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I think in general sense AI be the general manager of employers
Hello everyone, I thought long about this and I think a general solution to mitigate conflict, stressed out managers, perhaps even insensitive people generally is if AI computing be the manager of say an employer. So far it's great that Microsoft has now implemented CoPilot to give a contextual overview on any question asked but if it were the overseer of all administrative duties. I don't think AI is not quite a good team builder. If I say I don't like somebody working along with me if I could somehow resolve conflict or risky behaviour. Theirs also good scheduling apps like Motion too. I wouldn't want AI to be implemented on absolutely everything because then people will become even more reserved, unsocial "creatures". It's important to consider conscious and unconscious thoughts if the semblance of thought is good too.
10/29 event - Learn how Microsoft leveraged SharePoint Taxonomy Tagging feature for content mgmt!
Hi, we have a great Microsoft customer zero story coming up on LinkedIn on October 29th. Event URL:Microsoft Digital: AI tagging makes finding content easier | LinkedIn Discover how Microsoft is transforming content management with SharePoint Premium’s Taxonomy Tagging feature. Many employees often skip tagging their documents in SharePoint, making it harder to find relevant content later. Instead of relying on manual tagging, let AI do the work for you! This feature not only makes content easier to find but also simplifies the tedious task of tagging content, ensuring it is done consistently and efficiently. In this insightful session, Stan Liu and Ray Peer will showcase a powerful internal business use case from the Product Marketing Team’s Modern Marketing Playbook SharePoint site. Learn how Taxonomy Tagging was used to organize marketing knowledge articles in document libraries and leverage Content Query for an optimized finding experience. Stan and Ray will also discuss the importance of setting up the Taxonomy in the Term Store beforehand, a crucial step for enabling effective Taxonomy Tagging with Managed Metadata. Hope you can make it!I am developing an AI that can play TicTacToe using Reinforcement Q learning
So I recenly took an udemy course part of which reinforcement learning was included as part of a broad AI course. So based on what I learnt about Q Learning(a type of reinforcement learning, under stereotypical Artificial Intelligence) , even though the implementation was not discussed in any full details, I had to go through a provided book to understand how it's implemented in practice. And YouTube was not helping matters. I need any additional opinions I can get from you, thanks. Now in order for me to apply Q Learning to TicTacToe, I have to make the AI (Agent)always play X (makes the first move) for simplicity in my AI software development. Q Learning algorithm is based on Bellmans Equation. Literally it's about rewarding Favourable actions taken at different states and punishing unfavorable actions taken at different states also. Rewarding and punishing are one and the same number(variable) in the Bellmans equation. There is also the learning rate and discount factors, which are both variables too in the Bellmans equation. For every action the agent takes at every state it always get a tiny reward (positive or negative), then after winning or losing it gets a much larger reward (positive or negative). So how does the agent remembers all it has learnt, by looking at the q table,and checking for the action with the highest Q value. These Q values are updated by Bellmans equation whether positively or negatively. Now the first challenge is I have to pair all the possible valid board configurations (states space) to all the possible valid actions that can be taken for each states in a Q table dictionary, and map all the pairs to Q values of 0 (for initialization). I will write a Python code that will generate this mapping and remove all impossible states (where X is greater than 0 by more than 1 is definitely invalid). Also make Q values for actions where ever is occupied by either X or O as - 1.0 to prevent agent from making such moves. I will make 4 different players in different classes of the game software who would play with the agent at different stages of it's learning automatically to update the Q values of every actions taken by agent in each game state, instead of waiting for the final result before updating Q value(my initial mistake when I was still learning about Q Learning) . Below is for any of the 5 agents (Balanced, Quick Myopic, Quick Overplanner, Slow Myopic and Slow Overplanner) selected at the start of the training games. These agents have different combinations of hyperparameters (learning rate and discount factor) To train the agent I will make it play against 4 heuristics players (players programed to play only in a certain way) all using different playing strategies. For starting stage, agent will play with random player 1 for 2000 iterations of games and update it's Q values for all the state action pairs it encountered. Then for the next 2000 iterations, agent will play with a player 2 that always favor the center if available for its first move otherwise plays at any corner piece, otherwise plays any random available space. Then for the next 2000 iters, agent will play against a player 3 that plays randomly until Agent is about to make a winning move, then blocks it. Not really trying to move, just blocking agent winning moves. Then for the next 2000 iters, agent will play with a player 4 that tries to complete a line as soon as possible, by playing in corners that are impossible for agent to block, that is playing in triangular corners that leads to a definite win if agent doesn't win on time. Now create separate classes (.NET MAUI) for these four players that would train a selected agent chosen with options to pick the desired iterations of games for the training of the agent with that player. For the reward system, +1 reward on completing a line. - 1 for allowing opponent win and not blocking it. +0. 