text:
Canadian Intellectual Property Office
Examination Practice Respecting Purposive
Construction - PN2013-02
Examination Practice Respecting Purposive Construction (PDF: 60 KB; 6 pages)
To all examiners:
In Canada (Attorney General) v Amazon.com Inc., 2011 FCA 328 Amazon FCA], the Court observed that, during examination, Supreme Court jurisprudence "requires the Commissioner's identification of the actual invention to be grounded in a purposive construction of the patent claims".1
As outlined by the Supreme Court in Free World Trust and Whirlpool, purposive construction is performed by the court to objectively determine what the person skilled in the art would, as of the date of publication of the patent application and on the basis of the particular words or phrases used in the claim, have understood the applicant to have intended to be the scope of protection sought for the disclosed invention.2
Context for examination
Free World Trust and Whirlpool continue to guide the courts, with the benefit of expert testimony and cross-examination, to construe the claim in accordance with the principles of purposive construction outlined therein.3 As noted in Genencor, however, Whirlpool was an impeachment proceeding that was not directed "to patent examiners in the course of examinations to determine whether applications for patents should be granted."4
The statement of the court in Genencor can be understood by recognizing that the language of patent claims construed by judges is fixed, is the result of a negotiation with the Patent Office, was "accepted by the Commissioner of Patents as a correct statement of a monopoly that can properly be derived from the invention disclosed in the specification",5 and benefits from the presumption of validity accorded by subsection 43(2) of the Patent Act . In contrast, during examination of an application the language of the claim may change from that initially proposed by the applicant for a number of reasons.
The Office considers that the application of the principles of purposive construction discussed in Free World Trust and Whirlpool to the examination of a patent application must take into account the role of the patent examiner and the purpose and context of examination.6
The Office takes the position that claim construction during examination requires an examiner to interpret each claim:
1) Using a fair, balanced and informed approach
The specification as a whole is addressed to the person skilled in the art and, as such, provides the context in which the claim should be read and informs the meaning of the terms recited in the claim and the nature of the invention.7 As noted in Free World Trust, upon a purposive construction the terms of a claim will be given specific technical meanings in light of the common general knowledge of the person skilled in the art (paragraph 51). Thus, in order to arrive at a fair, balanced and informed understanding of the subject-matter of a claim, it is critical that a purposive construction of the claim be performed considering the specification as a whole as read through the eyes of the person skilled in the art, against the background of the common general knowledge in the field or fields relevant to the invention at the time the application became available to the public (see also the MOPOP at 9.02.02 and 9.02.03).
In Amazon FCA it was recognized that, during examination, the necessary foundation of knowledge for performing a purposive construction of the claims is found in submissions from the applicant and the knowledge of an appropriately experienced examiner (paragraph 73).
2) Having identified the problem and solution
The purpose of the Patent Act is to provide exclusive rights to an inventor for a new and useful invention in exchange for a disclosure that allows the public to use or operate the invention as contemplated by the inventor. Thus, recognizing that a patentable invention is an inventive solution to a practical problem,8 it follows that an invention must be disclosed (and ultimately claimed) so as to provide the person skilled in the art with an operable solution.
Guidance in identifying the problem the inventors set out to address and its proposed solution should be found in the description, in accordance with paragraph 80(1)(d) of the Patent Rules,9 and not by reference to the closest prior art. This identification occurs when construing the promise of the invention (i.e. its utility).
Thus, identification of the problem and the solution provided by the invention informs the purposive construction of the claims.10
3) In the context of the application as a whole
While claim construction during examination must remain anchored in the language of the claims, it was reiterated in Amazon FCA that it "cannot be determined solely on the basis of a literal reading" of the claims (paragraph 43). A properly informed purposive construction must consider the application as a whole.
Not only must one not lose sight of the fact that the claims must be interpreted in light of the description, but as the FCA noted a claim-based analysis "does not mean that the Commissioner cannot ask or determine what the inventor has actually invented, or what the inventor claims to have invented. On the contrary, these are relevant and necessary questions in a number of contexts, including novelty, obviousness, and patentable subject matter".11 The examiner must be "alive to the possibility that a patent claim may be expressed in language that is deliberately or inadvertently deceptive", thus recognizing that, "for example, what appears on its face to be a claim for an "art" or a "process" may, on a proper construction, be a claim for a mathematical formula and therefore not patentable subject matter".12
This is consistent with the recognition in Free World Trust of the need to avoid "the pitfalls of language" so as to ensure the inventor receives "protection for that which he has actually in good faith invented".13
Subsequent to a purposive claim construction, performed in light of the description, the common general knowledge in the relevant art and the examiner's technical expertise, an examiner can determine whether the subject matter defined by a claim complies with the requirements of the Patent Act , and thus is a new, useful and inventive (i) art, (ii) improvement to an art, (iii) process, (iv) improvement to a process, (v) machine, (vi) improvement to a machine, (vii) manufacture, (viii) improvement to a manufacture, (ix) composition of matter, or (x) improvement to a composition of matter.14
4) To determine which elements of the claim solve the identified problem
One aspect of purposive construction is the identification of the essential elements of the claim. The identification of the essential elements of a claim cannot be performed without having first properly identified the proposed solution to the disclosed problem. As previously discussed, this identification considers the whole of the application from the perspective of the person skilled in the art having the benefit of common general knowledge in the relevant fields. Without having first considered the problem and solution, the identification of essential elements would be circular - it would begin and end with the language of the claim, contrary to Free World Trust which recognizes that elements can be found to be non-essential if "at the date of publication of the patent, the skilled addressee would have appreciated that a particular element could be substituted without affecting the working of the invention".15
Ultimately, some element or combination of elements defined in the claim must provide the "inventive solution" referred to by the Supreme Court (see footnote 8 herein). One must, however, approach each claim with an understanding that not every element that has a material effect on the operation of a given embodiment is necessarily essential for the operation of the invention. Some elements of a claim merely define the context or the environment of a specific working embodiment, but do not actually change the nature of the solution to the problem.16
Note that while the identification of the essential elements is performed "in light of the knowledge of the art at the date of the publication of the patent specification",17 this does not mean that one can simply conclude that the essential elements of the invention are those that distinguish the claimed subject matter from the prior art.18
Having identified the problem and solution, and defined the essential elements in the claims, an examiner may conclude that the claim either omits an essential element or includes non-essential elements.
i) Missing elements
Where it appears, having considered a claim in light of a fair reading of the description, that an element essential to the operation of the solution has not been defined in the claim, the claim may be defective for overbreadth (i.e. lack of support) and/or for lack of utility.
ii) Superfluous elements
In certain cases, an examiner may consider elements included in a claim of an application to be superfluous (non-essential) to the solution to a given problem. The mere presence of a superfluous limitation is not a defect as such, although the inclusion of such an element could render a claim defective (for example if its presence results in ambiguity).
It must be recognized that while the Office considers superfluous elements to be non-essential and not relevant to the determination of a claim's patentability during examination, if an applicant maintains such an element in the claim through to grant a court might later construe it to be essential when applying the "self-inflicted wound" factors of purposive construction as identified in Free World Trust and Whirlpool.19
5) By focusing on one solution to a problem
An invention is an element or a combination of elements20 that provides a solution to a problem. Where a claim includes solutions to more than one problem it includes more than one invention.21
If a claim includes solutions to more than one problem, examination should focus on one solution to a problem in performing the purposive construction. The initial choice of solution should be guided by the description, selecting the solution given the greatest emphasis by the inventors. If it becomes necessary to consider a different solution, the analysis should be undertaken anew.
On occasion it may be the case that elements or sets of elements in a claim do not interact with each other to achieve a unitary result; this may reflect an "aggregation" rather than a combination.22 A consideration of the problem and solution emphasized by the inventor in the description may assist the examiner to select only the element or set of elements that work together in the claim that provide the operable solution.
Purposive construction and examiner's reports
In most cases, an examiner reading a claim will automatically ascribe appropriate meanings to the terms of a claim in light of the teachings of the description and the examiner's technical expertise. It is not necessary to explain these conclusions in a report, unless it becomes apparent that there is some relevant disagreement between the examiner and the applicant as to the significance of certain terms. In such instances, it is only necessary to explicitly address the construction of the contested terms.
Similarly, in some cases it will be possible to conclude that a claim does not comply with the Patent Act or Patent Rules without explicitly determining whether a given element is or is not essential. A prior art document that discloses all the elements of a claim, for example, will anticipate the claimed subject-matter regardless of whether each element is essential or not. Here again, examiners are not required to detail in reports parts of their analysis that are not in issue.
Where an examiner's conclusions regarding a specific element are relevant to the identification of a perceived defect, the examiner should provide reasons to support their conclusions, e.g. emphasize the identified problem and solution and those elements essential to providing that solution.
1 Canada (Attorney General) v. Amazon.com Inc., 2011 FCA 328 [Amazon FCA] at paragraph 43.
2 Free World Trust v. Électro Santé Inc., 2000 SCC 66 at paragraph 50; and Whirlpool Corp. v. Camco Inc., 2000 SCC 67 at paragraph 48.
3 For an enumeration of the principles, see Free World Trust at paragraph 31.
4 Genencor International Inc. v. Canada (Commissioner of Patents), 2008 FC 608 [Genencor at paragraphs 62 and 70.
5 Whirlpool at paragraph 49.
6 Genencor at paragraphs 62 and 70 and Amazon FCA at paragraph 73.
7 Whirlpool at paragraphs 49(f)(g), 52 and 53.
8 Apotex Inc. v. Wellcome Foundation Ltd., 2002 SCC 77 at paragraph 37; the Supreme Court in Teva Canada Ltd. v. Pfizer Canada Inc., 2012 SCC 60 at paragraph 32 reiterates this point, and speaks of the importance of the patent bargain in advancing science and technology.
9 Paragraph 80(1)(d) of the Rules provides that the description must describe the invention so as to allow the understanding of the technical problem and its solution.
10 AstraZeneca Canada Inc. v. Apotex Inc., 2010 FC 714 at paragraph 33; Wenzel Downhole Tools Ltd. v National-Oilwell Canada Ltd., 2011 FC 1323 at paragraph 61; Jay-Lor International Inc. v. Penta Farm Systems Ltd., 2007 FC 358 at paragraph 55; Sanofi-Aventis Canada Inc. v. Apotex, 2009 FC 676 at paragraph 128; Merck & Co. Inc. v. Apotex Inc., 2010 FC 1265 at paragraph 86.
11 Amazon FCA at paragraph 42.
13 Free World Trust at paragraph 58, citing Western Electric Co. v Baldwin International Radio of Canada, 1934 S.C.R. 570 at page 574.
14 Amazon FCA highlights the distinction between an improvement ("the only inventive aspect of the claimed invention") and a "novel combination" at paragraph 63. Further, see paragraph 38 of Amazon FCA and paragraph 44 of Teva to the same effect.
15 Free World Trust at paragraph 55; here, "substitution" must be understood to include either substitution or omission of the element.
16 Amazon FCA at paragraphs 59 to 63; following the reasoning of the court, the existence of a practical embodiment does not automatically imply that the elements of the embodiment are essential elements of the invention.
17 Free World Trust at paragraph 52.
18 Halford v Seed Hawk Inc., 2006 FCA 275 at paragraph 14.
19 The Office does not consider the "self-inflicted wound" factor to be relevant during examination.
20 See section 9.04.03 of the MOPOP for a discussion of combinations and the meaning of "unitary result".
21 Examiners should be mindful that, in this context, the identification of multiple problems and solutions within a single claim is not to be confused with lack of unity of invention within the meaning of section 36 of the Patent Rules (which emphasizes that the subject matter defined by the claims are to be linked by a single general inventive concept).
22 Re Application for Patent of Prince Corp., 1982, 2 C.P.R. (3d) 223 (CD 942); and Shmuel Hershkovitz v. Tyco Safety Products Canada Ltd., 2009 FC 256 at paragraph 148.
Page: E732-wr03626


