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)
Many factors contribute to a student’s success, and navigating the education system can be difficult — especially for first-time college students. One use case for machine learning in education is identifying and assisting at-risk students. Schools can use ML algorithms as an early warning system to identify struggling students, gauge their level of risk and offer appropriate resources to help them succeed. In part, this is due to the fact that the efficacy of methods and tools used in education need to be studied and understood before being deployed more broadly. As machine learning becomes more common, its influence on education has grown.
- Machine learning is best suited for this use case as it can scan through vast amounts of transactional data and identify patterns, i.e., if there is any unusual behavior.
- A good way to explain the training process is to consider an example using a simple machine-learning model, known as linear regression with gradient descent.
- It is the study of making machines more human-like in their behavior and decisions by giving them the ability to learn and develop their own programs.
- Each processing layer passes on a more abstract representation of the data to the next layer, with the final layer providing a more human-like insight.
- A neural network has to have at least one hidden layer to be classed as a neural network.
- Some known classification algorithms include the Random Forest Algorithm, Decision Tree Algorithm, Logistic Regression Algorithm, and Support Vector Machine Algorithm.
Hence, the objective of all the machine learning algorithms is to estimate a predictive model that best generalizes to a particular type of data. Although the learning task is not easy, with a better understanding of the different components of the machine learning and how they interact with each other, things will become clearer. In the subsequent posts, we will look at how the machine learning algorithms can be used to solve real-world problems. Unsupervised learning refers to a learning technique that’s devoid of supervision. Here, the machine is trained using an unlabeled dataset and is enabled to predict the output without any supervision.
How to Know Which Machine Learning Algorithms to Use: Techniques in Machine Learning
So how should executives manage the existing and emerging risks of machine learning? Developing appropriate processes, increasing the savviness of management and the board, asking the right questions, and adopting the correct mental frame are important steps. Second, the environment in which machine learning operates may itself evolve or differ from what the algorithms were developed to face.
What are the 3 types of machine learning?
The three machine learning types are supervised, unsupervised, and reinforcement learning.
Retailers use it to gain insights into their customers’ purchasing behavior. Locking doesn’t alter the fact that machine-learning algorithms typically base decisions on estimated probabilities. Though it’s difficult to understand how the accuracy (or inaccuracy) of decisions may change when an algorithm is unlocked, it’s important to try. The third reason machine learning can make inaccurate decisions has to do with the complexity of the overall systems it’s embedded in. With so many parameters, it’s difficult to assess whether and why such a device may have made a mistake, let alone be certain about its behavior.
Making The Model
Deep learning models use artificial neural networks to extract information. You may have also heard it being referred to as Deep Neural Network, where the term “Deep” relates to the number of hidden layers in the neural network. When a classification model processes data, it produces a probability that the input data matches one of the classes from the training data. It thus produces a prediction or correlation rather than a statement of causality. These patterns that machine learning systems can see are often so granular that no human could ever catch them.
![how machine learning 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)
This preprocessing layer must be adapted, tested and refined over several iterations for optimal results. Deep learning algorithms attempt to draw similar conclusions as humans would by constantly analyzing data with a given logical structure. To achieve this, deep learning uses a multi-layered structure of algorithms called neural networks. In supervised machine learning, the algorithm is provided an input dataset, and is rewarded or optimized to meet a set of specific outputs. For example, supervised machine learning is widely deployed in image recognition, utilizing a technique called classification.
What is a Classifier in Machine Learning?
Machine learning (ML) is one of the most impactful technological advances of the past decade, affecting almost every single industry and discipline. From helping businesses provide more advanced, personalized customer service, to processing huge amounts of data in seconds, ML is revolutionizing the way we do things every day. One of its own, Arthur Samuel, is credited for coining the term, “machine learning” with his research (PDF, 481 KB) (link resides outside IBM) around the game of checkers. Robert Nealey, the self-proclaimed checkers master, played the game on an IBM 7094 computer in 1962, and he lost to the computer. Compared to what can be done today, this feat seems trivial, but it’s considered a major milestone in the field of artificial intelligence. Machine learning programs can be trained to examine medical images or other information and look for certain markers of illness, like a tool that can predict cancer risk based on a mammogram.
