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ArticleController :: show

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Stateless check enabled
Usage
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Show trace 
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    "function" => "finishView"
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    "line" => 220
    "function" => "createView"
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    "line" => 181
    "function" => "show"
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    "type" => "->"
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    "class" => "Symfony\Component\HttpKernel\HttpKernel"
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    "function" => "run"
    "class" => "Symfony\Component\Runtime\Runner\Symfony\HttpKernelRunner"
    "type" => "->"
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      "/var/www/vhosts/dev.bbntimes.com/rct.dev.bbntimes.com/vendor/autoload_runtime.php"
    ]
    "function" => "require_once"
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]
Symfony\Component\Security\Core\Authentication\Token\Storage\UsageTrackingTokenStorage:41
Show trace 
[
  [
    "file" => "/var/www/vhosts/dev.bbntimes.com/rct.dev.bbntimes.com/vendor/symfony/security-core/Authentication/Token/Storage/UsageTrackingTokenStorage.php"
    "line" => 41
    "function" => "getMetadataBag"
    "class" => "Symfony\Component\HttpFoundation\Session\Session"
    "type" => "->"
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    "file" => "/var/www/vhosts/dev.bbntimes.com/rct.dev.bbntimes.com/vendor/symfony/twig-bridge/AppVariable.php"
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    "function" => "handleRaw"
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    "function" => "run"
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    ]
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]
Symfony\Component\Security\Http\Firewall\ContextListener:171
Show trace 
[
  [
    "file" => "/var/www/vhosts/dev.bbntimes.com/rct.dev.bbntimes.com/vendor/symfony/security-http/Firewall/ContextListener.php"
    "line" => 171
    "function" => "remove"
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    "function" => "onKernelResponse"
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    "function" => "__invoke"
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    "function" => "callListeners"
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Flashes

Flashes

No flash messages were created.

Server Parameters

Server Parameters

Defined in .env

Key Value
APP_ENV 
"dev"
APP_SECRET 
"0a988e63f011514eaabfc650b599af4d"
CORS_ALLOW_ORIGIN 
"*"
DATABASE_URL 
"mysql://bbndb_rctuser:33F5W25z40or0f7@localhost:3306/rct_bbntimes"
GOOGLE_RECAPTCHA_SECRET_KEY 
"6LdV5fgpAAAAANxzTG8ZMfIjil1wu-1vrQvnUt-x"
GOOGLE_RECAPTCHA_SITE_KEY 
"6LdV5fgpAAAAAENKcn73MJAhQrbtQeqgyC4wDLMP"
MAILER_DSN 
"smtp://no-reply%40rct.dev.bbntimes.com:Bl6%26gLD48%26Of919@rct.dev.bbntimes.com:465"
MARIADB_PASSWORD 
"i!87pK&!85ezc8"

