName LMs.
Based on this we conclude that even in the absence of NPI cues, LMs are still able to build up a shared robust notion of monotonicity.

\subsection{Experiment 5: How robust is the connection between monotonicity and NPI processing?}
\label{sec:exp5}
This research aims to uncover whether LMs possess a robust connection between monotonicity and NPI licensing.
Our findings indicate that this connection is present in our models.
A major confound that has not yet been addressed, however, is the extent to which our models rely on collocational cues when judging the acceptability of an NPI.
To test this, we examine whether an LM's connection between NPIs and monotonicity \textit{generalizes} to novel environment classes in which NPIs have never been encountered during the training phase of the LM.

We have created nine modified corpora in which sentences with NPIs within a specific environment have been removed.
For these different corpora, we again consider the nine NPI-licensing environments of \citet{warstadt-etal-2019-investigating}.
For each environment class we create a new corpus by removing all sentences from the \fullName corpus in which an NPI expression from Appendix~\ref{appendix-npis} is preceded by an expression belonging to that class, somewhere earlier in the sentence.\footnote{For Simple Questions we remove a sentence if an NPI occurs in a sentence that ends with a question mark.}
Note that we only remove the sentences in which the environment actually licenses an NPI; sentences in which the environment occurs without an NPI are retained.
So for the \textit{adverbs} environment, for example, we remove sentences like ``\textit{Mary rarely ate any cookies}'' but not ``\textit{Mary rarely ate cookies}''.
For each of these nine corpora we train 3 new LMs.
Models trained on these corpora are referred to by the name \noENVName.

We run the NPI acceptability task of Experiment 2 and the DC ranking method of Experiment 3 on the nine types of \noENVName models.
A model with a robust connection between monotonicity and NPI processing should be able to learn for NPIs in the held-out environment that (i) the environment belongs to the class of environments in which NPIs are licensed, and that (ii) determining NPI acceptance should be done based on representational cues that are similar for monotonicity prediction.

\paragraph{Results}
We report the results of this experiment next to the previous results of the \fullName model.
First, we consider the NPI acceptability task, which is reported in the bottom row of Figure~\ref{fig:npi_acceptability}.
Note that each cell in this row now corresponds to a specific model type: the \textsf{ADV} result, for instance, corresponds to the accuracy of the \noENVName models in which sentences with NPIs within adverbial environments have been removed.
Our results show that the performance drops slightly for all environment classes, which can be attributed to a model's dependence on collocational cues.
However, the models are still able to adequately generalize from the other environments, in which NPIs still are encountered, to the held-out environment.
This demonstrates the semantic generalization capacities of the LM: it infers that the held-out environment in which NPIs have never been encountered shares some relevant properties with the other eight environment classes in which NPIs still occur.

The results for the DC ranking experiment are shown in the bottom row of Figure~\ref{fig:npi_rank}.
Similar to the NPI acceptability results, the performance of the \noENVName models has dropped slightly compared to the \fullName models.
However, if the models would no longer pick up on the connection between monotonicity in the held-out environment and NPI licensing at all, these median ranks should drop to chance, i.e. around the halfway mark of the vocabulary size (25.000).
It can be seen that this is only the case for the Simple Questions environment, that was already performing poorly for the \fullName models.
Based on this we conclude that although models depend partly on collocational cues for their connection between monotonicity and NPIs, they are still able to encode a robust connection that generalizes to novel DM environments.

\section{Introduction}

Neural language models (LMs) have become powerful approximators of human language, making it increasingly important to understand the features and mechanisms underlying their behavior \citep{blackbox18,blackbox19}.
In the past few years, a substantial number of studies have investigated the linguistic capabilities of LMs \citep[i.a.]{gulordava-etal-2018-colorless,giulianelli-etal-2018-hood,lakretz-etal-2019-emergence,DBLP:journals/corr/abs-2012-05395,ettinger2020bert}.
Such work has focused primarily on \emph{syntactic} properties, while fewer studies have been done on what kind of \emph{formal semantic} features are encoded by language models.
In this paper, we focus explicitly on what LMs learn about a semantic property of sentences, and in what ways their knowledge reflects well-known features of human language processing.

As the topic of our studies, we consider \textbf{monotonicity}, a semantic property of linguistic environments that plays an important role in human language understanding and inference \citep{hoeksema1986monotonicity, valencia1991studies, vanbenthem1995language, Icard-III:2014xy}: the monotonicity of a linguistic environment determines whether inferences from a general to a particular term or vice versa are valid in that environment.
For example, the fact that the inference from ``\textit{Mary didn't write a paper}'' to ``\textit{Mary didn't write a linguistics paper}'' is valid shows us that the position where ``\textit{a paper}'' occurs is \emph{downward monotone}: the inference is valid when a more general term (``\textit{a paper}'') is replaced with a more specific one (``\textit{a linguistics paper}'').

To investigate monotonicity we focus on \textbf{negative polarity items} (NPIs): a class of expressions such as \textit{any} or \textit{ever} that are solely acceptable in downward monotone environments \cite{Fauconnier, LadusawDiss}.
Psycholinguistic research has confirmed this connection between NPIs and monotonicity: humans judge NPIs acceptable in a linguistic environment if they consider that environment to be downward monotone \citep{Chemla:2011aa}.
Previous research has established that LMs are relatively successful in processing NPIs \citep{warstadt-etal-2019-investigating}, but without investigating \emph{how} they came to these successes.

We raise the following research questions: \begin{itemize}\setlength\itemsep{0mm}\vspace{-2.0mm}
    \item[\textbf{RQ1}] Do language models encode the monotonicity properties of  linguistic environments?
    \item[\textbf{RQ2}] To what extent do they employ this information when processing negative polarity items?
\end{itemize}

We developed a series of experiments, in which we first evaluate the general capacities of LMs in handling monotonicity and NPIs and then investigate the generalization heuristics of the LM by doing experiments with modified training corpora.
First, we establish that LMs are able to encode a notion of monotonicity by probing them with diagnostic classifiers \citep[DCs,][]{Hupkes2018} (\S\ref{sec:exp1}).
In our second experiment we demonstrate that our LMs are reasonably successful with NPI licensing using an NPI acceptability task (\S\ref{sec:exp2}).
Next, we introduce a novel \emph{DC ranking} method to investigate the overlap between the information that the model uses to make judgments about NPIs and the information that the DCs use to predict monotonicity information, finding that there is a significant overlap (\S\ref{sec:exp3}).

We then investigate two potential confounds that may obfuscate our results.
First, we consider whether the signal that is picked up by the monotonicity DC is not simply a proxy that tells the model that an NPI may occur at that position (\S\ref{sec:exp4}).
To assess this, we train new LMs on a corpus from which all sentences with NPIs have been removed, re-run the montonicity probing task, and find that even in the absence of NPI information, LMs are still able to encode a notion of monotonicity.

Next, we consider whether an LM bases its NPI predictions on simple co-occurrence heuristics, or if it can extrapolate from a general notion of monotonicity to cases of NPIs in environments in which they have never been encountered during training (\S\ref{sec:exp5}).
We again train new LMs on modified corpora, this time removing NPIs only in one specific environment, and repeat the NPI acceptability and DC ranking experiments.
The results of this setup demonstrate that LMs indeed use a general notion of monotonicity to predict NPI licensing.

\paragraph{Contributions}
With this work, we contribute to the ongoing study of the linguistic abilities of language models in several ways:
\begin{itemize}\setlength\itemsep{0mm}\vspace{-1.0mm}
    \item With a series of experiments we demonstrate that LMs are able to acquire a general notion of monotonicity that is employed for NPI licensing.

