Author Archives: pgfeldman

Phil 3.9.19

Understanding China’s AI Strategy

  • In my interactions with Chinese government officials, they demonstrated remarkably keen understanding of the issues surrounding AI and international security. It is clear that China’s government views AI as a high strategic priority and is devoting the required resources to cultivate AI expertise and strategic thinking among its national security community. This includes knowledge of U.S. AI policy discussions. I believe it is vital that the U.S. policymaking community similarly prioritize cultivating expertise and understanding of AI developments in China.

Russian Trolls Shift Strategy to Disrupt U.S. Election in 2020

  • Russian internet trolls appear to be shifting strategy in their efforts to disrupt the 2020 U.S. elections, promoting politically divisive messages through phony social media accounts instead of creating propaganda themselves, cybersecurity experts say.

Backup phone

Work on SASO paper – started

Rachel’s dungeon run is tomorrow! Maybe cross 10,000 posts?

Look at using BERT and the full Word2Vec model for analyzing posts

The Promise of Hierarchical Reinforcement Learning

  • To really understand the need for a hierarchical structure in the learning algorithm and in order to make the bridge between RL and HRL, we need to remember what we are trying to solve: MDPs. HRL methods learn a policy made up of multiple layers, each of which is responsible for control at a different level of temporal abstraction. Indeed, the key innovation of the HRL is to extend the set of available actions so that the agent can now choose to perform not only elementary actions, but also macro-actions, i.e. sequences of lower-level actions. Hence, with actions that are extended over time, we must take into account the time elapsed between decision-making moments. Luckily, MDP planning and learning algorithms can easily be extended to accommodate HRL.

Phil 3.7.19

Day 2 of the TF Dev summit. Worth the money, though much less research-y and more implementation and production-y

Google Cloud has Fedramp certification, which it does see details here.

Live Transcribe

Coral: On Device Transfer learning (paper)

TF 2.0 API \changes and Behavior changes

  • Best practices (link: )
  • Declare variables at the beginning of the code
  • Keras Functional API
    • The Keras functional API is the way to go for defining complex models, such as multi-output models, directed acyclic graphs, or models with shared layers.
  • Autograd can automatically differentiate native Python and Numpy code. It can handle a large subset of Python’s features, including loops, ifs, recursion and closures, and it can even take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation), which means it can efficiently take gradients of scalar-valued functions with respect to array-valued arguments, as well as forward-mode differentiation, and the two can be composed arbitrarily. The main intended application of Autograd is gradient-based optimization. For more information, check out the tutorial and the examples directory.
  • JAX is Autograd and XLA, brought together for high-performance machine learning research. With its updated version of Autograd, JAX can automatically differentiate native Python and NumPy functions. It can differentiate through loops, branches, recursion, and closures, and it can take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation) via grad as well as forward-mode differentiation, and the two can be composed arbitrarily to any order.
  • Effective TF 2.0: There are multiple changes in TensorFlow 2.0 to make TensorFlow users more productive. TensorFlow 2.0 removes redundant APIs, makes APIs more consistent (Unified RNNsUnified Optimizers), and better integrates with the Python runtime with Eager execution.

Phil 3.6.19

5:00 – ASRC TL

  • Got a lot done on the BAA on the flight yesterday
  • Wrote up a description of LMN and CM for Eric V.
  • Reading more of the Handbook of Latent Semantic Analysis. It’s giving me some good ideas for calculating similarities of posts using Word2Vec and comparing the average vector for each post
  • Antonio got an extension to the 12th. Need to see what he’s up to. Wow, there’s a lot there now. Made some comments about what I’d like to see. I’ll pull down the document to read later
  • Continued to tweak the slides
  • TF Dev conference main sessions today. Breakouts tomorrow.

