Phil 10.30.19

7:00 – 5:00 GOES


  • Dissertation – finish up the maps chapter – done!
  • Try writing up more expensive information thoughts (added to discussion section as well)
    • Game theory comes from an age of incomplete information. Now we have access to mostly complete, but potentially expensive information
      • Expense in time – throwing the breakers on high-frequency trading
      • Expense in $$ – Buying the information you need from available resources
      • Expensive in resources – developing the hardware and software to obtain the information (Operation Hummingbird to TPU/DNN development)
    • By handing the information management to machines, we create a human-machine social structure, governed by the rules of dense/sparse,stiff/slack networks
      • AI combat is a very good example of an extremely stiff network (varies in density) and the associated time expense. Combat has to happen as fast as possible, due to OODA loop constraints. But if the system does not have designed-in capacity to negotiate a ceasefire (on both/all sides!), there may be no way to introduce it in human time scales, even though the information that one side is losing is readily apparent.
      • Online advertising is a case where existing information is hidden from the target of the advertiser, but available to the platform, and to a lesser degree, the client. Because this information asymmetry, the user’s behavior/beliefs are more likely to be exploited in a way that denies the user agency, while granting maximum agency to the platform and clients.
      • Deepfakes, spam and the costs of identifying deliberate misinformation
      • Call to action: the creation of an information environment impact body that can examine these issues and determine costs. This is too complex a process for the creators to do on their own, and there would be rampant conflict of interest anyway. But an EPA-like structure, where experts in this topic perform as a counterbalance to unconstrained development and exploitation of the information ecosystem
  • The Knowledge, Analytics, Cognitive and Cloud Computing (KnACC) lab in the Information Systems department in UMBC aims to address challenging issues at the intersection of Data Science and Cloud Computing. We are located in ITE 415.
  • GOES
    • Start creating NN that takes pitch/roll/yaw star tracker deltas and tries to calculate reaction wheel efficiency
      • input vector is dp, dr, dy. Assume a fixed timestep
      • output vector is effp, effr, effy
      • once everything trains up, try running the inferencer on the running sim and display “inferred RW efficiency” for each RW
      • Broke out the base class parts of TF2OptimizerTest. I just need to generate the test/train data for now, no sim needed


big ending news for the day