Finished Meltdown. Need to write up some notes.
Think about using a CMAC or Deep CMAC for function learning, because NIST. Also, can it be used for multi-dimensional learning?
- Cerebellar model articulation controller
- Adaptive Noise Cancellation Using Deep Cerebellar Model Articulation Controller
- RCMAC Hybrid Control for MIMO Uncertain Nonlinear Systems Using Sliding-Mode Technology
- A hybrid control system, integrating principal and compensation controllers, is developed for multiple-input-multiple-output (MIMO) uncertain nonlinear systems. This hybrid control system is based on sliding-mode technique and uses a recurrent cerebellar model articulation controller (RCMAC) as an uncertainty observer. The principal controller containing an RCMAC uncertainty observer is the main controller, and the compensation controller is a compensator for the approximation error of the system uncertainty. In addition, in order to relax the requirement of approximation error bound, an estimation law is derived to estimate the error bound. The Taylor linearization technique is employed to increase the learning ability of RCMAC and the adaptive laws of the control system are derived based on Lyapunov stability theorem and Barbalat’s lemma so that the asymptotical stability of the system can be guaranteed. Finally, the proposed design method is applied to control a biped robot. Simulation results demonstrate the effectiveness of the proposed control scheme for the MIMO uncertain nonlinear system
- Github CMAC TF projects
Phil 7:00 – 3:30 ASRC PhD
- Sprint review
- Reading Meltdown: Why our systems fail and What we can do about it, and I found some really interesting work that relates to social conformity, flocking, stampeding and nomadic behaviors:
- “We show that a deviation from the group opinion is regarded by the brain as a punishment,” said the study’s lead author, Vasily Klucharev. And the error message combined with a dampened reward signal produces a brain impulse indicating that we should adjust our opinion to match the consensus. Interestingly, this process occurs even if there is no reason for us to expect any punishment from the group. As Klucharev put it, “This is likely an automatic process in which people form their own opinion, hear the group view, and then quickly shift their opinion to make it more compliant with the group view.” (Page 154)
- Reinforcement Learning Signal Predicts Social Conformity
- Vasily Klucharev
- We often change our decisions and judgments to conform with normative group behavior. However, the neural mechanisms of social conformity remain unclear. Here we show, using functional magnetic resonance imaging, that conformity is based on mechanisms that comply with principles of reinforcement learning. We found that individual judgments of facial attractiveness are adjusted in line with group opinion. Conflict with group opinion triggered a neuronal response in the rostral cingulate zone and the ventral striatum similar to the “prediction error” signal suggested by neuroscientific models of reinforcement learning. The amplitude of the conflict-related signal predicted subsequent conforming behavioral adjustments. Furthermore, the individual amplitude of the conflict-related signal in the ventral striatum correlated with differences in conforming behavior across subjects. These findings provide evidence that social group norms evoke conformity via learning mechanisms reflected in the activity of the rostral cingulate zone and ventral striatum.
- When people agreed with their peers’ incorrect answers, there was little change in activity in the areas associated with conscious decision-making. Instead, the regions devoted to vision and spatial perception lit up. It’s not that people were consciously lying to fit in. It seems that the prevailing opinion actually changed their perceptions. If everyone else said the two objects were different, a participant might have started to notice differences even if the objects were identical. Our tendency for conformity can literally change what we see. (Page 155)
- Gregory Berns
- Dr. Berns specializes in the use of brain imaging technologies to understand human – and now, canine – motivation and decision-making. He has received numerous grants from the National Institutes of Health, National Science Foundation, and the Department of Defense and has published over 70 peer-reviewed original research articles.
- Neurobiological Correlates of Social Conformity and Independence During Mental Rotation
When individual judgment conflicts with a group, the individual will often conform his judgment to that of the group. Conformity might arise at an executive level of decision making, or it might arise because the social setting alters the individual’s perception of the world.
We used functional magnetic resonance imaging and a task of mental rotation in the context of peer pressure to investigate the neural basis of individualistic and conforming behavior in the face of wrong information.Results
Conformity was associated with functional changes in an occipital-parietal network, especially when the wrong information originated from other people. Independence was associated with increased amygdala and caudate activity, findings consistent with the assumptions of social norm theory about the behavioral saliency of standing alone.
These findings provide the first biological evidence for the involvement of perceptual and emotional processes during social conformity.
