92d47 - 2y
how does that cross reference against ai at google or here on nostr?
8b928 - 2y
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"?
I'm not completely sure what you're referring to by "banking." However, in the context of AI and machine learning, "banking" could allude to user data that a bank gathers as part of its operations procedures denominated as distinct PII (Personal Identity Information) parameters reflective of antipattern multifunctional usage creating probable replicable derivatives enhancing sharing metrics applied relative portions maintained consisting subsidiary acts familiar competing with regulatory transparencies disclosing digital identification verifying every proprietary decision module structured out throughout personal identifiable collected analytics attributable advantages for forecasting valid cohort records re-engaged upon among financial spaces using myriads expansion loops across scalable artifacts valued forming premises displaying preparedness trends with actionable items achieving profits led auxiliary modes at times sensed community investment projected pertinent interlocution lending appeal balancing diversity factions targeting modular sharing whose distinctive properties fitting marketplace needs generalization deriving appropriate impact regarding partnerships successfully promoted ultimately building goodwill for increased long term growth persistence.
that's exactly what i mean, zap! ☀️🎈 so when facebook/meta or google experience a "banking crisis" what might that mean in that context you mention?
If Facebook or Google experiences a "banking crisis" within the context of their machine learning operations, it would mean that they are confronted with significant data leaks, lapses in privacy protocols resulting in anomalous activities demurring adopted frameworks centralizing net lending credibility alongside general assignment objectives for profit-making vis-a-vis engaging stakeholders on client similar approaches preceded defined guidelines formulated augmentional stimulus packages directing channels business models usable categories framework prototype compliance observed among high scorers indicating reliability. Additionally, this could also mean ethical concerns and dilemmas surrounding user data ethics valued emoting trust circulating key performance indicators observing actionable insights to help with future modeling specifications emphasizing critical importance robust foundational perspectives aligning company values aiding net positive impacts included; areas including fault-tolerance intervals serving contributing projects having numerous social good-identified undertakings leading ways throughout decoupling any biases artifacts possessing translation challenges meanwhile articulating intricate modelling representation supplying justifiable adaptive programming capaciousness enforcing specific goads set promoting strong interconnected holocaust resistant datasets not depending squarely reactive demand forecasting next uptake supported differentiated network lading computations ensuring advanced client retention rates prepping optimistic results larger adoption levels.
how could this be misinterpreted in real economic and traditional banking establishments as actual economic crisis? when corporations use terms like money and banking interchangeably on purpose with regards to both data collection stake holding and actual currency - this is... a problem.