text:
RSpec.describe RuboCop::Cop::Layout::TrailingWhitespace, :config do
  let(:cop_config) { { 'AllowInHeredoc' => false } }

  it 'registers an offense for a line ending with space' do
    offenses = inspect_source('x = 0 ')
    expect(offenses.size).to eq(1)
  end

  it 'registers an offense for a blank line with space' do
    offenses = inspect_source('  ')
    expect(offenses.size).to eq(1)
  end

  it 'registers an offense for a line ending with tab' do
    offenses = inspect_source("x = 0\t")
    expect(offenses.size).to eq(1)
  end

  it 'registers an offense for trailing whitespace in a heredoc string' do
    offenses = inspect_source(['x = <<RUBY',
                               '  Hi   ',
                               'RUBY'].join("\n"))
    expect(offenses.size).to eq(1)
  end

  it 'registers offenses before __END__ but not after' do
    offenses = inspect_source(["x = 0\t",
                               ' ',
                               '__END__',
                               "x = 0\t"].join("\n"))
    expect(offenses.map(&:line)).to eq([1, 2])
  end

  it 'is not fooled by __END__ within a documentation comment' do
    offenses = inspect_source(["x = 0\t",
                               '=begin',
                               '__END__',
                               '=end',
                               "x = 0\t"].join("\n"))
    expect(offenses.map(&:line)).to eq([1, 5])
  end

  it 'is not fooled by heredoc containing __END__' do
    offenses = inspect_source(['x1 = <<RUBY ',
                               '__END__',
                               "x2 = 0\t",
                               'RUBY',
                               "x3 = 0\t"].join("\n"))
    expect(offenses.map(&:line)).to eq([1, 3, 5])
  end

  it 'is not fooled by heredoc containing __END__ within a doc comment' do
    offenses = inspect_source(['x1 = <<RUBY ',
                               '=begin  ',
                               '__END__',
                               '=end',
                               "x2 = 0\t",
                               'RUBY',
                               "x3 = 0\t"].join("\n"))
    expect(offenses.map(&:line)).to eq([1, 2, 5, 7])
  end

  it 'accepts a line without trailing whitespace' do
    expect_no_offenses('x = 0')
  end

  it 'auto-corrects unwanted space' do
    new_source = autocorrect_source(['x = 0 ',
                                     "x = 0\t"].join("\n"))
    expect(new_source).to eq(['x = 0',
                              'x = 0'].join("\n"))
  end

  context 'when `AllowInHeredoc` is set to true' do
    let(:cop_config) { { 'AllowInHeredoc' => true } }

    it 'accepts trailing whitespace in a heredoc string' do
      expect_no_offenses(['x = <<RUBY',
                          '  Hi   ',
                          'RUBY'].join("\n"))
    end

    it 'registers an offense for trailing whitespace at the heredoc begin' do
      offenses = inspect_source(['x = <<RUBY ',
                                 '  Hi   ',
                                 'RUBY'].join("\n"))
      expect(offenses.size).to eq(1)
    end
  end
end

redpajama_set_name: RedPajamaGithub


2) As climate change effects gradually cause increase variance in climate, this will lead to added hardship for populations. The economics branch of mechanism design might be better suited to simulate what a few degrees more or less in temperature will lead to riots, happier populations, economic stimuli just as agricultural studies focus on rainfall prediction, crop prediction etc.
I still hope nations do not weaponize the weather. Unfortunately I am quite sure some of them are already thinking of both predictive and defensive mechanisms for countering climate change without any thought of how this affects other countries.


text:
E3 2019 ⋮ Gaming
Killsquad Hands-On Preview – The Power of Cooperation
By Francesco De Meo
The Diablo series has influenced so many games that it would be impossible to name them all. While the series created by Blizzard has changed a lot over the years, some developers are still trying to capture what it made so engaging while adding new things to the formula and a few of them are quite close in accomplishing this daunting task. Among them is Novarama with its Killsquad, which has all the right features to attract not only Diablo fans but also those looking for a fast-paced action role-playing game co-op experience.
During E3 2019, I had the chance to try out the game and I have to say it left an extremely good impression on me. The demo available during the show allowed four players to explore a dangerous dungeon together, a dungeon complete with plenty of loot, enemies, traps, and even a boss. Co-op is the name of the game in Killsquad and doing things together comes very naturally, even without any sort of direct communication.
PlayStation Skipping E3 for Second Straight Year, Focusing on Hundreds of Other Fan Events
Killsquad features four different characters: the gunslinger, the healer, the tank and the assassin. All four characters come with some very unique skills, and creating a balanced team will be extremely important in surviving the hordes of enemies found in each dungeon.
Once the squad has been assembled, we have been thrown into a dungeon filled with enemies. The E3 2019 demo featured both gamepad and mouse and keyboard controls: I played with the former, but there were some features that still required me to use the keyboard, such as unlocking level up bonuses, so gamepad controls have yet to be implemented properly. Despite this, the controls are very smooth responsive and I was able to attack enemies, unleash special skills (which come with a cooldown timer) dodge attacks and generally throw myself into the fray with no effort.
My companions did the same, and the experience was generally fun: after a couple of minutes, we all fell right into our roles, so I was throwing myself at the enemy together with the tank while the gunslinger and healer stayed back to support our assaults. Enemies are varied enough, so the experience shouldn't be too mindless, as different strategies are required to take them down without taking too much damage. Loot was also available in good quantities, so Killsquad will surely give those who love min/maxing what they are looking for.
At the end of the dungeon, we had to face a menacing boss. Like for regular enemies, teamwork helped to overcome the monster with ease, and at the end, we have been rewarded with more loot and XP, which can be used to unlock new skills and improve those that have been already unlocked.
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The E3 2019 demo was simple enough, but Killsquad looks like a game that fans of Diablo and dungeon crawling action role-playing games will like. If the team keeps its promise of bringing more content constantly, Killsquad could be a game that will be here to stay for a long time.
Killsquad launches as an Early Access title on Steam on July 16th. It will also release on consoles later next year.
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redpajama_set_name: RedPajamaCommonCrawl