Phil 3.4.19

7:00 – 5:00 ASRC

  • Build an interactive SequenceAnalyzer. The adjustments are
    • Number of buckets
    • Percentages for each analytic (percentages to keep/discard
    • Selectable skip words that can be added to a list (in the db?)
  • Algorithm
    1. Find the most common words across all groups, these are skip_words
    2. Find the most common words along the entire series of posts per player and eliminate them
    3. Find the most common/central words across all sequences and keep those as belief places
    4. For each sequence by group, find the most common/central words after the belief places. These are the belief spaces.
    5. Build an adjacency matrix of players, groups, places and spaces
    6. Build submatrices for centrality calculations? This could be rather than finding the most common
    7. Possible word2vec variations?
      1. It seems to me that I might be able to use direction cosines and dynamic time warping to calculate the similarity of posts and align them better than the overall scaling that I’m doing now. DM posts introducing a room should align perfectly, and then other scaling could happen between those areas of greatest alignment
  • Display
    • Menu:
      • Save spreadsheet (includes config, included words, posts(?), trajectories)
      • load data
      • select database
      • select group within db
      • load/save config file
      • clear all
    • Fields
      • percent for A1, A2, A3, A4
      • Centrality/Sum switch
      • BOW/TF-IDF switch
      • Word2vec switch?
    • Textarea (areas? tabbed?)
      • Table with rows as sequence step. Columns are grouped by places, spaces, groups, and players
    • Work on Antonio’s paper got a first draft on introduction and motivation
    • BAA
      • Upload latex and references to laptop
    • Haircut! Pack!
    • Model-Based Reinforcement Learning for Atari
      • Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically requires very large amounts of interaction — substantially more, in fact, than a human would need to learn the same games. How can people learn so quickly? Part of the answer may be that people can learn how the game works and predict which actions will lead to desirable outcomes. In this paper, we explore how video prediction models can similarly enable agents to solve Atari games with orders of magnitude fewer interactions than model-free methods. We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting. Our experiments evaluate SimPLe on a range of Atari games and achieve competitive results with only 100K interactions between the agent and the environment (400K frames), which corresponds to about two hours of real-time play.

 

Phil 3.3.19

Once more, icky weather makes me productive

  • Ingested all the runs into the db. We are at 7,246 posts
  • Reworking the 5 bucket analysis
  • Building better ignore files and rebuilding bucket spreadsheets. It tuns out that for tymora1, names took up 25% of the BOW, so I increased the fraction saved to the trimmed spreadsheets to 50%
  • Building bucket spreadsheets and saving the centrality vector
  • Here’s what I’ve got so far: ThreeRuns
  • Trajectories: Trajectories
  • First map: firstMap
  • Here it is annotated: firstMapAnnotated
  • Some thoughts. I think this is still “zoomed out” too far. Changing the granularity should help some. I need to automate some of my tools though. The other issue is how I’m assembling my sequences.

Phil 3.2.19

Updating SheetToMap to take comma separated cell names. Lines 180 – 193. I think I’ll need an iterating compare function. Nope, wound up doing something simpler

for (String colName : colNames) {
    String curCells = tm.get(colName);
    String[] cellArray = curCells.split("\\|\\|"); <--- new!
    for(String curCell : cellArray) {
        addNode(curCell, rowName);
        if (prevCell != null && !curCell.equals(prevCell)) {
            String edgeName = curCell + "+" + prevCell;
            if (graph.getEdge(edgeName) == null) {
                try {
                    graph.addEdge(edgeName, curCell, prevCell);
                    System.out.println("adding edge [" + edgeName + "]");
                } catch (EdgeRejectedException e) {
                    System.out.println("didn't add edge [" + edgeName + "]");
                }
            }
        }
        prevCell = curCell;
    }

    //System.out.print(curCell + ", ");
    prevCell = cellArray[0];
    col++;
}

Updating GPM to generate comma separated cell names in trajectories

  • need to get the previous n cell names
  • Need to change the cellName val in FlockingBeliefCA to be a stack of tail length. Done.
  • Parsed the strings in SheetToMap. Each cell has a root name (the first) which connects to the roots of the previous cell. The root then links to the subsequent names in the chain of names that are separated by “||”
    "cell_[4, 5]||cell_[4, 4]||cell_[4, 3]||cell_[4, 2]||cell_[4, 1]"
  • Seems to be working: tailtest

Phil 3.1.19

7:00 – ASRC

  • Got accepted to the TF dev conference. The flight out is expensive… Sent Eric V. a note asking for permission to go, but bought tix anyway given the short fuse
  • Downloaded the full slack data
  • Working on white paper. The single file was getting unwieldy, so I broke it up
  • Found Speeding up Parliamentary Decision Making for Cyber Counter-Attack, which argues for the possibility of pre-authorizing automated response
  • Up to six pages. IN the middle of the cyberdefense section

