Home > Uncategorized > Weekly QuEST Discussion Topics and News, 5 May

Weekly QuEST Discussion Topics and News, 5 May

QuEST 5 May 2017

I assigned some homework last week – so we will start this week by discussing your answers and assigning grades.  Our colleague Igor provided a really interesting viewpoint and we will star there but I hope to have others chime in with their thoughts on the assignment.

Let me remind you the task:

We’ve defined autonomy via the behavioral characteristics:

1.1         Autonomy

1.1.1        What is an autonomous system (AS)?

An autonomous system (AS) possess all of the following principles:

 

  • Peer Flexibility: An AS exhibits subordinate, peer, or supervisor role.  Peer flexibility enables the AS to change that role with Airmen or other AS’s within the organization. That is, it participates in the negotiation that results in the accepted change requiring the AS to ‘understand’ the meaning of the new peer relationship to respond acceptably. For example, a ground collision avoidance system (GCAS) demonstrates peer flexibility by making the pilot subordinate to the system until it is safe for the pilot to resume positive control of the aircraft.
  • Task Flexibility: The system can change its task. For example, a system could change what it measures to accomplish its original task (like changing the modes in a modern sensor) or even change the task based on changing conditions. This requires seeing (sensing its environment) / thinking (assessing the situation) / doing (making decisions that help it reach its goal and then acting on the environment) – closing the loop with the environment ~ situated agency.
  • Cognitive Flexibility: The technique is how the AS carries out its task.  For example, in a machine learning situation, the system could change its decision boundaries, rules, or machine learning model for a given task, adaptive cognition. The AS can learn new behaviors over time (experiential learning) and uses situated cognitive representations to close the loop around its interactions in the battle space to facilitate learning and accomplishing its tasks.

 

Each of the three principles contains the idea of change. A system is not autonomous if it is not capable of changing at least one of the three principles of autonomy. No one principle is more important than the other. No one principle makes a system more autonomous than another. The importance of a principle is driven solely by the application.

Autonomy:  We’ve taken the position that an autonomous system is one that creates the knowledge necessary to remain flexible in its relationships with humans and machines (peer flexibility), tasks it undertakes (task flexibility), and how it completes those tasks (task flexibility).

To achieve our goal of making Autonomous systems our autonomy vision can thus be mapped to:  Timely Knowledge creation improving every Air Force decision!

Strategy to tasks:  A sequence of near / mid-term cross Directorate experiments with increasing complexities of the knowledge creation necessary for mission success culminating in an effort focused on situation awareness for tailored multi-domain effects.

This requires us to characterize knowledge complexity for each of these experiments and the really important task of characterizing the knowledge complexity required for autonomy (to be able to possess the three principles).

This led to the homework — all QuEST ‘avengers’ – associates of Captain Amerika – come up with a sequence of challenge problems and characterize the knowledge complexity for each.  The ultimate challenge problem should demonstrate the 3 principles of autonomy and the appropriate characterization of the knowledge to solve that challenge problem – again with the pinnacle being the multi-domain situation awareness.

1.2         Definitions & Foundational Concepts

1.2.1        What is intelligence? What is artificial intelligence?

Intelligence is the ability to gather observations, generate knowledge, and appropriately apply that knowledge to accomplish tasks. Artificial Intelligence (AI) is a machine that possesses intelligence.

1.2.2        What is an Autonomous system’s (AS’s) internal representation?

Current AS’s are programmed to complete tasks using different procedures.  The AS’s internal representation is how the agent structures what it knows about the world, its knowledge (what the AS uses to take observations and generate meaning), how the agent structures its meaning and its understanding.  For example, the programmed model used inside of the AS for its knowledge-base.  The knowledge base can change as the AS acquires more knowledge or as the AS further manipulates existing knowledge to create new knowledge.

1.2.3        What is meaning?  Do machines generate meaning?

Meaning is what changes in an Airman’s or Autonomous System’s (AS’s) internal representation as a result of some stimuli.  It is the meaning of the stimuli to that the Airman or the System. When you, the Airman, look at an American flag, the sequence of thoughts and emotions that it evokes in you, is the meaning of that experience to you at that moment. When the image is shown to a computer, and if the pixel intensities evoked some programed changes in that computers program, then that is the meaning of that flag to that computer (the AS). Here we see that the AS generates meaning that is completely different than what an Airmen does. The change in the AS’s internal representation, as a result of how it is programmed, is the meaning to the AS. The meaning of a stimulus is the agent specific representational changes evoked by that stimulus in that agent.  The update to the representation, evoked by the data, is the meaning of the stimulus to this agent.  Meaning is NOT just the posting into the representation of the data it is all the resulting changes to the representation.  For example, the evoking of tacit knowledge or a modification of the ongoing simulation (consciousness) or even the updating of the agent’s knowledge resulting from the stimuli is included in the meaning of a stimulus to an agent.  Meaning is not static and changes over time.  The meaning of a stimulus is different for a given agent depending on when it is presented to the agent.