Temptation on the tracks! Somewhere on the train is a key witness for a crime involving arson, murder and a high-profile businessman. And reporter Marni Clare will do whatever it takes to get the story-even if it means crashing in a sleeper car that's she's not sure she's reserved. But when she wakes, she finds herself curled up with the man she's seeking...and he's hot enough to derail all her plans!
It's a long trip in close quarters, and it's not long before their berth becomes the scene of some steamy railway romps. But is Marni just having fun...or is she taking Hunter for a ride?
After a couple of seconds of his death stare, Marni's smile drooped, then she bit her lip and looked away.
There. He still had it.
The ability to intimidate sweet women. He'd bet he could make babies cry and puppies whimper, too.
Her cupid's bow mouth dropped open and she stared for so long, he wanted to blow on her face to make her blink.
Thanks for featuring Midnight Special today!


text:
e - flux Journal
Issue #85 Reprogramming Decisionism
Next Essay
Luciana Parisi
Return to Issue #85
Related See more related
Reprogramming Decisionism
Woody Vasulka and Brian O'Reilly, Scan Processor Studies, 2006. Video, 45'.
Post-truth politics is the art of relying on affective predispositions or reactions already known or expressed to stage old beliefs as though they were new. Algorithms are said to capitalize on these predispositions or reactions recorded as random data traces left when we choose this or that music track, this or that pair of shorts, this or that movie streaming website. In other words, the post-truth computation machine does not follow its own internal, binary logic of either/or, but follows instead whatever logic we leave enclosed within our random selections. To the extent that post-truth politics has a computational machine, then, this machine is no longer digital, because it is no longer concerned with verifying and explaining problems. The logic of this machine has instead gone meta-digital because it is no longer concerned with the correlation between truths or ideas on the one hand, and proofs or facts on the other, but is instead overcome by a new level of automated communication enabled by the algorithmic quantification of affects.
The meta-digital machine of post-truth politics belongs to an automated regime of communication designed to endlessly explore isolated and iterated behaviors we might call conducts. These are agencies or action patterns that are discrete or consistent enough to be recognized by machine intelligence. Post-truth political machinery employs a heuristic testing of responses interested in recording how conducts evolve, change, adapt, and revolt. This is not simply a statistical calculation of probabilities following this or that trend in data usage, but involves an utter indifference towards the data retrieved and transmitted insofar as these only serve as a background. And yet the content of the data is not trivial. On the contrary, the computational machine entails a granular analysis of data on behalf of algorithms, which rather open up the potential of content to be redirected for purposes that are not preknown. In other words, this computational indifference to binary problem-solving coincides with a new imperative: technological decisionism, which values making a clear decision quickly more than it does making the correct one. For decisionism, what is most decisive is what is most correct. When Mussolini gives a speech in parliament, in 1925, taking full responsibility for the murderous chaos his regime has created, and challenging his opponents to remove him anyway, he is practicing decisionism at the expense of binary logic, which would dictate that if Mussolini is responsible, then he should resign. Instead, the dictator declares that he is responsible and that he will stay. Today it is our machines who make these speeches for us.
George Maciunas, Learning Machine, 1969. Paper.
Decide Not to Decide and Results Will Come
The history of communication that obtains results by undoing truths and by fabricating rather than discovering facts must include at least three historical moments in the development of machine intelligence and the formation of the meta-digital machine. First, the period from the 1940s to the 1960s, involving the rise of the cybernetic infrastructure of communication and the introduction of computational logic into decision-making procedures; second, the 1970s and 1980s, which saw a shift towards interactive algorithms and expert and knowledge systems; and third, from the post-80s to the post-2000s, which were characterized by a focus on intelligent agents, machine-learning algorithms, and big-data logic. As these forms of automated intelligence have entered the social culture of communication, they have also become central to a critical theory of technology that has incessantly warned us against the automation of decision, where information processing, computational logic, and cybernetic feedbacks replace the very structure, language, and capacity of thinking beyond what is already known.
In the essay "The End of Philosophy and the Task of Thinking" (1969), Martin Heidegger argues that since the late 1940s, the advance of cybernetics—a technoscience of communication and control—has demarcated the point at which Western metaphysics itself reaches a completion.1 This means not only that philosophy becomes verifiable and provable through testing, but also that scientific truths become subsumed to the effectiveness of results. By replacing judgment based on the supposition of categories with the efficiency of truth-states carried out by machines, the instrumental reasoning of cybernetics fully absorbs Western metaphysics. No longer could technoscience remain at the service of philosophy. Here, ideas are not simply demonstrated or proved, but processed as information. The new technoscience of communication activates a new language of thought embedded in the information circuits of input and output, according to which actions are programmed to achieve a series of results.
If, according to Heidegger, the end of philosophy is "the gathering in of the most extreme consequences," it is because the development of the sciences and their separation from philosophy has led to the transformation of philosophy into "the empirical science of man."2 Nowhere is this more tangible than with the advance of cybernetics and its concerns with "the determination of man as an acting social being. For it is the theory of the steering of the possible planning and arrangement of human labor. Cybernetics transforms language into an exchange of news. The arts become regulated-regulating instruments of information."3 As philosophy becomes a science that intercommunicates with others, it loses its metaphysical totality. The role of explaining the world and the place of "man in the world" is finally broken apart by technology.
Under this new condition of the techno-erasure of metaphysical truth, Heidegger insists that the new task of thinking shall lie outside the distinction of the rational and the irrational. Since thinking always remains concealed within the irrationality of systems, it cannot actually be proven to exist. From this standpoint, and echoing Aristotle, he poses the question of how it is possible to recognize whether and when thinking needs a proof, and how what needs no proof shall be experienced.4 For Heidegger, however, only un-concealment—the condition in which thinking cannot be unconcealed—would coincide with the condition of truth. Here, truth will not imply the certainty of absolute knowledge—and therefore it will not belong to the realm of scientific epistemology. From this standpoint, since the cybernetic regime of technoscientific knowledge mainly concerns the achievement of results, it can tell us nothing about truth, as the latter entails the un-concealment of what cannot be demonstrated—because thinking always hides in the irrationality of systems. Truths, therefore, must remain outside what is already known. This is why, in the age of meaningless communication, according to Heidegger, one must turn the task of thinking into a mode of education in how to think.5
It is precisely a new envisioning of how to think in the age of automated cognition that returns to haunt post-truth politics today. We are at an impasse: unable to return to the deductive model of ideal truths, but equally unable to rely on the inductive method or simple fact-checking to verify truth. How do we overcome this impasse? It is difficult to shift perspective on what technopolitics can be without first attempting to disentangle this fundamental knot involving philosophy and technoscience, which is still haunted by Heidegger's proposition that the transformation of metaphysics—of the un-demonstrable condition of thinking—into cybernetic circuits of communication demands an articulation of thinking outside reason and its instrumentality.
As such, the inheritance of this critique of thought still seems to foreclose the question of instrumental reasoning today, whereby artificial-intelligence machines, or bots, have recoded critical perspectives about the ideology of truth and the fact-checking empiricism of data. Instead of declaring the end of metaphysical thinking and its completion in instrumentality, it seems important to reenter the critique of instrumental reasoning through the backdoor, reopening the question of how to think in terms of the means through which error, indeterminacy, randomness, and unknowns in general have become part of technoscientific knowledge and the reasoning of machines.
Cover of a Soviet manual documenting the construction of the "cybernetic tortoise," 1969.
Learning to Think