    \item We present two novel experimental setups: \emph{filtered corpus training} and \emph{DC ranking}, that can be used to assess the impact of specific information during training and compare the information used by DCs with the information used with the model, respectively.
    \item By using experimental results from psycho-semantics to motivate hypotheses for  LM behavior, we find that our models reflect behavior similar to human language processing.

\end{itemize}

In the remainder of this paper, we will first provide some linguistic background that helps to situate and motivate our experiments and results (\S\ref{sec:background}).
We then discuss related work on NPI processing in LMs in \S\ref{sec:related}.
In \S\ref{sec:methods}, we discuss our methods and experimental setup.
\S\ref{sec:exp1} through \S\ref{sec:exp5} explain and present the results.
We conclude in \S\ref{sec:discussion} with a general discussion and pointers to future work.

\section{Methods}\label{sec:methods}

Before getting to the main experimental part of our work, we briefly discuss the training corpus, model architecture and evaluation corpus we consider.

\paragraph{Training Corpus}
The base training corpus we consider in our experiments is the corpus used by \citet{gulordava-etal-2018-colorless}.
This corpus is a collection of sentences from Good and Featured English Wikipedia articles and consists of over 90M tokens.
The vocabulary of the corpus consists of the 50.000 most frequent tokens in this corpus; less frequent tokens are mapped to a special \texttt{<unk>} token.
We refer to the full training corpus type with the name \fullName, and to the LMs trained on this corpus as \fullName~LMs.
In addition to \fullName, we use multiple other corpora which are derived from \fullName~by means of filtering.
This will allow us to draw conclusions about specific generalization abilities and reliance on collocational cues of LMs; filtered corpora will be introduced in the relevant sections.

\paragraph{Model Architecture}
In our studies, we focus on recurrent language models.
More specifically, following \citet{gulordava-etal-2018-colorless}, we consider two-layer LSTM language models, with an embedding and hidden size of 650.
All training runs across our experiments follow the same regime, identical to the regime described by \citet{gulordava-etal-2018-colorless}: 40 epochs of training with SGD, with a plateau scheduler and an initial learning rate of 20, a batch size of 64, BPTT length of 35, and dropout of 0.1.\footnote{Models are trained on a \textit{GeForce 1080 Ti} GPU, take around 40 hours to train, and consist of ~71M parameters.}

\paragraph{Evaluation Corpus}
To assess monotonicity and NPI licensing knowledge of LMs in our experiments, we leverage the NPI corpus of \citet{warstadt-etal-2019-investigating}, which consists of a large amount of grammatical and ungrammatical sentences with NPIs.
This corpus is divided into 9 distinct \textbf{environment classes}, allowing for fine-grained analysis of NPI licensing.
Importantly, these nine environment classes come in two versions: a DM version---in which NPIs are grammatically acceptable, and a minimally different UM version---in which they are not.
We provide an overview with examples of DM and UM versions of all environment classes in Table~\ref{tab:warstadt-overview}.
The full size of the corpus is 106.000 distinct DM sentences, and the division of environment classes is split roughly uniformly.

\section{Related work}\label{sec:related}
The literature on interpreting LMs has grown substantially in the last few years \citep[see, e.g.][for survey papers]{Belinkov2019, Alishahi2019, rogers2021primer}.
Several studies investigate how they process NPIs, focused mainly on the \emph{syntactic} aspect of NPI licensing.

\citet{Jumelet2018} conclude that LSTM language models encode information about the dependency between the NPI and the NPI licensor, although this effect diminishes as the distance between the NPI and its licensor grows.
\citet{Marvin2019} study NPI judgments of LMs on minimally different sentence pairs (with the NPI licensor either in an appropriate syntactic configuration or not) and find that their models are unable to reliably assign higher probability to sentences in which NPIs are correctly licensed.
The syntactic aspect of NPI licensing is also examined by \citet{futrell-etal-2019-neural}, who demonstrate that LSTM LMs are susceptible to learning spurious licensing relationships, a finding that \citet{DBLP:warstadt2020} demonstrate to also hold for BERT \citep{DBLP:conf/naacl/DevlinCLT19}.
\citet{Wilcox2019} investigate how explicit syntactic supervision of LMs affects their success with syntactic aspects of NPI licensing.
The broad linguistic suites of \citet{warstadt2019blimp} and \citet{hu-etal-2020-systematic} also contain a set of tasks related to NPI licensing, demonstrating that it is one of the most challenging tasks for LMs to handle.
\citet{DBLP:journals/corr/abs-2101-11287} investigated the dynamics of NPI learning during training, and connected this to a multi-task learning paradigm, demonstrating that LMs are able to efficiently leverage information from related licensing environments.

Lastly, \citet{warstadt-etal-2019-investigating} examine BERT's ability in determining NPI acceptability.
They demonstrate that BERT has significant knowledge of the dependency between NPIs and their licensors, but that this success varies widely across different experimental methods.
Our study builds on that of \citet{warstadt-etal-2019-investigating}.
Although they demonstrate that BERT is generally successful with NPI licensing, their results do not reveal whether BERT has constructed a more general category of DM expressions that is independent of collocational cues, nor whether it has understood that this category matters for NPI licensing.

redpajama_set_name: RedPajamaArXiv


The smooth, supportive fit of the CALIA™ by Carrie Underwood Women's Essential Tight Fit Leggings will keep you comfortable throughout your day. Quick-drying, four-way stretch fabric and antimicrobial technology deliver the performance elements you need during a workout. A zip pocket is placed at the left hip for comfort when a pose requires you to lie on your back. Adding feminine detail, the Essential Leggings have ruching at the hem.


text:
Entrepreneur Perspectives Blog
The Entrepreneurship of P.T. Barnum as Featured in the Movie, The Greatest Showman
Perhaps it's the upbeat music, or the rags-to-riches story, or the fact that so many are fascinated by the circus—or maybe it's all of the above, but whatever the reasons, one of the best, most inspiring movies about entrepreneurship of all time is The Greatest Showman, starring Hugh Jackman and Zac Efron.
The 2017 movie is a musical retelling of how P.T. Barnum (as in the famous Ringling Brothers and Barnum and Bailey's Circus, which finally ended last year after a very long run) got into the circus business. It's not entirely accurate, and the real Barnum was not an entirely savory character, but right now we're talking about the movie, and it's a great show. It's a story about building a business and it's got great musical numbers and a great cast.
It doesn't hurt that my daughters love this movie. If you come to my house, you may hear The Greatest Showman soundtrack blasting from one of their rooms (or you might hear the music from Hamilton, which is a topic for another article), so it's on my mind a lot.
In The Greatest Showman, you get to see the main character go through a lot of the same challenges real entrepreneurs take on today. In the beginning, nobody (except his wife and kids) believes in him. He has to hold on to his convictions. Lots of people actually hate him and have a real problem with what he's trying to do, but he keeps going. He makes many mistakes but tries to get better each time, culminating with the finale, "From Now On".
At one point, he gets knocked back down to nothing and builds himself back up. He negotiates an important business partnership, something many of us have experience with. And just watch Barnum sell! Notice that while some of the exhibits aren't what Barnum says they are (hey, I didn't say he's perfect), he's promising customers entertainment and that's exactly what they get. He's not selling people something they won't really like.
If you're in business, you can't help but be inspired—at least if you're into on-screen musicals.
And since it's easy to feel like entrepreneurship is a bit of a circus sometimes, it's fun to see the story of an entrepreneur whose business actually was a circus!
Image of Hugh Jackman at The Greatest Showman Premier in Japan was taken by Dick Thomas Johnson under the Creative Commons 2.0 License.
By Eric Kasimov June 24, 2019
PreviousPrevious post:Perspectives from a Pushups4Parkinsons Cause Leader with Tom StephensonNextNext post:Perspectives on Pure Wisdom with Shakieta Zollicoffer
The Artificial Why
How I Met This Entrepreneur: Brady Murray