Defined as regular env variables

Key Value
APP_DEBUG 
"1"
BASE 
"/public"
CONTEXT_DOCUMENT_ROOT 
"/var/www/vhosts/dev.bbntimes.com/rct.dev.bbntimes.com"
CONTEXT_PREFIX 
""
DOCUMENT_ROOT 
"/var/www/vhosts/dev.bbntimes.com/rct.dev.bbntimes.com"
FCGI_ROLE 
"RESPONDER"
GATEWAY_INTERFACE 
"CGI/1.1"
HTTPS 
"on"
HTTP_ACCEPT 
"*/*"
HTTP_ACCEPT_ENCODING 
"gzip, br, zstd, deflate"
HTTP_CONNECTION 
"close"
HTTP_COOKIE 
"PHPSESSID=rok4quoppnf7jjmnie2oocank3; handl_url_base=https%3A%2F%2Fabmcollege.com%2Fwp-json%2Fwp%2Fv2%2Fpages%2F1871; handl_landing_page=https%3A%2F%2Fabmcollege.com%2Fwp-json%2Fwp%2Fv2%2Fpages%2F1871; handl_ip=18.119.108.80; handl_url=https%3A%2F%2Fabmcollege.com%2Fwp-json%2Fwp%2Fv2%2Fpages%2F1871; user_agent=Mozilla%2F5.0%20AppleWebKit%2F537.36%20%28KHTML%2C%20like%20Gecko%3B%20compatible%3B%20ClaudeBot%2F1.0%3B%20%2Bclaudebot%40anthropic.com%29; HandLtestDomainNameServer=HandLtestDomainValueServer"
HTTP_HOST 
"rct.dev.bbntimes.com"
HTTP_USER_AGENT 
"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)"
PASSENGER_COMPILE_NATIVE_SUPPORT_BINARY 
"0"
PASSENGER_DOWNLOAD_NATIVE_SUPPORT_BINARY 
"0"
PATH 
"/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/snap/bin"
PHP_SELF 
"/public/index.php"
PP_CUSTOM_PHP_CGI_INDEX 
"plesk-php81-fastcgi"
PP_CUSTOM_PHP_INI 
"/var/www/vhosts/system/rct.dev.bbntimes.com/etc/php.ini"
QUERY_STRING 
""
REDIRECT_BASE 
"/public"
REDIRECT_HTTPS 
"on"
REDIRECT_PASSENGER_COMPILE_NATIVE_SUPPORT_BINARY 
"0"
REDIRECT_PASSENGER_DOWNLOAD_NATIVE_SUPPORT_BINARY 
"0"
REDIRECT_REDIRECT_HTTPS 
"on"
REDIRECT_REDIRECT_PASSENGER_COMPILE_NATIVE_SUPPORT_BINARY 
"0"
REDIRECT_REDIRECT_PASSENGER_DOWNLOAD_NATIVE_SUPPORT_BINARY 
"0"
REDIRECT_REDIRECT_SCRIPT_URI 
"https://rct.dev.bbntimes.com/science/what-s-fundamentally-wrong-with-machine-learning"
REDIRECT_REDIRECT_SCRIPT_URL 
"/science/what-s-fundamentally-wrong-with-machine-learning"
REDIRECT_REDIRECT_SSL_TLS_SNI 
"rct.dev.bbntimes.com"
REDIRECT_REDIRECT_STATUS 
"200"
REDIRECT_REDIRECT_UNIQUE_ID 
"Z7mNz5DyWLT8YV1VjBo7WAAAAIw"
REDIRECT_SCRIPT_URI 
"https://rct.dev.bbntimes.com/science/what-s-fundamentally-wrong-with-machine-learning"
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App\Entity\Article {#1094
  -id: 9550
  -title: "What's Fundamentally Wrong With Machine Learning?"
  -slug: "what-s-fundamentally-wrong-with-machine-learning"
  -introtext: "<p><span style="color: #000000;"><a href="technology/the-latest-trends-in-artificial-intelligence-ai-and-machine-learning-ml" target="_blank" rel="noopener">Machine le<span id="gmail-rmm"><span id="gmail-rmm">a</span></span>rning (ML), a subset of artificial intelligence (AI)</a>, has revolutionized the world but it has some fundamental flaws.</span></p>\r\n"
  -content: """
    \r\n
    <p><span style="color: #000000;">The rapid development of technology, processor power and <a href="https://searchdatacenter.techtarget.com/definition/parallel-processing" target="_blank" rel="noopener">computer parallelization</a>, has now made it possible to obtain and study large amounts of data with relative ease.</span></p>\r\n
    <p id="8045" class="ji jj hf jk b jl ll jn jo jp lm jr js jt ln jv jw jx lo jz ka kb lp kd ke kf gy ic" data-selectable-paragraph><span style="color: #000000;">Although, there are so<span id="rmm"><span id="rmm">m</span></span>e variations of how to define the types of machine learning algorithms but commonly they can be divided into categories according to their purpose. The main machine learning categories are as follow:</span></p>\r\n
    <ul>\r\n
    <li><span style="color: #000000;"><strong>Supervised learning</strong></span></li>\r\n
    <li><span style="color: #000000;"><strong>Unsupervised Learning</strong></span></li>\r\n
    <li><span style="color: #000000;"><strong>Semi-supervised Learning</strong></span></li>\r\n
    <li><span style="color: #000000;"><strong>Reinforcement Learning</strong></span><strong class="jk hg" style="background-color: inherit; color: inherit; font-family: inherit; font-size: 1rem;"></strong></li>\r\n
    </ul>\r\n
    <p><span style="color: #000000;">Here is the list of<a href="https://towardsdatascience.com/types-of-machine-learning-algorithms-you-should-know-953a08248861" target="_blank" rel="noopener"> supervised learning algorithms in machine learning</a>:</span></p>\r\n
    <ul>\r\n
    <li><strong><span style="color: #000000;">Neural Networks</span></strong></li>\r\n
    <li><strong><span style="color: #000000;">Naive Bayesian Model</span></strong></li>\r\n
    <li><strong><span style="color: #000000;">Classification</span></strong></li>\r\n
    <li><strong><span style="color: #000000;">Support Vector Machines</span></strong></li>\r\n
    <li><strong><span style="color: #000000;">Regression</span></strong></li>\r\n
    <li><strong><span style="color: #000000;">Random Forest Model</span></strong></li>\r\n
    </ul>\r\n
    <p><span style="color: #000000;">There are many different models in machine learning. Here are some of the most commonly used algorithms in the world today:</span></p>\r\n
    <ul>\r\n
    <li><strong><span style="color: #000000;">Gradient Boosting algorithms dimensionality Reduction Algorithms</span></strong></li>\r\n
    <li><strong><span style="color: #000000;">Random Forest</span></strong></li>\r\n
    <li><strong><span style="color: #000000;">K-Means</span></strong></li>\r\n
    <li><strong><span style="color: #000000;">KNN</span></strong></li>\r\n
