Reinforcement learning<\/span><\/li>\n<\/ul>\nLimited memory AI is used in all modern-day AI systems. Think of chatbots and virtual assistants like Siri, Alexa, and Cortana. They are all limited memory AI.<\/span><\/p>\nTheory of Mind<\/span><\/h3>\n<\/span><\/p>\nAI Theory of Mind is still in its beginning stages and most probably a long way off from being realized. It is the ability for a machine to understand what other people think and feel and then use that understanding of emotional behavior to react.<\/span><\/p>\nSelf-Awareness<\/span><\/h3>\n<\/span><\/p>\nThis is the final type of AI and the most complex of all.<\/span><\/p>\nIt\u2019s more than just a simple machine that can learn; it\u2019s a sentient being that can understand its own thoughts and emotions.<\/span><\/p>\nThis is the ultimate goal of AI and will enable it to make its own decisions and take control of its own actions. It uses logic and reasoning to make decisions and takes into account the consequences of those decisions. Self-aware AI interacts with other AIs and humans, and it\u2019s capable of learning from its own mistakes and improving.<\/span><\/p>\nSelf-Awareness AI outperforms humans in all aspects.<\/span><\/p>\nAlthough it\u2019s still very far away, it will probably never be seen in our lifetime. And depending on how you look at it, this might be a good thing considering it could advance society or eradicate humanity.<\/span><\/p>\n<\/span><\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/fitra.co.za\/design\/wp-content\/uploads\/sites\/8\/2025\/11\/Ai-with-Guy-Ai-Information-Post-Content-Ai-Classifications-1.jpg” alt=”A hypothetical self-aware AI holding a human hologram” title_text=”Ai-with-Guy-Ai-Information-Post-Content-Ai-Classifications” align=”center” _builder_version=”4.19.1″ _module_preset=”default” width=”80%” module_alignment=”center” box_shadow_style=”preset3″ global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n
Alternate AI classification<\/span><\/h2>\n<\/span><\/p>\nThere is an alternate classification of AI that\u2019s used more in science and tech circles.<\/span>\u00a0<\/span><\/p>\n<\/span><\/p>\n[\/et_pb_text][et_pb_pricing_tables featured_table_background_color=”RGBA(255,255,255,0)” featured_table_header_background_color=”#273147″ header_background_color=”#273147″ featured_table_bullet_color=”#7893B1″ bullet_color=”#7893B1″ show_featured_drop_shadow=”off” featured_table_price_background_color=”RGBA(255,255,255,0)” featured_table_price_color=”RGBA(255,255,255,0)” price_background_color=”RGBA(255,255,255,0)” _builder_version=”4.19.1″ _module_preset=”default” price_text_color=”#273147″ subheader_text_color=”#7792B2″ header_text_color=”#E8EFF2″ subheader_font=”Iceland||||||||” subheader_font_size=”25px” price_font_size=”15px” price_letter_spacing=”1px” price_line_height=”18px” currency_frequency_font_size=”0px” currency_frequency_line_height=”1.5em” excluded_font_size=”0px” custom_padding=”|20px|20px|20px|false|true” header_text_shadow_style=”preset3″ header_text_shadow_blur_strength=”0.15em” header_text_shadow_color=”#E8EFF2″ subheader_text_shadow_style=”preset3″ subheader_text_shadow_color=”#7893B1″ price_text_shadow_style=”preset1″ price_text_shadow_blur_strength=”0.28em” text_shadow_style=”preset3″ global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_pricing_table title=”Artificial Narrow Intelligence ” subtitle=”(ANI) ” currency=”$” sum=”Artificial narrow intelligence is a broad term used to describe a type of AI that can be trained to accomplish specific tasks. For example, a self-driving car able to perform lane changes and avoid obstacles.” _builder_version=”4.19.1″ _module_preset=”default” global_colors_info=”{}” sticky_transition=”on” theme_builder_area=”post_content”]<\/p>\n
All currently existing AI (reactive machines and limited memory) are artificial narrow intelligence.<\/span><\/span><\/p>\nWhile artificial narrow intelligence appears highly sophisticated, it only does what it\u2019s programmed to do. It cannot take the knowledge gained and apply it to another task.<\/span><\/span><\/p>\nArtificial narrow intelligence is therefore considered weak AI.<\/span><\/span><\/p>\n[\/et_pb_pricing_table][et_pb_pricing_table title=”Artificial General Intelligence ” subtitle=”(AGI)” currency=”$” sum=”Artificial general intelligence (AGI) is a hypothetical type of artificial intelligence that is intelligent enough to perform any intellectual task that a human being can do, including those requiring creativity and innovation.” _builder_version=”4.19.1″ _module_preset=”default” global_colors_info=”{}” sticky_transition=”on” theme_builder_area=”post_content”]<\/p>\n
AGI can learn, reason, plan, communicate and adapt.<\/span><\/p>\nIf AGI were developed, it would probably make human labor obsolete as it can potentially do everything a human can do, just better due to its computational power.<\/span><\/p>\nArtificial general intelligence doesn\u2019t exist yet. However, there are dozens of big organizations currently researching it.<\/span><\/p>\n[\/et_pb_pricing_table][et_pb_pricing_table title=”Artificial Superintelligence ” subtitle=”(ASI)” currency=”$” sum=”Artificial superintelligence (ASI) is another hypothetical stage of machine intelligence that is expected to exceed human intelligence in every way. Essentially developing cognitive thinking skills of its own. ” _builder_version=”4.19.1″ _module_preset=”default” global_colors_info=”{}” sticky_transition=”on” theme_builder_area=”post_content”]<\/p>\n
Artificial Intelligence capable of performing abstractions, interpretations, and other human-like cognitive processes at exponentially higher speeds and efficiencies than the human brain is can.