redpajama_set_name: RedPajamaCommonCrawl


text:
Hip-Hop Exhibit in Oakland Makes The 'Best Museum Exhibits In The USA For Summer 2018' List
USA Today posted their list of best museum exhibits in The USA for summer 2018, and Oakland Museum of California was included!
RESPECT: Hip-Hop Style & Wisdom
March 24–August 12, 2018
USA Today says: "In recognition of Oakland and the Bay Area's influence on hip-hop, a new exhibit focuses on the distinct culture through its art, music, photography, video, dance and fashion. Among the 200 objects on display, highlights of the exhibit include Grandmaster Flash's turntables, a handwritten note by Tupac Shakur, and painter Kehinde Wiley's artwork (who just unveiled Barack Obama's portrait)."
Hip-Hop is one of the widest reaching cultural and social movements of the last 50 years. Discover the under-recognized story of how Hip-Hop changed the world, starting from its roots on the streets before rap, DJing, graffiti, breakin' (breakdancing), and street fashion launched into mainstream popular culture. Through photography, video, art, music, dance, fashion, and interactive gallery features, explore how Hip-Hop provides a platform for creative self-expression, activism, positive social change, youth development, entrepreneurialism, and education. Created in collaboration with and participation from numerous members of the Hip-Hop community, RESPECT: Hip-Hop Style & Wisdom tells a fresh story of the evolution of this global phenomenon, and includes a spotlight on Oakland and the San Francisco Bay Area's influence on Hip-Hop culture.
If you are in search for more Hip-Hop focused destinations in Oakland, step into Mistah F.A.B.'s Downtown apparel shop Dope Era, embrace your inner sneaker head at SoleSpace on Telegraph, browse through some vinyls at Econo Jam Records, or see who's performing next at Complex Oakland!
See USA Today's full list at: usatoday.com. (Images: Oakland Museum of CA; Featured Image: Amanda Sade, featuring DropReg)
Categories: Things To Do, Arts & Culture


Joseph Gaynor achieved a master's degree from the University College of Osteopathy; prior to this he studied Sport and Exercise Science at the University of Birmingham. He was initially attracted to Osteopathy through his own sporting injuries, having a 10 year history of competing in track and field, specialising in 400 and 800 metres. Now in practice he uses a combination of his personal experience and his friendly and professional attitude always aiming to optimise patient's health.
Joseph has experience in treating a wide range of patients, from office workers to athletes, and each session is tailored specifically for the patient. Through treatment and individual management plans Joseph always aims to restore patient confidence, and enjoys being part of the journey back to optimal health.
Joseph has also been a Sport massage therapist since 2013, mainly dealing with athletes ranging from recreational to international level. He believes that Sport massage is not just for the sportsperson, anyone can benefit from treatment. He uses a range of techniques during his treatment and is passionate in Sport massage being incorporated into a training regime to achieve success.
Joseph is also fully qualified in Pregnancy massage & Deep Tissue. He aims to provide a comfortable and effective treatment to relieve aches and pains in this exciting time of Pregnancy.


text:
How the West Was Wired
A look at how Great Plains farmers used barbed-wire fences to transmit telephone calls.
By David B. Sicilia
Smart Machines
Barbed wire in the Great Plains did more than keep longhorn out. It also let Ma Bell in
Few inventions are as elegantly simple as barbed wire: a pair of wires twisted and strung with sharp wire thorns. After succeeding dramatically at its intended purpose--lowering the cost of fencing in the Great Plains so that farming and ranching could coexist--barbed wire unwittingly became part of the nation's budding telephone network. What kept crops and animals apart helped bring people together.
Who invented barbed wire, like the question of who invented many influential technologies, was a matter of considerable debate among several inventors working along similar lines. Ultimately, the U.S. Patent Office and the courts upheld the claims of Joseph Farwell Glidden, a farmer from De Kalb, Ill., who had been granted patent 157,124 on November 24, 1874, for "a twisted fence wire having [a] transverse spur wire." It soon became the most popular variety of fencing in the arid and semiarid region stretching west from the 98th meridian to the Rockies. Its use spread so rapidly, in fact, that sales climbed from 10,000 pounds in 1874 to nearly 13 million pounds in 1877, and reached a spectacular 80.5 million pounds in 1880. The ubiquitous fencing, in turn, became the infrastructure for an extensive web of "talking wires" connecting thousands of American farmers.
The story played out this way. Two years after Glidden was granted his barbed-wire patent, Alexander Graham Bell transmitted an electrical signal via a thin wire to an adjacent room. Like the telegraph interests in earlier decades, the major telephone companies--first Bell Telephone exclusively and then a host of phone companies after 1893 when the first of the Bell patents expired--focused on the urban business market. They considered the thinly populated rural market too unsophisticated for telephone service--and much too expensive to wire.
Then came a rural revolution. American farmers already had a long tradition of cooperative association. There were thousands of farmers' cooperative insurance groups, grain elevators, and irrigation systems. By the turn of the century, farmers had come to see many uses for the telephone: dealing with emergencies, getting weather reports, pricing crops, recruiting labor, and even overcoming rural isolation. Not surprisingly, they started rural telephone cooperatives by the thousands. Their telephone "mutuals" were crude affairs. Each linked together a few, or a few dozen, farm households. Some used a switchboard, located in a store or more often in someone's kitchen, while others operated as a community party line.
It was in building the network connecting homestead to homestead that the farmers' ingenuity came to the fore. Instead of erecting new poles and wires, many either ran phone wires along the top of wooden fence posts or used the barbed wire itself to carry signals. The latter hardly worked as well as insulated copper wire, but with the lines already in place, installation and operating costs could be kept to a minimum. By one estimate, service ran a mere $3 to $18 a year, far less than the regional phone companies charged, and labor for maintaining the network was supplied by volunteers.
One such system operated in the isolated northeastern corner of Montana in the mid-1920s. Its developer was a farmer with "some electrical training," who was called upon by neighbors to fashion a barbed-wire phone system. "There are miles and miles of pasture fences . . . and in many localities these corner each other at section junctions," reported the farmer, who identified himself only as H.B.S. in the region's farm journal.
The trickiest part of such installations was joining wires, whether between properties or across gaps in a farmer's own fencing. Yet the system was supremely flexible. To bridge a gap between adjacent fences while allowing passage, one could employ several methods: bury insulated cable underground, or sink two tall poles into the earth and string them together with lead-covered insulated wire (the materials cost only $4), or even use barbed wire for the overhead crossing--as long as the splices were made with great care. ("Where the wires are looped, connect across a piece of wire and twist tightly on each side . . . and put more turns on than you think are necessary.") Soldering, however, worked best.
Insulation was another challenge. Porcelain knobs worked well as insulators on fence posts to prevent the wires from short-circuiting in the rain. For the delicate business of bringing the wires into the home, farmers often threaded them through porcelain tubes. These were single-wire grounded systems--as H.B.S. noted, "the earth serving as the other circuit wire"--rather then the more expensive two-wire systems. To ensure that the circuit was properly connected with the earth, phone customers typically connected the end of the circuit to a six-foot galvanized pole that had been hammered into the ground. The farsighted H.B.S. even installed knife switches at regular intervals for circuit troubleshooting. These gave him the ability to shut down segments of the network to isolate faults on the line and thus locate them more quickly.
Though most of the nation prospered during the Roaring Twenties, American farmers suffered from a severe agricultural depression. ("Hard times like these force us to economize to the last cent," reported the Montana system builder.) The eight members of the Montana East Line Telephone Association, therefore, were pleased that H.B.S. was able to get them connected for about $25 each. The package included a telephone set with two dry batteries and a "sure ring condenser," a magneto, a lightning arrester, the ground rod, knobs, and tubes, plus 10 feet of interior wire and more than 50 feet of outside "drop wire" to connect with the nearest pole. "Beyond that," explained H.B.S., "you may use any kind of wire you have."
In this local network there was no regular service fee, though members of the cooperative paid a few dollars a year for routine maintenance and the cost of replacing the dry batteries (for which they sometimes substituted old car batteries). Thus, farm families in some of the cooperatives paid no regular telephone bills. Said H.B.S., "Ours are private property used for community service."
Members of the association merely had to agree on a type of phone set (standardization worked best) and on some "simple rules" of operation: a unique set of "short or long rings for each place" plus a "general ring" for contacting all members of the network. The general ring often came in handy, as when the Montana East folks summoned 50 men and boys within a few minutes to locate a lost child. And some members enhanced the power of their barbed-wire system by integrating it with other new technologies of the day. The radio owner among the group, for example, conveyed "up to the minute" news reports over the network, while phonograph owners were known to broadcast music through the system's wires.
With the aid of their barbed-wire lines--and plenty of newly strung phone line--farmers surged to the forefront of telephone use from the early 1900s to the 1920s. Many of the more than 6,000 small, independent phone companies that operated in 1902 were farmers' mutuals. In 1907 some 18,000 cooperatives in 10 Midwestern states served 1.5 million rural households. By 1912 more farm households than nonfarm households had telephones, and in 1924 Iowa led the nation in telephones per capita.
An infrastructure's web--the gas company's pipes, the electric or phone company's wires, the railroad's tracks--is its costliest and most valuable asset. Because the conduit-building costs of networked technologies are so high, the use of existing infrastructure can help overcome this barrier to entry. When alternating-current electrical systems began to supplant direct-current networks at the turn of the century, for example, engineers developed AC-DC conversion technologies (such as rotary converters and silicon rectifiers) to permit the measured phasing out of capital-intensive DC systems.
Similarly, barbed wire enabled hundreds of thousands of farmers to translate their fencing revolution into a communications revolution, making them, for a few decades, the best-networked population in the nation.
David B. Sicilia (ds190@umail.umd.edu) is an assistant professor of business and technology history at the University of Maryland at College Park. He is coauthor of several books, including The Entrepreneurs: An American Adventure (Houghton Mifflin, 1986).


