Welcome to the Future!! ---Science Ambassador Scholarship

This is made for the Science Ambassador Scholarship AI and Machine Learning are fairly new topics that are revolutionizing the world already!! This is a brief video about these fascinating concepts that will hopefully inspire others to pursue a career within the STEM field. Here is my script: So what is AI? AI or Artificial Intelligence is defined as the theory and development of computer systems able to perform tasks that normally require human intelligence. This means that for a device to be artificially intelligent it must mimic the cognitive thinking of a human mind like contemplation, judgment, and intention. Artificial Intelligence covers a broad spectrum of topics like robotics, deep learning, big data, machine learning, and much more. These topics are categorized into two types of AI: 1.Artificial Narrow Intelligence 2.Artificial General Intelligence Artificial Narrow Intelligence or “weak” AI are devices that outperform humans in only a specific task. Examples of this are self-driving cars or virtual assistants. Narrow AI is not conscious or driven by emotion the way humans are and operate based on pre-determined algorithms. Artificial General Intelligence or “strong” AI refers to machines that can complete any human task and exhibit human intelligence. Examples of this are robots we see in sci-fi films. For a long time codders thought they could achieve strong this AI by creating an algorithm that directs a device with a long list of rules; however, this proved to be ineffective and scientists turned to a different approach. This is where machine learning comes into play. Machine learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning algorithms build models based on data which is called “training data” that helps an AI device make decisions. Let me illustrate this concept. Imagine you have a robot that is given the instruction to get from point A to point B. On its first attempt, it might take one step and fall down. The second time it might take two steps and then fall down. However, the robot uses the data it collects to quickly teach itself how to walk and eventually by attempt number 100 it will successfully accomplish its goal. There are four main machine learning methods: 1.Supervised machine learning 2.Unsupervised machine learning 3.Semi-supervised machine learning 4.Reinforcement machine learning In Supervised Learning algorithms, devices use preexisting data to predict the outcome of future events. An example of this is a device that predicts the amount of time it takes for you to get home from work, school, or any other place. The algorithm uses data like weather conditions, traffic, and time of day to estimate your arrival. Unsupervised Learning is used when the training data is unlabeled and an algorithm identifies patterns and relationships within the data to form its output. An example of this is creating a device that tells apart the difference between cats and dogs. The algorithm identifies patterns between the two (like the shape of ears, eyes, face, etc) and based on these patterns recognizes the difference. Semi-supervised Learning is a mixture of unlabeled and labeled data which guides the model to make independent conclusions. The combination of two data types in one training data set allows the algorithm to learn how to label the unlabeled data. A great example of this is speech analysis. And finally reinforced machine learning provides a data set that uses a “reward and punishment” system, offering the algorithm a way to learn from its mistakes by trial and error. The robot learning how to walk is a great example of reinforced machine learning. As we enter a new decade of learning and innovation, AI and Machine learning will become more and more prevalent within our society, making this only the beginning. Sources: https://datascience.berkeley.edu/blog... https://stratechery.com/topic/ai-mach... https://plato.stanford.edu/entries/ar... https://www.brookings.edu/research/wh... https://dataprivacylab.org/people/swe... https://dataprivacylab.org/people/swe... https://towardsdatascience.com/hot-to... https://www.techopedia.com/definition...   / distinguishing-between-narrow-ai-general-a...   https://www.intel.com/content/www/us/... https://becominghuman.ai/top-machine-... https://expertsystem.com/machine-lear... https://www.guru99.com/supervised-vs-...