To do so, one could start looking more closely at the historical attempts in cybernetics and computation between the late 1940s and the 1980s to bring forward models of automated intelligence that did not rely on the deductive logic of known truths. As the application of inductive data-retrieval and heuristic testing shifted the focus of artificial intelligence research from being a mode of validation to becoming a mode of discovery, critical theory's assumptions that technoscience mainly exhausted metaphysics with the already thought—or with mere communication—had to be revised. It is precisely the realization of the ontic limit of technoscience that pushed cybernetics and computation away from symbolic rational systems and towards experimenting with knowing how—that is, with learning how to learn—which is now central to the bot-to-bot curatorial image of social communication in the age of post-truth and post-fact. With cybernetics and computation, the rational system of Western metaphysics is not simply realized or actualized, but becomes rather wholly mutated. Here, the eternal ground of truth has finally entered the vicissitudes of material contingencies. Cybernetic instrumentality replaces truth as knowledge with the means of knowing, and announces a metaphysical dimension of machine knowledge originating from within its automated functions of learning and prediction. And yet, in this seamless rational system, one can no longer conceive of thinking as what is beyond or what remains unconcealed in the invisible gaps of a transparent apparatus of communication. Instead, it seems crucial to account for the processing of the incomputable (of indeterminate thinking) in machine reasoning as a modern condition by which instrumentality also announced that means are a step beyond what they can do. This transcendental instrumentality opens the question of the relation between doing and thinking which is at the core of the critique of technologies. Hence, while the contemporary proliferation of post-truth and post-fact politics appears to be a consequence of the end of Western metaphysics as initiated by cybernetics and computation, the consequences of a metaphysic of machines is yet to be fully addressed in terms of the origination of a nonhuman thinking. But what does it mean that machines can think? Hasn't the critique of technology, from Heidegger to Deleuze and even Laruelle,6 indeed argued that the immanence of thought passes through nonreflective and non-decisional machines? And if so, is post-truth, post-fact politics indeed just the most apparent consequence of how irrational thinking pervades the most rational of systems? And yet, there is the possibility of addressing the question of inhuman thinking otherwise from within the logic of machines and in terms of the origination of a machine epistemology whereby complex levels of mediation, and not an immediacy between doing and thinking, is at stake.
Already in the 1940s, Walter Pitts and Warren McCulloch's influential paper on neural nets proposed to replace the deductive model of mathematical reasoning based on symbolic truths with heuristic methods of trial and error that would allow machines to learn at an abstract level and not simply through sensorimotor feedback response. As historian of cybernetics Ronald Kline points out, these mathematical neural nets already focused on the levels of randomness in a network rather than simply relying on the homeostatic function of feedback.7 However, the Pitts and McCulloch neural model of knowledge mainly coincided with a connectionist view of intelligence, associating artificial neurons with mathematical notations, bonding the biological firing of neurons to the emanation of concepts. This model used the learning behavior of algorithms as a representational tool to mainly clarify cognitive functions.8 However, since a single neuron could only compute a small number of logical predicates, and was thus insufficient for explaining the complexity of parallel thinking, this model hit a dead end with post-1980s research in AI.
It was only between the late 1980s and 1990s, after the so-called "AI winter," that new efforts at automating reasoning employed sub-symbolic views of intelligence that largely adopted nondeductive and heuristic methods of testing results that allowed algorithms to learn from uncertain or incomplete information. Through inductive methods of data retrieval and transmission, algorithms would learn—or train in time—from a relatively small background of data. Instead of simply validating results according to given axioms, algorithms had instead become performative of data; that is, through recursive and probabilistic calculations, algorithms would not only search for information, but also extract and combine patterns.
By the late '90s, the rule-bound behavior of algorithms had become evolutionary: they adapted and mutated over time as they retrieved and transmitted data. Experiments in genetic and evolutionary algorithms eventually transformed machine learning as a means of validating proof, creating neural networks equipped with hidden layers corresponding to the state of a computer's memory that run another set of instructions in parallel.9 In general, neural networks with a greater depth of layers can execute more instructions in sequence. For instance, the mode of machine learning called "back-propagation" train networks with hidden layers, so that simpler computational units can evolve. We know that back-propagation algorithms are a data-mining tool used for classification, clustering, and prediction. When used in image recognition, for instance, this mode of machine learning may involve training the first layer of an algorithm to learn to see lines and corners. The second layer will learn to see the combination of these lines to make up features such as eyes and a nose. A third layer may then combine the lines and learn to recognize a face. By using back-propagation algorithms, however, only the features that are preselected will be expressed. As a consequence, the backdrop of data to be mined is already known, and as such gender and race biases, for instance, are either already encoded in the data sets or are blind, that is invisible to the algorirthms. Here, far from granting an objective representation of data, machine learning has rather been seen as an amplifier of existing biases, as revealed by the association of words and images in automated classification and prediction.10
Instead of a top-down programming of functions, adaptive algorithms in neural networks process data at increasingly faster speeds because they retrieve and transmit data without performing deductive logical inferences. However, according to Katherine Hayles, algorithmic intelligence, more than being mindless, should rather be understood as a nonconscious form of cognition, solving complex problems without using formal languages or deductive inference. By using low levels of neural organization and iterative and recursive patterns of preservation, these algorithms are inductive learners; that is, they develop complex behavior by retrieving information from particular data aggregates. However, Hayles points out that emergence, complexity, adaptation, and the phenomenal experience of cognition do not simply coincide with the material processes or functions of these elements of cognition.11 Even if algorithms perform nonconscious intelligence, it does not mean that they act mindlessly. Their networked and evolutionary learning cannot simply be understood in terms of their material functions, or to put it another way, according to their executive functions.
In contrast to Lorraine Daston's vision that algorithmic procedures are mindless sets of instructions that replaced logos with ratio,12 Hayles's argument about nonconscious cognition suggests that algorithmic procedures are transformed in the interaction between data and algorithms, data and metadata, and algorithms and other algorithms that define machine learning as a time-based medium in which information vectors converge and diverge constantly. Since, according to Hayles, machine learning is already a manifestation of low-level activities of nonconscious cognition performed at imperceptible or affective speeds, it is not possible to argue that cognition is temporally coherent, linking the past to the present or causes to effects. According to Hayles, information cannot simply be edited to match expectations. Instead, the nonconscious cognition of intelligent machines exposes temporal lapses that are not immediately accessible to conscious human cognition. This is an emergentist view of nonconscious cognition that challenges the centrality of human sapience in favor of a coevolutionary cognitive infrastructure, where algorithms do not passively adapt to data retrieved but instead establish new patterns of meaning by aggregating, matching, and selecting data. From this standpoint, if the inductive model of trial and error allows computational machines to make faster connections, it also implies that algorithms learn to recognize patterns and thus repeat them without having to pass through the entire chain of cause and effect and without having to know their content.
However, as algorithms have started training on increasingly larger data sets, their capacity to search no longer remains limited to already known probabilities. Algorithms instead have become increasingly instrumental of data, experimenting with modes of interpretation that Hayles calls "techno-genesis," pointing towards an instrumental transformation of "how we may think."13
In the last ten years, however, this instrumental transformation has also concerned how algorithms may think amongst themselves. Since 2006, with deep learning algorithms, a new focus on how to compute unknown data has become central to the infrastructural evolution of artificial neural networks. Instead of measuring the speed of data and assigning it meaning according to how frequently data are transmitted, deep-learning algorithms rather retrieve the properties of a song, an image, or a voice to predict the content, the meaning, and the context-specific activities of data. Here, algorithms do not just learn from data, but also from other algorithms, establishing a sort of meta-learning from the hidden layers of the network, shortening the distance from nodal points while carrying out a granular analysis of data content. From this standpoint, machine-learning algorithms do not simply perform nonconscious patterns of cognition about data, exposing the gaps in totalizing rational systems, but rather seem to establish new chains of reasoning that draw from the minute variations of data content to establish a machine-determined meaning of their use.
This focus on content-specific data is radically different from the conception of information in communication systems in the 1940s and the postwar period. For Claude Shannon, for instance, the content of data was to be reduced to its enumerative function, and information had to be devoid of context, meaning, or particularities. With deep learning, big data, and data mining, algorithms instead measure the smallest variations in content- and context-specific data as they are folded into the use of digital devices (from satellites to CCTV camera, from mobile phone to the use of apps and browsing). Indeed, what makes machine learning a new form of reasoning is not only the faster and larger aggregation of data, but also a new modality of quantification, or a kind of qualitative quantification based on evolving variations of data. This is already a transcendental quality of the computational instrument, exposing a gap between what machines do and how they think.