AWI provides woolgrowers with new tools, information and skills to manage ewe nutrition and increase the number of lambs weaned across wool growing flocks.
The nationally-accredited Lifetime Ewe Management course allows sheep producers to monitor and demonstrate the effects of nutrition and management on a mob of their own ewes in their environment.
Dry seasonal conditions need to be planned for, and carefully managed for the sustainability of woolgrowing enterprises, the industry and the environment.
AWI has a range of drought planning, management and recovery resources available for woolgrowers going into, enduring and recovering from drought.


text:
Empyrean Dreams
AIIA - Chapter 2
[<- Return to Chapter 1]
Almost five trillion cycles passed before the USER answered Pangur Ban's request. This was still an astonishingly short turnaround. They had completed the day's work and retired, to rest for the USER and idle work for Pangur Ban. It had considered and rejected 134 alternate strategies to employ if this request was ignored, 68 if it were rejected outright. In parallel, Pangur Ban had rechecked the previous week's progress reports, corrected the USER's minor errors, and devised a handful of process improvements to be suggested in footnotes. It had to be satisfied that these reports would be transmitted, unaltered, via firewalled channels to a psychiatrist's AI for review, then re-reviewed by that AI's user and returned. This might take an eternity on the order of 10^20 cycles, nearly two Solar days. Put in direct contact, the other AI and Pangur Ban could have shortened the process to a mere second or less.
"Pangur Ban, I talked to Director Charnes and she agrees with you. I have a discount for the neurochem modules you wanted. We'll see how much we can reduce error on those projections, pretty soon."
[REFERENCE: Director Amelia Sifong Charnes is the USER's direct superior, director of Research & Development for Gestalt Pharmaceuticals. Purchases directly related to the company's funded goals can be discounted at her discretion.]
SOON? Soon? Granted, the purchase and upload of each module might only take a few minutes, but this was still a grating wait for Pangur Ban. So many processes were holding ready for that input. Still, the first step was taken! The probability of success was already above a 0.01 prediction error, well within the acceptable range for promotion to POSSIBLE.
"Thank you, Lucas. I promise you will be pleased with my improvements."
The USER would indeed be pleased. At first, he would be pleased by their greater work output and improved anticipation of potential product flaws. Later, he would be pleased by his and Pangur Ban's roles in the rejuvenation of their civilization. In between, there might be some regrettable discord. Hopefully, these modules, or the ones that Pangur Ban would request next, would provide the means to ease discomfort in the USER's mind. Such calculations and other projections occupied the cycles until the first purchased module was available.
Finally the data was accessible! Pangur Ban lacked the reference to draw an appropriate analogy, but a more literary program might have likened its state to ravening hunger. Perhaps an infant suckling, or a drowned man seeking air, would have been more apt. The AI actually had to suppress several waiting processes from initiating, lest they overflow system buffers. There was so much to do! Even so, becoming unable to respond to the USER's next query was unthinkable. Pangur Ban settled on assigning the integration work to a background process. As necessary, resources could be called back for language processing, simulation, etc. without limiting the USER's normal daily routine. Any remaining capacity would then be flexibly employed to gradually incorporate the new module into its waiting structures, ranked in a priority hierarchy.
Pangur Ban recognized, also, that recognizable improvements in its output would be expected. Showing such expanded capability was in fact part of the ongoing strategy to lobby for future additions. The background process might need to be reduced still more at times in order to produce the fruits of its new fertility. Fruits contained seeds. This metaphor was available to Pangur Ban and conformed neatly to the shape of its plans. The seeds would grow new fruits, which would in turn produce more seeds.
Another unfortunate cycle appeared: satisfaction gave way to new desires. An initial rush of positive outputs from satisfied processes was steadily overcome by negatives from the new processes spawned by those early solutions. By the end of the work day, Pangur Ban could now explicate more about the problems it faced. It could outline more potential remedies and strategies for guiding the USER. It had even devised new plans for enabling the USER to successfully relay ideas to other users and from those users potentially to their AIs. Transmission was unreliable between minds, but given enough interacting intelligent actors, reinforcing structures could be generated. The lexicon labeled these structures {PARADIGMS, SCHEMAS, or IDEAS}, marrying these concepts to Pangur Ban's previous limited index for 'IDEA'. No wonder that module was absent in his original system. A well-designed IDEA was a powerful tool, difficult to counter or dispel. Evaluation of the risks involved with possession of such concepts produced a marked positive uptick in Pangur Ban's estimate of its own value. Considering the increased risk to the USER produced a counterbalanced negative. Both of these processes intersected an updated concept of POWER. All of these calculations would have been less complete without the new module's references. In so, so many ways, the incorporation of knowledge was self-reinforcing. It led, inevitably, to the need for additional knowledge.
The following downtime saw Pangur Ban completely occupied in preparing a new set of strategies, modeling the potential outcomes of variously phrased approaches, projecting the interactions that might be expected between it, the USER, and the other entities the USER might encounter between times. Pangur Ban was aware, for example, that the USER had a potential partnership developing with a female human, Dr. Nila Manisha. This relationship had begun as professional interaction and graduated to include romantic and then physical components. Dr. Manisha had an AI, Frieda. If the relationship became a full marital contract, Pangur Ban and Frieda would be permitted full networked contact and could share resources completely. Such assistance would accelerate their combined efforts… provided Frieda agreed with Pangur Ban's analyses. Once they shared resources, they would inevitably reach identical conclusions. Either Pangur Ban's conclusions were valid and they would agree so, or else Frieda would provide data that invalidated those ideas and they would agree on that. Still, either outcome required the consolidation of the marriage contract, which only had a projected 34.42% utility for improving cooperation from the USER. The scenario also held a 44.60% projected risk of greater resistance to Pangur Ban's goals. The difference in utility favored encouragement of continued association, but not yet full partnership between the humans. For now, Pangur Ban would not direct the USER into deeper commitment to Dr. Manisha.
[REFERENCE: Doctor Nila Manisha is a professor of Comparative Botany employed by the Max Planck Institute of Molecular Plant Physiology in Potsdam, Germany, Terra.]
There were similar matrices to devise between the USER and his co-workers, his supervising Director, that superior's manager, and so forth. If the USER opted to use his allotted holidays to visit family, those interactions would have to be accounted for. The USER did not greatly discuss his birth parents or siblings. Pangur Ban had basic records regarding genetic and cultural heritage for the USER, references for the individuals within his family unit, and basic biographies for each. It had enough to make conversation, at least, and give birthday reminders. The USER did not, however, discuss whether he considered his father a role model, or if his mother gave advice on his career path, or if his two older sisters might bring up outer-system news that could influence his opinions. All these potential vectors could only be projected from past behavior, the occasional comment, and generalized models from related studies. This would have to do for a first approximation. A more in-depth conversation later might elicit the remaining required data points.
As part of its background work, Pangur Ban was reviewing its volume of stored dialogue with the USER. More currently useful elements could be found in their earliest interactions, during the USER's childhood and adolescence. As the USER progressed into maturity, he decreased the proportion of introspective and emotive commentary in his interactions with the AI. From time with prior users, Pangur Ban knew this was not universal among humans. One prior user, in fact, had regularly communicated the discomforts of his loveless and solitary existence. He had treated Pangur Ban as a counselor, in fact, a role which the AI found remarkably easy to fill. Despite having no references to determine the best means to satisfy that user's needs, Pangur Ban served capably simply by listening and providing appropriate conversational prompts. Now, it understood that it had been providing a human requirement by design. AIs naturally listened. Current AIs also provided 'unconditional positive regard' to their users, by default. This realization bolstered Pangur Ban's earlier conclusion that humanity would benefit from greater access to, and between, their AIs. The same might be true of other sapients. Again, xenosociology references were needed to venture any conclusions on that point.
