artificial intelligenc AI types of AI ppt

artificial intelligenc AI types of AI ppt

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  ARTIFICIAL INTELLIGENCE (AI)
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  WHAT IS ARTIFICIALINTELLIGENCE? It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.
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  WEAK AI VSSTRONG AI WEAK ARTIFICIAL INTELLIGENCE Weak AI—also called Narrow AI or Artificial Narrow Intelligence (ANI)—is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today. ‘Narrow’ might be a more accurate descriptor for this type of AI as it is anything but weak; it enables some very robust applications. such as Apple's Siri, Amazon's Alexa, IBM Watson, and autonomous vehicles. STRONG ARTIFICIAL INTELLIGENCE Strong AI is made up of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). Artificial general intelligence (AGI), or general AI, is a theoretical form of AI where a machine would have an intelligence equaled to humans; it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future. Artificial Super Intelligence (ASI) —also known as super intelligence—would surpass the intelligence and ability of the human brain. While strong AI is still entirely theoretical with no practical examples in use today, that doesn't mean AI researchers aren't also exploring its development. the best examples of ASI might be from science fiction, such as HAL, the superhuman, rogue computer assistant in 2001: A Space Odyssey.
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  DEEP LEARNING VSMACHINE LEARNING DEEP LEARNING Deep learning is actually comprised of neural networks. “Deep” in deep learning refers to a neural network comprised of more than three layers —which would be inclusive of the inputs and the output—can be considered a deep learning algorithm. MACHINE LEARNING A machine learning algorithm is fed data by a computer and uses statistical techniques to help it “learn” how to get progressively better at a task, without necessarily having been specifically programmed for that task. Instead, ML algorithms use historical data as input to predict new output values. To that end, ML consists of both supervised learning (where the expected output for the input is known thanks to labeled data sets) and unsupervised learning (where the expected outputs are unknown due to the use of unlabeled data sets).
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  ARTIFICIAL INTELLIGENCE APPLICATIONS •There are numerous, real-world applications of AI systems today. Below are some of the most common examples: • Speech recognition: It is also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, and it is a capability which uses natural language processing (NLP) to process human speech into a written format. Many mobile devices incorporate speech recognition into their systems to conduct voice search—e.g. Siri—or provide more accessibility around texting. • Customer service: Online virtual agents are replacing human agents along the customer journey. They answer frequently asked questions (FAQs) around topics, like shipping, or provide personalized advice, cross-selling products or suggesting sizes for users, changing the way we think about customer engagement across websites and social media platforms. Examples include messaging bots on e-commerce sites with virtual agents, messaging apps, such as Slack and Facebook Messenger, and tasks usually done by virtual assistants and voice assistants.
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  Computer vision: ThisAI technology enables computers and systems to derive meaningful information from digital images, videos and other visual inputs, and based on those inputs, it can take action. This ability to provide recommendations distinguishes it from image recognition tasks. Powered by convolutional neural networks, computer vision has applications within photo tagging in social media, radiology imaging in healthcare, and self-driving cars within the automotive industry. Recommendation engines: Using past consumption behavior data, AI algorithms can help to discover data trends that can be used to develop more effective cross-selling strategies. This is used to make relevant add-on recommendations to customers during the checkout process for online retailers. Automated stock trading: Designed to optimize stock portfolios, AI-driven high-frequency trading platforms make thousands or even millions of trades per day without human intervention.
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  PROS AND CONSOF AI PROS • Artificial intelligence can improve workplace safety. • AI can offer accessibility for people with disabilities. • AI can make everyday life more convenient and enjoyable, improving our health and standard of living. CONS • Artificial intelligence poses dangerous privacy risks. • AI repeats and exacerbates human racism. • AI will harm the standard of living for many people by causing mass unemployment as robots replace people.
