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Artificial Intelligence: A Guide To Studying AI

Instructions for Creating a Teachable Machine

Teachable Machine is a tool from Google in which users can use a type of AI called Machine Learning to train the machine to recognize images, sounds, and poses by inputting data for the machine to “learn”.

To create your own visit <a href="http://teachablemachine.withgoogle.com">Teachable Machine with Google</a>

Teachable Machine Introduction, Step-By-Step

Teachable Machine Audio Model Step-by-Step

With so many new and emerging technologies, it is important to be cautious when surfing the internet. Avoid sharing personal information online, as well as being careful of what you say and post with social media. Be critical of things you see online and make sure you check the sources and are aware of possible “fake news”.

11 Internet Safety Tips for Your Online Security

Training a machine to get better at a task without explicit programming

Using multi-layered networks for machine learning 

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Natural Language Processing (NLP) is the part of AI that helps computers understand, interpret, and manipulate human language in the pursuit of filling the gap between human language and computer understanding.&nbsp;&nbsp; NLP helps computers communicate with humans in our language and makes other language-related tasks more efficient. With NLP, computers can read text, hear speech, interpret words, measure sentiment, and determine which parts are important.

Natural Language Processing In 5 Minutes | What Is NLP And How Does It Work? | Simplilearn

AI has many applications, including natural language processing, image and speech recognition, robotics, and expert systems.&nbsp; The direction and future of AI is very promising. With the development of more sophisticated algorithms, faster processing speeds, and improved data collection methods, AI is becoming more powerful and capable of tackling more complex problems. This has the potential to revolutionize a variety of fields, such as healthcare, transportation, finance, and education.&nbsp; In the future, we can expect to see more advanced AI-powered technologies that will help us address some of the world's most pressing challenges, such as climate change, disease prevention, and poverty reduction. For example, AI can be used to improve the efficiency of energy systems, develop new drugs and treatments, and support precision agriculture.&nbsp; However, we must also be aware of the challenges and risks associated with AI. For instance, AI can be used to automate jobs, which can lead to job displacement and economic inequality. Additionally, there are concerns about privacy, security, and ethical considerations, particularly in areas like facial recognition, surveillance, and autonomous weapons.

You may have heard of the term, Artificial Intelligence or A.I., but do you really know what A.I. is? AI, or artificial intelligence, refers to the ability of machines or computer programs to perform tasks that typically require human intelligence, such as learning, problem-solving, decision-making, and understanding natural language. AI is achieved through the development and use of algorithms and statistical models that enable machines to analyze data, recognize patterns, and make predictions or decisions based on that analysis.

Caltech Science Exchange: What Is AI?

MACHINE PERCEPTION

Machine perception refers to the ability of machines (such as computers or robots) to understand and interpret information from the world around them using sensors, cameras, microphones, and other devices. Machine perception involves processing and analyzing large amounts of data, such as images, sounds, and text, in order to extract meaning and insights.&nbsp; It can enable machines to recognize objects, understand natural language, detect patterns and anomalies, and make decisions based on sensory input.

How Computer Vision Works

* Natural Language Processing (NLP): This is a type of AI technology that enables machines to understand and process human language. We encounter NLP technology in virtual assistants like Siri and Alexa, chatbots, and language translation apps. * Machine Learning (ML): This is a type of AI technology that allows machines to learn from data and improve their performance over time. We encounter ML technology in personalized recommendations on streaming services, fraud detection in banking and finance, and image and speech recognition. * Robotics: This is a type of AI technology that enables machines to perform physical tasks in a variety of settings. We encounter robotics technology in manufacturing and assembly lines, automated delivery drones, and autonomous vehicles. * Computer Vision: This is a type of AI technology that allows machines to interpret visual information from the world around them. We encounter computer vision technology in facial recognition software, security cameras, and self-driving cars. * Expert Systems: These are AI systems that replicate the decision-making abilities of a human expert in a specific field. We encounter expert systems in medical diagnosis tools, legal research and analysis software, and financial forecasting models.