5 for playing a position that can lead to a win in its next move. - 0.5 by playing in a position that cannot lead to a win in its next move (that is a move that doesn't form a straight line of three with an empty cell anywhere in the line) and - 0.5 for playing along a line that is played by an opponent. This is the reward system rules. So bells formula would be used for updating the Q value for every action taken for every state in the Q Table already defined in their respective Q Table json file for the particular agent being trained. We would use both learning rate, discount factor and reward from reward system for every action taken at every state. Below is the link to the Q table (the brain of the AI); https://www.kaggle.com/datasets/adedapoadeniran/tictactoe-q-learning-table And below is the link to the code that generated the Q table. https://www.kaggle.com/code/adedapoadeniran/reinforcement-learning-for-tictactoe-ai/ Thanks for your attention. This was really mentally tasking to come up withandfigureout.What is Convolutional Neural Network — CNN (Deep Learning)
Convolutional Neural Networks (CNNs) are a type of deep learning neural network architecture that is particularly well suited to image classification and object recognition tasks. A CNN works by transforming an input image into a feature map, which is then processed through multiple convolutional and pooling layers to produce a predicted output. Convolutional Neural Network — CNN architecture In this blog post, we will explore the basics of CNNs, including how they work, their architecture, and how they can be used for a wide range of computer vision tasks. We will also provide examples of some real-world applications of CNNs, and outline some of the benefits and limitations of this deep-learning architecture. Working of Convolutional Neural Network: A convolutional neural network starts by taking an input image, which is then transformed into a feature map through a series of convolutional and pooling layers. The convolutional layer applies a set of filters to the input image, each filter producing a feature map that highlights a specific aspect of the input image. The pooling layer then downsamples the feature map to reduce its size, while retaining the most important information. The feature map produced by the convolutional layer is then passed through multiple additional convolutional and pooling layers, each layer learning increasingly complex features of the input image. The final output of the network is a predicted class label or probability score for each class, depending on the task. The architecture of Convolutional Neural Network: A typical CNN architecture is made up of three main components: the input layer, the hidden layers, and the output layer. The input layer receives the input image and passes it to the hidden layers, which are made up of multiple convolutional and pooling layers. The output layer provides the predicted class label or probability scores for each class. The hidden layers are the most important part of a CNN, and the number of hidden layers and the number of filters in each layer can be adjusted to optimize the network’s performance. A common architecture for a CNN is to have multiple convolutional layers, followed by one or more pooling layers, and then a fully connected layer that provides the final output. Applications of Convolutional Neural Network: CNNs have a wide range of applications in computer vision, including image classification, object detection, semantic segmentation, and style transfer. Image classification:Image classification is the task of assigning a class label to an input image. CNNs can be trained on large datasets of labeled images to learn the relationships between the image pixels and the class labels, and then applied to new, unseen images to make a prediction. Object detection:Object detection is the task of identifying objects of a specific class in an input image and marking their locations. This can be useful for applications such as security and surveillance, where it is important to detect and track objects in real time. Semantic segmentation:Semantic segmentation is the task of assigning a class label to each pixel in an input image, producing a segmented image that can be used for further analysis. This can be useful for applications such as medical image analysis, where it is important to segment specific structures in an image for further analysis. Style transfer:Style transfer is the task of transferring the style of one image to another image while preserving the content of the target image. This can be useful for applications such as art and design, where it is desired to create an image that combines the content of one image with the style of another. Layers of Convolutional neural network: The layers of a Convolutional Neural Network (CNN) can be broadly classified into the following categories: Convolutional Layer:The convolutional layer is responsible for extracting features from the input image. It performs a convolution operation on the input image, where a filter or kernel is applied to the image to identify and extract specific features. Convolutional Layer Pooling Layer:The pooling layer is responsible for reducing the spatial dimensions of the feature maps produced by the convolutional layer. It performs a down-sampling operation to reduce the size of the feature maps and reduce computational complexity. MaxPooling Layer Activation Layer:The activation layer applies a non-linear activation function, such as the ReLU function, to the output of the pooling layer. This function helps to introduce non-linearity into the model, allowing it to learn more complex representations of the input data. Activation Layer Fully Connected Layer:The fully connected layer is a traditional neural network layer that connects all the neurons in the previous layer to all the neurons in the next layer. This layer is responsible for combining the features learned by the convolutional and pooling layers to make a prediction. Fully Connected Layer Normalization Layer:The normalization layer performs normalization operations, such as batch normalization or layer