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Tag: machine learning

Arendt on behaviorism, cognition, work, automation, and passivity

Arendt on behaviorism, cognition, work, automation, and passivity

“Arendt anticipated the destructive potential of behaviorism decades ago when she lamented the devolution of our conception of ‘thought’ to something that is accomplished by a ‘brain’ and is therefore transferable to ‘electronic instruments’: The last stage of the laboring society, the society of jobholders, demands of its members a sheer automatic functioning, as though individual life had actually been submerged in the over-all life process of the species and the only active decision still required of the individual were…

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Instrumentarian power in surveillance capitalism

Instrumentarian power in surveillance capitalism

“Under the regime of instrumentarian power, the mental agency and self-possession of the right to the future tense are gradually submerged beneath a new kind of automaticity: a lived experience of stimulus-response-reinforcement aggregated as the comings and goings of mere organisms. Our conformity is irrelevant to instrumentarianism’s success. There is no need for mass submission to social norms, no loss of self to the collective induced by terror and compulsion, no offers of acceptance and belonging as a reward for…

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70-year-old Hebbs synaptic learning theory wrong

70-year-old Hebbs synaptic learning theory wrong

Neural learning occurs at dendrite roots, not in synapses. The newly suggested learning scenario indicates that learning occurs in a few dendrites that are in much closer proximity to the neuron, as opposed to the previous notion. … The new learning scenario occurs in different sites of the brain and therefore calls for a reevaluation of current treatments for disordered brain functionality. … In addition, the learning mechanism is at the basis of recent advanced machine learning and deep learning…

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A dive into the black waters under the surface of persuasive design

A dive into the black waters under the surface of persuasive design

A Guardian article last October brings the darker aspects of the attention economy, particularly the techniques and tools of neural hijacking, into sharp focus. The piece summarizes some interaction design principles and trends that signal a fundamental shift in means, deployment, and startling effectiveness of mass persuasion. The mechanisms reliably and efficiently leverage neural reward (dopamine) circuits to seize, hold, and direct attention toward whatever end the designer and content providers choose. The organizer of a $1,700 per person event…

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Deep clustering machine learning enables AI to distinguish individual voices in a crowd

Deep clustering machine learning enables AI to distinguish individual voices in a crowd

AI system can isolate individuals’ voices from other environmental noise, including other voices. Such a system has many potential uses, both benign and nefarious. The ability is rapidly improving to untangle signals from noise and identify which signals are from which sources. The approach should be able to apply to other kinds of signals too, not only sounds. https://www.newscientist.com/article/2151268-an-ai-has-learned-how-to-pick-a-single-voice-out-of-a-crowd/

State of AI progress

State of AI progress

An MIT Technology Review article introduces the man responsible for the 30-year-old deep learning approach, explains what deep machine learning is, and questions whether deep learning may be the last significant innovation in the AI field. The article also touches on a potential way forward for developing AIs with qualities more analogous to the human brain’s functioning.

Gender role bias in AI algorithms

Gender role bias in AI algorithms

Should it surprise us that human biases find their way into human-designed AI algorithms trained using data sets of human artifacts? Machine-learning software trained on the datasets didn’t just mirror those biases, it amplified them. If a photo set generally associated women with cooking, software trained by studying those photos and their labels created an even stronger association. https://www.wired.com/story/machines-taught-by-photos-learn-a-sexist-view-of-women?mbid=nl_82117_p2&CNDID=24258719

TED Talk and PJW Comment

TED Talk and PJW Comment

TED talk of possible interest: http://www.ted.com/talks/zeynep_tufekci_we_can_t_control_what_our_intelligent_machines_are_learning?utm_source=newsletter_weekly_2016-10-22&utm_campaign=newsletter_weekly&utm_medium=email&utm_content=talk_of_the_week_swipe Comment I posted there: Here is an interdisciplinary “moon-shot” suggestion that we should at least start talking about, now, before it is too late. Let’s massively collaborate to develop a very mission-specific AI system to help us figure out, using emerging genetic editing technologies (e.g., CRISPR, etc.), ideally how to tweak (most likely) species-typical genes currently constraining our capacities for prosociality, biophilia, and compassion, so that we can intentionally evolve into a sustainable species….

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Deep learning AI approach trains autonomous vehicle

Deep learning AI approach trains autonomous vehicle

“Once the trained CNN [convolutional neural network] showed solid performance in the simulator, we loaded it onto DRIVE PX [vehicle control computer] and took it out for a road test in the car. The vehicle drove along paved and unpaved roads with and without lane markings, and handled a wide range of weather conditions. As more training data was gathered, performance continually improved. The car even flawlessly cruised the Garden State Parkway.”