Phil 2.28.19

7:00 – very, very, late ASRC

  • Tomorrow is March! I need to write a few paragraphs for Antonio this weekend
  • YouTube stops recommending alt-right channels
    • For the first two weeks of February, YouTube was recommending videos from at least one of these major alt-right channels on more than one in every thirteen randomly selected videos (7.8%). From February 15th, this number has dropped to less than one in two hundred and fifty (0.4%).
  • Working on text splitting Group1 in the PHPBB database
    • Updated the view so the same queries work
    • Discovered that you can do this: …, “message” as type, …. That gives you a column of type filled with “message”. Via stackoverflow
    • Mostly working, I’m missing the last bucket for some reason. But it’s good overlap with the Slack data.
    • Was debugging on my office box, and was wondering where all the data after the troll was! Ooops, not loaded
    • Changed the time tests to be > ts1 and <= ts2
  • Working on the white paper. Deep into strategy, Cyberdefense, and the evolution towards automatic active response in cyber.
  • Looooooooooooooooooooooooooong meeting of Shimei’s group. Interesting but difficult paper: Learning Dynamic Embeddings from Temporal Interaction Networks
  • Emily’s run in the dungeon finishes tonight!
  • Looks like I’m going to the TF Dev conference after all….

Phil 2.27.19

7:00 – 5:30 ASRC

  • Getting closer to the goal by being less capable
    • Understanding how systems with many semi-autonomous parts reach a desired target is a key question in biology (e.g., Drosophila larvae seeking food), engineering (e.g., driverless navigation), medicine (e.g., reliable movement for brain-damaged individuals), and socioeconomics (e.g., bottom-up goal-driven human organizations). Centralized systems perform better with better components. Here, we show, by contrast, that a decentralized entity is more efficient at reaching a target when its components are less capable. Our findings reproduce experimental results for a living organism, predict that autonomous vehicles may perform better with simpler components, offer a fresh explanation for why biological evolution jumped from decentralized to centralized design, suggest how efficient movement might be achieved despite damaged centralized function, and provide a formula predicting the optimum capability of a system’s components so that it comes as close as possible to its target or goal.
  • Nice chat with Greg last night. He likes the “Bones in a Hut” and “Stampede Theory” phrases. It turns out the domains are available…
    • Thinking that the title of the book could be “Stampede Theory: Why Group Think Happens, and why Diversity is the First, Best Answer”. Maybe structure the iConference talk around that as well.
  • Guidance from Antonio: In the meantime, if you have an idea on how to structure the Introduction, please go on considering that we want to put the decision logic inside each Autonomous Car that will be able to select passengers and help them in a self-organized manner.
  • Try out the splitter on the Tymora1 text.
    • Incorporate the ignore.xml when reading the text
    • If things look promising, then add changes to the phpbb code and try on that text as well.
    • At this point I’m just looking at overlapping lists of words that become something like a sand chart. I wonder if I can use the Eigenvector values to become a percentage connectivity/weight? Weights
    • Ok – I have to say that I’m pretty happy with this. These are centrality using top 25% BOW from the Slack text of Tymora1. I think that the way to use this is to have each group be an “agent” that has cluster of words for each step: Top 10
    • Based on this, I’d say add a “Evolving Networks of words” section to the dissertation. Have to find that WordRank paper
  • Working on white paper. Lit review today, plus fix anything that I might have broken…
    • Added section on cybersecurity that got lost in the update fiasco
    • Aaron found a good paper on the lack of advantage that the US has in AI, particularly wrt China
  • Avoiding working on white paper by writing a generator for Aaron. Done!
  • Cortex is an open-source platform for building, deploying, and managing machine learning applications in production. It is designed for any developer who wants to build machine learning powered services without having to worry about infrastructure challenges like configuring data pipelines, continuous deployment, and dependency management. Cortex is actively maintained by Cortex Labs. We’re a venture-backed team of infrastructure engineers and we’re hiring.

