{"id":470635,"date":"2025-08-13T21:00:59","date_gmt":"2025-08-13T21:00:59","guid":{"rendered":"http:\/\/savepearlharbor.com\/?p=470635"},"modified":"-0001-11-30T00:00:00","modified_gmt":"-0001-11-29T21:00:00","slug":"","status":"publish","type":"post","link":"https:\/\/savepearlharbor.com\/?p=470635","title":{"rendered":"<span>\u041f\u0435\u0440\u0435\u043f\u0438\u0441\u0430\u043b \u0441\u0432\u043e\u044e \u00ab\u043e\u043f\u0435\u0440\u0430\u0446\u0438\u043e\u043d\u043d\u0443\u044e \u0441\u0438\u0441\u0442\u0435\u043c\u0443\u00bb \u043f\u043e\u0441\u043b\u0435 \u0430\u043c\u043f\u0443\u0442\u0430\u0446\u0438\u0438: \u0432\u0437\u0433\u043b\u044f\u0434 GenAI-\u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a\u0430<\/span>"},"content":{"rendered":"<div><!--[--><!--]--><\/div>\n<div id=\"post-content-body\">\n<div>\n<div class=\"article-formatted-body article-formatted-body article-formatted-body_version-2\">\n<div xmlns=\"http:\/\/www.w3.org\/1999\/xhtml\">\n<p><strong>\u041f\u0440\u043e\u043b\u043e\u0433: RuntimeError<\/strong><\/p>\n<p>\u041a\u043e\u0433\u0434\u0430 \u0445\u0438\u0440\u0443\u0440\u0433 \u0441\u043a\u0430\u0437\u0430\u043b \u00ab\u0430\u043c\u043f\u0443\u0442\u0430\u0446\u0438\u044f\u00bb, \u043c\u043e\u0439 \u043c\u043e\u0437\u0433 \u0432\u044b\u0434\u0430\u043b \u0438\u0441\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435 LifeCriticalInterrupt. \u041a\u0430\u043a Python-\u0438\u043d\u0436\u0435\u043d\u0435\u0440, \u0434\u043e\u043e\u0431\u0443\u0447\u0430\u0432\u0448\u0438\u0439 \u0442\u0440\u0430\u043d\u0441\u0444\u043e\u0440\u043c\u0435\u0440\u044b, \u044f \u0441\u0442\u043e\u043b\u043a\u043d\u0443\u043b\u0441\u044f \u0441 \u0444\u0430\u0442\u0430\u043b\u044c\u043d\u044b\u043c \u0441\u0431\u043e\u0435\u043c \u0432 \u0431\u0430\u0437\u043e\u0432\u043e\u0439 \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u0435 \u2014 \u0441\u043e\u0431\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u043c \u0442\u0435\u043b\u0435. \u041f\u043e\u0442\u0435\u0440\u044f \u043d\u043e\u0433\u0438 \u043a\u0430\u0437\u0430\u043b\u0430\u0441\u044c \u043a\u0440\u0430\u0445\u043e\u043c pipeline\u2019\u0430 \u0436\u0438\u0437\u043d\u0438. \u041d\u043e \u0433\u043e\u0434 \u0441\u043f\u0443\u0441\u0442\u044f \u044f \u043f\u043e\u043d\u044f\u043b: \u044d\u0442\u043e \u043d\u0435 \u0431\u0430\u0433, \u0430 \u0442\u0440\u0435\u0431\u043e\u0432\u0430\u043d\u0438\u0435 \u043a \u043f\u0435\u0440\u0435\u0441\u0431\u043e\u0440\u043a\u0435 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u044b.