Durkheim, Elementary Forms of Religious Life

A World Beyond Physics: The Emergence and Evolution of Life

The British cabinet is the progenitor of Israel, as well as America's progenitor. On one hand, American land is a land of extremes and contradictions that rebelled first against the crown and then there were many noble sacrifices made by revolutionaries made…

Ultimately, statements like "there is not an America," "there is not an India," or "there is not an England" (t. Sam Kriss "30,000 Years of Hurt") are firey and do serve a purpose, and make a point, but in reality, the idea of a place is self-fulfilling, once it is made it is not so easily unmade. The truth is: if we want to see the world turn red, if we want to see the world made into something worth living in, without borders, the whole world, we must not fall into the age-old reactionary trap of saying to ourselves, "these places are fake" as much as the pretenses on which they were created were indeed pulled from thin air and erroneous to start with. They are no longer fake; what are we to do about it?

We may either make them worth living in, or we may destroy everything and everyone. The latter answer is actually the answer of the current billionaire class. I do not concur. We will not capitulate to that, this tendency towards further isolation, acceleration of "safe spaces" and cloisters, ultimately leaving everyone who cannot afford the best safe rooms to fend for themselves in the wastes like as if it's Mad Max. The only cloister we will abide by is the one which exists to protect an instance of the effort to open the world up and reverse this tendency.

Ultimately, neither the land nor its people should be divided or disowned as a solution. We must call people to work together. "I didn't want your help anyway" is the death rattle of a movement. Among the children of Hindu men, there are those who wish they did not live this way, that there was a better way that is less bigoted and self-defeating and preacarious. Just as among the children of slavers, there are abolitionists. Among the children of colonizers, there are those who want friendship, coexistence, and to pay reparations. We do call on Americans to actually make this a place where there is justice for all. It began as a cheap slogan, like everything else that is fake and a mere cover for something else. It should not remain a cheap slogan. It should be true. Through our actions, investigations, and work, we could make it true.

The greatest Americans were patriots. We’re the country of Douglas, Truth, Whitman, Emerson, Ursula Le Guin, Muhammad Ali… We made radio and computers, landed on the Moon… we made San Francisco… an absolutely absurd topography on which to build a city. The seesawing roads… the byzantine knots of transmission lines, all of them presumably serving some function… everything miraculously sustained. Hiking the hills of San Francisco you cannot help but feel overwhelmed by a sense of collective unity, a longing for collaboration and peaceful coexistence. Even if this project is incomplete, even if it falls short of its stated ambitions, we cannot hide my admiration.

VI. Predictive Coding

1. A large body of experimental evidence has shown that, following training with temporal sequences of sensory stimuli, BOLD response is greater when a stimulus violates the temporal pattern established by prior training, than when a stimulus is consistent with that learned pattern.

2. A cortical region learns temporal models of its input and uses these models to continuously generate predictions based on its current input in the context of its prior inputs. Prediction errors are then used to update the learned models.

3. The predictive coding hypothesis accounts for the decreased activity due to predicted stimuli, as well as for simple repetition suppression and priming effects. With a hierarchical model of predictive coding, whereby each level of cortex generates top-down predictions which are compared with novel input at a lower level.

4. Only the difference between the two, the prediction error, is transmitted back up to the higher level.

V. Temporal Slowness

1. One reason visual invariance problems are difficult is because they are not easily solved by either of these methods. Two input patterns representing the same object with some variation in position (or scale, rotation, etc.) may be separated by a very large Euclidean distance, while two patterns representing different objects may be much closer.

2. Error driven learning has been proven more capable of making the correct discriminations, but is not very good at generalizing these discriminations beyond its training set.

3. Temporal slowness is based on the idea that sensory data changes on a much more rapid timescale than do the relevant properties of the world. While the identities of nearby objects and their configuration in the environment tend to change only gradually, the visual input reflecting that environment can change markedly from one moment to the next, due for example to the movement of objects or a change in view angle.

4. Temporal slowness takes advantage of the fact that the many possible visual impressions that can be projected by an individual object make up a single, contiguous manifold in the high dimensional space of visual input. Because of this, different visual impressions of the same object will tend to transform continuously from one to another, tracing a path on that manifold.

5. This information can be used to learn invariant representations in an unsupervised manner, or rather by using time as a supervisor; those input representations that tend to occur adjacently in time are transformed into a common, invariant output representation.

6. Slow feature analysis restates the temporal slowness principle as a nonlinear optimization problem, in which, given an input signal, it will find the functions that will produce those output signals that vary most slowly over time, while still carrying significant information.

7. Unlike the earlier models, however, slow feature analysis remains a mathematical algorithm; there is not yet a biological theory for how it may be implemented by the cortex. However, it presents a credible solution to the question of how the disjunctive cell properties in a MHWA model such as HMAX may be learned.

Notes on "Towards Universal Cortical Algorithm" by Michael Ferrier

IV. Multi-Stage Hubel Wiesel Architectures

1. MHWA models are composed of alternating layers of conjunctive and disjunctive units. According to MHWA, simple cells (conjunctive units) respond mostly to lines of a particular orientation, spatial frequency, and position, while complex cells (disjunctive units) introduce some degree of invariance to position.

2. The conjunctive units perform template matching, responding to a particular combination of inputs from the previous layer, such as a contrasting edge at a particular orientation and position (often described using a Gabor filter function).

3. The disjunctive units are wired to respond when any of several related conjunctive units from a local area in the previous layer are active, for example when any unit is active that represents a given line orientation and spatial frequency, but at any position within a local range. Each disjunctive unit pools over a particular set of conjunctive input units.

4. The disjunctive unit layer then feeds into a second conjunctive unit layer, which learns to respond to specific combinations of those partially invariant representations, and so on. The result is that, over the course of several levels, representations are learned that are selective to individual whole objects but are also invariant to changes in position and scale.

Shortcomings :-

a. However it does not address how all of those response properties may develop in the first place. Specifically, HMAX's disjunctive units are hard-wired to the particular set of conjunctive units that they pool over, in order to respond invariantly to the activity of any of those conjunctive units and so introduce spatial and scale invariance; these connections are set parametrically, rather than through a learning process. The HMAX model remains agnostic to the nature of this learning process.

b. It is unclear what role the disjunctive layers may play in non-visual cortical functions. In addition, there are other cortical functions such as the representation of temporal sequences, crucial for auditory and motor processing, that are not modeled at all within the HMAX framework.

c. Even within the domain of visual object recognition, HMAX relies on a separate, supervised mechanism to learn other types of invariance, such as pose, rotation and lighting invariance.