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Archive for May, 2017

Weekly QuEST Discussion Topics 26 May

QuEST 26 May 2017

This week we want to start by taking the position that the third wave of machine

learning / artificial intelligence is 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 (cognitive

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 technical

integration experiments (TIE) 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 week we want to discuss some candidate TIEs in terms of knowledge

complexity and have that discussion from the perspective of the first and second

wave knowledge representations.

One of those TIEs pushes on the idea of an agile system of system – where we

posit a key knowledge complexity challenge is the idea of estimating another

agents representation to facilitate the sharing of relevant knowledge. We will use

this need to finally discuss the following article:

A second thread is relevant to the idea of generating a model of another agent’s

representation and current meaning it has created associated with some

observations. The article that has been at the core of this thread that we haven’t

found time to get to:

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?

There also is an associated youtube TeDX talk:

Another thread going for the last couple of weeks is associated with epiphany

learning –

https://www.sciencedaily.com/releases/2017/04/170417154847.htm

http://www.pnas.org/content/114/18/4637.abstract

the topic was proposed by our colleague Prof Bert P – and then that was also

supported by our recuperating colleague Robert P – from Robert:

This so-called 'epiphany' learning is more commonly known as insight problem

solving and the original report on the phenomenon was Wallas in 1926 (he called

it 'illumination'). There are many papers in the literature on insight, and a well-

known 1995 edited book is really great. …

What has attracted me to study insight is that it represents meaning making in a

way that is tractable because the meaning making (insight or epiphany) occurs

suddenly– exactly at the time the person get the insight, we know they have made

meaning (i.e., the insight can be taken as a sign denoting a solution to a problem).

Also, Bob E. and I have argued recently that insight is an intuitive cognition

phenomenon (occurs suddenly from unconscious processing).

If anyone wants background to this paper, I have a lot of articles on insight I can

send…

The last thread has to do with the engineering of QuEST agents using a

combination of DL for the sys1 calculations and cGANs for the generation of the

qualia vocabulary – recall one application we were pursuing in this thread was the

solution to the chatbot problem – there is a news article this week associated

with this thread:

1 Ray Kurzweil is building a chatbot for Google

12

1.1 It's based on a novel he wrote, and will be released later this

year

by Ben Popper  May 27, 2016, 5:13pm EDT

  SHARE

  TWEET

  LINKEDIN

Inventor Ray Kurzweil made his name as a pioneer in technology that helped

machines understand human language, both written and spoken. These days

he is probably best known as a prophet of The Singularity, one of the leading

voices predicting that artificial intelligence will soon surpass its human

creators — resulting in either our enslavement or immortality, depending on

how things shake out. Back in 2012 he was hired at Google as a director of

engineering to work on natural language recognition, and today we got

another hint of what he is working on. In a video from a recent Singularity

conference Kurzweil says he and his team at Google are building a

chatbot, and that it will be released sometime later this year.

Kurzweil was answering questions from the audience, via telepresence robot

naturally. He was asked when he thought people would be able to have

meaningful conversations with artificial intelligence, one that might fool

you into thinking you were conversing with a human being. "That's very

relevant to what I'm doing at Google," Kurzweil said. "My team, among other

things, is working on chatbots. We expect to release some chatbots you can

talk to later this year.

One of the bots will be named Danielle, and according to Kurzweil, it will draw

on dialog from a character named Danielle, who appears in a novel he wrote

— a book titled, what else, Danielle. Kurzweil is a best selling author, but so

far has only published non-fiction. He said that anyone will be able to create

their own unique chatbot by feeding it a large sample of your writing, for

example by letting it ingest your blog. This would allow the bot to adopt your

"style, personality, and ideas."

news summary (55)

Categories: Uncategorized

Weekly QuEST Discussion Topics and News 19 May

news summary (54)QuEST 19 May 2017

There are several threads of discussions that we want to pick up on and catch up

on this week.

Topic on the progression of Knowledge complexity (characterizations of the

representation) to achieve the principles of autonomy – peer, task and cognitive

flexibility. Our colleague, Mike M has generated an extremely interesting cut on

this task and we will want to discuss that.

Let me remind you the task:

We’ve defined autonomy via the “principles of autonomy” – 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 (cognitive 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 technical

integration experiments (TIE) 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).

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.

A second thread is relevant to the idea of generating a model of another agent’s

representation and current meaning it has created associated with some

observations. The article that has been at the core of this thread:

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?

There also is an associated youtube TeDX talk:

Another thread going this week is associated with epiphany learning –

https://www.sciencedaily.com/releases/2017/04/170417154847.htm

http://www.pnas.org/content/114/18/4637.abstract

the topic was proposed by our colleague Prof Bert P – and then that was also

supported by our recuperating colleague Robert P – from Robert:

This so-called 'epiphany' learning is more commonly known as insight problem

solving and the original report on the phenomenon was Wallas in 1926 (he called

it 'illumination'). There are many papers in the literature on insight, and a well-

known 1995 edited book is really great. …

What has attracted me to study insight is that it represents meaning making in a

way that is tractable because the meaning making (insight or epiphany) occurs

suddenly– exactly at the time the person get the insight, we know they have made

meaning (i.e., the insight can be taken as a sign denoting a solution to a problem).

Also, Bob E. and I have argued recently that insight is an intuitive cognition

phenomenon (occurs suddenly from unconscious processing).