normalization, to ensure that the activations of each layer are well-conditioned and prevent overfitting. Dropout Layer:The dropout layer is used to prevent overfitting by randomly dropping out neurons during training. This helps to ensure that the model does not memorize the training data but instead generalizes to new, unseen data. Dense Layer:After the convolutional and pooling layers have extracted features from the input image, the dense layer can then be used to combine those features and make a final prediction. In a CNN, the dense layer is usually the final layer and is used to produce the output predictions. The activations from the previous layers are flattened and passed as inputs to the dense layer, which performs a weighted sum of the inputs and applies an activation function to produce the final output. Dense layer Benefits of Convolutional Neural Network: Feature extraction:CNNs are capable of automatically extracting relevant features from an input image, reducing the need for manual feature engineering. Spatial invariance:CNNs can recognize objects in an image regardless of their location, size, or orientation, making them well-suited to object recognition tasks. Robust to noise:CNNs can often handle noisy or cluttered images, making them useful for real-world applications where image quality may be variable. Transfer learning:CNNs can leverage pre-trained models, reducing the amount of data and computational resources required to train a new model. Performance:CNNs have demonstrated state-of-the-art performance on a range of computer vision tasks, including image classification, object detection, and semantic segmentation. Limitations of Convolutional Neural Network: Computational cost: Training a deep CNN can be computationally expensive, requiring significant amounts of data and computational resources. Overfitting: Deep CNNs are prone to overfitting, especially when trained on small datasets, where the model may memorize the training data rather than generalize to new, unseen data. Lack of interpretability: CNNs are considered to be a “black box” model, making it difficult to understand why a particular prediction was made. Limited to grid-like structures: CNNs are limited to grid-like structures and cannot handle irregular shapes or non-grid-like data structures. Conclusion: In conclusion, Convolutional Neural Networks (CNNs) is a powerful deep learning architecture well-suited to image classification and object recognition tasks. With its ability to automatically extract relevant features, handle noisy images, and leverage pre-trained models, CNNs have demonstrated state-of-the-art performance on a range of computer vision tasks. However, they also have their limitations, including a high computational cost, overfitting, a lack of interpretability, and a limited ability to handle irregular shapes. Nevertheless, CNNs remain a popular choice for many computer vision tasks and are likely to continue to be a key area of research and development in the coming years.Fundamentals of machine learning
Machine learning is in many ways the intersection of two disciplines - data science and software engineering. The goal of machine learning is to use data to create a predictive model that can be incorporated into a software application or service. To achieve this goal requires collaboration between data scientists who explore and prepare the data before using it totraina machine learning model, and software developers who integrate the models into applications where they're used to predict new data values (a process known asinferencing). What is machine learning? Machine learning has its origins in statistics and mathematical modeling of data. The fundamental idea of machine learning is to use data from past observations to predict unknown outcomes or values. For example: The proprietor of an ice cream store might use an app that combines historical sales and weather records to predict how many ice creams they're likely to sell on a given day, based on the weather forecast. A doctor might use clinical data from past patients to run automated tests that predict whether a new patient is at risk from diabetes based on factors like weight, blood glucose level, and other measurements. A researcher in the Antarctic might use past observations automate the identification of different penguin species (such asAdelie,Gentoo, orChinstrap) based on measurements of a bird's flippers, bill, and other physical attributes. Machine learning as afunction Because machine learning is based on mathematics and statistics, it's common to think about machine learning models in mathematical terms. Fundamentally, a machine learning model is a software application that encapsulates afunctionto calculate an output value based on one or more input values. The process of defining that function is known astraining. After the function has been defined, you can use it to predict new values in a process calledinferencing. Let's explore the steps involved in training and inferencing. The training data consists of past observations. In most cases, the observations include the observed attributes orfeaturesof the thing being observed, and the known value of the thing you want to train a model to predict (known as thelabel). In mathematical terms, you'll often see the features referred to using the shorthand variable namex, and the label referred to asy. Usually, an observation consists of multiple feature values, soxis actually avector(an array with multiple values), like this:[x1,x2,x3,...]