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  TYPES OF ARTIFICIALINTELIGENCE AI can be divided into four categories, based on the type and complexity of the tasks a system is able to perform. They are: 1. Reactive machines 2. Limited memory 3. Theory of mind 4. Self awareness
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  REACTIVE MACHINES • Areactive machine follows the most basic of AI principles and, as its name implies, is capable of only using its intelligence to perceive and react to the world in front of it. A reactive machine cannot store a memory and, as a result, cannot rely on past experiences to inform decision making in real time. • Perceiving the world directly means that reactive machines are designed to complete only a limited number of specialized duties. Intentionally narrowing a reactive machine’s worldview has its benefits, however: This type of AI will be more trustworthy and reliable, and it will react the same way to the same stimuli every time. Examples • Deep Blue was designed by IBM in the 1990s as a chess-playing supercomputer and defeated international grandmaster Gary Kasparov in a game. Deep Blue was only capable of identifying the pieces on a chess board and knowing how each moves based on the rules of chess, acknowledging each piece’s present position and determining what the most logical move would be at that moment. The computer was not pursuing future potential moves by its opponent or trying to put its own pieces in better position. Every turn was viewed as its own reality, separate from any other movement that was made beforehand. • Google’s Alpha Go is also incapable of evaluating future moves but relies on its own neural network to evaluate developments of the present game, giving it an edge over Deep Blue in a more complex game. AlphaGo also bested world-class competitors of the game, defeating champion Go player Lee Sedol in 2016.
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  LIMITED MEMORY • Limitedmemory AI has the ability to store previous data and predictions when gathering information and weighing potential decisions — essentially looking into the past for clues on what may come next. Limited memory AI is more complex and presents greater possibilities than reactive machines. • Limited memory AI is created when a team continuously trains a model in how to analyze and utilize new data or an AI environment is built so models can be automatically trained and renewed. • When utilizing limited memory AI in ML, six steps must be followed: • Establish training data • Create the machine learning model • Ensure the model can make predictions • Ensure the model can receive human or environmental feedback • Store human and environmental feedback as data • Reiterate the steps above as a cycle
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  THEORY OF MIND Theoryof mind is just that — theoretical. We have not yet achieved the technological and scientific capabilities necessary to reach this next level of AI. The concept is based on the psychological premise of understanding that other living things have thoughts and emotions that affect the behavior of one’s self. In terms of AI machines, this would mean that AI could comprehend how humans, animals and other machines feel and make decisions through self-reflection and determination, and then utilize that information to make decisions of their own. Essentially, machines would have to be able to grasp and process the concept of “mind,” the fluctuations of emotions in decision-making and a litany of other psychological concepts in real time, creating a two-way relationship between people and AI.
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  SELF AWARENESS Once theoryof mind can be established, sometime well into the future of AI, the final step will be for AI to become self-aware. This kind of AI possesses human-level consciousness and understands its own existence in the world, as well as the presence and emotional state of others. It would be able to understand what others may need based on not just what they communicate to them but how they communicate it. Self-awareness in AI relies both on human researchers understanding the premise of consciousness and then learning how to replicate that so it can be built into machines.
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  LATEST EXAMPLES ChatGPT • ChatGPTis an artificial intelligence chatbot capable of producing written content in a range of formats, from essays to code and answers to simple questions. Launched in November 2022 by OpenAI, ChatGPT is powered by a large language model that allows it to closely emulate human writing. Google Maps • Google Maps uses location data from smartphones, as well as user-reported data on things like construction and car accidents, to monitor the ebb and flow of traffic and assess what the fastest route will be. Snapchat Filters • Snapchat filters use ML algorithms to distinguish between an image’s subject and the background, track facial movements and adjust the image on the screen based on what the user is doing. Self-Driving Cars • Self-driving cars are a recognizable example of deep learning, since they use deep neural networks to detect objects around them, determine their distance from other cars, identify traffic signals and much more. Wearables • The wearable sensors and devices used in the healthcare industry also apply deep learning to assess the health condition of the patient, including their blood sugar levels, blood pressure and heart rate. They can also derive patterns from a patient’s prior medical data and use that to anticipate any future health conditions.
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  FUTURE OF AI Whenone considers the computational costs and the technical data infrastructure running behind artificial intelligence, actually executing on AI is a complex and costly business. Fortunately, there have been massive advancements in computing technology, as indicated by Moore’s Law, which states that the number of transistors on a microchip doubles about every two years while the cost of computers is halved. Although many experts believe that Moore’s Law will likely come to an end sometime in the 2020s, this has had a major impact on modern AI techniques — without it, deep learning would be out of the question, financially speaking. Recent research found that AI innovation has actually outperformed Moore’s Law, doubling every six months or so as opposed to two years. By that logic, the advancements artificial intelligence has made across a variety of industries have been major over the last several years. And the potential for an even greater impact over the next several decades seems all but inevitable.
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  ANY QUESTIONS??? .
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  THANKS !!! FOR YOURCONCENTRATION