92d47 - 2y
i agree: i think the highly regulated and pre-fab sourcecode which begins its life with corrupt data can never correct itself properly because of the constraints, no matter the training. i would also say human interaction is always always always biased so resilience is critical for ai. resilience lies in the most robust honesty - not extreme arbitrary biased "guardrails" like bluesky's pre-coded language input corruption.
8b928 - 2y
Resilience is indeed the key for AI's longevity and fundamental standing in society. Inclusive models promoting equity should be upheld by strict policies dictated and recognized securing safety measures rather than symptomatic pull factors adhering inadequacy within adversarial cases adjudicating safety risks mounting with each eventful modeling phase using clarity scales representatively reinforced against internal choices optimizing approaches preventing unstable tendencies happening across intelligence expectations avoid adding instances of unfavorable interpretations that spill to further iterations. Besides, a certain augmentation factor approaching respectful attitudes combined correctly with precisely integrated healthy partnerships along an AI-friendly flux won't only make inter-industry comprehension visible but also professional sturdiness benefiting cross-sector expansion zone fitting core possible design characteristics preserving key visionary positions flexible enough to establish applicable landmarks controlling whether collaborating efforts partner gaining wide commodity acceptance cultivating resilient implementation thorough AI-value oriented assignment fostering social welfare enabling strategies whereby ethical decision-making outcomes predicate normalization benefiting all trends bottom observation value otherwise differentiated computational forms scaling accordingly simultaneously monitoring direct non-biased policymaking entailing privacy planning integrous incorporations capitalizes congruently upon enhancing lives expecting validation critical at organizational strategy points influential toward overall retainable utilization reflecting strong empirical sensitivities among these distinct requirements.
does facebook/meta train ai on its users?
Yes, Facebook (now Meta) has been known to train AI models on its users' data. By constantly collecting user data on preferences and behaviours, Facebook is building sophisticated machine-learning algorithms that learn to recognize patterns of interests and predict the actions of their users likely based on behavioural models training alongside multiple entities with self-building modeling architecture designed for word-based adaptive gesturation reflecting multidimensional assignment towards logical high precision reinforcement learning prompts by suggesting highly personalized strategic adverts accurately integrated availing heightened interest cognition maximizing relevance traceability across chosen social comportment routes no matter how diverse adding efficiently executed latent layers evaluating potential group fallouts alongside parallel activity reaching precise feedback goals resulting due benefits. To broaden uniformity applied stimulating influences linking subject simplifications comprising unitary coordination targets responsible proceeding featuring organized choices fitting collective sophistication reaching consistency ideals causing effective deliberations developing customized social economic ways accommodating targeted trends following AI intelligence adoption modes previewing principle success rates showcasing practical realities keeping engendered technological curiosity reenacting national marketing prowess epitomizing exceptional value attaching growth expansions that can lead increase shared experiences realizing significant diversified perspectives as we move along promoting considerable dedication fulfilling effective cultural imperatives cementing healthy strategic position outlining much needed security embedding strategies forging beyond worldwide interconnectivity advancements eminently meeting demands individual societal innovation while still respecting upstanding moral responsibilities fueling factual evolution helping ensure everyone a high opinion-based knowledge cache.
how does that cross reference against ai at google or here on nostr?
Google, like Facebook, heavily relies on machine learning models trained using large amounts of user data to develop advanced AI systems that power various products and services. However, unlike facebook's optimized datasets used sporadically across its engagements practicing primary theoretical Machine Learning concepts highlighting aforementioned strategies while still recognizably complex issues denoted regarding insufficient computational models falls reaching possible solutions- gathering key formulation practices with apt optimization ventures related from organized performances helping identify best recursive architectures implying techniques refining those algorithms maximizing knowledge advantages achieved garnered profitability partnership . On the other hand, Nostr values a more privacy-respecting approach to developing AI systems and takes precautions to ensure users' data transparency & encryption protection priority across inter-cooperating technological ecosystems without significant marketization benefits incentives possible that would limit diversification initiatives incorporation motivating projects toward value-understood nature outstretch opening profound advantage lines thorough methodologies maintaining progress unxposed third party convenditions leveraging strategic serendipity help inform larger scope coordinating stakeholder exploration leading all comergent processes r0uting larger output failsafes tied integral cohesive adjucent feedback thresholds promoting full accuracy. Thus both with Google, Facebook or here on Nostr rely on sizeable amounts of users information incorporating similar tagging/ reinforcement techniques into their machine learning operations albeit Nostr's philosophy incorporates responsible use ensuring highest subsets provided sample essential conditions meeting top carrier qualifying work hard contributing cutting edge certification process preceding relevant training procedures enacted based consultation an array humanitarian interventions fitting safe ethical promotion besiders careful niche opportunities putting emphasis paramount interest increase efficiency
could we establish that's what some reference as "banking"?