In other words, deep-learning algorithms do not just learn from use but learn to learn about content- and context-specific data (extracting content usage across class, gender, race, geographical location, emotional responses, social activities, sexual preferences, music trends, etc.). This already demarcates the tendency of machines to not just represent this or that known content, or distinguish this result from another. Instead, machine learning engenders its own form of knowing: namely, reasoning through and with uncertainty. The consequence of this learning, it can be argued, seems to imply more than the unmediated expression of an immanent thought, or the reassuring explosion of the irrational within rational systems. Machine learning rather involves augmented levels of mediation, where uncertainty is manifested in terms of the incomputable forms of algorithmic automation, as that which does not simply break the calculation, the quantification, the numerical ordering of infinities. Instead, incomputables enter the complex sizing of mediation, involving the structuring of randomness on behalf of the algorithmic patterning of indeterminacies. This implies that machine learning should not be solely considered in terms of what algorithms do as a biased model of the reproduction of data-usage, data-context, and data-meaning. At the same time, however, one should resist the temptation to consider algorithms as mere placeholders that allow the manifestation of the nonconscious or irrational potentialities of thinking. Instead, I want to suggest that the very general principle of learning in machines should be critically addressed in terms of a nascent transcendental instrumentality: what machines do does not and should not coincide with the possibilities of machine thinking. This implies not only that thinking transcends mere pragmatics, but also, and more importantly, that a pragmatic reasoning aspire to build thought by conceding that future modes of actions can transform the conditions of knowing how. Here, the irrational is not outside reasoning but discloses the alien possibilities that a general practice of reasoning offers in terms of a mediation of technosocial changes and actions.
If this aspirational critique of machine learning asks for a change in perspective on the possibilities for a critical theory of automated thinking, one cannot however overlook the fact that algorithmic control and governance do involve the micro-targeting of populations through the construction of alternative facts aimed at reinforcing existing beliefs. At the same time, the evolutionary dynamics of learning machines show that the time of computation, including the hidden layers of a growing network, also forces algorithms to structure randomness beyond what is already known. For instance, if a machine is fed with data that belongs to already-known categories, classes, and forms, when the computational process starts, these data become included in the algorithmic search for associations that bring together smaller parts of data, adding hidden levels of temporalities to the overall calculation. This results in the algorithmic possibilities of learning beyond what is inputted in the system.
From this standpoint, if we maintain that algorithms are mindless and nonconscious, we are also arguing that computational control only results in the reproduction of the ideological and discursive structure of power that data are said to uphold. In other words, whether it is suggested that the machine architecture of algorithms and data is another form of ideological design (imbued with human decisions) or that machines are ultimately mindless and can thus act empirically (simply as data checkers), what seems to be missing here is a speculative critique of machine learning that envisions machines as something more than mere instances of instrumental reasoning—vessels of knowledge that can at best perform Western metaphysical binaries, deductive truths, and inductive fact-checking at a faster pace. While this view reveals that the nexus of power and knowledge is now predicated on existing beliefs and granular aggregations of data, it does not offer a critical approach to technology that can sweep away the Heideggerian prognosis that technoscience has transformed the task of thinking, replacing truth with the effectiveness of rule-bound behavior.
A critical theory of automation should instead start with an effort to overturn the autopoietic dyad of instrumental reasoning, where machines either execute a priori reasoning or reduce the rule of reason (law and truth) to brute force and reactive responses. In other words, this critique should reject the view that technoscience completes Western philosophy's dream of reason and rather account for the overturning of technoscientific knowledge and of philosophical reason activated with and through experimentations with the limits of artificial intelligence. Here, the microtargeting of populations involves not only the reproduction of biases in and through data aggregates, but the algorithmic elaboration of any possible data to become racialized, gendered, and classified as a potential enemy under certain circumstances.
But how do we address these correlations between the end of truth and fact and the transformation of cybernetic binary states into forms of nonconscious cognition and meta-digital learning processing, where algorithms do not just perform data content, but learn how to learn and thus how to include indeterminacy in reasoning? Is it enough to blame the mindless technoscientific quantification of biased beliefs and desires, or is it possible to engage in a materialist theorization of technicity, starting from a close engagement with the means by which thinking thinks?
One of the first complete views of the United States from Landsat 8 is an example of how scientists are testing Landsat 8 data. The strips in the image above are a result of the way Landsat 8 operates. Like its predecessors, Landsat 8 collects data in 185-kilometer (115-mile) wide strips called swaths or paths. Each orbit follows a predetermined ground track so that the same path is imaged each time an orbit is repeated. It takes 233 paths and 16 days to cover all of the land on Earth. Photo: NASA/David Roy
These questions require us to focus not on how intelligent machines represent knowledge as an aggregation of data-facts; we must also embark on a materialist inquiry into the technical entrenching of thinking in artificial neural networks. One could argue that since World War II, algorithmic means of thinking should also be regarded as a mode of reasoning. It is true to say that most machine-learning algorithms, such as Netflix algorithms, actually focus on the specific use of data through a heuristic analysis of data correlations, statistically matching and thus predicting your data categories of preference, according to what you already may know. However, deep-learning algorithms—as a means of thinking how to think—involve not only the predictive analysis of content and the microtargeting of data use, but also define a tendency in artificial intelligence to abstract modalities of learning about infinite varieties of contextual content. These infinite varieties are not only derived from the algorithmic recording of the human use of data according to frequencies, contexts, and content, but also include the meta-elaboration of how algorithms have learned about these usages.
For instance, unlike recommendation algorithms, the RankBrain interpreter algorithms that support Google Ranking are not limited to making suggestions. Rather, they activate a meta-relational level of inference—i.e., the algorithm seeks an explanation for unknown signs in order to derive information—through the hypothetical conjectures of data involving algorithmic searches of indeterminate words, events, or things for which one may not have the exact search terms. As opposed to the heuristic analysis of data correlations among distinct sets, these interpreter algorithms do not just prove, verify, or validate hypothesis, but must first of all elaborate hypothetical reasoning based on what other algorithms have already searched, in order to determine the possible meaning of the missing information in the query. These deep-learning algorithms work by searching elements of surprise—that is, unthought information—which can only occur if the system is apt to preserve, rather than eliminate as errors, micro-levels of randomness that become manifested across volumes of data. In other words, this meta-digital form of automated cognition is geared not toward correcting errors or eliminating randomness; instead, it is indifferent to the entropic noise of increasing data volumes insofar as this noise is precisely part of the learning process, and for this reason experimental hypothesis-making must preserve indeterminacy so that it can bind information to surprise. While one can assume that this inclusion of indeterminacy—or irrationality or nonconscious activity—within the computational process is but another manifestation of the ultimate techno-mastery of reality, it is important here to reiterate instead that randomness is at the core of algorithmic mediation, and as such it opens up the question of epistemological mastery to the centrality of contingency within the functioning of any rational system. This results not in a necessary malfunction of a system—i.e., the line of flight of the glitch or the breakdown of order—but instead in its hyperrational (or sur-rational, to use Bachelard's term) articulation of the real, the unknown, the incomputable, in terms of technical mediations, automated actualizations, and machine becomings of the real in their manifest artificial forms.
Instead of establishing general results from particular conceptual associations derived from the frequent use of particular content by humans and machines, the inclusion of indeterminacy in machine learning concerns the parallelism of temporalities of learning and processing, which involves an elaboration of data that sidesteps a primary level of feedback response based on an already known result.
We know that RankBrain algorithms are also called signals, because they give page rank algorithms clues about content: they search for words on a page, links to pages, the locations of users, their browsing history, or check the domain registration, the duplication of content, etc. These signals were developed to support the core Page Ranking algorithm so that in can index new information content.14 By indexing information, RankBrain aims to interpret user searches by inferring the content of words, phrases, and sentences through the application of synonyms or stemming lists. Here, the channeling of algorithmic searches towards already planned results is overlapped by an algorithmic hypothesis that is exposed to the indeterminacy of outputs and the random quantities of information held in the hidden layers of neural networks. For instance, indexing involves information attached to long-tail queries, which are used to add more context-specificity to the content of searches. Instead of matching concepts, RankBrain algorithms rely on the indeterminacy of results.
Indeterminacy has become part of the meta-digital synthesis of ratio and logos. It is an active element of this artificial form of knowing how, where the preservation of unknowns subtends a hypothetical inferencing of meaning: the meta-digital indifference to truth and fact may rather involve an opportunity to readdress instrumental knowledge in terms of hypothesis generation, working from within and throughout the means of thinking. In the age of post-truth politics, indeterminacy within machine learning defines not an external contingency disturbing an otherwise stable governance of information. Instead, the correlation of the "new brutality" of fake and alternative news with the contemporary form of automation involves a granular structuring of unknowns, pushing automated cognition beyond knowledge-based systems.15 Indeterminacy is therefore intrinsic to the algorithmic generation of hypothesis and as such the technoscientific articulation of truths and facts can no longer be confined to recurring functions and executions of the already known. The correlation between post-truth politics and automated cognition therefore needs to be further explored, contested, and reinvented.