- The Pain of Independence: Compared to behavioral research of conformity, comparatively little is known about the mechanisms of non-conformity, or independence. In one psychological framework, the group provides a normative influence on the individual. Depending on the particular situation, the group’s influence may be purely informational – providing information to an individual who is unsure of what to do. More interesting is the case in which the individual has definite opinions of what to do but conforms due to a normative influence of the group due to social reasons. In this model, normative influences are presumed to act through the aversiveness of being in a minority position
- A Neural Basis for Social Cooperation
- Cooperation based on reciprocal altruism has evolved in only a small number of species, yet it constitutes the core behavioral principle of human social life. The iterated Prisoner’s Dilemma Game has been used to model this form of cooperation. We used fMRI to scan 36 women as they played an iterated Prisoner’s Dilemma Game with another woman to investigate the neurobiological basis of cooperative social behavior. Mutual cooperation was associated with consistent activation in brain areas that have been linked with reward processing: nucleus accumbens, the caudate nucleus, ventromedial frontal/orbitofrontal cortex, and rostral anterior cingulate cortex. We propose that activation of this neural network positively reinforces reciprocal altruism, thereby motivating subjects to resist the temptation to selfishly accept but not reciprocate favors.
- Working on Antonio’s paper. I think I’ve found the two best papers to use for the market system. It turns out that freight has been doing this for about 20 years. Agent simulation and everything
7:00 – 9:00, 12:00 – ASRC PhD
- Reading the New Yorker piece How Russia Helped Swing the Election for Trump, about Kathleen Hall Jamieson‘s book Cyberwar: How Russian Hackers and Trolls Helped Elect a President—What We Don’t, Can’t, and Do Know. Some interesting points with respect to Adversarial Herding:
- Jamieson’s Post article was grounded in years of scholarship on political persuasion. She noted that political messages are especially effective when they are sent by trusted sources, such as members of one’s own community. Russian operatives, it turned out, disguised themselves in precisely this way. As the Times first reported, on June 8, 2016, a Facebook user depicting himself as Melvin Redick, a genial family man from Harrisburg, Pennsylvania, posted a link to DCLeaks.com, and wrote that users should check out “the hidden truth about Hillary Clinton, George Soros and other leaders of the US.” The profile photograph of “Redick” showed him in a backward baseball cap, alongside his young daughter—but Pennsylvania records showed no evidence of Redick’s existence, and the photograph matched an image of an unsuspecting man in Brazil. U.S. intelligence experts later announced, “with high confidence,” that DCLeaks was the creation of the G.R.U., Russia’s military-intelligence agency.
- Jamieson argues that the impact of the Russian cyberwar was likely enhanced by its consistency with messaging from Trump’s campaign, and by its strategic alignment with the campaign’s geographic and demographic objectives. Had the Kremlin tried to push voters in a new direction, its effort might have failed. But, Jamieson concluded, the Russian saboteurs nimbly amplified Trump’s divisive rhetoric on immigrants, minorities, and Muslims, among other signature topics, and targeted constituencies that he needed to reach.
- Twitter released IRA dataset (announcement, archive), and Kate Starbird’s group has done some preliminary analysis
- Need to do something about the NESTA Call for Ideas, which is due “11am on Friday 9th November“
- Continuing with Market-Oriented Programming
- Some thoughts on what the “cost” for a trip can reference
- Ticket price
- provider: Current price, refundability, includes taxes
- consumer: Acceptable range
- Travel time
- Departure time
- Arrival time (plus arrival time confidence)
- comfort (legroom, AC)
- Number of stops (related to convenience)
- Number of passengers
- Time to wait
- Externalities like airport security, which adds +/- 2 hours to air travel
- Divisibility (ship as one or more items)
- Physical state for shipping (packaged, indivisible solid, fluid, gas)
- Waste to food grade to living (is there a difference between algae and cattle? Pets? Show horses?
- Aggregators provide simpler combinations of transportation options
- Any exchange that supports this format should be able to participate. Additionally, each exchange should contain a list of other exchanges that a consumer can request, so we don’t need another level of hierarchy. Exchanges could rate other exchanges as a quality measure
- It also occurs to me that there could be some kind of peer-to-peer or mesh network for degraded modes. A degraded mode implies a certain level of emergency, which would affect the (now small-scale) allocation of resources.
- Some stuff about Mobility as a Service. Slide deck (from Canada Intelligent Transportation Service), and an app (Whim)
- PSC AI/ML working group 9:00 – 12:00, plus writeup
- PSC will convene a working group meeting on Thursday, Oct. 18 from 9am – 10am to identify actions and policy considerations related to advancing the use of AI solutions in government. Come prepared to share your ideas and experience. We would welcome your specific feedback on these questions:
- How can PSC help make the government a “smarter buyer” when it comes to AI/ML?
- How are agencies effectively using AI/ML today?
- In what other areas could these technologies be deployed in government today?
- Looking for bad sensors on NOAA satellites
- What is the current federal market and potential future market for AI/ML?
- How to help our members – federal contracts. Help make the federal market frictionless
- Kevin – SmartForm? What are the main gvt concerns? Is it worry about False positives?
- Competitiveness – no national strategy
- Appropriate use, particularly law enforcement
- Robotic Process Automation (RPA) Security, Compliancy, and adoption. Compliancy testing.
- Data trust. Humans make errors. When ML makes the same errors, it’s worse.