<\/p>\n<hr\/>\n<h4>1. \u0410\u0443\u0434\u0438\u0442 legacy-\u0441\u0438\u0441\u0442\u0435\u043c\u044b<\/h4>\n<p><\/p>\n<p>\u041c\u043e\u0439 \u00ab\u0436\u0438\u0437\u043d\u0435\u043d\u043d\u044b\u0439 \u0441\u043a\u0440\u0438\u043f\u0442\u00bb \u0434\u043e \u0442\u0440\u0430\u0432\u043c\u044b:<\/p>\n<pre><code>def daily_routine():     while True:         train_llm(overfit=True)         ignore_body_warnings(lr=0.001)          consume_nutrients(type=\"junk\", batch_size=32)         sleep = nn.Dropout(p=0.8)<\/code><\/pre>\n<p><strong>\u0414\u0438\u0430\u0433\u043d\u043e\u0437:<\/strong> \u0438\u0433\u043d\u043e\u0440\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0441\u0438\u0433\u043d\u0430\u043b\u043e\u0432 \u0442\u0435\u043b\u0430. \u0410\u043c\u043f\u0443\u0442\u0430\u0446\u0438\u044f \u0441\u0442\u0430\u043b\u0430 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u044b\u043c \u0443\u0434\u0430\u043b\u0435\u043d\u0438\u0435\u043c \u00ab\u043d\u0435\u0438\u0441\u043f\u0440\u0430\u0432\u043d\u043e\u0433\u043e \u043c\u043e\u0434\u0443\u043b\u044f\u00bb.<\/p>\n<hr\/>\n<h4>2. Transfer Learning<\/h4>\n<p>\u041f\u0440\u043e\u0442\u0435\u0437 \u2014 \u043c\u043e\u0439 hardware-\u0430\u043f\u0434\u0435\u0439\u0442, \u0442\u0440\u0435\u0431\u0443\u044e\u0449\u0438\u0439 \u0442\u043e\u043d\u043a\u043e\u0439 \u043d\u0430\u0441\u0442\u0440\u043e\u0439\u043a\u0438.<\/p>\n<p><strong>\u0424\u0438\u0437\u0438\u043e\u0442\u0435\u0440\u0430\u043f\u0438\u044f = RL-finetuning:<\/strong> \u043a\u0430\u0436\u0434\u043e\u0435 \u0434\u0432\u0438\u0436\u0435\u043d\u0438\u0435 \u2014 backpropagation \u0447\u0435\u0440\u0435\u0437 \u0431\u043e\u043b\u044c:<\/p>\n<pre><code>for epoch in range(1000):     loss = attempt_walk(prosthetic_leg)     optimizer.step(loss)     if loss &lt; threshold: reward += 1<\/code><\/pre>\n<ul>\n<li>\n<p><strong>\u0424\u0430\u043d\u0442\u043e\u043c\u043d\u044b\u0435 \u0431\u043e\u043b\u0438 = ghost gradients.<\/strong><\/p>\n<\/li>\n<li>\n<p><strong>Data fusion:<\/strong> \u0441\u0438\u043d\u0445\u0440\u043e\u043d\u0438\u0437\u0430\u0446\u0438\u044f \u0441\u0435\u043d\u0441\u043e\u0440\u043e\u0432 \u0442\u0438\u0442\u0430\u043d\u0430 \u0438 \u0431\u0438\u043e\u043b\u043e\u0433\u0438\u0438.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>\u041a\u0430\u0436\u0434\u043e\u0435 \u043f\u0430\u0434\u0435\u043d\u0438\u0435 \u2014 KeyboardInterrupt, \u0437\u0430\u0441\u0442\u0430\u0432\u043b\u044f\u044e\u0449\u0438\u0439 \u043f\u0435\u0440\u0435\u0441\u043c\u043e\u0442\u0440\u0435\u0442\u044c \u0432\u0435\u0441\u0430 \u0434\u0432\u0438\u0436\u0435\u043d\u0438\u0439.<\/p>\n<\/blockquote>\n<hr\/>\n<h4>3. \u041f\u0435\u0440\u0435\u0441\u0431\u043e\u0440\u043a\u0430 self-image<\/h4>\n<p>\u0421\u0442\u0430\u0440\u044b\u0435 embedding\u2019\u044b \u0443\u0441\u0442\u0430\u0440\u0435\u043b\u0438:<\/p>\n<pre><code>- self_embedding = model.encode(\"\u0446\u0435\u043b\u044c\u043d\u044b\u0439 \u0447\u0435\u043b\u043e\u0432\u0435\u043a\") + self_embedding = model.encode(\"\u0431\u0438\u043e\u043d\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0433\u0438\u0431\u0440\u0438\u0434\")<\/code><\/pre>\n<p>\u0425\u0440\u043e\u043c\u043e\u0442\u0430 \u2014 \u043d\u0435 noise, \u0430 feature engineering. \u041c\u043e\u044f \u043f\u043e\u0445\u043e\u0434\u043a\u0430 \u2014 live demo dropout-\u0443\u0441\u0442\u043e\u0439\u0447\u0438\u0432\u043e\u0441\u0442\u0438.