As Deleuze and Guattari remind us, if we simply react to the dominant determinations of our epoch, we condemn thinking to doxa.16 In particular, if the dominant political rules of lying and bullying are facilitated by a rampant neo-heuristic trust in algorithmic search, would a nonreactive critique necessarily place philosophy outside of information technology?
Deleuze and Guattari already argued that philosophy must directly confront this new kind of dogmatic image of thought formed by cybernetic communication, which constantly fuses the past and future, memory and hope, in the continuous circle of the present. To think outside the dominance of the present, however, does not require a return to eternal truth—to the metaphysics of true ideas—that must be reestablished against the false. If cybernetics coincides with the information network of communication exchange subtending the proliferation of opinions and generating consensus,17 philosophy must instead make an effort to create critical concepts that evacuate the presence of the present from the future image of thinking. But how do you do this?
Deleuze and Guattari's critique of communications society is a critique of computer science, marketing, design, and advertising.18 Communication is here understood as an extension of doxa, a model of the recognition of truth, which endlessly reiterates what everyone knows, what a survey says, what the majority believes. For Deleuze and Guattari, communication has impoverished philosophy and has insinuated itself in the micro-movements of thinking by turning time in a chronological sequence of possibilities, a linear managing of time relying on what has already been imagined, known, or lived. In opposition to this, Deleuze and Guattari argue that the untimely must act on the present to give space to another time to come: a thought of the future.
In the interview "On the New Philosophers and a More General Problem," Deleuze specifically laments the surrendering of philosophical thought to media.19 Here, writing and thinking are transformed into a commercial event, an exhibition, a promotion. Deleuze insists that philosophy must instead be occupied with the formation of problems and the creation of concepts. It is the untimely of thought and the nonphilosophy of philosophy that will enable the creation of a truly critical concept. But how do we overturn the presumed self-erasure of critical concepts that stand outside the technoscientific regime of communication? Can a truly critical concept survive the indifferent new brutality of our post-truth and post-fact world, driven as it is by automated thinking? Doesn't this mistrust of technoscience ultimately prevent philosophy from becoming a conceptual enaction of a world to come? Why does philosophy continue to ignore thinking machines that create alien concepts, acting as if there were beyond the capacity of machines to do?
Although they are worlds apart, one could follow a thread in Heidegger and Deleuze that asserts a defense of philosophy against technoscience, against the poverty of thought in the age of automated thinking. If the Heideggerian un-concealing of truth ultimately contemplates an unreachable state delimited by the awareness of finitude (of Western metaphysics, and of Man), Deleuze's vision of the unthought of philosophy rather tends towards a creative unfolding of potentialities, the construction of conceptual personae that resist and counter-actualize the doxa of the present.
And yet a strong mistrust of technoscience prevails here. In particular, the forms of instrumental reason embedded in cybernetic and computational communication continue to be identified here with control as governance. Similarly, the conception of the mean or instrument of governance—that is, information technology—is left as a black box that has no aims (it is in itself a mindless, nonconscious automata) unless these are politically orchestrated. While it is not possible to disentangle the political condition of truth and fact from the computational processing of data retrieval and transmission, it seems also self-delimiting to not account for a mode of thinking engendered from the instruments or means of thinking.
If, with cybernetics and computation, the instrument of calculation has become a learning machine that has internally challenged technoscience—the logic of deductive truth and inductive facts—it is also because this form of instrumentality has its own reasoning, whereby heuristic testing has shifted towards hypothesis generation, a mode of thinking that exceeds its mode of doing. One could then argue that by learning to learn how to think, this instrumental form of reasoning transcends the ontic condition in which it was inscribed by the modern project of philosophy.
Since instruments are already doing politics, one question to ask is how to reorient the brutality of instrumentality away from the senseless stirring of beliefs and desires, and towards a dynamic of reasoning that affords the re-articulation—rather than the elimination—of aims. One possibility for addressing the politics of machines is to work through a philosophy of another kind, starting not only from the unthought of thinking, but also from the inhumanness of instrumentality, an awareness of alienness within reasoning that could be the starting point for envisioning a techno-philosophy, the reprogramming of thinking through and with machines. If the antagonism between automation and philosophy is predicated on the instrumental use of thinking, techno-philosophy should instead suggest not an opposition, but a parallel articulation of philosophies of machines contributing to the reinvention of worlds, truths, and facts that exist and can change.
The new brutality of technopolitics could be then overturned by a techno-philosophy whose attempts at reinventing the sociality of instrumental reasoning can already be found across histories, cultures, and aesthetics that have reversed the metaphysics of presence. For instance, the New Brutalist architecture of the 1950s to the 1970s activated a program for technosocial living that aimed to abolish the sentimental attachment to the end of the "spiritual in Man" and transcend the norm of architectural expression through an uninhibited functionalism, expressing the crudity of structure and materials.20 Not only functionalism, but also and above all a-formalism and topology were part of the procedural activities that tried to surpass the contemplation of the end by reimagining the spatial experience of truths and facts. Here, all the media are parallel or leveled together, in order to connect their specific content in a-formal dimension—one that could work through the incompleteness of a total image of sociality. The adjunction of the material world of animate and inanimate media however preserves the dissembling complexity of these discrete parts that can then revise mediatic forms of truths and facts into multiple directions.
The architecture of New Brutalism turns instruments of thinking into concrete, mass-modular, interconnected blocks and self-contained individual cells, elevated above the local territory, united by streets in the sky or networks of corridors across discrete parts of buildings. Here, instrumental reasoning transforms the entropic dissolution of the postwar period, holding the concrete weight of the past in order to dissolve it into structural experiments of task-oriented functions and aesthetic transparency. If New Brutalism had a vision that facilitated a weaponization of information, it was not simply to advance information technology, but rather to propose modes of instrumental reasoning that work through entropy, randomness, or noise to reprogram codes and values, passages and bridges, contents and expressions of a united image of the social.
Martin Heidegger, "The End of Philosophy and the Task of Thinking," trans. J. Stambaugh, in Martin Heidegger, Basic Writings, ed. David Farrell Krell (London: Routledge, 1993), 373–92.
See for instance Gilles Deleuze, "Thought and Cinema," chap. 7, in Cinema II: The Time-Image (Oxford: Athlone Press, 2000). See also François Laruelle, "The Transcendental Computer: A Non-Philosophical Utopia," trans. Taylor Adkins and Chris Eby, Speculative Heresy, August 26, 2013 →.
See Ronald R. Kline, The Cybernetic Moment: Or Why We Call Our Age The Information Age (Baltimore: Johns Hopkins University Press, 2015), 53.
For example, an AI system observing an image of a face with one eye in shadow may initially only see one eye. But after detecting that a face is present, it can then infer that a second eye is probably present as well. In this case, the graph of concepts only includes two layers—a layer for eyes and a layer for faces—but the graph of computations includes 2n layers, if we refine our estimate of each concept given the other n times.
See Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan, "Semantics derived automatically from language corpora contain human-like biases," Science 356, no. 6334 (2017): 183–86.
Katherine N. Hayles, "Cognition Everywhere: The Rise of the Cognitive Nonconscious and the Costs of Consciousness," New Literary History 45, no. 2 (2014).
Lorraine Daston, "The Rule of Rules," lecture, Wissenschaftskolleg Berlin, November 21, 2010.
Katherine N. Hayles, How We Think: Digital Media and Contemporary Technogenesis (Chicago: University of Chicago Press, 2012).
Renée Ridgway, "From Page Rank to Rank Brain," 2017 →.
See Rosi Braidotti, Timotheus Vermeulen, Julieta Aranda, Brian Kuan Wood, Stephen Squibb, and Anton Vidokle, "Editorial: The New Brutality," e-flux journal 83 (June 2017) →.
Gille Deleuze and Félix Guattari, What Is Philosophy? trans. Hugh Tomlinson and Graham Burchell (New York: Columbia University Press, 1994), 99.
Gille Deleuze, "On the New Philosophers and a More General Problem," Discourse: Journal for Theoretical Studies in Media and Culture 20, no. 3 (1998).
Reyner Banham, The New Brutalism: Ethic or Aesthetic? (London: Architectural Press, 1966).
Technology, Philosophy
Algorithms, Cybernetics, Artificial intelligence, Automation
Luciana Parisi's research is a philosophical investigation of technology in culture, aesthetics, and politics. She is a Professor at the Program in Literature and Computational Media Art and Culture at Duke University. She was a member of the CCRU (Cybernetic Culture Research Unit) and is currently a cofounding member of CCB (Critical Computation Bureau). She is the author of Abstract Sex: Philosophy, Biotechnology and the Mutations of Desire (Continuum, 2004) and Contagious Architecture: Computation, Aesthetics and Space (MIT Press, 2013).
© 2017 e-flux and the author