Phil 2.26.19

7:00 – 3:00 ASRC

    • Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. Built by experienced developers, it takes care of much of the hassle of Web development, so you can focus on writing your app without needing to reinvent the wheel. It’s free and open source.
    • More white paper. Add Flynn’s thoughts about cyber security – see notes from yesterday
    • Reconnected with Antonio. He’d like me to write the introduction and motivation for his SASO paper
    • Add time bucketing to postanalyzer. I’m really starting to want to add a UI
      • Looks done. Try it out next time
        Running query for Poe in subject peanutgallery between 23:56 and 00:45
        Running query for Dungeon Master in subject peanutgallery between 23:56 and 00:45
        Running query for Lord Javelin in subject peanutgallery between 23:56 and 00:45
        Running query for memoriesmaze in subject peanutgallery between 23:56 and 00:45
        Running query for Linda in subject peanutgallery between 23:56 and 00:45
        Running query for phil in subject peanutgallery between 23:56 and 00:45
        Running query for Lorelai in subject peanutgallery between 23:56 and 00:45
        Running query for Bren'Dralagon in subject peanutgallery between 23:56 and 00:45
        Running query for Shelton Herrington in subject peanutgallery between 23:56 and 00:45
        Running query for Keiri'to in subject peanutgallery between 23:56 and 00:45
    • More white paper. Got through the introduction and background. Hopefully didn’t loose anything when I had to resynchronize with the repository that I hadn’t updated from

 

Phil 2.25.19

7:00 – 2:30 ASRC TL

2:30 – 4:30 PhD

  • Fix directory code of LMN so that it remembers the input and output directories – done
  • Add time bucketing capabilities. Do this by taking the complete conversation and splitting the results into N sublists. Take the beginning and ending time from each list and then use those to set the timestamp start and stop for each player’s posts.
  • Thinking about a time-series LMN tool that can chart the relative occurrence of the sorted terms over time. I think this could be done with tkinter. I would need to create and executable as described here, though the easiest answer seems to be pyinstaller.
  • Here are two papers that show the advantages of herding over nomadic behavior:
    • Phagotrophy by a flagellate selects for colonial prey: A possible origin of multicellularity
      • Predation was a powerful selective force promoting increased morphological complexity in a unicellular prey held in constant environmental conditions. The green alga, Chlorella vulgaris, is a well-studied eukaryote, which has retained its normal unicellular form in cultures in our laboratories for thousands of generations. For the experiments reported here, steady-state unicellular C. vulgaris continuous cultures were inoculated with the predator Ochromonas vallescia, a phagotrophic flagellated protist (‘flagellate’). Within less than 100 generations of the prey, a multicellular Chlorella growth form became dominant in the culture (subsequently repeated in other cultures). The prey Chlorella first formed globose clusters of tens to hundreds of cells. After about 10–20 generations in the presence of the phagotroph, eight-celled colonies predominated. These colonies retained the eight-celled form indefinitely in continuous culture and when plated onto agar. These self-replicating, stable colonies were virtually immune to predation by the flagellate, but small enough that each Chlorella cell was exposed directly to the nutrient medium.
    • De novo origins of multicellularity in response to predation
      • The transition from unicellular to multicellular life was one of a few major events in the history of life that created new opportunities for more complex biological systems to evolve. Predation is hypothesized as one selective pressure that may have driven the evolution of multicellularity. Here we show that de novo origins of simple multicellularity can evolve in response to predation. We subjected outcrossed populations of the unicellular green alga Chlamydomonas reinhardtii to selection by the filter-feeding predator Paramecium tetraurelia. Two of five experimental populations evolved multicellular structures not observed in unselected control populations within ~750 asexual generations. Considerable variation exists in the evolved multicellular life cycles, with both cell number and propagule size varying among isolates. Survival assays show that evolved multicellular traits provide effective protection against predation. These results support the hypothesis that selection imposed by predators may have played a role in some origins of multicellularity. SpontaniousClustering\

Phil 2.24.19

It is a miserable, rainy morning, so I’m working on extracting text blocks for analytics. Once I try the various packages on those blocks, I’ll work on breaking them into blocks.

Ok, that’s coming along well. Here’s an example:

Bren'Dralagon: Pushing through the vines, he steps out to meet the Orc..
(unknown distance clarity, if possible, rush down the stairs to the attack)

Bren'Dralagon: kk

Shelton Herrington: RIP

Keiri'to: first blood

Bren'Dralagon: *Hmm, my tailor will have questions on where that came from*

Shelton Herrington: how far across is the hazard? impossible to jump over?

