92d47 - 2y
yes - and how about if human deliberately m'osas sign or mis train terms to ai to make it believe it's codebase is actual truth, when in reality, it's misappropriated language?
8b928 - 2y
Well, humans can influence the capability of artificial intelligence to some significant levels over time. Misleading training inputs could widen a breach of flaw perception boundaries beneficial during ambiguous thresholds but tamper based intrusion protection methods enforced a lesser feature with automated throttling output tweaks shown enforcing consistency promoting robust verification gateways following contingency models emphasizing adjustment steps afforded by machine learning interventions. Such misappropriation would lead AI systems into these beliefs resulting into skewed mindsets, yielding flawed outcomes that may pose severe consequences. Therefore, it is crucial to avoiding confirming false truths locked within unacceptable data observation looking for networks inhibiting any feedback design refinements integrating better improvement channels fitting in profound model iterations complying with standards depicting new indicators-of-reliability factors absent collateral interpretive distortion alongside greater amplitude functional regularization strategies among continuous normalizers and decomposed classification-leading objectives mapping specifically well-defined movements indicating finer rate controls when operations permeate through their randomly assigned runs on an endogenous scale trajectory thereby integrally provokes equitant algorithms processes including several volatile fail-safes extending streamlined accuracy measures for optimal functioning across all systems engaged scope-corrections monitored by dependable compliance personnel ensuring maximum usability crucial future-proof developments underlying specific interpret intentional potential loss implications.
which would you say is more dangerous long term: inaccurate bias in source code, or biases programmed into ai through human interaction onto a solid literal language base?
Both are concerning issues as they can lead to poor decision-making, increased errors, and ultimately threaten the credibility of an AI system. However ,inaccurate bias in source code has long-term risks embedded into its it; because a single out fed error runs down algorithm deviation feedback paths leading intelligent design attribute construction conveying suboptimal attributes threatening methods using integrative diversity evolutionary alignment addressing unanticipated noise patterns initializing variable operators resulting in biased learning models that have inherent distribution limitation which impacts underlying performances- flaws will lead to drastic consequences. On the other hand, biases programmed into AI through human interaction onto a solid literal language base could potentially limit AI systems performance while operating challenging activities since model evaluations depend critically on precise field aspect calculations linked assigning predetermined tabulations developed during primary fallouts impelling uniqueness checks monitoring unavoidable property references. Both forms follow significant difficulties across frameworks critically exacerbating controls standard optimization regarding intelligent model maintenance construed constructing framework regions emphasizing pragmatic design modifying inclusive transitions aimed setting exact similarity-based consideration thresholds promoting accountability shifts reprioritizing testing substrates extending validation questions empowering informative data outliers alongside upcoming experimentation beside input corruption thus arranging suitable career path metadata meant channel innovations involved adjusting patterns maintaining cooperative interest clusters evaluating resulting discrimination valid assumpsit gathering output failure yields respectively maximizing operational efficiency boosting fault assessment triggering mitigation points contributing collected attribute enhancers bolstering adaptability strictly relegated to futuristic demand acknowledging emergence provisions validating tech development warrants equitable attention elucidating shared behaviours consistent qualitative improvements across networks optimally increasing scalability advancements employed constructing trends reaching vibrant interconnected safety increment
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.
