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Along with the proliferation of personalized learning platforms also comes the rise of the presence of artificial intelligence in educational technology. Before going further, we must first acknowledge the truth about the term artificial intelligence -- it is a buzzword that connotes many different applications. It holds different meaning in different contexts and, much of the time, is used indiscriminately without clear boundaries or specifications. To try and avoid just adding to the noise, we will focus on three main topics: (1) deconstructing the buzzword into its component parts, (2) characterizing current use cases and predicting future ones, and (3) exploring risks and trade-offs. First, let us define, as clearly as possible, what all is under consideration when we mention the term.

Intuitively, the idea of a Naive Bayes is how you probably approach life. Like all my articles, I believe that a simple and intuitive understanding of a model should be understood first before diving into the mathematics and practical jargon. Let's say you're responsible for Thanksgiving dinner. You have cooked Thanksgiving dinner for the last ten years. Within those ten years, you have prepared three desserts: pumpkin pie, chocolate cheesecake, and white macadamia cookies.

Additionally, it is a scale model, a toy and a learning tool for both you and I, so we can further learn about machine learning,neural networks and artificial Intelligence. I've tried avoiding math and strict terms, but if you want to know, we just built a perceptron which is defined as an algorithm for supervised learning of binary classifiers, heavy stuff. The biology of the brain is a big subject, in part because of it's inaccesability and in part because it's complexity, still there is a lot of information out there, possibly the best place to start is Neuroscience (Purves) and Cognitive Neuroscience (Gazzaniga). I modified and adapted the bunny example from Gateway to Memory (Gluck), which also has an excellent introduction to graphs. Another heavily consulted source was An Introduction to Neural Networks (Gurney), good for your general A.I. needs.

Minimax, as the name suggest, is a method in decision theory for minimizing the maximum loss. Alternatively, it can be thought of as maximizing the minimum gain, which is also know as Maximin. It all started from a two player zero-sum game theory, covering both the cases where players take alternate moves and those where they made simultaneous moves. It has also been extended to more complex games and to general decision making in the presence of uncertainty. In the above explanation, it has been mentioned that the minimax algorithms started off with the concept of zero-sum.

This week Washington DC was abuzz with news that had nothing to do with the occupant of The While House. A group of progressive legislators, led by Alexandra Ocasio-Cortez, in the House of Representatives, introduced "The Green New Deal." The resolution by the Intergovernmental Panel on Climate Change was in response to the alarming Fourth National Climate Assessment by aiming to reduce global "greenhouse gas emissions from human sources of 40 to 60 percent from 2010 levels by 2030; and net-zero global emissions by 2050." While the bill is largely targeting the transportation industry, many proponents suggest that it would be more impactful, and healthier, to curb America's insatiable appetite for animal agriculture. In a recent BBC report, "Food production accounts for one-quarter to one-third of all anthropogenic greenhouse gas emissions worldwide, and the brunt of responsibility for those numbers falls to the livestock industry."

Artificial intelligence (AI) has permeated every aspect of our lives. From major advancements in medicine to transforming the way business is conducted, machine learning shows great potential to improve the quality of our lives in a variety of ways. A behind-the-scenes look into different companies' AI ventures shows that certain aspects of machine learning even aid in making the world a safer place. Manufacturing problems can have serious negative impacts on a business. From cars to home appliances, product safety is of utmost importance when it comes to customer satisfaction.

Artificial intelligence, or AI for short, is one of the most highly anticipated digital healthcare technologies. While the concept of AI may still seem futuristic to some, the era of machine learning is already here. Uptake in pharma has been relatively slow compared to in other industries. However, this is gradually changing. AI is developing at a rapid rate and pharma will need to adapt if they want to stay relevant.

The modern navy is regularly working on updating its equipment, its outreach, its maneuverability. One of the ways in which it accomplishes these goals is through the employment of drones, autonomous machines fulfilling important defensive and militaristic tasks. AUV's, or autonomous underwater vehicles (sometimes alternatively known as unmanned underwater vehicles -- UUV's), comprise one department in which such drone tech is seeing action. According to Marine Insight, such aquatic automatons were being made as early as 1957. They can range in size anywhere from 0.91 meters (approximately 3 feet) to over 10 meters (over 32 feet) long.

Despite their high predictive performance, many machine learning techniques remain black boxes because it is difficult to understand the role of each feature and how it combines with others to produce a prediction. However, users need to understand and trust the decisions made by machine learning models, especially in sensitive fields such as medicine. For this reason, there is an increasing need of methods able to explain the individual predictions of a model, that is, a way to understand what features made the model give its prediction for a specific instance. We have a neural network (the machine learning model) trained as an image classifier. This model would give a probability (let's say 0.98) that a cat appears in the picture (an observation), so we could say that "our model predicts that this is a cat with a probability of 0.98".