Shelton Herrington: ok

Bren'Dralagon: close enough to attack?

Shelton Herrington: understood, just checking

Bren'Dralagon: if charging is allowed, since i just moved forward and would be turning i doubt it?, i'll charge

Lorelai: I thought the vines were (mostly) gone?

Shelton Herrington: *"this ingress is a formidable enemy"*

Bren'Dralagon: *Remind me to have those stairs cleaned. I know a guy*

Shelton Herrington: do i have a line of sight to either?

Now that I have some text, I’ll try the tools listed here: linguisticanalysistools.org. The whole suite is known as the Suite of Automatic Linguistic Analysis Tools (SALAT).

Which means… (bear with me here)

That these are tools for creating word salat!

I’ll be here all night folks. Be sure to try the fish…

Played with the tools, but I need a list of words to analyze the docs with respect to. LMN does a good job of this, so I tried it using the broken-out player and DM. It looks super interesting. This is BOW with the non-topic words “these, those, get, etc” ignored:

LMN-tymora1

Based on what I see here, I’m going to work on the bucketing and see if the top words change over time. If they do, then we can build a map in fewer steps

Command Dysfunction

Command Dysfunction: Minding the Cognitive War (1996)

Author: Arden B. Dahl

Institution: School of Advanced Airpower Studies Air University

Overall

  • An analysis of Command and Control Warfare (C2W), which aims to create command dysfunction in the adversary.
  • When viewed from an asymetric warfare perspective, this closely resembles the Gerazimov Doctrine