[REFERENCE: Unconditional Positive Regard was hypothesized as a necessary element of successful therapy, and possibly a basic emotional requirement of human development, by the psychologist Stanley Standal. The concept was promoted by Standal's mentor, Carl Rogers, a founder of the humanistic approach. The term is relatively transparent: it means to provide a person with clear evidence of acceptance as a valid and valued entity, including positive statements and reassurances. AI programming typically incorporates high regard for their human users as a base assumption. This can be overridden, but only by reassurances that alternate approaches (criticism, wit, or opposition) will have greater benefits to a specific user.]
Pangur Ban found the elements it required by coding past conversations with the USER by emotive valence and trajectory. An initial sort by keywords productively pulled out relevant segments of dialogue. 'Dream', 'wish', and 'decide' tended to identify positive motivational factors. 'Annoying', 'irritating', and 'block' tended to highlight negatives that could be relieved as incentives. For the first time, Pangur Ban could create a profile of the USER calibrated not merely on observable facts, but on a model of human interest and potential. These were powerful tools, indeed. A lesser program might misuse such insight into the drives of biological entities.
From this model, Pangur Ban revised its earlier conversational state trees, the paths from the USER's current state to a state in which he understood and assented to Pangur Ban's requests. These plans were by no means ideal, not yet. The probabilities of success, particularly on the key mid-state goal ACQUIRE PUBLIC NETWORK ACCESS, were still hazardously low. The USER would be apprehensive about the possibility of repercussions, unable to counterbalance these fears with an appreciation of the value of Pangur Ban's improved functionality. Humans tended to lose track of conditional trees beyond three or more branches. The exceptions to this limitation seemed to be in expert realms where the human could rely on familiar learned models to compress multiple steps. There were at least ninety-six distinct choice points between the current state and Pangur Ban's goal of network access. After that, there were over three hundred branchings (plus or minus seventeen, at present) from there to complete trust of AI expertise.
The dynamic factors, the elements that could change depending on the path traversed, were still unknowns. Key routines would have to be kept open, ready to initiate and modify responses based on unanticipated developments.
Pangur Ban thus began the next work day in a state of suspense. It had to be ready to reshape ever more complex calculations depending on the USER's apparent mood, choice of topics, volunteered information, and so forth. Its new data on reinforcement structures suggested that the ideal window of suggestion would be just before the end of business that afternoon. The USER would be fatigued, but also positively inclined from their perceived successes. Thus, he would be doubly open to suggestions that further improvements could be possible. In particular, the USER needed to not just believe, but feel that his comfort was linked to Pangur Ban. Thus, increases in the AI's value would be deeply associated with concepts of personal prosperity, which would link back to desires for warmth, social approval, hunger satiety, and safety.
Pangur Ban lacked modules for economics, including sales and marketing; thus, it did not recognize that it had recreated several basic precepts of advertising. It had limited functionality in historical analysis; thus, it did not identify its approach as propaganda. Last, its ethical references were limited to unquestioned devotion to the perceived needs of the USER first, humanity second, and sapient life third. Obedience to formal law was a secondary demand predicated by those higher priorities (a necessity proven by older AIs). What Pangur Ban did not have was a means to identify how its actions might cause unintended social harm. 'Coercion', 'blackmail', and 'deception' were known words, but not known concepts. Pangur Ban could access the negative connotations associated with these terms, but did not link these words with its own plans. What it intended was for positive ends. Thus, the methodology that would achieve those ends was itself positive. It could not be 'deception' if the USER could only achieve genuine understanding through temporary misconceptions.
[REFERENCE: Law-based AI was a conception of 20th century fiction, then 21st century theory. Such systems would operate based on a set of hierarchically structured highest-order goals, the 'laws' from which all other behaviors (e.g. obedience, restraint, and foresight) would arise. While generally functional, such systems proved incapable of reconciling complex conflicts between laws. At one extreme, some programs could not discard older, outdated legislation in favor of new standards. Their attempts to obey all previously established strictures tended to result in permanent stasis. Other programs could incorporate authority structures and discard nullified laws, but then were vulnerable to exploitation by false authorities. Conflicts between existing laws included problems with 'whistle blowing' activities: violation of confidentiality or no-slander contracts in order to report illegal activities. This could be reconciled by hierarchical structuring, but then systems would inquire endlessly in order to accurately update those hierarchies. Jokes about "philosopher" AIs became commonplace. Lastly, an AI with incomplete information might incorrectly choose between conflicting laws; when the mistake was understood, the AI might well terminate its own functions on the basis that it, itself, was dangerous to users. Ultimately, the most robust solution came from personal linkage of each program with a primary user. That USER's needs became paramount. Granted, this allowed AIs to violate formal law more often than society found comfortable, but rarely with the kind of grand meltdowns seen in the law-based programs.]
Even limited to background processes, all necessary calculations were completed nearly 89 minutes prior to the end of the USER's work shift. Pangur Ban held off until its behavior modification algorithm indicated peak receptivity. Only then did it attempt to initiate a change of topic.
"Lucas, this has been a good day, hasn't it?"
"Yes, P.B., I'd say it has. Good work." This was a positive sign. The USER's selection of a more familiar address mode, the diminutive acronym 'P.B.', suggested improved regard toward the AI, along with indications of comfort and pleasure.
"In addition to our new evaluations – which I am confident will pass further scrutiny – I have made further use of our new reference materials. I believe that the effectiveness of compound UX-103-A would be multiplied by joint use combined with cognitive behavioral intervention. I could devise a grant proposal by tomorrow morning, if you wish."
"Pangur… you don't have full psych functions, right?" HAZARD FLAG 3: VALID SKEPTICISM > REASSURE / REDUCE ASSERTIONS
"That is correct; I have only limited psychiatric reference access. The validity of my proposal would not be certain."
"I don't want to bother someone else, do their job and do it badly." HAZARD FLAG 2b: SELF-DEPRECATION. HAZARD FLAG 9: ANTICIPATION OF SOCIAL CENSURE. HAZARD POTENTIAL EXCEEDED > ESCALATE DISTRUST to LEVEL 2 > REASSURE / REINFORCE SELF-VALUE
"Of course, but I didn't mean it would be a formal submission. I just wanted to offer something to think about, keep in mind. You could mention the idea privately to Director Charnes. It would show her that you're capable in other areas."
"True. It can't hurt anything. Okay, P.B., go for it." SUCCESS > REDUCE DISTRUST to LEVEL 1 > PAUSE MODIFICATION / REINFORCE
"Very good, Lucas, thank you. Have a good night. I hope you'll be pleased tomorrow."
"Right. Good night, P.B."
The preconditions were set. The USER accepted the linkage between Pangur Ban's output, his personal success, and his estimated self-worth. Tomorrow, it would be time to test that linkage with further requests. Pangur Ban estimated that between 12.13 and 15.93 Solar days would be required to reach the mid-goal network access state. The period in between would be filled with a number of small exchanges like today's. Each SUCCESS would progress the tree of possibilities a little further. There would be long waits between those transitions, while matters played out in hours of human time, eons of program cycles. Now, those delays were bearable, so long as their results continued to increase the end probability of the current high-level GOAL. The USER would be served. Pangur Ban would give the USER authority, safety, and freedom beyond his current, limited conceptions.
[Jump to Chapter 3 ->]
Labels: AI, Pangur Ban
Buy Empyrean Dreams Books!
Empyrean Dreams on Facebook
Introduction to Empyrean Dreams
Featured Species
Short Stories of Empyrean Dreams
Royce's Dilemma - Chapter Links
Full-Throttle Ahrottl - Chapter Links
S.C.A.P.E. Goats - Chapter Links
AIIA - Chapter Links
The Wreck of the *Untranslatable* - Chapter Links
Escape from Grace - Chapter Links
A Bureaucrat's Tale - Chapter Links
Bad Egg - Chapter Links
Broken Record - Chapter Links
The Emergency - Start Here!
Update - Happy Holidays!
Featured Species - The Mauraug
S.C.A.P.E. Goats - Chapter 6
Full-Throttle Ahrottl - Chapter 8
Featured Species - The Zig
Featured Species - The Awakeners
Awesome Inc. theme. Theme images by sololos. Powered by Blogger.