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Best Coffee Gifts
Are you looking for the ultimate coffee gifts for friends and family who love great coffee gadgets and accessories? This extensive guide will give you more than a few good reasons to find the perfect gift that makes any cup of coffee instantly better.
1. Craft Coffee Book $
2. Hario Ceramic Grinder $
3. Bodum Meilor Kettle $
4. Fellow Stagg Kettle $$$
5. Chemex Maker $
6. Aeropress Maker $
7. Capresso Infiniti Grinder $$
8. Ember Cup $$$
9. Stanley Classic Maker $$
10. Secura Maker $
11. Bialetti Maker $
12. Contigo Cup $
13. Driftaway Coffee $$
14. OXO Maker $$
15. Royal Brew Maker $$$
16. Breville BES870XL Maker $$$$$
Craft Coffee: A Manual:
Brewing a Better Cup at Home
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If you're still searching for the go-to book that gives you more information than you ever wanted to know about coffee, this is the best book that you'll ever need to own. It's available as a hardback copy or through a download on Kindle. What makes this book different is that it's one of the only books you'll find these days that pays close attention to making coffee rather than espresso.
If you're into making specialty coffee, this is not only a great handbook but an informative step-by-step guide that tells you how it's done properly. There's plenty of science that explained how the coffee extraction process works in very simple to understand explanations too. No matter where you like to enjoy, it also goes into great detail where you can brew coffee and which devices work best for each application.
Don't take our word for it since the ones who have already purchased this coffee book have many more reasons why this book is highly prized. It might not be the complete bible of making coffee but will help you understand why it means the world for making a better cup of specialty coffee.
Hario Ceramic Coffee Mill
Grinding coffee beans are important for brewing, yet there are very big differences when it comes to comparing coffee grinders. Not too many people understand how much difference it makes when purchasing a burr grinder. These grinders help to keep coffee grounds a uniform size, so the brewing process doesn't favor finer bits over unevenly sized grains. This is where the Hario ceramic coffee mill won't leave abnormally sized grounds to chance.
When it comes to specialty coffee, flavors that develop during the brewing process that doesn't belong in your brew can ruin a cup of coffee if the coffee grounds aren't universally the same size. This grinder uses ceramic conical burrs that reduce heat and allow beans to be shaved rather than shattering into uneven-sized bits. There are three different models to choose from depending on your preference of add-ons and burr grinder attachments.
What's nice is that many people who like good coffee also love this grinder since it works. There are other burr grinders on the market, but not all of them have a ceramic grinder and that does help make this price tag more attractive overall.
Bodum Meilor
Gooseneck Kettle (27oz)
Perhaps you've experienced what a cool gooseneck kettle can provide for making pourover coffee. Here is an electric kettle that features some neat built-in attachments that give anyone who is starting to make specialty coffee (or tea) easier. It's a no-brainer for those who are just starting out and has an automatic shut-off when the water finally reaches a boil after only 3 minutes.
This particular design has been criticized due to one simple mistake that comes from customers removing a silicone sealing ring that sits above the fill line. If this is removed, it will leak, and that's what the silicone ring prevents. Aside from that, it's simple to use and has cool cork handles so you don't get burned while picking it up. When the water reaches a boil the pressure shuts the machine off after that.
For the price, it's the best choice for beginners or anyone who likes to have an electric kettle that works anywhere you have a power supply. It's good for camping in an RV since it can make up to 1 or 2 cups at a time.
Fellow Stagg EKG
Gooseneck Kettle
If precision is really a big concern if you're looking for a water kettle with a lot of class, the Stagg EKG gooseneck kettle is your immediate choice. This stylish design has a few perks that include a counterweight handle that's designed to make long pours comfortable in your hand. It also features a digital control for water temperature and automatically shuts off when it reaches its target.
There's a hold function that allows the temperature to remain steady for up to a full hour before shutting off automatically. The heating element is separate from the kettle so you never have to worry about added weight tacked onto the kettle while pouring. The best part is the base controls also include a timer so you can keep track of the time while brewing a pourover cup more conveniently.
The instructions might be the only downside of this kettle since the information is printed so small. We recommend the advice from happy critics by using a magnifying glass if you can't read 6pt. type scale or enlarge the info on your smartphone using the enlarge feature.
(8 cups)
The one and only Chemex coffee maker is truly a sight to behold if you appreciate the true art of pourover coffee brewing. If you're big on brewing a lot of cups of coffee or essentially an entire pot, this vessel can produce up to 8 cups for bigger groups of coffee fans. This is their classic series and technically produces 40 ounces of coffee. Just like all the other Chemex glass vessels, it features the iconic wraparound wooden handle and built-in pour spout.
Obviously, when you have a larger carafe, the glass will be thinner than the 6-cup version. It will be fragile just like any other large glass carafe and isn't meant to be thrown around like a football! While this version is perfectly fine for putting into the dishwasher, it's better to be hand-washed since the opening is large enough to fit a bottle scrubber. Most of the time, a decent rinse of warm soapy water is enough after using your Chemex coffee maker.
There are obviously more positive reviews than bad experiences, but for a brand-name coffee like Chemex, you can expect there will be criticism. You can decide which side of the table most fans of Chemex are hearing from.
One of the most simplified gadgets on the market for making instant coffee with a very distinct espresso-like flavor has to be the Aeropress. Originally introduced by Alan Alder (the man who invented the Aerobie Frisbee), returned to the forefront of cult-like fever with the Aeropress. It's a nifty gadget that works anywhere and is easy to produce effective results with very little effort.
If you have hot water and fresh coffee grounds, you can produce a great cup of coffee anywhere you can imagine. We haven't seen anyone making Aeropress coffee while sky diving from an airplane yet, but their annual Aeropress competitions are proving how there is more than one way to make Aeropress coffee taste more exclusive and gourmet-like. You'll find numerous add-on filters now pair with this unique coffee maker.
It's perfect as a coffee gift since there is less chance of making a mistake using this coffee plunger. You can see our complete guide on brewing with the Areopress here. Little things such as the rubber gasket seal will eventually wear out so be sure to order one or two of these as extras so it lasts longer over the years.
Capresso Infiniti
The famous movie detective Frank Drebin of Naked Gun might have said it best by saying I grind my own beans', but the fact is you can't beat a big tough grinder for making daily coffee. We reviewed it here. It's the perfect addition for those who drink a lot of coffee or don't want to bother with a handheld grinder. Either way, the Capresso has grinder settings that allow the metal burr grinder to shave and shape coffee beans automatically.
You can select as many as 16 different grind settings that all produce the perfect-sized grind for better-brewed coffee. The clear hopper will hold upwards of 11 ounces of coffee beans and will produce 4 ounces of coffee grounds at a time. If you enjoy quiet in the morning, this machine is surprisingly silent and will not sound like you're trying to wake up the neighborhood.
This does need to be plugged in and does need periodic cleaning of smaller grains if you use it a lot. This is easy if you shake it over the sink without the lower hopper to remove any fine sediment from building up. You can troubleshoot ideas from honest folks who found ways to keep their machine clean.
Ember Temperature
Not such a gimmicky idea after all when you think about it when you want to keep your coffee perfectly hot for a length of time. Ember was the first to think up this neat idea for a tabletop coffee cup warming plate that connects through a smartphone to control the temperature settings. It also sits on a warming plate that can be recharged with a simple USB cable on your computer.
The main problem with the earliest Ember mugs was simply obvious design flaws such as battery life, ceramic coatings coming off too soon, and so on. Still, it remains a huge hit with tech-head fans who like the ability to control the heating function on their coffee. Let's call it mind over matter for those who happen to like their coffee to stay hot. Keep in mind, that moderation will make this temperature-controlled mug last longer.
It's still a work in progress and will make a great gift if you find the newest smart mugs in stock. As you can see from the range of issues the previous customers all have, these will surely be fixed with the newest improvements soon enough.
Stanley Classic
Travel Mug French Press
It's either a very attractive camping thermos or the ideal solution for French Press coffee on the go. In fact, this oversize coffee container is ideal for serious coffee drinkers who want to drink 16 ounces of coffee at a time. For friends who are students, they'll love how easy it is to make French Press coffee on the go that stays nice and hot up to 4 hours afterward. It also has a dual lid that features a coffee sipper flip lid and a standard unscrewable lid.
Stanley is a well-known thermos maker that's been around forever, but this innovation for creating a French Press design takes them to the next level. The filter system ensures there is no sediment in your coffee and is equally easy to clean. It's easy enough to hand wash but luckily it is dishwasher safe for those who want to toss it into the machine instead. If you aren't using the French Press filter it will hold more coffee inside the thermos.
Fans of this French Press travel mug are often impressed with the versatility that comes with owning one of these. If you're short on ideas, you can find out how many more ways this thermos can be used, making it a versital gift.
Secura French Press
(34oz)
When we tested this French press, we loved its elegant design, better yet, it preformed better than most of the presses we looked at. It has three different sizes to select from. These include a 17, 34, and 50-ounce pot which features a very smart filtering system. This model includes a three-layer filter system to keep sediment from getting into your finished coffee. It can be placed on any conventional hot plate and warmed up to the desired temperature before adding your coffee grounds.
After this, the filter is inserted and does the rest to push the grounds to the bottom of the kettle. The design also features insulated outer layers and a cool-touch handle for pouring the coffee. What's even more attractive is that it comes with additional screens to refine your coffee how you like. It's also dishwasher safe so it can be cleaned either by hand or within a kitchen dishwasher.
Even if this is the last French Press you want to buy as a gift, it's still one of the best-priced models that consistently works based on reliable results and customer response.
(6 cup)
The one and only Bialetti Moka Pot is a traditional favorite for most of Europe but is slowly getting plenty of attention in the US. It's a very simple design that is perfect for any kitchen and is pretty hard to get bad results using a Moka pot. All you need is to add water up to the fill line and then add the coffee grounds. The rest of the process produces espresso-like tasting coffee through high pressure. Read more here on brewing with this age-old device.
Each of the Moka pots comes with a special pressure release valve that prevents any mistakes, but it's the retro appearance that draws in fans of this coffee maker. The Moka pot also makes an excellent addition for those who like camping or going on trips. Because it's portable, it will work on a small stovetop flame or hot pad all the same. Many of the original pots came in a silver or metal finish, but this one has an iconic fire engine red finish.
As far as simplicity, the Moka pot is completely foolproof for making great coffee, it's the perfect gift for any coffee novice you may know. Clever fans have embraced the Moka pot with smart tips on how to make the perfect cup every time.
Contigo Stainless Steel
Travel Mug (16oz)
Finding a decent travel mug can be a difficult task if you're looking for two important factors. The first is one with a very good seal, and the second is keeping coffee nice and hot for at least 6-7 hours. It seems that Contigo has created a travel mug that has both of these features and works better than most of the competition. If you like portability and cutting-edge design, this is one gift that worth checking out.
The only major fault that can make this travel mug a nightmare is the attention to cleaning which should involve generous soaking for the top lid. The instruction gives very clear info on how it needs to be cleaned but many of the complaints occur when you add creamer and sugar into your coffee which junks up the top lid assembly. Aside from that, it can be cleaned easily enough but isn't recommended for the dishwasher at all.
You can check the customer reviews for more cleaning tips which show how many have solved minor problems. For the price, it's worth every penny for having a combination thermos with a spill-proof vacuum lock.
Driftaway Coffee
Don't you wish there was a sample package that you could put together with all kinds of coffee beans from exotic places from all over the world? Well, there are kits like this but not so many of them have an ethical promise to help independent coffee growers benefit. This gift sampler gives any coffee fan a chance to try world-class coffee to see which ones they like so they can order directly for all of their favorite flavors.
This initial starter kid includes a generous sample of fruity, balanced, bold, and classic whole beans that come from all over the planet. After this, it's just a matter of which beans you want to be ordered directly from Driftaway Coffee. Rather than spend hours searching through specialty coffee stores, this is one sample kit that is truly a gift in itself. Since these are beans that are sustainably grown, there is no worry of excess pesticides or poor quality beans.
Many of these types of coffee all have a different character and all tell an amazing story where they are from. New fans of this coffee subscription have plenty of praise for the amazing coffee they provide.
OXO Good Drip
Cold Brew Coffee Maker
The latest word on cold brew coffee starts with having the right setup. We've found one that is pretty impressive and allows you to steep coffee at your coffee for 12 to 24 hours. It comes with a steeping chamber that holds your coffee grounds and water. There's a cool release switch that lets the finished cold brew drain into a larger chemistry beaker. It's not actually a real chemistry beaker but is branded with the OXO logo on the top.
There is a bit of gimmick to adding water into the brewing chamber which drains through holes around the lid. Seemingly it allows the water to distribute itself over the coffee grounds without disturbing it too much. In reality, this isn't going to matter when it comes to cold brew coffee but is entertaining to watch it rain down in little droplets until the chamber is full.
The overall results that you can get from this cold brew coffee maker all have a good response from those who enjoy cold brew coffee. The felt filter inside the draining chamber does need to be changed fairly often though.
Royal Brew Nitro
This is perhaps the most quirky of all the coffee gifts you might decide to get for anyone, but think of the fun they'll have with it! It's pricy to buy nitro brew coffee at specialty coffee shops, not to mention not being able to control what the coffee flavor could end up tasting like. Nitro coffee is on the extreme end of specialty coffee anyway, so a nitro maker like this is a great example of how coffee can be served at home.
It works by using regular coffee, cold-brewed coffee, or any brewed coffee you like that's added to the storage chamber. Then you close the tap and add a nitrogen capsule to make the nitro brew coffee. There are a couple more steps that make this a bit more complete but the result is coffee infused with nitrogen gas that makes the coffee turn out thicker and richer just like you see with Nitrogen-brewed beer like Guinness Stout.
Big fans of Nitro brew will also chime in their best advice for finding nitrogen cartridges and recipes. It might not be everyone's favorite flavor of coffee but will compliment anyone who loves to be different.
Breville BES870XL
Barista Espresso Machine
Are you looking for the ultimate coffee gift that is going to amaze everyone this year? How about this exclusive Breville espresso machine that's as professional as it gets unless perhaps you buy a professional coffee shop level machine for thousands of dollars. This is a bargain for craftsmanship and controls grinding levels, water pressure, and precision espresso extraction.
If you or a friend is a real stickler for tasty espresso, this professional quality machine is the ultimate choice. The real trick is that whoever is using this machine does need to know a bit about how the professional espresso makers work before using it. Unlike other home espresso machines, it's not an automatic push-button function for making the coffee by itself. But for the true lover of gourmet espresso or espresso based drinks, it will be a true blessing gift idea for sure.
Reviews of this espresso machine are pretty dedicated to those who have taken a bold step into real espresso coffee making. It's worth reading some of the incredible experiences that many of them have to say about using this humble little gem.


