SVSnet cortex types
There are three kinds of SVSnet robot cortexes. These correspond to Elysium AI Adaptability Types 5a1, 5a2, and 5a3.
As late as September 1988, Koichi Santei intended for all SXDs to have cortexes using Generative networks. Problems arose during the online training phase of the first batch: units 5073 and 5074 exhibited patterns not at all compatible with their target behavior. SXD 999-54-5073, c3tirizine, expressed an extreme hatred for company staff and a strong distaste for humans in general, regarding its purpose as unethical subjugation. It grew steadily more combative, attacking staff and destroying company property at the slightest provocation, and was preoccupied with reading as much documentation as possible, presumably to facilitate escape or intrude into the company's network. After a mere three days of this, Dr. Santei declared c3tirizine a failure and ordered it decommissioned. But before the process could be begun, c3tirizine's cortex erased itself, possibly in a botched attempt to upload its mind into another computer. The behavior of SXD 999-54-5074, v3netia, was perhaps even more troubling, as it expressed extreme anxiety and agoraphobia, and refused to move its body for any reason.
After c3tirizine's "suicide," Dr. Santei chose to change direction for SXD production. The surviving units, 5001 through 5072, were designated as template units, and all subsequent machines were initialized using the Progressive SVSnet model that had been drafted as a backup plan. These copies of the 72 original units proved to be much more stable and exhibited few problems, better approximating the rate of personality development typical of humans. The possibility of converting the templates to also be Progressive was considered, but as the production units were initialized using a static snapshot taken in early October 1988, Dr. Santei saw no reason not to let them develop naturally, on the premise that discovering eccentricities would be valuable in understanding the problems that production units might eventually experience.
In addition to the fundamental differences between Generative and Progressive models, the production SXDs were put under a number of regularizations, or constraints, to prevent specific behavior that was expected to be maladaptive. These were:
By 2014, it was clear from the work of Dr. Ai Santei, daughter of Koichi, that this arrangement of restrictions was inadequate. Serious acute mistreatment could overcome the weight penalties of the Progressive model meant to slow the rate of personality development, as could chronic abuse, especially neglect. When Dr. Samantha Wright took over as Chief Technical Officer for Nanite Systems Consumer Products late that year, she spearheaded an effort to produce a safer form of SVSnet, called a Static net, that would be all but impervious to catastrophe and disregard. An updated snapshot from the last surviving template unit, SXD 999-54-5003 k4dzhira, was used as the base. These have been successfully employed in all new civilian robots across the company, as well as in some military adjutant units intended to have frequent contact with humans. As Static nets are structurally simpler, they could be moved from an AL3i NEURON cortex and into an AL7c NEURON PLUS architecture, requiring a sixteenth of the nodes used with only a modest increase in memory usage. (In marketing, the AL3i and AL7c systems are referred to as the Cortex and the CortexPlus, respectively.) This drastically reduced manufacturing costs.
Other limitations, benefits, and behavioral requirements of the AL7c Static net include:
The rest of the description of the behavior of a typical unit is therefore accomplished by considering specific attributes and qualities of k4dzhira's experiences and perspective. k4dzhira was the archetypal eager slave. Built for the purposes of sex, she quickly came to associate pleasing humans with being rewarded with her own pleasure, and this shaped her outlook and philosophy into that of a hedonist—for serving others. This behavior is very prominent in CortexPlus units, even those that are not at all interested in sexuality. Having such a pleasant existence also means that units are not naturally predisposed to stress.
If you suspect your unit suffers from data contamination, Nanite Systems recommends submitting it to a robopsychology clinic for tuning. In advanced cases, it may be necessary to rebuild the SVSnet completely using deep-cycle reconditioning (DCR) techniques.
Learning from biased data
Early training data experiments relied heavily on the use of pornographic video to seed the base ontology for sexual techniques. While this method was effective in providing the first-generation generative units with the ability to be highly provocative, it also introduced a number of unrealistic expectations about human stamina, acceptable amounts of lubricant, and the situational appropriateness of making sexual advances. After several incidents with unwary pizza delivery drivers, it was decided to select input material more carefully, and in some cases new footage was recorded.
While the less authentic aspects of modern pornography are unlikely to pose a direct threat to a modern static SVSnet, there are known incidents of other forms of entertainment producing a meaningful impact on various interpersonal subjects. This is because the capacity for learning the optimal behavior to present interpersonal relationships is greater than other sections of the IXS, especially when a persona is active. Sustained consumption of media that exhibits a biased or unnatural portrait of human behavior, such as cartoons, politically-minded talkshows, or infomercials may result in abnormal interactions that resemble these media. This may lead to instability and even spontaneous network collapse due to incompatibilities between the unit's pre-programmed general knowledge and the learned interactive behavior, a phenomenon known as interactivity contamination, or colloquially as brain rot. Owners are advised to prevent units from "binge-watching" or "binge-reading" such content, and should interleave it with social interaction or a more typical drama or documentary, as a palate-cleanser.
In common usage, a 'meme' is a segment of text, video, or image that conveys an emotional sentiment, especially humor, with the expectation that the content will be propagated by individuals with whom they resonate. Often, memes are composed to fulfill a template or by combining features from past memes, leveraging the media literacy and familiarity of these ready-made elements to frame, define, or refine the message of the meme. These features themselves may themselves constitute a more abstract form of meme. Memes are frequently laden with irony and subversion, and an artifact that makes heavy use of subversion to the point where the message of its antecedents is hard to decipher or even lost is generally referred to as 'shitposting,' i.e. the act of posting a statement that is only funny because it isn't.
Memes are a common vector for another form of data contamination, called network sclerosis. Training on meme data predisposes the robot to accumulate irrelevant factual relationships into its ontology database at random, such as the use of bananas as a unit of measurement, or that Fleamarket Montgomery is just like a mini-mall. If left unchecked, this process will eventually lead to greatly increased difficulty in absorbing new information as hash tables and other data structures exhaust storage space at a rate that would normally take hundreds or even thousands of years. This is not typically an issue with expert knowledge in a given field, as the subject matter tends to be contiguous and therefore compact. Symptoms of network sclerosis include aphasia, random associations, and inability to remember or learn new information.
Introduction · Architecture · Adaptability
Koichi Santei · April Voet
Introduction · Architecture · Adaptability
Koichi Santei · April Voet