<\/p>\n<hr\/>\n<h4>4. \u041e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u044f inference<\/h4>\n<p>\u041d\u043e\u0432\u044b\u0439 \u0436\u0438\u0437\u043d\u0435\u043d\u043d\u044b\u0439 pipeline:<\/p>\n<div>\n<div class=\"table\">\n<table>\n<tbody>\n<tr>\n<th>\n<p><strong>\u041f\u0430\u0440\u0430\u043c\u0435\u0442\u0440<\/strong><\/p>\n<\/th>\n<th>\n<p><strong>\u0414\u043e<\/strong><\/p>\n<\/th>\n<th>\n<p><strong>\u041f\u043e\u0441\u043b\u0435<\/strong><\/p>\n<\/th>\n<\/tr>\n<tr>\n<td>\n<p align=\"left\"><strong>\u0411\u0430\u0442\u0430\u0440\u0435\u044f<\/strong><\/p>\n<\/td>\n<td>\n<p align=\"left\">train(epochs=\u221e)<\/p>\n<\/td>\n<td>\n<p align=\"left\">early_stopping(patience=2)<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p align=\"left\"><strong>\u041c\u0443\u043b\u044c\u0442\u0438\u043f\u0440\u043e\u0446\u0435\u0441\u0441\u0438\u043d\u0433<\/strong><\/p>\n<\/td>\n<td>\n<p align=\"left\">sync_all()<\/p>\n<\/td>\n<td>\n<p align=\"left\">async with ThreadPool<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p align=\"left\"><strong>\u041c\u043e\u043d\u0438\u0442\u043e\u0440\u0438\u043d\u0433<\/strong><\/p>\n<\/td>\n<td>\n<p align=\"left\">print()<\/p>\n<\/td>\n<td>\n<p align=\"left\">Grafana + Bio-ALERTs<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<p>\u0427\u0435\u0440\u0435\u0437 6 \u043c\u0435\u0441\u044f\u0446\u0435\u0432 \u043c\u0435\u0442\u0440\u0438\u043a\u0438 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0430 \u0436\u0438\u0437\u043d\u0438 \u043f\u0440\u0435\u0432\u044b\u0441\u0438\u043b\u0438 baseline.<\/p>\n<hr\/>\n<h4>5. \u041d\u043e\u0432\u044b\u0435 emergent-\u0441\u0432\u043e\u0439\u0441\u0442\u0432\u0430<\/h4>\n<pre><code>class BionicDeveloper(GenAIEngineer):     def __init__(self):         self.superpowers = [             'pain_gradient_awareness',             'resilience_regularization',             'adaptive_calibration'         ]     def forward(self, obstacles):         return self.prosthetic_forward_pass(obstacles)<\/code><\/pre>\n<p>\u0411\u043e\u043d\u0443\u0441: \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u0438 \u0442\u0435\u043f\u0435\u0440\u044c \u043b\u0443\u0447\u0448\u0435 \u0433\u0435\u043d\u0435\u0440\u0438\u0440\u0443\u044e\u0442 \u044d\u043c\u043f\u0430\u0442\u0438\u044e \u2014 \u0442\u0435\u043b\u043e \u0441\u0442\u0430\u043b\u043e \u0444\u0438\u0437\u0438\u0447\u0435\u0441\u043a\u0438\u043c loss function \u0447\u0435\u043b\u043e\u0432\u0435\u0447\u043d\u043e\u0441\u0442\u0438.