this will change the way you understand AI FOREVER
Publicado no IA Dots ·
AGIAI SignalANIASIdot-agidot-anidot-artificial_intelligencedot-asidot-generative_aidot-metaidioma-enInglêsyt-SDMENdTBiic
Vídeo em Inglês, publicado pelo canal Gritap.
About this content: The summary, key points and topics below are generated automatically from the video transcript. They reflect the content, opinions, claims and predictions presented in the video and do not represent a position, validation or fact-check by IA Dots.
Video summary
This video provides a comprehensive overview of artificial intelligence, covering its definition, subfields, capabilities, functionalities, machine learning pillars, and current applications and limitations.
Key points
- Artificial intelligence is defined as the use of computers to perform tasks requiring human intelligence through automated decision-making.
- AI types based on capabilities include Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI).
- AI types based on functionalities comprise reactive machine AI, limited memory AI, theory of Mind AI, and self-aware AI.
- The four pillars of machine learning discussed are Supervised Learning, Unsupervised Learning, Reinforcement Learning, and Deep Learning.
- Current practical applications of AI include Natural Language Processing, Computer Vision, and Predictive Analytics.
- Limitations of AI involve challenges with linguistic context, biases in computer vision, generalizability across different environments, and the black box problem of machine learning models.
Topics
Artificial IntelligenceMachine LearningCapabilities of AIFunctionalities of AIPillars of Machine LearningDeep LearningApplications of AILimitations of AI
Video transcript
Transcript from available captions; it may contain errors.
Is AI really shaping the world around us? or is it`s impact blown out of proportion? where is AI actually being used effectively and could it be impacting your life in ways that you may not indeed realize? these days we observe advances in AI that imply that the technology is similar to human beings the many subfields the terminologies the AI techniques, the AI errors and the hype may cause confusion regarding the clear understanding of the space by the end of this video you're going to have a clear overview of AI this will help you to make informed decisions along the way. Badru here let's get started the first step is the one that is by far the most important and yet also most overlooked what is artificial intelligence AI is basically the use of computers to do jobs or tasks that frequently require human intelligence through automated decision making this particular field is dedicated to the idea of reproducing human abilities such as creativity, self improvement language use and many others the early forms of the AI operations we constantly use were built on traditional machine learning models these models depend on algorithms drafted and managed by data scientists in substance, traditional machine learning (ML) models need to reuse new data and address tasks that go beyond their original training (the ML model can be saved as a file (like a .pkl file) which you can load into other software applications) at the moment, AI encompasses many subfields our understanding of both practical and theoretical AI is evolving which means that AI terminologies or language can vary and occasionally overlap between different sources nevertheless, the different types of AI can be distributed into two main areas that is AI capabilities and AI functionalities what are the three kinds of AI grounded on capabilities Artificial Narrow Intelligence (ANI) ANI, also referred to as weak AI is the only kind of AI that is currently in use any other form of AI is theoretical it can be trained to perform a single or narrow task often far more quickly than the human mind is capable of it can't perform outside of its defined task rather it targets a single subset of capabilities of the mind and devotes that field Apples AI, Meta AI and Claude are examples of narrow AI Artificiall General Intelligence (AGI) Artificial General Intelligence also known as strong AI is at the moment nothing further than a theoretical concept without the requirement for human training of the underlying models AGI will leverage prior learnings and skills to navigate new tasks in a different environment because of this skill AGI will be able to learn and carry out any intellectual work that a person is capable of. Artificial Super Intelligence (ASI) ASI the term Super AI usually refers to Artificial Super Intelligence and like AGI, is theoretical if Super AI were to be developed it would presumably reason The operations that benefit from Super AI skills will have advanced past the point where they can comprehend human emotions and gestures, feel emotions, have needs and hold on to their own beliefs and requests what are the four types of AI grounded on functionalities reactive machine AI. These are AI systems with no memory and are designed to perform an extremely specific task since they can't recollect former issues or opinions they only work with presently available data Reactive AI is based on statistical computation and can analyze large amounts of data to create a seemingly helpful conclusion some of the examples of reactive machine AI is the recommendation engine on Netflix Netflix uses models to analyze large datasets of past