![how machine learning 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)
This resurgence follows a series of breakthroughs, with deep learning setting new records for accuracy in areas such as speech and language recognition, and computer vision. The next step will be choosing an appropriate machine-learning model from the wide variety available. Each have strengths and weaknesses depending on the type of data, for example some are suited to handling images, some to text, and some to purely numerical data. An example of reinforcement learning is Google DeepMind’s Deep Q-network, which has beaten humans in a wide range of vintage video games. The system is fed pixels from each game and determines various information about the state of the game, such as the distance between objects on screen.
What is an example of a machine learning application?
Because some ML applications use models written in different languages, tools like machine learning operations (MLOps) can be particularly helpful. Machine learning and AI tools are often software libraries, toolkits, or suites that aid in executing tasks. However, because of its widespread support and multitude of libraries to choose from, Python is considered the most popular programming language for machine learning. Across the business world, as machine-learning-based artificial intelligence permeates more and more offerings and processes, executives and boards must be prepared to answer such questions. In this article, which draws on our work in health care law, ethics, regulation, and machine learning, we introduce key concepts for understanding and managing the potential downside of this advanced technology. The use of prompts and parameters is critical in the functioning of those models, as it determines the context and output of the generated text.
![how machine learning 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)
In order to understand how machine learning works, first you need to know what a “tag” is. To train image recognition, for example, you would “tag” photos of dogs, cats, horses, etc., with the appropriate animal name. In classification tasks, the output value is a category with a finite number of options.
What are the differences between data mining, machine learning and deep learning?
ML applications are fed with new data, and they can independently learn, grow, develop, and adapt. Machine learning teaches machines to learn from data and improve incrementally without being explicitly programmed. Terry Sejnowski’s and Charles Rosenberg’s artificial neural network taught itself how to correctly pronounce metadialog.com 20,000 words in one week. Complex models can produce accurate predictions, but explaining to a lay person how an output was determined can be difficult. Machine learning projects are typically driven by data scientists, who command high salaries. These projects also require software infrastructure that can be expensive.
To combat GPU shortage for generative AI, startup works to optimize … – VentureBeat
To combat GPU shortage for generative AI, startup works to optimize ….
Posted: Thu, 08 Jun 2023 13:42:36 GMT [source]