<\/p>\n<h4>\u042d\u043f\u0438\u043b\u043e\u0433: \u041f\u0435\u0440\u0435\u0437\u0430\u0433\u0440\u0443\u0437\u043a\u0430<\/h4>\n<pre><code>new_life = Pipeline(     Input(ambition) &gt;&gt;     Layer([ProstheticAdapter(), BioSensors()]) &gt;&gt;     Optimizer(Resilience(\u03b2=0.9)) &gt;&gt;     Output(life_quality_metric) )<\/code><\/pre>\n<p>\u0410\u043c\u043f\u0443\u0442\u0430\u0446\u0438\u044f \u043d\u0430\u0443\u0447\u0438\u043b\u0430: \u0442\u0435\u043b\u043e \u2014 \u0441\u0430\u043c\u044b\u0439 \u0441\u043b\u043e\u0436\u043d\u044b\u0439 generative model. \u0415\u0433\u043e \u043d\u0435\u043b\u044c\u0437\u044f \u0434\u043e\u043e\u0431\u0443\u0447\u0430\u0442\u044c \u0431\u0435\u0437 catastrophic forgetting \u0434\u0443\u0448\u0438. \u041a\u0430\u0436\u0434\u044b\u0439 \u0448\u0430\u0433 \u043f\u0440\u043e\u0442\u0435\u0437\u0430 \u2014 forward pass \u0432 \u043d\u043e\u0432\u0443\u044e \u0440\u0435\u0430\u043b\u044c\u043d\u043e\u0441\u0442\u044c, \u0433\u0434\u0435 \u043e\u0433\u0440\u0430\u043d\u0438\u0447\u0435\u043d\u0438\u044f \u2014 \u0433\u0438\u043f\u0435\u0440\u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u044b \u0442\u0440\u0430\u043d\u0441\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0438.<\/p>\n<hr\/>\n<\/div>\n<\/div>\n<\/div>\n<p><!----><!----><\/div>\n<p><!----><!----><br \/> \u0441\u0441\u044b\u043b\u043a\u0430 \u043d\u0430 \u043e\u0440\u0438\u0433\u0438\u043d\u0430\u043b \u0441\u0442\u0430\u0442\u044c\u0438 <a href=\"https:\/\/habr.com\/ru\/articles\/936924\/\"> https:\/\/habr.com\/ru\/articles\/936924\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<div><!--[--><!--]--><\/div>\n<div id=\"post-content-body\">\n<div>\n<div class=\"article-formatted-body article-formatted-body article-formatted-body_version-2\">\n<div xmlns=\"http:\/\/www.w3.org\/1999\/xhtml\">\n<p><strong>\u041f\u0440\u043e\u043b\u043e\u0433: RuntimeError<\/strong><\/p>\n<p>\u041a\u043e\u0433\u0434\u0430 \u0445\u0438\u0440\u0443\u0440\u0433 \u0441\u043a\u0430\u0437\u0430\u043b \u00ab\u0430\u043c\u043f\u0443\u0442\u0430\u0446\u0438\u044f\u00bb, \u043c\u043e\u0439 \u043c\u043e\u0437\u0433 \u0432\u044b\u0434\u0430\u043b \u0438\u0441\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435 LifeCriticalInterrupt. \u041a\u0430\u043a Python-\u0438\u043d\u0436\u0435\u043d\u0435\u0440, \u0434\u043e\u043e\u0431\u0443\u0447\u0430\u0432\u0448\u0438\u0439 \u0442\u0440\u0430\u043d\u0441\u0444\u043e\u0440\u043c\u0435\u0440\u044b, \u044f \u0441\u0442\u043e\u043b\u043a\u043d\u0443\u043b\u0441\u044f \u0441 \u0444\u0430\u0442\u0430\u043b\u044c\u043d\u044b\u043c \u0441\u0431\u043e\u0435\u043c \u0432 \u0431\u0430\u0437\u043e\u0432\u043e\u0439 \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u0435 \u2014 \u0441\u043e\u0431\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u043c \u0442\u0435\u043b\u0435. \u041f\u043e\u0442\u0435\u0440\u044f \u043d\u043e\u0433\u0438 \u043a\u0430\u0437\u0430\u043b\u0430\u0441\u044c \u043a\u0440\u0430\u0445\u043e\u043c pipeline\u2019\u0430 \u0436\u0438\u0437\u043d\u0438. \u041d\u043e \u0433\u043e\u0434 \u0441\u043f\u0443\u0441\u0442\u044f \u044f \u043f\u043e\u043d\u044f\u043b: \u044d\u0442\u043e \u043d\u0435 \u0431\u0430\u0433, \u0430 \u0442\u0440\u0435\u0431\u043e\u0432\u0430\u043d\u0438\u0435 \u043a \u043f\u0435\u0440\u0435\u0441\u0431\u043e\u0440\u043a\u0435 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u044b.<\/p>\n<hr\/>\n<h4>1. \u0410\u0443\u0434\u0438\u0442 legacy-\u0441\u0438\u0441\u0442\u0435\u043c\u044b<\/h4>\n<p><\/p>\n<p>\u041c\u043e\u0439 \u00ab\u0436\u0438\u0437\u043d\u0435\u043d\u043d\u044b\u0439 \u0441\u043a\u0440\u0438\u043f\u0442\u00bb \u0434\u043e \u0442\u0440\u0430\u0432\u043c\u044b:<\/p>\n<pre><code>def daily_routine():     while True:         train_llm(overfit=True)         ignore_body_warnings(lr=0.001)          consume_nutrients(type=\"junk\", batch_size=32)         sleep = nn.Dropout(p=0.8)<\/code><\/pre>\n<p><strong>\u0414\u0438\u0430\u0433\u043d\u043e\u0437:<\/strong> \u0438\u0433\u043d\u043e\u0440\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0441\u0438\u0433\u043d\u0430\u043b\u043e\u0432 \u0442\u0435\u043b\u0430. \u0410\u043c\u043f\u0443\u0442\u0430\u0446\u0438\u044f \u0441\u0442\u0430\u043b\u0430 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u044b\u043c \u0443\u0434\u0430\u043b\u0435\u043d\u0438\u0435\u043c \u00ab\u043d\u0435\u0438\u0441\u043f\u0440\u0430\u0432\u043d\u043e\u0433\u043e \u043c\u043e\u0434\u0443\u043b\u044f\u00bb.<\/p>\n<hr\/>\n<h4>2. Transfer Learning<\/h4>\n<p>\u041f\u0440\u043e\u0442\u0435\u0437 \u2014 \u043c\u043e\u0439 hardware-\u0430\u043f\u0434\u0435\u0439\u0442, \u0442\u0440\u0435\u0431\u0443\u044e\u0449\u0438\u0439 \u0442\u043e\u043d\u043a\u043e\u0439 \u043d\u0430\u0441\u0442\u0440\u043e\u0439\u043a\u0438.<\/p>\n<p><strong>\u0424\u0438\u0437\u0438\u043e\u0442\u0435\u0440\u0430\u043f\u0438\u044f = RL-finetuning:<\/strong> \u043a\u0430\u0436\u0434\u043e\u0435 \u0434\u0432\u0438\u0436\u0435\u043d\u0438\u0435 \u2014 backpropagation \u0447\u0435\u0440\u0435\u0437 \u0431\u043e\u043b\u044c:<\/p>\n<pre><code>for epoch in range(1000):     loss = attempt_walk(prosthetic_leg)     optimizer.step(loss)     if loss &lt; threshold: reward += 1<\/code><\/pre>\n<ul>\n<li>\n<p><strong>\u0424\u0430\u043d\u0442\u043e\u043c\u043d\u044b\u0435 \u0431\u043e\u043b\u0438 = ghost gradients.<\/strong><\/p>\n<\/li>\n<li>\n<p><strong>Data fusion:<\/strong> \u0441\u0438\u043d\u0445\u0440\u043e\u043d\u0438\u0437\u0430\u0446\u0438\u044f \u0441\u0435\u043d\u0441\u043e\u0440\u043e\u0432 \u0442\u0438\u0442\u0430\u043d\u0430 \u0438 \u0431\u0438\u043e\u043b\u043e\u0433\u0438\u0438.