If anyone wants background to this paper, I have a lot of articles on insight I can

send…

The last thread has to do with the engineering of QuEST agents using a

combination of DL for the sys1 calculations and cGANs for the generation of the

qualia vocabulary – recall one application we were pursuing in this thread was the

solution to the chatbot problem – there is a news article this week associated

with this thread:

2 Ray Kurzweil is building a chatbot for Google

12

2.1 It's based on a novel he wrote, and will be released later this

year

by Ben Popper  May 27, 2016, 5:13pm EDT

  SHARE

  TWEET

  LINKEDIN

Inventor Ray Kurzweil made his name as a pioneer in technology that helped

machines understand human language, both written and spoken. These days

he is probably best known as a prophet of The Singularity, one of the leading

voices predicting that artificial intelligence will soon surpass its human

creators — resulting in either our enslavement or immortality, depending on

how things shake out. Back in 2012 he was hired at Google as a director of

engineering to work on natural language recognition, and today we got

another hint of what he is working on. In a video from a recent Singularity

conference Kurzweil says he and his team at Google are building a

chatbot, and that it will be released sometime later this year.

Kurzweil was answering questions from the audience, via telepresence robot

naturally. He was asked when he thought people would be able to have

meaningful conversations with artificial intelligence, one that might fool

you into thinking you were conversing with a human being. "That's very

relevant to what I'm doing at Google," Kurzweil said. "My team, among other

things, is working on chatbots. We expect to release some chatbots you can

talk to later this year.

One of the bots will be named Danielle, and according to Kurzweil, it will draw

on dialog from a character named Danielle, who appears in a novel he wrote

— a book titled, what else, Danielle. Kurzweil is a best selling author, but so

far has only published non-fiction. He said that anyone will be able to create

their own unique chatbot by feeding it a large sample of your writing, for

example by letting it ingest your blog. This would allow the bot to adopt your

"style, personality, and ideas."

Categories: Uncategorized

Weekly QUEST Discussion Topics and News, 12 May

QuEST 12 May 2017

Unfortunately due to some visitors local today, Cap will have to cancel the in-person meeting for QuEST this week.  There are several threads of discussions (virtual QuEST) ongoing that are described below – if you have interest in joining any of these discussions let Cap know by chiming in with thoughts – we will pick up on these topics when Cap gets back in town for the QuEST meeting on the 19th of May.

Topic: on the progression of Knowledge complexity (characterizations of the representation) to achieve the principles of autonomy – peer, task and cognitive flexibility.  We had assigned some homework – Our colleague, Mike M has generated an extremely interesting cut on this task and we will want to discuss that.

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.

A second thread: is relevant to the idea of generating a model of another agent’s representation and current meaning it has created associated with some observations.  The article that has been at the core of this thread:

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?

There also is an associated youtube TeDX talk:

https://www.youtube.com/watch?v=y53EfXv3bII

 

Another thread: going this week is associated with epiphany learning –

https://www.sciencedaily.com/releases/2017/04/170417154847.htm

 

http://www.pnas.org/content/114/18/4637.abstract

 

the topic was proposed by our colleague Prof Bert P – and then that was also supported by our recuperating colleague Robert P – from Robert:

This so-called ‘epiphany’ learning is more commonly known as insight problem solving and the original report on the phenomenon was Wallas in 1926 (he called it ‘illumination’). There are many papers in the literature on insight, and a well-known 1995 edited book is really great. …

 

What has attracted me to study insight is that it represents meaning making in a way that is tractable because the meaning making (insight or epiphany) occurs suddenly–exactly at the time the person get the insight, we know they have made meaning (i.e., the insight can be taken as a sign denoting a solution to a problem). Also, Bob E. and I have argued recently that insight is an intuitive cognition phenomenon (occurs suddenly from unconscious processing).

 

If anyone wants background to this paper, I have a lot of articles on insight I can send…

 

The last thread: has to do with the engineering of QuEST agents using a combination of DL for the sys1 calculations and cGANs for the generation of the qualia vocabulary – recall one application we were pursuing in this thread was the solution to the chatbot problem – there is a news article this week associated with this thread:

2       Ray Kurzweil is building a chatbot for Google

12

2.1   It’s based on a novel he wrote, and will be released later this year

by Ben Popper  May 27, 2016, 5:13pm EDT

 

 

Inventor Ray Kurzweil made his name as a pioneer in technology that helped machines understand human language, both written and spoken. These days he is probably best known as a prophet of The Singularity, one of the leading voices predicting that artificial intelligence will soon surpass its human creators — resulting in either our enslavement or immortality, depending on how things shake out. Back in 2012 he was hired at Google as a director of engineering to work on natural language recognition, and today we got another hint of what he is working on. In a video from a recent Singularity conference Kurzweil says he and his team at Google are building a chatbot, and that it will be released sometime later this year.

Kurzweil was answering questions from the audience, via telepresence robot naturally. He was asked when he thought people would be able to have meaningful conversations with artificial intelligence, one that might fool you into thinking you were conversing with a human being. “That’s very relevant to what I’m doing at Google,” Kurzweil said. “My team, among other things, is working on chatbots. We expect to release some chatbots you can talk to later this year.

 

 

One of the bots will be named Danielle, and according to Kurzweil, it will draw on dialog from a character named Danielle, who appears in a novel he wrote — a book titled, what else, Danielle. Kurzweil is a best selling author, but so far has only published non-fiction. He said that anyone will be able to create their own unique chatbot by feeding it a large sample of your writing, for example by letting it ingest your blog. This would allow the bot to adopt your “style, personality, and ideas.”news summary (53)

Categories: Uncategorized

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.

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