Machine Learning models are trained on human-selected data. Sometimes AI systems display algorithmic bias, where the systems have outcomes that are unfair in a systematic way. Bias in this context is defined as having practical differences in gender classification error rates between groups. Automated systems are not inherently neutral. They reflect the priorities, preferences, and prejudices - the coded gaze - of those who have the power to mold artificial intelligence.

Gender Shades

These AI-powered style transfer tools use pre-trained neural networks that have been taught to do three things: identify essential content elements in one image, identify essential stylistic elements in a second image, and combine the two models to create a third merged image. When you provide the neural network with content and style images, it analyzes them, identifies the most essential characteristics, and uses that data as the last step or layer in the neural network. It then synthesizes the most essential content and style characteristics to create a third merged image.

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AI holds a lot of potential for effecting social good via applications that solve humanitarian, social, and environmental problems around the world. It also holds the potential for reinforcing bias and systematically discriminating against vulnerable groups of people. Awareness of the limitations of AI is a key aspect of ethical use. Like all technologies, AI algorithms can make mistakes, so it’s important to maintain oversight when using automated AI algorithms for things such as grading assignments.

Top 9 Ethical Dilemmas of AI and How to Navigate Them in 2023

<a href="https://research.aimultiple.com/ai-ethics/">READ ARTICLE</a>

Generative AI uses examples in data that has been inputted to create something entirely new based on that information such as video, audio, images, and text. The AI uses something called a Generative Adversarial Network (GAN) that is made up of a generator (which produces something new based on the distinct characteristics of the items in the training data) and a discriminator (which tries to determine if the created item is real or fake).&nbsp; If the discriminator can tell it is fake, the generator will make adjustments and repeat the process until the discriminator can no longer tell the difference.

The generative AI revolution, explained

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Image Create by A.I. with the Query: Infant interacts with A.I.

Machine Learning is a type of Artificial Intelligence in which computers analyze data to make predictions and recognize patterns without being explicitly programmed. The more data that is analyzed, the more accurate the results. Some examples of machine learning in our everyday lives are things such as smart assistants like Siri and Alexa; the way streaming services personalize our experience through show/movie recommendations, as well as email services that have “smart replies” when we are responding to an email.

ARTIFICIAL INTELLIGENCE vs MACHINE LEARNING vs DEEP LEARNING | Machine Learning with Python

ChatGPT is an artificial intelligence program that can converse with people in natural language. It uses a large neural network, a type of machine learning model, to understand and generate human-like responses to questions and statements. Essentially, it's a computer program that can chat with you like a human would, using a lot of data and fancy math to understand what you're saying and give you an appropriate response.

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<a href="https://docs.google.com/document/d/1rH7X1SbrFh062oF8_MvH2tYujnZoRxDlcS2TXdrF_Hw/edit?usp=sharing">Click Here</a>

Try this <a href="https://replit.com/@zparisher93/chatbot">teacher created chat bot</a>

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Generative AI music is music that is created by artificial intelligence (AI) algorithms, rather than by human musicians. The AI algorithms use machine learning techniques to analyze existing music and then generate new pieces of music based on that analysis.&nbsp; Generative AI music has the potential to revolutionize the music industry by enabling new forms of creativity and expression. It can also be used to create personalized music for individuals or to provide background music for various types of media, such as video games or films.

Deepfakes are created by generative AI in which images, audio, and video are made of people doing or saying things that they did not do. One danger of deepfakes is the spreading of false information from what looks to be trusted sources. 

How Dangerous are Deepfakes? | Explained

The Chronicles of DeepTomCruise Part3 (Viral Tiktok Videos)

<a href="https://docs.google.com/document/d/1FvUuDEtuCmnudJ3NqP2z9sM1ONPrBZJc7KvyfdKmjW0/edit?usp=sharing">Standards and Objectives</a>

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