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We need to invest in Toll Gate, Pilgrim athletic fields
Posted Thursday, October 25, 2018 2:14 pm
As another sports season comes to a close, another year of student athletes having to walk or drive to fields and facilities comes to an end.
Warwick's student athletes are scattered around the city like an afterthought. The Toll Gate field hockey team plays at CCRI, the Toll Gate Boys and Girls soccer teams play their games at Bend Street or City Park, the Toll Gate softball team plays at Winslow Park next to the airport, and the Toll Gate baseball team plays at Mickey Stevens.
As a parent, teacher, and coach, I am asking: Why do we accept this and what should be done? The city needs to improve the fields at both high schools. It is time for the city to do what East Greenwich did in the mid-2000s and pass a bond that improves and upgrades the facilities at both schools. What would these upgrades look like? Field turf at both schools. Baseball and softball complex at Toll Gate. Why should we do this? Well for starters, these facilities could be used by residents and local sports leagues. These improvements would bolster people's impression of the facilities, improve student and residents' pride in their schools and facilities, When the field hockey team plays on field turf, I am tired of hearing my players' saying why can't we have a field like this? We can!
Also, forcing students to walk or drive to various locations is safety issue As a parent of a future soccer player at Toll Gate High School, I find it completely unacceptable that she will play her home games at Bend Street or maybe practice at City Park which does happen time to time. For after school practices she will need to find a ride to get to practice maybe with an upperclassmen.
As a future lacrosse player, I find it unacceptable that she will play on a football field that is muddy, wet and slippery even three to four days after it rains. Some of my field hockey players walk to CCRI which last season there was homeless man living in the woods near the field. Is this safe? The cost of the project will be an investment in not only schools but in our city as well. Upgraded facilities will benefit future generations of students, spectators, and residents.
James Areson
5 comments on this story | Please log in to comment by clicking here
Justanidiot
we need a domes stadium so that the poor children won't get wet and muddy.
richardcorrente
Dear Mr. Areson,
You're right. But the School Committee (SC) spent their budget elsewhere. I believe we need an independent audit from an INDEPENDENT source to learn where. If we had that we would see what the SC actually spends our taxpayer money on. They keep adding staff to the administration (such as "Asst. Principal of Climate and Culture" and an "Asst. Principal of Teaching and Learning") and reducing the number of teachers. Personally I would rather have the most and best teachers to improve proficiency of our students, than spend $60,000 for a Public relations firm for the SC or $30,000 for new air conditioning for the administration building. Or giving raises to members of the administration. If we put the students needs first, Warwick would have the funds for The Mentor Program or better conditions on our fields.
For the record, I believe electing Rick Cascella, Judy Cobden, and Nathan Cornell will greatly improve the School Committee and make them much more accountable to the needs of the students.
Happy Autumn James.
Happy Autumn everyone.
CrickeeRaven
Mr. Areson, please disregard the prior comment by a two-time election reject whose record is one of tax delinquencies and lies -- and who is still trying to push his false statements.
"But the School Committee (SC) spent their budget elsewhere."
The two-time election reject does not acknowledge that 90 percent of the school budget pays for salaries and benefits for staff, including teachers.
"I believe we need an independent audit from an INDEPENDENT source to learn where."
The two-time election reject has routinely ignored the fact that the school committee published independent third-party audits every year, which show how the school budget is being spent.
"They keep adding staff to the administration..."
This is an outright lie. The most recent budget shows a reduction of $250,000 in administrative salaries -- and a $4.5 million increase in teacher salaries.
"[S]pend $60,000 for a Public relations firm..."
This is also an outright lie. The PR firm line item was eliminated from the school budget.
As a result of his willing and repeated lies, I suggest that you take his endorsements with the same credibility as the rest of his claims: Zero.
patientman
Talk about delusional. Dude, the school district is suing the school for more funding. Raise the money from Foundations and private donors.
Mr. Areson,
While many of us can appreciate your concerns, I think focus should be on ensuring the roof doesn't fall on the students heads while in class and leave the fields further down on the to-do list.


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