<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p>\u041a\u0430\u0436\u0434\u043e\u0435 \u043f\u0430\u0434\u0435\u043d\u0438\u0435 \u2014 KeyboardInterrupt, \u0437\u0430\u0441\u0442\u0430\u0432\u043b\u044f\u044e\u0449\u0438\u0439 \u043f\u0435\u0440\u0435\u0441\u043c\u043e\u0442\u0440\u0435\u0442\u044c \u0432\u0435\u0441\u0430 \u0434\u0432\u0438\u0436\u0435\u043d\u0438\u0439.<\/p>\n<\/blockquote>\n<hr\/>\n<h4>3. \u041f\u0435\u0440\u0435\u0441\u0431\u043e\u0440\u043a\u0430 self-image<\/h4>\n<p>\u0421\u0442\u0430\u0440\u044b\u0435 embedding\u2019\u044b \u0443\u0441\u0442\u0430\u0440\u0435\u043b\u0438:<\/p>\n<pre><code>- self_embedding = model.encode(\"\u0446\u0435\u043b\u044c\u043d\u044b\u0439 \u0447\u0435\u043b\u043e\u0432\u0435\u043a\") + self_embedding = model.encode(\"\u0431\u0438\u043e\u043d\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0433\u0438\u0431\u0440\u0438\u0434\")<\/code><\/pre>\n<p>\u0425\u0440\u043e\u043c\u043e\u0442\u0430 \u2014 \u043d\u0435 noise, \u0430 feature engineering. \u041c\u043e\u044f \u043f\u043e\u0445\u043e\u0434\u043a\u0430 \u2014 live demo dropout-\u0443\u0441\u0442\u043e\u0439\u0447\u0438\u0432\u043e\u0441\u0442\u0438.<\/p>\n<hr\/>\n<h4>4. \u041e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u044f inference<\/h4>\n<p>\u041d\u043e\u0432\u044b\u0439 \u0436\u0438\u0437\u043d\u0435\u043d\u043d\u044b\u0439 pipeline:<\/p>\n<div>\n<div class=\"table\">\n<table>\n<tbody>\n<tr>\n<th>\n<p><strong>\u041f\u0430\u0440\u0430\u043c\u0435\u0442\u0440<\/strong><\/p>\n<\/th>\n<th>\n<p><strong>\u0414\u043e<\/strong><\/p>\n<\/th>\n<th>\n<p><strong>\u041f\u043e\u0441\u043b\u0435<\/strong><\/p>\n<\/th>\n<\/tr>\n<tr>\n<td>\n<p align=\"left\"><strong>\u0411\u0430\u0442\u0430\u0440\u0435\u044f<\/strong><\/p>\n<\/td>\n<td>\n<p align=\"left\">train(epochs=\u221e)<\/p>\n<\/td>\n<td>\n<p align=\"left\">early_stopping(patience=2)<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p align=\"left\"><strong>\u041c\u0443\u043b\u044c\u0442\u0438\u043f\u0440\u043e\u0446\u0435\u0441\u0441\u0438\u043d\u0433<\/strong><\/p>\n<\/td>\n<td>\n<p align=\"left\">sync_all()<\/p>\n<\/td>\n<td>\n<p align=\"left\">async with ThreadPool<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p align=\"left\"><strong>\u041c\u043e\u043d\u0438\u0442\u043e\u0440\u0438\u043d\u0433<\/strong><\/p>\n<\/td>\n<td>\n<p align=\"left\">print()<\/p>\n<\/td>\n<td>\n<p align=\"left\">Grafana + Bio-ALERTs<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<p>\u0427\u0435\u0440\u0435\u0437 6 \u043c\u0435\u0441\u044f\u0446\u0435\u0432 \u043c\u0435\u0442\u0440\u0438\u043a\u0438 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0430 \u0436\u0438\u0437\u043d\u0438 \u043f\u0440\u0435\u0432\u044b\u0441\u0438\u043b\u0438 baseline.