Rationally Biased Learning

Are human perception and decision biases grounded in a form of rationality? You return to your camp after hunting or gathering. You see the grass moving. You do not know the probability that a snake is in the grass. Should you cross the grass - at the risk of being bitten by a snake - or make a long, hence costly, detour? Based on this storyline, we consider a rational decision maker maximizing expected discounted utility with learning. We show that his optimal behavior displays three biases: status quo, salience, overestimation of small probabilities. Biases can be the product of rational behavior

The Blind Viewer

After watching Peter Gidal’s Room Film 1973, Michael Snow commented: ‘Your film had to be worked at. I felt… as if it was made by a blind man. I felt that searching tentative quality, the quality of trying to see’ (1.). The desire to see anew, as if it was for the first time, a learning to unlearn (2.), is one of the most enduring aspects of Gidal’s film practice and theory. A body of material as rich with possibilities as unresolved questions, paradoxes and dead ends. And yet, at a time when artists’ moving image has too often become a sheer repository for discourse, a reinvestment in Gidal’s attentiveness to the ‘coming into presence’ of the film may shed light on what these images and sounds do, rather than say. The question remains: how is a critique of image-production enacted, instead of represented? Notes: (1.) Snow, Michael, September 1973, quoted by Peter Gidal, ‘Theory and Definition of Structural / Materialist Film’, Structural Film Anthology, London: BFI, 1978, p.17 (2.) Borrowing a famous line from the poem, ‘what we see of things is things’ by Alberto Caeiro. Pessoa, Fernando, The Collected Poems of Alberto Caeiro, Exeter: Shearsman Books, 200

TensorFlow Enabled Genetic Programming

Genetic Programming, a kind of evolutionary computation and machine learning algorithm, is shown to benefit significantly from the application of vectorized data and the TensorFlow numerical computation library on both CPU and GPU architectures. The open source, Python Karoo GP is employed for a series of 190 tests across 6 platforms, with real-world datasets ranging from 18 to 5.5M data points. This body of tests demonstrates that datasets measured in tens and hundreds of data points see 2-15x improvement when moving from the scalar/SymPy configuration to the vector/TensorFlow configuration, with a single core performing on par or better than multiple CPU cores and GPUs. A dataset composed of 90,000 data points demonstrates a single vector/TensorFlow CPU core performing 875x better than 40 scalar/Sympy CPU cores. And a dataset containing 5.5M data points sees GPU configurations out-performing CPU configurations on average by 1.3x.Comment: 8 pages, 5 figures; presented at GECCO 2017, Berlin, German

Sim2Real View Invariant Visual Servoing by Recurrent Control

Humans are remarkably proficient at controlling their limbs and tools from a wide range of viewpoints and angles, even in the presence of optical distortions. In robotics, this ability is referred to as visual servoing: moving a tool or end-point to a desired location using primarily visual feedback. In this paper, we study how viewpoint-invariant visual servoing skills can be learned automatically in a robotic manipulation scenario. To this end, we train a deep recurrent controller that can automatically determine which actions move the end-point of a robotic arm to a desired object. The problem that must be solved by this controller is fundamentally ambiguous: under severe variation in viewpoint, it may be impossible to determine the actions in a single feedforward operation. Instead, our visual servoing system must use its memory of past movements to understand how the actions affect the robot motion from the current viewpoint, correcting mistakes and gradually moving closer to the target. This ability is in stark contrast to most visual servoing methods, which either assume known dynamics or require a calibration phase. We show how we can learn this recurrent controller using simulated data and a reinforcement learning objective. We then describe how the resulting model can be transferred to a real-world robot by disentangling perception from control and only adapting the visual layers. The adapted model can servo to previously unseen objects from novel viewpoints on a real-world Kuka IIWA robotic arm. For supplementary videos, see: https://fsadeghi.github.io/Sim2RealViewInvariantServoComment: Supplementary video: https://fsadeghi.github.io/Sim2RealViewInvariantServ

Dewey, Bruner, and Seas of Stories in the High Stakes Testing Debate

This paper proposes that many of the questions surrounding high stakes testing being debated today are important, yet fall short of moving teachers, parents, students, administrators and legislators to think deeply about how optimal teaching and learning can be achieved in a high stakes testing environment. Finally, the high stakes testing debate is viewed, to borrow a term from Bruner, as a sea of stories in which the stakeholders see the same things, but come away with remarkably differing stories of what is happening (1996, p. 147). The principles of learning espoused by Dewey and Bruner put these seas of stories into a different light by offering alternative ways of perceiving learning and teaching

Dewey, Bruner, and Seas of Stories in the High Stakes Testing Debate

This paper proposes that many of the questions surrounding high stakes testing being debated today are important, yet fall short of moving teachers, parents, students, administrators and legislators to think deeply about how optimal teaching and learning can be achieved in a high stakes testing environment. Finally, the high stakes testing debate is viewed, to borrow a term from Bruner, as a sea of stories in which the stakeholders see the same things, but come away with remarkably differing stories of what is happening (1996, p. 147). The principles of learning espoused by Dewey and Bruner put these seas of stories into a different light by offering alternative ways of perceiving learning and teaching

Risk-Seeking versus Risk-Avoiding Investments in Noisy Periodic Environments

We study the performance of various agent strategies in an artificial investment scenario. Agents are equipped with a budget, $x(t)$, and at each time step invest a particular fraction, $q(t)$, of their budget. The return on investment (RoI), $r(t)$, is characterized by a periodic function with different types and levels of noise. Risk-avoiding agents choose their fraction $q(t)$ proportional to the expected positive RoI, while risk-seeking agents always choose a maximum value $q_{max}$ if they predict the RoI to be positive ("everything on red"). In addition to these different strategies, agents have different capabilities to predict the future $r(t)$, dependent on their internal complexity. Here, we compare 'zero-intelligent' agents using technical analysis (such as moving least squares) with agents using reinforcement learning or genetic algorithms to predict $r(t)$. The performance of agents is measured by their average budget growth after a certain number of time steps. We present results of extensive computer simulations, which show that, for our given artificial environment, (i) the risk-seeking strategy outperforms the risk-avoiding one, and (ii) the genetic algorithm was able to find this optimal strategy itself, and thus outperforms other prediction approaches considered.Comment: 27 pp. v2 with minor corrections. See http://www.sg.ethz.ch for more inf