viewing behaviour to suggest material for users based on what they are more likely to appreciate or enjoy Limited memory AI. Unlike reactive machine AI this type of AI has the ability to remember past occurrences problems and analyze particular items or circumstances over time in order to decide on the optimal course of action for reaching a particular goal, limited memory AI can analyse both previous and current data even yet, limited memory AI is only able to use one piece of data at a time limited memory AI`s performance can improve overtime as it has more experience and is taught on more data examples of limited memory AI, 1. we have generative AI Chat GPT, Meta AI and Gemini are examples of generative AI systems that use limited memory theory of Mind AI. This is a functional class of AI that falls underneath the general AI although an unrealised form of AI at the moment it would be able to comprehend the emotions and research of different worlds this would in theory enable the AI to mimic human like connections such as distinct emotional needs Researchers studying artificial intelligence think that the technology will be able to analyze sounds images and other types of data to mimic, predict, aid and react emotionally to humans Self Aware AI this is a kind of functional AI class or operations that would retain super AI capabilities like the theory of Mind AI self aware AI is strictly theoretical however, it would have the capability to understand its own internal conditions and traits along with human feelings and studies if ever achieved it would also have its own set of feelings, requirements and beliefs to more understand how these different branches of AI fit together an illustration will help us visualize it AI broadly speaking refers to machines that think and behave like humans machine learning, a branch of artificial intelligence has developed methods that could allow computers to learn without clear guidance or instructions in order to create intellectual machines enhance achieve artificial intelligence machine learning practitioners such as AI scientists, engineers technicians and others must integrate a number of machine learning techniques in fact when considered separately every learning technique that relies on gaining knowledge from data examples like the supervised learning technique is a machine learning approach what are the four pillars of machine learning Supervised Learning (SL) Supervised Learning technique in machine learning involves learning via labelled samples Essentially, the process involves instructing a model behaving like a student for example on how to choose the right response or output or a given input as a result each data set that the teacher provides that student is divided into two sections features for the input and labels for the output the values we want a model to predict are called labels the challenge of learning is referred to as classification when the output y falls inside limited range of values regression however is the learning problem when the output y is a number Unsupervised Learning (UL) the unsupervised learning technique is as clusters which are categorised groups of related objects or cluster analysis to learn from unlabeled samples. Without a label the unsupervised learning model classifies the given data automatically it does however exist in different forms first there is the self supervised learning subset of U L which depends on the object encoding rather than clustering and grouping as it's primary focus the next type of learning is called semi supervised learning and it involves using both labelled and unlabelled data this combines elements of both supervised learning and unsupervised learning unlabeled data can be data such as images videos and audio whereas labelled data can be uh some data set data in an Excel file with defined features or items that can be used as features to train the model reinforcement learning (RL) the term reinforcement learning RL refers to a behaviour learning approach that is based on the world model or what we call the model best or the mathematical model we call the model free it essentially lies on the kind of reward system in which the RL model or what we call the agent always seeks to maximize or optimize reward value in accordance with a given strategy or policy that instructs the agent on how to have nevertheless they are situations when a sub optimal value rather than the maximum value of the stated reward achieves the system's ideal performance we assume no prior knowledge of the reward functions or the environment in reinforcement learning for a passive reinforcement landing scheme we only consider a fixed policy of state action players for an active reinforcement lining scheme we only consider a self decision making approach regarding what to do Deep Learning (DL) the process of landing using brain like layered models is known as deep learning. Here, the learning process is repeatedly running the data through the DL model providing feedback and adjusting the parameters until the model reaches a steady state that may be suitable for. A number of AI concepts that AI scientists adopted were focused on developing a brain language examining its physical characteristics in order to tackle AI please note that language refers to the internal processing of the brain that allow it to express itself or respond to the outside world for them building