<\/p>\n<hr\/>\n<h4>5. \u041d\u043e\u0432\u044b\u0435 emergent-\u0441\u0432\u043e\u0439\u0441\u0442\u0432\u0430<\/h4>\n<pre><code>class BionicDeveloper(GenAIEngineer):     def __init__(self):         self.superpowers = [             'pain_gradient_awareness',             'resilience_regularization',             'adaptive_calibration'         ]     def forward(self, obstacles):         return self.prosthetic_forward_pass(obstacles)<\/code><\/pre>\n<p>\u0411\u043e\u043d\u0443\u0441: \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u0438 \u0442\u0435\u043f\u0435\u0440\u044c \u043b\u0443\u0447\u0448\u0435 \u0433\u0435\u043d\u0435\u0440\u0438\u0440\u0443\u044e\u0442 \u044d\u043c\u043f\u0430\u0442\u0438\u044e \u2014 \u0442\u0435\u043b\u043e \u0441\u0442\u0430\u043b\u043e \u0444\u0438\u0437\u0438\u0447\u0435\u0441\u043a\u0438\u043c loss function \u0447\u0435\u043b\u043e\u0432\u0435\u0447\u043d\u043e\u0441\u0442\u0438.<\/p>\n<h4>\u042d\u043f\u0438\u043b\u043e\u0433: \u041f\u0435\u0440\u0435\u0437\u0430\u0433\u0440\u0443\u0437\u043a\u0430<\/h4>\n<pre><code>new_life = Pipeline(     Input(ambition) &gt;&gt;     Layer([ProstheticAdapter(), BioSensors()]) &gt;&gt;     Optimizer(Resilience(\u03b2=0.9)) &gt;&gt;     Output(life_quality_metric) )<\/code><\/pre>\n<p>\u0410\u043c\u043f\u0443\u0442\u0430\u0446\u0438\u044f \u043d\u0430\u0443\u0447\u0438\u043b\u0430: \u0442\u0435\u043b\u043e \u2014 \u0441\u0430\u043c\u044b\u0439 \u0441\u043b\u043e\u0436\u043d\u044b\u0439 generative model. \u0415\u0433\u043e \u043d\u0435\u043b\u044c\u0437\u044f \u0434\u043e\u043e\u0431\u0443\u0447\u0430\u0442\u044c \u0431\u0435\u0437 catastrophic forgetting \u0434\u0443\u0448\u0438. \u041a\u0430\u0436\u0434\u044b\u0439 \u0448\u0430\u0433 \u043f\u0440\u043e\u0442\u0435\u0437\u0430 \u2014 forward pass \u0432 \u043d\u043e\u0432\u0443\u044e \u0440\u0435\u0430\u043b\u044c\u043d\u043e\u0441\u0442\u044c, \u0433\u0434\u0435 \u043e\u0433\u0440\u0430\u043d\u0438\u0447\u0435\u043d\u0438\u044f \u2014 \u0433\u0438\u043f\u0435\u0440\u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u044b \u0442\u0440\u0430\u043d\u0441\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0438.<\/p>\n<hr\/>\n<\/div>\n<\/div>\n<\/div>\n<p><!----><!----><\/div>\n<p><!----><!----><br \/> \u0441\u0441\u044b\u043b\u043a\u0430 \u043d\u0430 \u043e\u0440\u0438\u0433\u0438\u043d\u0430\u043b \u0441\u0442\u0430\u0442\u044c\u0438 <a href=\"https:\/\/habr.com\/ru\/articles\/936924\/\"> https:\/\/habr.com\/ru\/articles\/936924\/<\/a><br \/><\/br><\/br><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-470635","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/posts\/470635","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=470635"}],"version-history":[{"count":0,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/posts\/470635\/revisions"}],"wp:attachment":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=470635"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=470635"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=470635"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}