- A system that takes time to get accurate watching people perform is not the kind of system that the government can buy.
- This implies that there has to be immediate benefit, and can have the possibility of downstream benefit.
- Dell would love to participate (in what?) Something about cloud
- Replacing legacy processes with better approaches
- Fedramp-like compliance mechanism for AI. It is a requirement if it is a cloud service.
- Perceived, implicit bias is the dominant narrative on the government side. Specific applications like facial recognition
- Take a look at all the laws that might affect AI, to see how the constraints are affecting adoption/use with an eye towards removing barriers
- Chris ?? There isn’t a very good understanding or clear linkage between the the promise and the current problems, such as staffing, tagged data, etc
- What does it mean to be reskilled and retrained in an AI context?
- President’s Management Agenda
- The killer app is cost savings, particularly when one part of government is getting a better price than another part.
- Federal Data Strategy
- Send a note to Kevin about data availability. The difference between NOAA sensor data (clean and abundant), vs financial data, constantly changing spreadsheets that are not standardized. Maybe the creation of tools that make it easier to standardize data than use artisanal (usually Excel-based) solutions. Wrote it up for Aaron to review. It turned out to be a page.
7:00 – 4:00 Antonio Workshop
- Thought of a title: Transportation as a Service: Concepts, Implementations, and Black Swans
- Starting on Market-oriented programming.
- Would Kaufman’s patches apply here to localize markets? Certainly the idea of long jumps on the fitness landscapes initially (between patches), then short jumps on a single patch could make a lot of sense. For example, it might make sense to fly from BWI to dulles for an important meeting if traffic is very bad. This is why Sao Paulo and Mexico City have helicopters.
- Can A* be applied to create a “best path” through a set of resources (based on time, money, etc) to get to a destination?
- Some interesting belief space work: “Plotto”: Generating Truly Offensive Stories Since 1928.
- “Plotto: The Master Book of All Plots” (Amazon title!) was written by William Cook in 1928. I have had the physical book for years, which is a gorgeous object, but I have always wanted a digital database of the contents.
7:00 – 4:00 ASRC DARPA
- Steve had some good questions about quantitative measures:
- I think there are some good answers that we can provide here on determining the quality of maps. The number of users is an educated guess though. In my simulations, I can generate enough information to create maps using about 100 samples per agent. I’m working on a set of experiments that will produce “nosier” data that will provide a better estimate, but that won’t be ready until December. So we can say that “simulations indicate that approximately 100 users will have to interact through a total of 100 threaded posts to produce meaningful maps”
- With respect to the maps themselves, we can determine quality in four ways. The mechanism for making this comparison will be bootstrap sampling (https://en.wikipedia.org/wiki/Bootstrapping_(statistics)), which is an extremely effective way of comparing two unknown distributions. In our case, the distribution will be the coordinate of each topic in the embedding space.
- Repeatability: Can multiple maps generated on the same data set be made to align? Embedding algorithms often start with random values. As such embeddings that are similar may appear different because they have different orientations. To determine similarity we would apply a least-squares transformation of one map with respect to the other. Once complete, we would expect a greater than 90% match between the two maps in success.
- Resolution: What is the smallest level of detail that can be rendered accurately? We will be converting words into topics and then placing the topics in an embedding space. As described in the document, we expect to do this with Non-Negative Matrix Factorization (NMF). If we factor the all discussions down to a single topic (i.e. “words”), then we will have a single point map that can always be rendered with 100% repeatability, but it has 0% precision. If, on the other hand, we can place every word in every discussion on the map, but the relationships are different every time, then we can have 100% precision, but 0% repeatability. As we cluster terms together, we need to compare repeated runs to see that we get similar clusters each time. We need to find the level of abstraction that will give us a high level of repeatability. A 90% match is our expectation.
- Responsiveness: Maps change over time. A common example is a weather map, though political maps shift borders and physical maps reflect geographic activity like shoreline erosion. This duration may reflect the accuracy of the map, with slow change happening across large scales while rapid changes are visible at higher resolutions. A change at the limit of resolution should ideally be reflected immediately in the map and not adjust the surrounding areas.
- More frantic flailing to meet the deadline. DONE!!!
4:00 – 5:30 Antonio Workshop
- Started to read up about market-based scheduling
All day on the DARPA proposal effort
7:00 – 12:00, 2:00 – 5:00 ASRC Research
- Finish up At Home in the Universe notes – done!
- Get started on framing out Antonio’s paper – good progress!
- Basically, Aaron and I think there is a spectrum of interaction that can occur in these systems. At one end is some kind of market, where communication is mediated through price, time, and convenience to the transportation user. At the other is a more top down, control system way of dealing with this. NIST RCS would be an example of this. In between these two extremes are control hierarchies that in turn interact through markets
- Wrote up some early thoughts on how simulation and machine learning can be a thinking fast and slow solution to understandable AI