a model that replicates every aspect of the biological brain is building an artificial brain model considering their diverse methods for developing a brain language we can conclude that researchers in artificial intelligence develop machine intelligence by observing brain functions infections such as behavioral, intentional or brain states all of which are produced by the same biological brain in fact developing a sufficiently trustworthy language from a biological brain may result from a careful examination of the facts obtained from the observation of the brain functions does this imply therefore that the artificial brain languages or models developed from the study of the human brains are the true biological brain language or the precise means by which the human brain actually communicates? we are not entirely certain. In contrast the artificial neurons connection are binary rather than analogical however because of our current computer capability each artificial neuron might be coupled to a relatively small number of other neurons less than 10,000. Through a variety of topologies, Artificial Neural Networks (ANN) can simulate the functions of the human brain and aid in the development of artificial intelligence however, even if these apps simulate human brain functions that is behavior, purposeful or brain state they are not the exact means by which the human brain functions and so they are not the real human brain language the artificial brains language cannot in fact be the perfect replica of the biological brains own language cause of the differences in the biological brains connections and internal leakages naturally artificial brain language functions effectively and replicates key aspects of human intelligence it may even surpass humans in some tasks but we are not the same species we believe that special tasks will only be achieved by humans the biological human brain imposes dimensions of data that are unknown and unexamined which with the artificial brain it overlooks them understanding these branches helps us grasp where technologies like large language models fit in and how they contribute to the broader field of AI so what can AI do at the moment? Natural Language Processing (NLP) this is AI's capacity to comprehend and produce human language applications include automated chatbots, customer support, Virtual assistants like Siri and Google Assistant and even programs that aid with writing code or emails Computer Vision. This area of artificial intelligence enables machines to comprehend and decide on the basis of visual data that is images and videos it is utilized in medical imaging where a practitioner may use a visual a computer vision tool or application to identify cancer in a CT scan of a particular patient and this can help that profession to decide on the next step or action to take. There is also another application of computer vision especially in facial identification especially when it comes to security cameras as well as other particular sectors and even in smartphones whereby cameras can be used to improve the picture quality Predictive Analytics artificial intelligence is very good at analyzing actual facts to forecast future events. this is applicable in a number of industries including healthcare where disease outbreak predictions can be made finance where consumer behaviour predictions can also be made however it's crucial to acknowledge that AI has its limitation Natural Language Processing (NLP) is impressive but it still has trouble understanding linguistic context it does not put in context whether this person asking who they are, where they are from, what are some of the past experience they have gone through what is influencing this person to ask this particular question or start this particular conversation and so many other examples despite its strength computer vision is prone to bias and readily tricked due to
the fact that machine learning algorithms rely on pre existing data, marginalized groups who have experienced systematic and human biases may receive inaccurate rejections or have certain resources withheld from them take an example an application is devoped and trained in Uganda or a certain group of people is set to predict certain certain behaviour in the way they purchase goods there are high chances that if this particular model is taken to America or any other part of the world the output that that you get from that model in that other country may not be the same and may not be accurate compared to that model being used in the area where the that group of people uh the data was gotten from so depending on that, there is some challenge of generalizability using this model in all the different environments using the same pre existing data that was trained on as they say garbage in garbage out applies to predictive analytics which is only as good as the data it is trained on even while AI and machine learning have many advantages they are worries about the hazards and difficulties that black box machine learning models may present when decision makers are unaware of the reasons behind the current results and are unaware of how the system will generate results in the future they find it difficult to trust the procedures
Assistir ao vídeo original no YouTube
Assuntos: AGI, ANI, ASI. Publicado no YouTube em 2026-09-26.
Seleção automática por assunto e idioma. O vídeo pertence ao canal de origem.