    <li><strong><span style="color: #000000;">Naive Bayes</span></strong></li>\r\n
    <li><strong><span style="color: #000000;">SVM</span></strong></li>\r\n
    <li><strong><span style="color: #000000;">Decision Tree</span></strong></li>\r\n
    <li><strong><span style="color: #000000;">Logistic Regression</span></strong></li>\r\n
    <li><strong><span style="color: #000000;">Linear Regression</span></strong></li>\r\n
    </ul>\r\n
    <p><img src="/images/Everything_is_sketched_in_the_diagram_below.png" alt="Everything_is_sketched_in_the_diagram_below.png" /></p>\r\n
    <p><span style="color: #000000;">Despite its numerous advantages, machine learning isn’t perfect. Here are 8 issues that are harming the progress of machine learning and what's fundamentally wrong:</span></p>\r\n
    <h2><span style="color: #10228b;">1. Lack of Data</span></h2>\r\n
    <p><span style="color: #000000;">Machine Learning requires massive data sets to train on, and these should be inclusive/unbiased, and of good quality. There can also be times where they must wait for new data to be generated.</span></p>\r\n
    <h2><span style="color: #10228b;">2. Time Consuming</span></h2>\r\n
    <p><span style="color: #000000;">Machine learning requires enough time to let the algorithms learn and develop enough to fulfill their purpose with a considerable amount of accuracy and relevancy. It also needs massive resources to function. This can mean additional requirements of computer power for you.</span></p>\r\n
    <h2><span style="color: #10228b;">3. Weak Interpretation of Results</span></h2>\r\n
    <p><span style="color: #000000;">Another major challenge is the ability to accurately interpret results generated by the algorithms. You must also carefully choose the algorithms for your purpose.&nbsp;</span></p>\r\n
    <div>\r\n
    <h2><span style="color: #10228b;">4. High Error-Susceptibility</span></h2>\r\n
    <p><span style="color: #000000;">Machine Learning&nbsp;is autonomous but highly susceptible to errors. Suppose you train an algorithm with data sets small enough to not be inclusive. You end up with biased predictions coming from a biased training set. This leads to irrelevant advertisements being displayed to customers. In the case of machine learning, such blunders can set off a chain of errors that can go undetected for long periods of time. And when they do get noticed, it takes quite some time to recognize the source of the issue, and even longer to correct it.</span></p>\r\n
    <h2><span style="color: #10228b;">5. Ethical Issues</span></h2>\r\n
    <p><span style="color: #000000;">The idea of trusting data and algorithms more than our own judgment has its pros and cons. Obviously, we benefit from these algorithms, otherwise, we wouldn’t be using them in the first place. These algorithms allow us to automate processes by making informed judgments using available data. Sometimes, however, this means replacing someone’s job with an algorithm, which comes with ethical ramifications. Additionally, who do we blame if something goes wrong?</span></p>\r\n
    <h2><span style="color: #10228b;">6. Absence of Skilled Resources</span></h2>\r\n
    <p><span style="color: #000000;">Machine learning is still a relatively new technology. From the start code to the maintenance and monitoring of the process, machine learning experts are required to maintain the process. Artificial Intelligence and machine learning industries are still freshers to the market. Finding enough resources in the form of manpower is also difficult. Hence, there is a lack of talented representatives available to develop and manage scientific substances for machine learning. Data researchers regularly need a mix of space insight just as top to bottom knowledge of mathematics, technology, and science.</span></p>\r\n
    <h2><span style="color: #10228b;">7. Deficient Infrastructure</span></h2>\r\n
    <p><span style="color: #000000;">Machine learning requires a tremendous amount of data stirring abilities. Inheritance frameworks can’t deal with the responsibility and clasp under tension. You should check if your infrastructure can deal with issues in Machine Learning. If it can’t, you should hope to upgrade it completely with good hardware and adaptable storage.&nbsp;</span></p>\r\n
    <h2><span style="color: #10228b;">8. Slow Results and Bias</span></h2>\r\n
    <p><span style="color: #000000;">Machine learning is time-consuming.&nbsp;Due to an overload of data and requirements, it takes longer than expected to provide results.&nbsp;Focusing on particular features within the database in order to generalize the outcomes is very common in machine learning models, which lead to bias.</span></p>\r\n
    <h2><span style="color: #10228b;">Conclusion</span></h2>\r\n
    <p><span style="color: #000000;">Machine Learning has taken over many aspects of our life. Although it's not perfect, machine learning is a continuously evolving field with high demand. Without human intervention, it delivers real-time results using the already existing and processed data. It generally helps analyze and assess large amounts of data with ease by developing data-driven models.&nbsp;&nbsp;Although there are many issues in machine learning, it's an evolving field.&nbsp;&nbsp;From medical diagnosis, vaccine development to advanced trading algorithms, machine learning has become vital to scientific progress.&nbsp;<br /></span></p>\r\n
    </div>
    """
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