1.2.4        What is understanding?  Do machines understand?

Understanding is an estimation of whether an AS’s meaning will result in it acceptably accomplishing a task. Understanding occurs if it raises an evaluating Airman or evaluating AS’s belief that the performing AS will respond acceptably. Meaning is the change in an AS’s internal representation resulting from a query (presentation of a stimulus). Understanding is the impact of the meaning resulting in the expectation of successful accomplishment of a particular task.

1.2.5        What is knowledge?

Knowledge is what is used to generate the meaning of stimuli for a given agent.  Historically knowledge comes from the species capturing and encoding via evolution in genetics, experience by an individual animal or animals via culture communicating knowledge to other members of the same species (culture).  With the advances in machine learning it is a reasonable argument that most of the knowledge that will be generated in the world in the future will be done by machines.

1.2.6        What is thinking? Do machines think?

Thinking is the process used to manipulate an AS’s internal representation; a generation of meaning, where meaning is the change in the internal representation resulting from a stimuli. If an AS can change or manipulate its internal representation, then it can think.

1.2.7        What is reasoning? Do machines reason?

Reasoning is thinking in the context of a task.  Reasoning is the ability to think about what is perceived and the actions to take to complete a task. If the system updates its internal representation, it generates meaning, and is doing reasoning when that thinking is associated with accomplishing a task. If the system’s approach is not generating the required ‘meaning’ to acceptably accomplish the task, it is not reasoning appropriately.

We still didn’t get to it so one of the deliverables out of this week’s conversation / discussion will be a simple to understand example thread to capture where we are in making autonomous systems and what the world will look like when we actually deliver these systems in this simple example thread – the hope is that the homework for this week will allow us to clearly explain from a knowledge perspective of what is the missing link.

The one example that I continual to use: putting into a hotel room Alexa / Siri / Cortana … and having it be a ubiquitous aid.  For example, handling on-demand the HVAC (temp / air in the room) and the audio visual (channel location / movie options / radio …), local information to include weather / transportation / exercise / eating…  The discussion is not to build the widgets that facilitate the physical / cyber connectivity but building the joint cognitive solutions – that is what is necessary in the Alexa representation to facilitate her to be able to understand a set of request she has not been programmed to accomplish.  The suspicion is the knowledge representational complexity required to handle ‘meaning-making’ for the unexpected query will include ‘simulation’.

The major focus has been on the expectation that solutions for many of the mission capabilities we seek will require an Agile/autonomous System of systems (ASoS).  Agility in this phrase is meant to capture the dynamic nature of the composition of the SoS as well as the dynamic nature of the range of tasks this SoS needs to accomplish, to include the unexpected query.

This system (made up of both human and computer agents) has to solve the issue of collaboration between its agents.  Collaboration will require inter-agent communication.  We seek to have agile communication versus having to standardize a communication protocol to maintain maximum agility.  We expect agents will join and depart from these collaborations and some of the required mission capabilities will not be pre-defined.  It seems logical that these agents have to be intelligent, see definition above ~ creates new knowledge and appropriately uses it later.  Do we need these agents to be able to share knowledge or meaning or both?  What is required for two agents to be able to share knowledge or meaning?  Where do goals and intent fit in our framework?  The goal of collaboration is to accomplish some task that requires the ASoS have an understanding, meaning associated with expected successful completion of the task.  What is required for multiple agents to collaboratively achieve understanding for a given task?

Last week we introduced the idea of ‘meaning translators’ – we want to return to that discussion to pull on the thread of how can this be accomplished and what is the knowledge complexity required to accomplish that – what impact does a dual model system have on such a goal?  Does the ability to do simulation facilitate ‘meaning-translation’?  Is that the key to Theory of Mind?