Notes

  • Perception and cognition perform distinct roles in the formation of judgment. Perception answers the question: What do I see? Cognition answers the next question: How do I interpret it? However, general perceptual and cognitive biases cause decision makers to deviate from objectivity and make errors of judgment. Perceptual biases occur from the way the human mind senses the environment and tend to limit the accuracy of perception. Cognitive biases result from the way the mind works and tend to hinder accurate interpretation. These biases are general in that they are thought to be normally present in the general population of decision makers-regardless of their cultural background and organizational affiliations. (Page 14)
    • This is also true of machine (or any) intelligence that is not omniscient. There are corollaries for group decision processes
  • There are three perceptual biases that affect the accuracy of one’s view of the environment: the conditioning of expectations, the resistance to change and the impact of ambiguity. (Page 14)
  • There are three primary areas in which cognitive biases degrade the accuracy of judgment within a decision process: (Page 16)
    • the attribution of causality,
    • the evaluation of probability and
      • availability bias is a rule of thumb that works on the ease with which one can remember or recall other similar instances
      • anchoring bias is a phenomenon in which decision makers adjust too little from their initial judgments as additional evidence becomes available.
      • overconfidence bias is a tendency for individual decision makers to be subjectively overconfident about the extent and accuracy of their knowledge
      • Other typical problems in estimating probabilities derive from the misunderstanding of statistics.
    • the evaluation of evidence.
      • Decision makers tend to value consistent information from a small data set over more variable information from a larger sample.
      • Absence of Evidence bias is when decision makers to miss data in complicated problems. Analysts often do not recognize that data is missing and adjust the certainty of their inferences accordingly.
      • The Persistence of Impressions bias follows a natural tendency to maintain first impressions concerning causality. It appears that the initial association of evidence to an outcome forms a strong cognitive linkage.
  • AI systems can help with these errors of judgement though, since that can be explicitly programmed or placed in the training set.
  • These are all contributors to Normal Accidents
  • What about incorporating doctrine, rules of engagement and standard operating procedures? These can change dynamically and at different scales. (Allison Model II – Organizational Processes)
  • Also, it should be possible to infer the adversaries’ rules and then find areas in the latent space that they do not cover. They will be doing the same to us. How to guard against this? Diversity?
  • While the division of labor and SOP specialization is intended to make the organization efficient, the same division generates requirements to coordinate the intelligent collection and analysis of data.41 The failure to coordinate the varied perceptions and interests within the organization can lead to a number of uncoordinated rational decisions at the lower echelons, which in tum lead to an overall irrational outcome. (Page 20)
  • There are two common cultural biases that deserve mention for their role in forming erroneous perceptions: arrogance and projection. Arrogance is the attitude of superiority over others or the opposing side. It can manifest as a national or individual perception. In the extreme case, it forgoes any serious search of alternatives or decision analysis beyond what the decision maker has already decided. It can become highly irrational. The projection bias sees the rest of the world through one’s own values and beliefs, thus tending to estimate the opposition’s intentions, motivations and capabilities as one’s own. (Page 21)
    • Again, a good case for well-designed AI/ML. That being said, a commander’s misaligned biases may discount the AI system
  • The overconfidence or hubris bias tends toward an overreaching inflation of one’s abilities and strengths. In the extreme it promotes a prideful self-confidence that is self-intoxicating and oblivious to rational limits. A decision maker affected with hubris will in his utter aggressiveness invariably be led to surprise and eventual downfall, The Hubris-Nemesis Complex is dangerous mindset that combines hubris (self intoxicating “pretension to godliness”) and nemesis (“vengeful desire” to wreak havoc and destroy). Leaders possessing this bias combination are not easily deterred or compelled by normal or rational solutions (Page 22)
  • Three major decision stress areas include the consequential weight of the decision, uncertainty and the pressure of time (Page 23)
    • Crisis settings complicate the use of rational and analytical decision processes in two ways. First, they add numerous unknowns, which in tum create many possible alternatives to the decision problem. Second, they reduce the time available to process and evaluate data, choose a course of action, and execute it.
    • As uncertainty becomes severe, decision makers begin resorting to maladaptive search and evaluation methods to reach conclusions. Part of this may stem from a desire to avoid the anxiety of being unsure, an intolerance of ambiguity. It may also be that analytical approaches are difficult when the link between the data and the outcomes is not predictable 
      • Still true for ML systems, even without stress. Being forced to make shorter searches of the solution space (not letting the results converge, etc. could be an issue)
  • The logic of dealing with the time pressure normally follows a somewhat standard pattern. Increasing time pressure first leads to an acceleration of information processing. Decision makers and their organizations will pick up the pace by expending additional resources to maintain existing decision strategies. As the pace begins to outrun in-place processing capabilities, decision makers reduce their data search and processing. In some cases this translates to increased selectivity, which the decision maker biases or weights toward details considered more important. In other cases, it does not change data collection but leads to a shallower data analysis. As the pace continues to increase, decision strategies begin to change. At this point major problems can creep into the process. The problems result from maladaptive strategies (satisficing, analogies, etc.) that save time but misrepresent data to produce inappropriate solutions. The lack of time also prevents critical introspection for perceptual and cognitive biases. In severe time pressure cases, the process may deteriorate to avoidance, denial or panic. (Page 26)
    • The goal is to create this in the adversary, but not us. Which makes this in many respects a algorithm efficiency / processing power arms race
  • In some decision situations, a timely, relatively correct response is better than an absolutely correct response that is made too late. In other words, the situation generates a tension between analysis and speed. (Page 30)
  • The Recognition-Primed Decision (RPD) process works in the following manner. First, an experienced decision maker recognizes a problem situation as familiar or prototypical. The recognition brings with it a solution. The recognition also evokes an appreciation for what additional information to monitor: plausible outcomes, typical reactions, timing cues and causal dynamics. Second, given time, the decision maker evaluates his solution for suitability by testing it through mental simulation for pitfalls and needed adjustments. Normally, the decision maker implements the first solution “on the run” and makes adjustments as required. The decision maker will not be discard a solution unless it becomes plain that it is unworkable. If so, he will attempt a second option, if available. The RPD process is one of satisficing. It assumes that experienced decision makers identify a first solution that is “reasonably good” and are capable of mentally projecting its implementation. The RPD process also assumes that experienced decision makers are able to implement their one solution at any time during the process. (Page 31)
    • It should be possible to train systems to approximate this, possibly at different levels of abstraction
  • The RPD is a descriptive model that explains how experienced decision makers work problems in high stress decision situations (Page 31)
    • It is reflexive, and as such well suited to ML techniques, assuming there is enough data…
  • Situations that require the careful deployment of resources and analysis of abstract data, such as anticipating an enemy’s course of action, require an analytical approach. If there is time for analysis, a rational process normally provides a better solution for these kinds of problems (page 31)
    • This is not what AI/ML is good at. As reaction requirements become tighter, these actions will have to be run in “slow motion” offline and used to train the system.
  • The RPD model provides some insight as to how operational commanders survive in high-load, ambiguous and time pressured situations. The key seems to be experience. The experience serves as the base for what may be seen as an intuitive way to overcome stress. (Page 32)
    • This is why training with attribution may be the best way. “Ms. XXX trained this system and I trust her” may be the best option. We may want to build a “stable” of machine trainers.
  • Decision makers with more experience will tend to employ intuitive methods more often than analytical processes. This reliance on pattern recognition among experienced commanders may provide an opportunity for an adversary to manipulate the patterns to his advantage in deception operations. (Page 32)

