Category Archives: machine learning

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.

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

Excellent article on the history and recent advances in AI

This NY Times article is worth your time, if you are interested in AI–especially if you are still under the impression AI has ossified or lost its way.

AI Creativity

Google and others are developing neural networks that learn to recognize and imitate patterns present in works of art, including music. The path to autonomous creativity is unclear. Current systems can imitate existing artworks, but cannot generate truly original works. Human prompting and configuration are required.

Google’s Magenta project’s neural network learned from 4,500 pieces of music before creating the following simple tune (drum track overlaid by a human):

Click Play button to listen->

Is it conceivable that AI may one day be able to synthesize new made-to-order creations by blending features from a catalog of existing works and styles? Imagine being able to specify, “Write me a new musical composition reminiscent of Rhapsody in Blue, but in the style of Lynyrd Skynyrd.

There is already at least one human who could instantly play Rhapsody in Blue in Skynyrd style, but even he does not (to my knowledge) create entirely original pieces.

Original article: https://www.technologyreview.com/s/601642/ok-computer-write-me-a-song/

See also: https://www.technologyreview.com/s/600762/robot-art-raises-questions-about-human-creativity/

15 Nov 16 Discussion on Transhumanism

Good discussion that covered a lot of ground. I took away that none of us have signed on to be early adopters of brain augmentations, but some expect development of body and brain augmentations to continue and accelerate. We also considered the idea of bio-engineered and medical paths to significant life-span, health, and cognitive capacity improvements. I appreciated the ethical and value questions (Why pursue any of this? What would/must one give up to become transhuman? Will the health and lifespan enhancements be equally available to all? What could be the downsides of extremely extended lives?) Also, isn’t there considerable opportunity for smarter transhumans, along with AI tools, to vastly improve the lives of many people by finding ways to mitigate problems we’ve inherited (disease, etc.) and created (pollution, conflict, etc.)?