The below articles are still relevant as well as the articles we’ve previously discussed on generative models – they seem to be a great approach to instantiate the ‘simulation’ necessary for knowledge representation complexity. From the news this week you can read an article on the commercialization of these networks:

https://www.technologyreview.com/s/604270/real-or-fake-ai-is-making-it-very-hard-to-know/?set=604310

 

Intelligent Machines

Real or Fake? AI Is Making It Very Hard to Know

Learning Multiagent Communication with Backpropagation
Sainbayar Sukhbaatar
Dept. of Computer Science
Courant Institute, New York University …

  • Many tasks in AI require the collaboration of multiple agents. Typically, thecommunication protocol between agents is manually specified and not altered during training. In this paper we explore a simple neural model, called CommNet, that uses continuous communication for fully cooperative tasks.
  • The model consists of multiple agents and the communication between them is learned alongside their policy. We apply this model to a diverse set of tasks, demonstrating the ability of the agents to learn to communicate amongst themselves, yielding improved performance over non-communicative agents and baselines.
  • In some cases, it is possible to interpret the language devised by the agents, revealing simple but effective strategies for solving the task at hand.

Emergence of Grounded Compositional Language in Multi-Agent Populations
Igor Mordatch

arXiv:1703.04908v1 [cs.AI] 15 Mar 2017

It Begins: Bots Are Learning to Chat in Their Own Language

Igor Mordatch is working to build machines that can carry on a conversation. That’s something so many people are working on. In Silicon Valley, chatbot is now a bona fide buzzword. But Mordatch is different. He’s not a linguist. He doesn’t deal in the AI techniques that typically reach for language. He’s a roboticist who began his career as an animator. He spent time at Pixar and worked on Toy Story 3, in between stints as an academic at places like Stanford and the University of Washington, where he taught robots to move like humans. “Creating movement from scratch is what I was always interested in,” he says. Now, all this expertise is coming together in an unexpected way

Two other articles that have been in conversation threads this week are:

Neural Decoding of Visual
Imagery During Sleep
T. Horikawa,1,2 M. Tamaki,1* Y. Miyawaki,3,1† Y. Kamitani1,2

SCIENCE VOL 340 3 MAY 2013

  • Visual imagery during sleep has long been a topic of persistent speculation, but its private nature has hampered objective analysis. Here we present a neural decoding approach in which machine-learning models predict the contents of visual imagery during the sleep-onset period, given measured brain activity, by discovering links between human functional magnetic resonance imaging patterns and verbal reports with the assistance of lexical and image databases.
  • Decoding models trained on stimulus-induced brain activity in visual cortical areas showed accurate classification, detection, and identification of contents. Our findings demonstrate that specific visual experience during sleep is represented by brain activity patterns shared by stimulus perception, providing a means to uncover subjective contents of dreaming using objective neural measurement.

The question in this thread was does this show that machine learning can decipher the neural code?  Cap contends it can’t but we want to discuss what these experiments do show.

Another thread was:

Experimental evidence of massive-scale emotional
contagion through social networks
Adam D. I. Kramera,1, Jamie E. Guilloryb,2, and Jeffrey T. Hancockb,c
aCore Data Science Team, Facebook, Inc., Menlo Park, CA 94025; and Departments of bCommunication and cInformation Science, Cornell University, Ithaca,
NY 14853

  • Emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness.
  • Emotional contagion is well established in laboratory experiments, with people transferring positive and negative emotions to others.
  • Data from a large real-world social network, collected over a 20-y period suggests that longer-lasting moods (e.g., depression, happiness) can be transferred through networks [Fowler JH, Christakis NA (2008) BMJ 337:a2338], although the results are controversial.
  • In an experiment with people who use Facebook, we test whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the News Feed.
  • When positive expressions were reduced, people produced fewer positive posts and more negative posts; when negative expressions were reduced, the opposite pattern occurred.
  • These results indicate that emotions expressed by others on Facebook influence our own emotions, constituting experimental evidence for massive-scale contagion via social networks.
  • This work also suggests that, in contrast to prevailing assumptions, in-person interaction and nonverbal cues are not strictly necessary for emotional contagion, and that the observation of others’ positive experiences constitutes a positive experience for people.

Significance:

  • We show, via a massive (N = 689,003) experiment on Facebook, that emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness.
  • We provide experimental evidence that emotional contagion occurs without direct interaction between people (exposure to a friend expressing an emotion is sufficient), and in the complete absence of nonverbal cues.

The relationship of this thread is the fact that emotional state can be inferred / impacted by text communications – it doesn’t require face-to-face where other cues are available.

news summary (52)

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