A step beyond human-level machine intelligence. As such, it is considered to be the pinnacle of AI Research.
The point at which AI eclipses human intelligence.<\/p>\n
[\/et_pb_pricing_table][\/et_pb_pricing_tables][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.19.1″ _module_preset=”default” min_height=”1261.4px” custom_margin=”|auto|-37px|auto||” custom_padding=”||0px|||” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.19.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.19.1″ _module_preset=”default” custom_padding=”0px|||||” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n
Now let\u2019s look at some of the essential terms in artificial intelligence.<\/strong><\/span><\/p>\n<\/span><\/p>\n<\/span><\/h2>\nMachine Learning<\/span><\/h2>\n<\/span><\/p>\n\u201cthe field of study that gives computers the ability to learn without explicitly being programmed.\u201d – Arthur Samuel (AI pioneer)<\/span><\/p>\nMachine learning (ML) refers to a field of artificial intelligence that uses statistical techniques to \u201cteach\u201d computers how to perform tasks. In other words, it focuses on making computers learn to perform tasks by analyzing data and patterns.<\/span><\/p>\nMachine learning methods are used to analyze data sets and make predictions based on what it has learned. This is accomplished by training the ML algorithm with example data. Training takes place over a period of time and requires a significant amount of data.<\/span><\/p>\nOnce trained, the ML algorithm can be applied to new data without being retrained, making it useful for real-time applications.<\/span><\/p>\nMachine learning is a crucial element of artificial intelligence, as it provides the ability for machines to learn without being explicitly programmed. And it gradually improves over time with more data.<\/span><\/p>\nThere are four models of machine learning techniques.<\/span><\/p>\n\n- Supervised machine learning<\/span><\/li>\n
- Unsupervised machine learning<\/span><\/li>\n
- Semi-supervised machine learning<\/span><\/li>\n
- Reinforcement machine learning<\/span><\/li>\n<\/ul>\n
<\/span><\/p>\n[\/et_pb_text][et_pb_tabs active_tab_background_color=”#E8EFF2″ inactive_tab_background_color=”#273147″ active_tab_text_color=”#273147″ _builder_version=”4.19.1″ _module_preset=”default” tab_text_color=”#E8EFF2″ tab_font=”Iceland|||on|||||” tab_font_size=”17px” tab_line_height=”1.9em” box_shadow_style=”preset3″ global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_tab title=”Supervised ML” _builder_version=”4.19.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n
Supervised machine learning is a type of machine learning where the training data set consists of samples labeled with class labels. Supervised machine learning algorithms learn to recognize patterns in the data. These algorithms take into account the class labels to make their predictions.<\/span><\/p>\nFor example, if you want to build a computer that can detect whether a person is crying or laughing, it would be necessary to provide the algorithm with labeled images of these emotions. The trained algorithm would then be able to identify the appropriate features that differentiate between the two classes.<\/em><\/p>\nBoth the labeled data inputs and the outputs are specified.<\/span><\/p>\nThis means that the model learns from the training data and applies it to new data.<\/span><\/p>\n[\/et_pb_tab][et_pb_tab title=”Unsupervised ML” _builder_version=”4.19.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n
Unsupervised machine learning is a subset of machine learning that focuses on learning patterns from unlabeled data without any input from a human.<\/span><\/p>\nIt\u2019s helpful when the data is already available, but there are no labels to train it with.<\/span><\/p>\nIn this sense, unsupervised learning is different from supervised learning, which requires labeled data to produce results.<\/span><\/p>\nFor example, it can find similar objects by grouping them based on their features like customer segmentation.<\/span><\/p>\nMost neural networks, probabilistic clustering methods, and deep learning use unsupervised machine learning.<\/span><\/p>\n[\/et_pb_tab][et_pb_tab title=”Semi-Supervised ML” _builder_version=”4.19.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n
As the name suggests, semi-supervised machine learning is a middle ground between supervised and unsupervised machine learning.<\/span><\/p>\nIn this approach, a model is trained using both labeled and unlabeled data.<\/span><\/p>\nIt\u2019s usually used when not enough labeled data is available to train the system. Therefore, it uses smaller labeled datasets and then applies that knowledge to larger unlabeled datasets to improve accuracy.<\/span><\/p>\n[\/et_pb_tab][et_pb_tab title=”Reinforcement ML” _builder_version=”4.19.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n
Reinforcement learning is a machine learning technique that allows a computer system to learn a task by trial and error, reinforcing successful outcomes.<\/span><\/p>\nThe computer system repeatedly observes a particular action taken in response to a stimulus and decides the most likely following action based on its experiences. The system learns from its mistakes and adapts accordingly.<\/span><\/p>\nIn reinforcement machine learning, the machine learns through trial and error. However, the algorithm is programmed on specific rules to accomplish the task.<\/span><\/p>\n[\/et_pb_tab][\/et_pb_tabs][et_pb_text _builder_version=”4.19.1″ _module_preset=”default” custom_margin=”||-7px|||” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n