. To make this clearer, let's consider the examples described previously: In the ice cream sales scenario, our goal is to train a model that can predict the number of ice cream sales based on the weather. The weather measurements for the day (temperature, rainfall, windspeed, and so on) would be thefeatures(x), and the number of ice creams sold on each day would be thelabel(y). In the medical scenario, the goal is to predict whether or not a patient is at risk of diabetes based on their clinical measurements. The patient's measurements (weight, blood glucose level, and so on) are thefeatures(x), and the likelihood of diabetes (for example,1for at risk,0for not at risk) is thelabel(y). In the Antarctic research scenario, we want to predict the species of a penguin based on its physical attributes. The key measurements of the penguin (length of its flippers, width of its bill, and so on) are thefeatures(x), and the species (for example,0for Adelie,1for Gentoo, or2for Chinstrap) is thelabel(y). Analgorithmis applied to the data to try to determine a relationship between the features and the label, and generalize that relationship as a calculation that can be performed onxto calculatey. The specific algorithm used depends on the kind of predictive problem you're trying to solve (more about this later), but the basic principle is to try tofita function to the data, in which the values of the features can be used to calculate the label. The result of the algorithm is amodelthat encapsulates the calculation derived by the algorithm as afunction- let's call itf. In mathematical notation: y = f(x) Now that thetrainingphase is complete, the trained model can be used forinferencing. The model is essentially a software program that encapsulates the function produced by the training process. You can input a set of feature values, and receive as an output a prediction of the corresponding label. Because the output from the model is a prediction that was calculated by the function, and not an observed value, you'll often see the output from the function shown asŷ(which is rather delightfully verbalized as "y-hat"). Types of machine learning There are multiple types of machine learning, and you must apply the appropriate type depending on what you're trying to predict. A breakdown of common types of machine learning is shown in the following diagram. Supervised machine learning Supervisedmachine learning is a general term for machine learning algorithms in which the training data includes bothfeaturevalues and knownlabelvalues. Supervised machine learning is used to train models by determining a relationship between the features and labels in past observations, so that unknown labels can be predicted for features in future cases. Regression Regressionis a form of supervised machine learning in which the label predicted by the model is a numeric value. For example: The number of ice creams sold on a given day, based on the temperature, rainfall, and windspeed. The selling price of a property based on its size in square feet, the number of bedrooms it contains, and socio-economic metrics for its location. The fuel efficiency (in miles-per-gallon) of a car based on its engine size, weight, width, height, and length. Classification Classificationis a form of supervised machine learning in which the label represents a categorization, orclass. There are two common classification scenarios. Binary classification Inbinary classification, the label determines whether the observed itemis(orisn't) an instance of a specific class. Or put another way, binary classification models predict one of two mutually exclusive outcomes. For example: Whether a patient is at risk for diabetes based on clinical metrics like weight, age, blood glucose level, and so on. Whether a bank customer will default on a loan based on income, credit history, age, and other factors. Whether a mailing list customer will respond positively to a marketing offer based on demographic attributes and past purchases. In all of these examples, the model predicts a binarytrue/falseorpositive/negativeprediction for a single possible class. Multiclass classification Multiclass classificationextends binary classification to predict a label that represents one of multiple possible classes. For example, The species of a penguin (Adelie,Gentoo, orChinstrap) based on its physical measurements. The genre of a movie (comedy,horror,romance,adventure, orscience fiction) based on its cast, director, and budget. In most scenarios that involve a known set of multiple classes, multiclass classification is used to predict mutually exclusive labels. For example, a penguin can't be both aGentooand anAdelie. However, there are also some algorithms that you can use to trainmultilabelclassification models, in which there may be more than one valid label for a single observation. For example, a movie could potentially be categorized as bothscience fictionandcomedy. Unsupervised machine learning Unsupervisedmachine learning involves training models using data that consists only offeaturevalues without any known labels. Unsupervised machine learning algorithms determine relationships between the features of the observations in the training data. Clustering The most common form of unsupervised machine learning isclustering. A clustering algorithm identifies similarities between observations based on their features, and groups them into discrete clusters. For example: Group similar flowers based on their size, number of leaves, and number of petals. Identify groups of similar customers based on demographic attributes and purchasing behavior. In some ways, clustering is similar to multiclass classification; in that it categorizes observations into discrete groups. The difference is that when using classification, you already know the classes to which the observations in the training data belong; so the algorithm works by determining the relationship between the features and the known classification label. In clustering, there's no previously known cluster label and the algorithm groups the data observations based purely on similarity of features. In some cases, clustering is used to determine the set of classes that exist before training a classification model. For example, you might use clustering to segment your customers into groups, and then analyze those groups to identify and categorize different classes of customer (high value - low volume,frequent small purchaser, and so on). You could then use your categorizations to label the observations in your clustering results and use the labeled data to train a classification model that predicts to which customer category a new customer might belong.Best Azure Certifications For 2024
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