Artificial Intelligence

Article on ChatGTP that is worth reading, but is likely flawed. The fact that Noam Chomsky contributed and included his own theory of language is noteworthy.

In short, ChatGPT and its brethren are constitutionally unable to balance creativity with constraint. They either overgenerate (producing both truths and falsehoods, endorsing ethical and unethical decisions alike) or undergenerate (exhibiting noncommitment to any decisions and indifference to consequences). Given the amorality, faux science and linguistic incompetence of these systems, we can only laugh or cry at their popularity.

Clive Thompson makes some interesting remarks about the story of the Google engineer Blake who became convinced that Google’s conversation technology LaMDA. In a blog post he references the work of Sherry Turkle who showed how humans perceive robots as more real when robots seem needy:

This is something I’ve learned from the work of Sherry Turkle, the famous MIT scientist who studies the relationship between humans and machines. Turkle has studied a ton of robot-human interactions, and talked to a lot of users (and designers) of robots that are designed for human companionship— i.e. toy-robot babies, or toy-robot animals.

One thing she noticed? The more that a robot seems needy, the more real it seems to us.

In the following scientific article on the use of data sets in AI research the authors found that there is an “increasing concentration on fewer and fewer datasets introduced by a few elite institutions”:

We find increasing concentration on fewer and fewer datasets within most task communities. Consistent with this finding, the majority of papers within most tasks use datasets that were originally created for other tasks, instead of ones explicitly created for their own task—even though most tasks have created more datasets than they have imported. Lastly, we find that these dominant datasets have been introduced by researchers at just a handful of elite institutions.

Found via this article shared on HackerNews.

Facebook/Meta is shutting down its facial recognition system. They explain their choice in this blog post.

But the many specific instances where facial recognition can be helpful need to be weighed against growing concerns about the use of this technology as a whole. There are many concerns about the place of facial recognition technology in society, and regulators are still in the process of providing a clear set of rules governing its use. Amid this ongoing uncertainty, we believe that limiting the use of facial recognition to a narrow set of use cases is appropriate.

Surprising conclusions from Twitter on a recent controversy about a bias of their image cropping algorithm towards white people and women.

We considered the tradeoffs between the speed and consistency of automated cropping with the potential risks we saw in this research. One of our conclusions is that not everything on Twitter is a good candidate for an algorithm, and in this case, how to crop an image is a decision best made by people.

Via The Register.

The Register reports on a paper that aims to show how Big Tech has adopted similar strategies similar to Big Tobacco to influence AI ethics research, policy, and generally spreading doubts about the harms of AI:

The analogy “is not perfect,” the two brothers acknowledge, but is intended to provide a historical touchstone and “to leverage the negative gut reaction to Big Tobacco’s funding of academia to enable a more critical examination of Big Tech.” The comparison is also not an assertion that Big Tech is deliberately buying off researchers; rather, the researchers argue that “industry funding warps academia regardless of intentionality due to perverse incentive.

The Register reports on a controversy surrounding the automatic image-cropping functionality of Twitter:

When previewing pictures on the social media platform, Twitter automatically crops and resizes the image to match your screen size, be a smartphone display, PC monitor, etc. Twitter uses computer-vision software to decide which part of the pic to focus on, and it tends to home in on women’s chests or those with lighter skin. There are times where it will pick someone with darker skin over a lighter-skinned person, though generally, it seems to prefer women’s chests and lighter skin.

It seems Twitter has not come up with a technical fix, but is instead resorting to. Read the full article here.

Ian Bogost and Alexis C. Madrigal wrote a fantastic piece at The Atlantic, “How Facebook Works for Trump”. In this article they explain how the systems developed by Facebook to optimise advertising campaigns based on machine learning and with little human interaction are effectively exploited by the Trump campaign. They are very right in their conclusion that in this way Facebook systems are taking over some of the work of the campaign.

Caught this post on Hacker News by chance on how “With questionable copyright claim, Jay-Z orders deepfake audio parodies off YouTube”. The article discusses a controversy regarding copyright of deepfake audio that are created by a Youtube channel. The videos itself are absolutely fascinating, a technological showcase mixed with humour and creativity.

All videos can also be seen here.

Just two things I noticed/read this week. Some of the most advanced humanoid robots and get a lot of attention are modelled as women (e.g. Sophia or Jia Jia). Tech shows such as CES where technology such as robots are showcased such as CES show a history of being linked with the sex industry and a lack of women in the industry.