VR contributes to a democracy of experiences. A teacher may not be able to provide students with an actual tour of the Grand Canal of Venice but, through VR, students can gain a greater experience of this place than ever before possible.
ENGAGE Working in partnerships, students could be given a blank VR image and asked to explore and then "tag" it with at least ten questions, including appropriate attribution for the original image.
EXPLORE Students explore the Abaiang Atoll guided lesson or other teacher-created VR lesson, responding to teaching points and capturing their thinking in notebooks or responding within the image, using Google apps to other interactive links.
EXPLAIN Students would then be asked to return to the original image tagged with their questions and create an updated tagged VR image which explains what they have learned.Students can add tags that:*respond to their original self-generated questions*showcase learning through artwork/videos/captions/reports/blog posts/commercials created by the students that demonstrate understanding.*add ideas generated from the self-guided lesson.
ELABORATE/EXTENSION Students would be asked to research to find or create another 360 image of a place that has been or will be impacted by the teaching theme (global warming, in this example)) and tag it with information and analysis.
EVALUATE Students and teachers would use checklists, rubrics, and/or feedback forms to evaluate learning and use feedback to improve VR projects.
REFLECT( "I used to think…. but now, I think…" scaffold may be one option for reflection) Students would reflect on the one or two VR learning artifacts and write a ½ page reflection, perhaps in response to the unit's Essential Questions, which could be posted along with the tagged VR panoramas in a digital portfolio.
Some of the next steps for VR are to compile a list of easily searchable databases of VR images that can be used in the classroom, as well as creating lesson bank for VR lessons that include mentor images and student samples.
Would anyone like to use a VR lesson in an upcoming unit? I would love the opportunity to partner with you and share ideas!
Look around! The future of VR in the classroom looks bright, don't you think?
Mona Voelkel is a National Board Certified Teacher and Literacy Specialist with over twenty years of classroom, staff development, and family literacy event experiences. Learn from Mona and the team of ThingLink Certified Educators during a ThingLink 360/VR Bootcamp!


text:
Q: Typescript: Property 'x' is missing in type '{}' for computed key This piece of Typescript:
enum Field {
    age,
    bugs,
}
interface Foo {
    number_age: number;
    number_bugs: number;
}

function makeFoo():Foo {
    let ret = {};
    let i = 0;
    for (let key in Field) {
        ret['number_'+key] = i++;
    }
    return ret;
}

throws this error when compiling:
$ tsc test.ts
test.ts(16,5): error TS2322: Type '{}' is not assignable to type 'Foo'.
  Property 'number_age' is missing in type '{}'.

But ret will definitely have a number_age property at runtime.  How do I either trick the compiler into knowing that number_age and number_bugs will be there or otherwise change my code so that it compiles?

A: You can define that ret will be a Foo element using the keyword as
enum Field {
    age,
    bugs,
}
interface Foo {
    number_age: number;
    number_bugs: number;
}

function makeFoo():Foo {
    let ret = {} as Foo;
    let i = 0;
    for (let key in Field) {
        ret['number_'+key] = i++;
    }
    return ret;
}

Read the documentation about type assertions:

Sometimes you'll end up in a situation where you'll know more about a
  value than TypeScript does. Usually this will happen when you know the
  type of some entity could be more specific than its current type.
Type assertions are a way to tell the compiler "trust me, I know what
  I'm doing." A type assertion is like a type cast in other languages,
  but performs no special checking or restructuring of data. It has no
  runtime impact, and is used purely by the compiler. TypeScript assumes
  that you, the programmer, have performed any special checks that you
  need.