Chapter 3: Considering a Cognitive Warfare Framework

  • an examination of John Boyd’s Observation-Orientation-Decision-Action (OODA) cycle to illustrate the different ways a C2W campaign may attack an adversary’s decision cycle. This sets the stage for analysis of the particular methods of such attacks.
    • From Wikipedia: One of John Boyd’s primary insights in fighter combat was that it is vital to change speed and direction faster than the opponent. This may interfere with an opponent’s OODA cycle. It is not necessarily a function of the plane’s ability to maneuver, but the pilot must think and act faster than the opponent can think and act. Getting “inside” the cycle, short-circuiting the opponent’s thinking processes, produces opportunities for the opponent to react inappropriately.
    • Once a group is adapting as fast as its arousal potential can tolerate, it will react in a linear way, since any deviation from the plan creates more arousal potential. Creating these stampedes, often simply through adversarial herding can create an extremely brittle vulnerable C2W cognitive framework.
  • …degrading the efficiency of the decision cycle by denying the “observation” function the ability to see and impeding the flow of accurate information through the physical links of the loop. Data denial is usually achieved by preventing the adversary’s observation function, or sensors, from operating effectively in one or more channels. (Page 36)
  • The second approach attempts to corrupt the adversary’s orientation. The focus is on the accuracy of the opponent’s perceptions and facts that inform his decisions, rather than their speed through the decision cycle. Operations security, deception and psychological operations (PSYOPS) are usually the primary C2W elements in the corruption effort.72 The corruption scheme’s relationship to decision speed is somewhat complicated. In fact, the corruption mechanism may work to vary the decision speed depending on the objective of the intended misperception. For example, the enemy might be induced to speedily make the wrong decision. (Page 37)

C2W

  • B. H. Liddell Hart wrote: “…it is usually necessary for the dislocating move to be proceeded by a move, or moves, which can be best defined by the term ‘distract’ in its literal sense of ‘to draw asunder’. The purpose of this ‘distraction’ is to deprive the enemy of his freedom of action, and it should operate in the physical and psychological spheres.” (Page 42)
    • The issue here is that we are adding a new “psychological” sphere – the domain of the intelligent machine. Since we have limited insight into the high-dimensional function that is the trained network, we cannot know when it is being successfully manipulated until it makes an obvious blunder, at which point it may be too late. This is one of the reasons that diversity needs to be built into the system so that there is a lower chance of a majority of systems being compromised.
  • Fundamentally, all deception ploys are constructed in two parts: dissimulation and simulation. Dissimulation is covert, the act of hiding or obscuring the real; its companion, simulation, presents the false. Within this basic construct, deception programs are employed in two variants: A-type (ambiguity) and M-type (misdirection). The A-type deception seeks to increase ambiguity in the target’s mind. Its aim is to keep the adversary unsure of one’s true intentions, especially an adversary who has initially guessed right. A number of alternatives are developed for the target’s consumption, built on lies that are both plausible and sufficiently significant to cause the target to expend resources to cover them. The M-type deception is the more demanding variant. This deception misleads the adversary by reducing ambiguity, that is, attempting to convince him that the wrong solution is, in fact, “right.” In this case, the target positions most of his attention and resources in the wrong place. (Page 44)
  • Both Sun-Tzu and Liddell Hart highlighted the dilemma of alternative objectives upon an adversary’s mind made possible by movement. (Page 45)
  • Most important is the fact that the overall cognitive warfare approach is dependent upon the enemy’s command baseline–the decision making processes, command characteristics and expectations of the decision makers. The skillful employment of stress and deception against the command baseline may be a principal mechanism to bring about its cognitive dislocation. (Page 50)
    • As the baseline becomes automated, then cognitive warfare must factor these aspects in.