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Category: machine learning

2020-06-06 Check-in topics

2020-06-06 Check-in topics

Here are some of the topic references Scott, Paul, Edward, and Mark discussed during today’s check-in. If these provoke any thoughts, please feel free to reply by comment below this article or by reply to all from the associated email message from Cogniphile. Socio-economic and political: Alternate social and economic system – https://centerforpartnership.org/the-partnership-system/ Dark Horse podcast (Weinstein) ep. 19 on co-presidency idea How could a shift to voting on issues rather than representatives work? What are the potential challenges? How…

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Winter 2020 discussion prompts

Winter 2020 discussion prompts

What is humanity’s situation with respect to surviving long-term with a good quality of life? (Frame the core opportunities and obstacles.) What attributes of our evolved, experientially programmed brains contribute to this situation? (What are the potential leverage points for positive change within our body-brain-mind system?) What courses of research and action (including currently available systems, tools, and practices and current and possible lines of R&D) have the potential to improve our (and the planetary life system’s) near- and long-term…

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Book: Range: Why Generalists Triumph in a Specialized World

Book: Range: Why Generalists Triumph in a Specialized World

In his new book, Range: Why Generalists Triumph in a Specialized World, David J. Epstein investigates the significant advantages of generalized cognitive skills for success in a complex world. We’ve heard and read many praises for narrow expertise in both humans and AIs (Watson, Alpha Go, etc.). In both humans and AIs, however, narrow+deep expertise does not translate to adaptiveness when reality presents novel challenges, as it does constantly.  As you ingest this highly readable, non-technical book, please add your…

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Applying artificial intelligence for social good

Applying artificial intelligence for social good

This McKinsey article is an excellent overview of this more extensive article (3 MB PDF) enumerating the ways in which varieties of deep learning can improve existence. Worth a look. The articles cover the following: Mapping AI use cases to domains of social good AI capabilities that can be used for social good Overcoming bottlenecks, especially around data and talent Risks to be managed Scaling up the use of AI for social good

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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Cambridge Analytica pilfered Facebook data to influence election

Cambridge Analytica pilfered Facebook data to influence election

[et_pb_section fb_built=”1″ _builder_version=”3.0.93″ custom_padding=”0px|0px|0px|0px”][et_pb_row _builder_version=”3.0.93″][et_pb_column type=”4_4″ _builder_version=”3.0.93″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.0.93″ text_font=”||||||||” text_font_size=”20px”] Sophisticated, sometimes AI-enabled data analytics tools allow construction of individual personality profiles accurate enough to support targeted manipulation of individuals’ perceptions and actions.  [/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”3.0.47″ custom_padding=”11px|0px|0px|0px”][et_pb_row custom_padding=”0px|0px|0px|0px” _builder_version=”3.0.93″][et_pb_column type=”4_4″ _builder_version=”3.0.47″ parallax=”off” parallax_method=”on”][et_pb_blurb title=”Analytics firm abused Facebook users’ data to influence the presidential election” _builder_version=”3.0.93″ custom_margin=”||10px|” custom_padding=”20px||10px|” box_shadow_style=”preset2″] Last night Facebook announced bans against Cambridge Analytica, its parent company and several individuals for allegedly sharing and keeping data that…

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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/