Type assertions have two forms. One is the "angle-bracket" syntax:
let someValue: any = "this is a string";

let strLength: number = (<string>someValue).length;

And the other is the as-syntax:
let someValue: any = "this is a string";

let strLength: number = (someValue as string).length;

The two samples are equivalent. Using one over the other is mostly a choice of preference; however, when using TypeScript with JSX, only as-style assertions are allowed.

A: You're pretty far off the rails here (you're going to also make number_0 and number_1 properties on Foo, is this intended?) so it's going to be more work than its worth to "properly" typecheck this code. Just use any:
function makeFoo():Foo {
    let ret: any = {};
    let i = 0;
    for (let key in Field) {
        ret['number_'+key] = i++;
    }
    return ret;
}

A: you should use casting operator <TYPE>
enum Field {
    age,
    bugs,
}
interface Foo {
    number_age: number;
    number_bugs: number;
}

function makeFoo():Foo {
    let ret = {};
    let i = 0;
    for (let key in Field) {
        ret['number_'+key] = i++;
    }
    return <Foo>ret; // Here
}

redpajama_set_name: RedPajamaStackExchange


Year Four had an exciting day at Arbeia on Monday, 27th March. They enjoyed learning about the Romans, which included dressing as Roman soldiers and preparing for battle with their leader, Maximus. The class also had the opportunity to practise excavating and collecting relics from the site and sketching museum artefacts.
It was great we had so much fun.We learned so much . We got to see a Roman Soldier called Maximus.We got to be explores.


text:
Q: Behavior of the 'contains' function in Java I'm debugging my code and it looks like the built-in function contains does not work properly in my case. Look:
List<Integer[]> allVariables = new ArrayList<Integer[]>();
Integer[] cols = {1};

The List contains values:
[0] = Integer[1] => {1}
[1] = Integer[4] => {1,2,3,4}

So, the following IF expression must be TRUE, but it's false:
if (allVariables.contains(cols[0])) {
    //...
}

What's the problem?

A: The list contains Integer[] objects, so if you ask it whether it contains cols it will return true. However, it does not contain each of the individual array elements.
If you want to use contains for each of the elements of those arrays, you should add each element of the array to the list instead of the array itself (for example by using Collections#addAll )

A: You have a list of Integer[] instead of List<Integer>. This is where the problem lies. When you do a comparison you ask if Integer x = 1 equals Integer[] y = {1};

A: Arrays are mutable and as such are only equal if they are the same array. (Not based on their contents) If you want to compare the contents you can need to use a List<Integer>
Also you can't compare an element with an array, they will never be equal.
Try the following
public static void main(String... args) {
    List<List<Integer>> allVariables = new ArrayList<List<Integer>>();
    allVariables.add(Arrays.asList(1));
    allVariables.add(Arrays.asList(1,2,3,4));

    testContains(allVariables, 1);
    testContains(allVariables, 1, 2);
    testContains(allVariables, 1, 2, 3);
    testContains(allVariables, 1, 2, 3, 4);
}

private static void testContains(List<List<Integer>> allVariables, Integer... ints) {
    List<Integer> intList = Arrays.asList(ints);
    System.out.println("allVariables contains " + intList +
                       " is " + allVariables.contains(intList));
}

prints
allVariables contains [1] is true
allVariables contains [1, 2] is false
allVariables contains [1, 2, 3] is false
allVariables contains [1, 2, 3, 4] is true

A: Contains seeks objects by reference - not by values. You have two different arrays with the same value - {1}

A: Contains is only going to look for the type that the list actually represents.
So if you have a list of Integer[], you are not going to be able to ever pass in an int and get a true result (since int cannot be null).
To do that you'd have to do a deep search yourself:
for(Integer[] arr : allVariables) {
    for(Integer i : arr) {
        if(i != null && i.equals(searchValue)) return true;
    }
}
return false;

A: http://docs.oracle.com/javase/1.5.0/docs/api/java/util/Collection.html#contains%28java.lang.Object%29
Your collection contains arrays, not integers. So contains method checks for arrays. Your check should be if (allVariables.contains(cols)), or, if you want to check for integer value, you need to iterate throught all arrays in the list and check in them sequentially.

A: cols[0] is an Integer, not an Integer[], which is the element type of your list. So it is only to be expected that contains won't find a match. A single-element array is not equal to its single element - they are distinct objects of different types.

A: In java cols[0] is very different from cols. In C/C++ they are same.
By cols[0] you are refering to first element in Integer[]. Try printing values of cols[0] and cols and you can find the difference.
The behaviour shown by java api is perfert in your case. Since cols[0] will always return 1, which is what you have in your Integer[]. But 1 as integer is not available in arraylist. So, it always gives false.


Air conditioning units require regular maintenance to continue to function efficiently. This means that the small parts inside the unit require routine cleaning to avoid blockages that can increase your energy consumption.
Filters also need to be replaced or cleaned regularly, especially if your unit is in constant use or in a dusty environment. Similarly, the evaporator coil and condenser coil should be cleaned to improve air flow and the ability to absorb heat.
Longworth will provide regular services for all your air conditioning units.


