Category Archives: machine learning

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 could it be better? (There’s not a lot of easily discoverable analysis on this.)
  • Perspective: Despite our challenges and structural societal issues, most people in the U.S. enjoy more security—i.e., most Americans don’t need to worry about being violently attacked or starving to death. I think we agreed on this general point. It in no way lessens the obvious needs for systemic improvements.

    I add an after note, however, that a succession of unfortunate events, especially if medical issues and their crippling expenses are involved, can quickly deplete the average American’s finances and put them on the streets. A homeless person’s capacity to be resourceful literally includes their ability to carry and protect resources which become much more difficult to retain due to space in a car (or backpack) and increased exposure to crime. Social stigma becomes self-reinforcing to the homeless person and we who encounter them. Nearly all doors close. ‘Structural invisibility’ results—’society’ just stops seeing them (or can only see them as choosing or deserving their situations) and predators take society’s disregard as open season on the homeless.

    So, while it is true the threshold of personal disaster is farther from the average American than from the average, say, Zimbabwean or Eritrean, once an American crosses that threshold it can certainly be a devastating and nearly intractable circumstance. There are many trap doors leading down and few ladders leading back up. Thoughts?

Entertainment we’ve enjoyed recently:

  • Edward: Killing Eve – Bored British intelligence agent, Eve, is overly interested in female assassins, their psychologies and their methods of killing. She is recruited by a secret division within MI6 chasing an international assassin who calls herself Villanelle. Eve crosses paths with Villanelle and discovers that members within both of their secret circles may be more interconnected than she is comfortable with. Both women begin to focus less on their initial missions in order to desperately learn more about the other.
  • Mark: Devs (FX network sci-fi thriller series) – Atmospherically dark and brooding exploration of the implications of a quantum computing system capable of peering into past and future. Also a meditation on two competing physics theories, deterministic and indeterministic (Copenhagen interpretation – aka, ‘many worlds,’ ‘multiple universes’). From a genre perspective, it is a thriller.
  • Scott: After Life (Ricky Gervais) – follows Tony, whose life is turned upside down after his wife dies from breast cancer. He contemplates suicide, but instead decides to live long enough to punish the world for his wife’s death by saying and doing whatever he wants.
  • Paul: Exhalation (book of short sci-fi stories) Ted Chiang

    Mark would like to base a few future discussions on the following stories:
    • The Lifecycle of Software Objects “follows Ana Alvarado over a twenty-year period, during which she “raises” an artificial intelligence from being essentially a digital pet to a human-equivalent mind.”
    • The Truth of Fact, the Truth of Feeling – A study in memory and meaning told from interwoven future and past stories. “a journalist observes how the world, his daughter, and he himself are affected by ‘Remem’, a form of lifelogging whose advanced search algorithms effectively grant its users eidetic memory of everything that ever happened to them, and the ability to perfectly and objectively share those memories. In a parallel narrative strand, a Tiv [African tribal] man is one of the first of his people to learn to read and write, and discovers that this may not be compatible with oral tradition.” (Wikipedia)
    • The Great Silence – Mutimedia collaboration version here. An earthbound alien wonders about humanity’s fascination with missing space aliens and lack of interest of intelligences among us.
    • Omphalos – On an Earth on which science has long-since proven the planet is precisely as old as the bible states, an anthropologist following the trail of a fake artifact stumbles onto a shattering discovery.
    • Anxiety is the Dizziness of Freedom (title is a Kirkegaard quote) – “the ability to glimpse into alternate universes necessitates a radically new examination of the concepts of choice and free will.” (SFWA)

  • Scott: Who are some of your favorite fiction authors?

     

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 prospects?

Following is a list of (only some!) of the resources some of us have consumed and discussed online, in emails, or face-to-face in 2019. Sample a few to jog your thoughts and provoke deeper dives. Please add your own additional references in the comments below this post. For each, give a short (one line is fine) description, if possible.

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 observations to the comments below. 

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

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 achievements. The change in the learning paradigm opens new horizons for different types of deep learning algorithms and artificial intelligence based applications imitating our brain functions, but with advanced features and at a much faster speed.

Source: https://www.sciencedaily.com/releases/2018/03/180323084818.htm

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 they had promised to delete. This data reportedly included information siphoned from hundreds of thousands of Amazon Mechanical Turkers who were paid to use a “personality prediction app” that collected data from them and also anyone they were friends with — about 50 million accounts. That data reportedly turned into information used by the likes of Robert Mercer, Steve Bannon and the Donald Trump campaign for social media messaging and “micro-targeting” individuals based on shared characteristics.

 

https://www.engadget.com/2018/03/17/facebook-cambridge-analytica-data-analysis-chris-wylie/?sr_source=Facebook 

[/et_pb_blurb][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ fullwidth=”on” _builder_version=”3.0.93″][et_pb_fullwidth_code _builder_version=”3.0.93″ use_background_color_gradient=”on” background_color_gradient_start=”#3f310c” background_color_gradient_end=”#b57926″ text_orientation=”center” custom_padding=”20px||24px|” animation_style=”fold”]<iframe width="560" height="315" src="https://www.youtube.com/embed/FXdYSQ6nu-M?rel=0" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>[/et_pb_fullwidth_code][/et_pb_section]

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 convened to show marketers and technicians “how to manipulate people into habitual use of their products,” put it baldly.

subtle psychological tricks … can be used to make people develop habits, such as varying the rewards people receive to create “a craving”, or exploiting negative emotions that can act as “triggers”. “Feelings of boredom, loneliness, frustration, confusion and indecisiveness often instigate a slight pain or irritation and prompt an almost instantaneous and often mindless action to quell the negative sensation”

Particularly telling of the growing ethical worry are the defections from social media among Silicon Valley insiders.

Pearlman, then a product manager at Facebook and on the team that created the Facebook “like”,  … confirmed via email that she, too, has grown disaffected with Facebook “likes” and other addictive feedback loops. She has installed a web browser plug-in to eradicate her Facebook news feed, and hired a social media manager to monitor her Facebook page so that she doesn’t have to.
It is revealing that many of these younger technologists are weaning themselves off their own products, sending their children to elite Silicon Valley schools where iPhones, iPads and even laptops are banned. They appear to be abiding by a Biggie Smalls lyric from their own youth about the perils of dealing crack cocaine: never get high on your own supply.

If you read the article, please comment on any future meeting topics you detect. I find it a vibrant collection of concepts for further exploration.

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

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.