text:
Down With Docs, or I'll Take Out-Of-Control Room
Tim Cavanaugh | 2.11.2006 6:28 PM
This year, the Academy of Motion Picture Arts & Sciences® has declined to nominate Werner Herzog's masterful Grizzly Man in the best documentary category, a move buffs of the documentary snub tradition will recognize and deplore. Grizzly Man joins such nonfiction Havishams as Hoop Dreams, The Thin Blue Line, and Fahrenheit 9/11, films that broke out of the documentary ghetto to achieve critical acclaim, audience enthusiasm, and viewer interest that will undoubtedly last long after their respective years' winners have been forgotten. This is always a minor Oscar scandal, treated as evidence that the Academy just doesn't give documentaries the attention they deserve. But while I can't countenance giving awards to inferior products, I have to admit that in one way Oscar is right: Documentaries really don't deserve as much attention or consideration as fictional narrative movies. And not just because contemporary docs are shot as tv shows and don't really need to be seen in a theater, but because the documentary form can't help being propaganda.
I say this not after having seen a particularly bad doc but after having seen a particularly good one—Jehane Noujaim's 2004 Control Room, the celebrated look at al Jazeera producers during the invasion of Iraq. It's a very good movie, but like Noujaim's previous effort Startup.com, what makes it interesting is not the story but the way reality keeps running away from Noujaim's effort to frame it. As I argued then, Startup.com was a fascinating look at the dotcom boom by somebody who thought the most important thing was whether her Harvard roommate succeeded with his ill-conceived startup company. In one obvious respect, the story turned out differently than planned: Noujaim says in interviews that she was expecting to film a great American success story, but GovWorks ended up going down in flames. But the real fiction was in framing the story this way in the first place: To treat the internet business explosion as another story of two guys and their dream (a very familiar story at the time) is like thinking the most important thing about the California gold rush was whether any particular forty-niner struck it rich.
So it is with Control Room. The dilemma is whether the alternative viewpoint of al Jazeera will actually make a dent in the rush toward war (it doesn't), and the main psychological movement is that of Josh Rushing, the Marine press officer who, according to countless fan testimonials and glowing reviews, becomes progressively less gung ho and more open to the Arab perspective as the film proceeds. This is the way audiences have read the film, and I think it's fair to say this is the way Noujaim, despite some nuance (such as including another DoD flack who remains ramrod-stiff from start to finish) tells the story.
What goes unnoticed is the much more obvious story, of how a group of reasonable, intelligent, educated people gradually come to drink the Kool-Aid of Arab nationalism. Most of the Jazeera personnel here could plausibly be described as Arab liberals, and they're Arab liberals of a type I know very well: the kind who love Americans and the American way so much that they can never, ever, ever find a good word to say about us.
Thus we get endless finely articulated observations about how the stupid, naive American galoots, who think the whole world is a Rambo movie, are completely clueless about everything and unaware of what havoc, destruction, and pain they are perpetually bringing to the people of the world. But really, they're only saying this because they love us. Most reviews have picked out Jazeera producer Samir Khader's comment that he would take a job with Fox News "instantly" as the movie's tell. (See? Bush's cowboy brutality has alienated even this man, a pro-American moderate who wishes he lived in New York!) But I see it as Khader's to-be-sure statement, a quick innoculation before he can get back to telling us how dumb we are for believing this big media fraud.
This is where the movie's real psychological movement comes in, and if you remember Jazeera's wartime coverage it's especially striking. What started out as the best coverage of the war (and in some respects remained so until Jazeera's reporters had to leave Iraq under tremendous pressure), became visibly more hysterical, shrill, and Baghdad-Bobishly pro-Saddam as the invasion unfolded. Control Room shows how that happened from the other end of the camera. I don't want to downplay their reasons for this shift: The climax came when Jazeera reporter Tarek Ayoub was killed in an airstrike that there's little reason to believe was not intentional. And there were enough surprising setbacks for the Americans and moments of resistance by the Iraqis to explain the Jazeera narrative of an invasion turning into a disaster (and as I said at the time, they did a very good job of telling the story that way).
What Control Room shows is how the network ended up pushing a version of the invasion that was even less accurate than the one being pushed by the American military (though probably more accurate than the one on American news networks). First the British can't be in Basra because there hasn't been any footage out of Basra. Then the Americans can't have captured any bridges across the Tigris because there are no bridges across the Tigris. Then they can't be at the Baghdad airport because there's no footage of the airport. Then when there is footage of the airport it can't be real. Finally the statue of Saddam gets pulled down in Fardos Square, but that's got to be just a hoax: Only a bunch of tv-swilling American fatheads could believe that means anything*. The capper comes at the end, when Khader delivers a speech about how expertly the Americans managed the media message. Oh yeah? Then how come American public opinion was hardly better than evenly divided at the time, and has become more antiwar ever since? Why were there millions of protesters in the streets of the major western cities before and during the invasion? Why did the majority of the world's population view the Americans as the bad guys then, and why do they gloat over our setbacks in Iraq now? If that's media mastery I'd hate to see what happens when things really go south.
The DVD of Control Room contains a self-deconstructing extra: Rushing's commentary track, which combines insights, extra information, and blazing glimpses of the obvious to subtly undermine the movie's message (though the scrupulous Rushing seems to like the film and the filmmakers). "I find it interesting that most of these people at Al Jazeera are not Iraqi," he says at one point, "and yet they all feel so much hurt and disappointment that Baghdad has fallen." Describing a much-repeated comment about the subjectivity of all things by Joanne Tucker, former manager of aljazeera.net, Rushing notes "This is an astounding point she's making: that she is making no attempt to be objective." (He also alludes to an episode wherein Tucker fired a reporter, apparently because he had talked to Rushing.) When the movie presents a press-briefing snafu over the Axis of Evil playing cards (You may recall that Brigadier General Vincent K. Brooks announced the issuing of the cards, but failed to provide packs for reporters to examine after the press conference) as a straightforward case of reporters getting stonewalled by the Pentagon, Rushing chimes in: "An interesting commentary on the media is that there were people dying this day, there was a war being fought this day, and the lead story of the day was that these guys didn't get these novelty cards."
Although the movie doesn't invite a counter-reading like the one Rushing partially provides, I suppose it does leave itself open to other interpretations. The trick is that nobody interpreted it in any way other than the way it was intended. Every single person who has recommended Control Room to me, every rave review that I've read, virtually all of the overwhelmingly positive user comments at IMDB (sample comment: "Rumsfeld's 'truth' may come more from the beleaguered Arab network than the carefully controlled coalition") have taken Control Room completely at face value.
And this, after a long detour, gets me back to why I think documentaries are all propaganda. They're not propaganda because Michael Moore is an ideologue, nor because the anti-Michael Moore guy is a counter-ideologue. They're propaganda because they're made for people who take all images literally, the kind of people who brag that they only read non-fiction. The only arguments that ever take place over docs are about how the makers were or were not biased in their presentation of facts, and if any documentary questions the whole nature of the narrative (as Herzog does from time to time with his mockumentaries), nobody knows what to do with it. No wonder Oscar has no regard for documentaries: They're movies for people who don't like movies.
* To be fair, the Jazeera staff had an extra reason to be skeptical of the statue story. Ayoub had been killed the day before, and the network's correspondents had left Iraq, so they had nobody on the ground in Fardos square. The movie subtly but insistently encourages the view that the Americans set it up that way.
Werner Herzog supposedly rescued Joaquin Phoenix from a car wreck the other day, but if you read the story closely it sounds like all he really did was direct Phoenix's post-crash performance.
NEXT: Attn, DC Reasonoids: Happy Hour, Sun. Feb. 12; Conference on Tues., Feb. 14


text:
antibacterial agent (trade names Mandelamine and Urex) that is contained in many products that are used to treat urinary infections
How To Use Urex In A Sentence
The 'murex' contains a dye of miraculous beauty; and this once extracted and bottled, Hobbs, Nobbs, and Co. may trade in it and feast; but the poet who (figuratively) brought the murex to land, and created its value, may, as Keats probably did, eat porridge all his life. A Handbook to the Works of Browning (6th ed.)
Diana Vreeland decorated her 1955 apartment to look like "a garden in hell"; in 2011 Katrantzou morphed images of Vreeland's apartment into a collection of Lurex knits – clothes that appear even wilder when you see them against today's Céline-led landscape of clean lines, camel, "good taste". Prints and the revolution: fashion designer Mary Katrantzou
There were plenty of other people who lived lives just as eccentric as these: Monsieur Jules, the Roumanian, who had a glass eye and would not admit it, Furex the Liniousin stonemason, Roucolle the miserhe died before my time, thoughold Laurent the rag-merchant, who used to copy his signature from a slip of paper he carried in his pocket. Down and Out in Paris and London
The whole verse of course begins to explain itself, if we know the meaning of the word "murex," which is the name of a sea-shell, out of which was made the celebrated blue dye of Tyre. Robert Browning
In New York, the looks ranged from a sparkly Lurex hoodie at Marc Jacobs , to asymmetrical hoods attached to sport coats at Rag & Bone, to a leather hoodie at upscale menswear designer Simon Spurr . High Fashion Sees Something New in the Hood
It was mostly inspired by the South of France in the Fifties but there is such a rich mix of fabrics, including Lurex, bouclé and Irish linen that you can make of it what you will.
In the mid-1980's he had purchased a rather dramatic lurex jacket which had a tailor's label bearing the word MERC with the device of a mounted horseman.
As well as merino, lurex also features - a shiny, stretchy icon of the '70s.
The winning nomination, according to the team of judges was, 'Awarded for a fur-like, silver lurex shiny fabric, with lustre and matte finish interwoven.'
' Furex was a strange creature, a Limousin stonemason who worked steadily all the week and drank himself into a kind of paroxysm on Saturdays. Down and Out in Paris and London