Therefore, they’re a great way to improve reinforcement learning algorithms. In this case, the model uses labeled data as an input to make inferences about the unlabeled data, providing more accurate results than regular supervised-learning models. One of the most common types of unsupervised learning is clustering, which consists of grouping similar data.
What Is Deep Learning?
For instance, an algorithm may be given datasets containing images of animals. The algorithm classifies the animals according to their features like fur, ears, tail, etc. Unsupervised learning is a basis for many data mining techniques in machine learning. There are different types of Activation Functions, and their choice has a large impact on the performance of the neural network. Activation Functions in the hidden layer control how well the neural network model learns on the training dataset.
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H8d9t/Acn57XvGqvHaU0GaPbKd6oVlVzEofzyGPfNxIOOg5ZGCHBJVW1kuu5aq9Vluu1oMFPTA93UqmI5uoCsp5nPIciVwOOAMtnOgPnx9KN3H8d9t/Acn57R9KN3H8d9t/Acn57X0n0aA+bH0o3cfx3238ByfntH0o3cfx3238ByfntfSfRoD5cb0/uXW4NnWuK5ydsNvqhLULBwWzOmMqzZz3p/W/wC3Rr6C9t/2KUn3QT8XJo0BbNofYnZfudTfil020p2h9idl+51N+KXTbQBqifo3dnH6J6djvs3J7a3DFaL0WXBAhMxPeceGOAznPn089Xs68VJJH9MZhg7yISFZH7vie8I9iXHLPLBX1Yx0Jz69dey4EcfPm/hi34Hk/Fduxdh5DklefEjF79E7t6HtXRo0a5D1g0uv1/tW2bdJd7zO0FJCCZJRGzBFAJLNxBwAAep6aY6qHarVpRbMrJ5JKREGEdqsEwcWypDqCOQIJXHXJYdDoCFU9uPZfRVFwpa3dMNNNaTELhHNFJG1GZX4Rd6GUd3zbovLHL1Z1F3f2m2Os2rcINubnntV2qYO5ttXJQyYjq5B7gCskZBy5UFSOviHmDjVF1uNvhu+7kmu+zadqaajaRbhH3j0jNVJwevPpI7zmQO4Ph4lT4jjK2Wl9hIbfA1XQW+J5Ku2EvUKD3swalAY+6DnLwTgkmW4PHksAhRALP2f9sW1anZlse8byS83SAR0F0rKWkkMZufHMsICphGDhgIz4gAAevm7p+2bs5q72u26W/mW6uzotGlNKZWZC3NQvHJK8TyA6qME4yM6b2PUWyq21JJ3m3K1ob0IlegTCwTCOVTHIe+b9PAEo8mVyzY6Z4GXY6mgbtcFKk+3JCZ5+7paZeNb3glqy5ibv/DKObNM/HxrKnXxcpAN9bc3RZN2UPsnYKw1VKWKiXu2UMQSDjkBnBBHT1gj1aa6pPZTFFBt8ww09HCqOycKNOEAIdwQiZPHBBBHTxBjjrq7aANV+u+z6zfce5/jqLVg1X677PrN9x7n+OotAWDRo0aASb03lt7s+2zW7v3VWmktVuCNUTCNn4BnVAeKgk+Jh6tYti772z2kbapt3bRrnq7XWFxDK0LxFuJwfC4BHUfBqifRXgHsA3UGzjFFnGf8Mg+Aj+caT/QVNE/0O22ZIJI5I375leJSsbAuTyRSSVU+YBPQYGpzKqyyv6q5vZf1dXAzccTlM3KQy1+C3yl/UluydF8dDeejRo1BoGqtuHtN2dtWuW3X64zU0zusaj0SZ1LMkrr4lUjqsE2OvUxsB16atOvPXbhcYjv6x2WprrGaaR3mmoa0GSadTR3JORTvQGhILRhe7Y9446HPGQDaF57ZOzvblNUVt/vpt1NSfXE9TSyxxwjiWy7soCjiM9T61/XDOu5O3Ta9+7aUtu3u1eohs+2aKtXdFjNokDJWxSJFADI8HJIz6RK0mHyTBTFSq973i3tVqbbSU1yrKhtuUIV0Woqb1H3tKIxDU8UqT3o7xfG2F5Hj30Z4jkO8012dvYf/AIhe1VY6ahE0VVWNWem+6pHKLnCUebD9eICMGz4QueSgclnMlo4yd9KVpdcuC9SsoTlrHEhFLepOpS6oLplxXDU9aHty7L1WnZtzovpdUtBAGp5QZKoxrIIACuTKUdW7v32MnHQ6kT9sXZ7TSVMU97dHo52pqhTSTZjmVGkaNhx6OERm4+eBnHUa0ia63JaNvg1u1omlu0ULekryknlFHTFYI/dx+nwqriTxYWNugxxRvfYqaoh3UKWnszdxV1wAji90hm9ErWcN7p9clDIWfA5I/wBsDiSCx6L0aNGgDVf2N+otT92Lt/vCo1YNV/Y36i1P3Yu3+8KjQFg0aNGgKJujtt7Odm73tXZ3uC8y09+vXdehU4pJXEveOUXxqpUdQc5PQdTq968WfRIsi/RkdmatJGrO1tChgSzgVMhwh5ALjzOQcgfJr2nrq2jAjgww5Rf4lZ5mwbbibVi42HNKoSpdgaNGjXKemRrjcaa1Uj1tYZBEhVT3cbSNlmCjCqCT1I9Wqi/bR2bxp3km4Qi+gC68mp5QDREIfSfe/UsSJ4/ejkOunG/Kh6TatbVxTxwPAYpFllYqkZWRTzchl8Ixk+IdAeutLXWe1wtWr/8ALtKE2okqx1cfjigApsyMDKv6RXpyiwvkPL1AbQp+2Ps7ulH6RbtxPNFNSelxzU9NK+YG5BZlIQ5QlThhkHpjORmo9mfa3a7NtmooO0vtIp7vuK3VLi5zx0ZiSnjeZo6XkiRgR94ixt4vfO7lTx4gVGxyUFftaoER2xWzVO3Ze7lpqYy084Y1XdOIxMxkoyzZWFWY8XAHVgX6VVfZpLvuoGq2m8UVRASk6iSSnRqwmIVpE57zJPKHoOPIH90QNx1nbF2e2+tW2197emqmaNBDNSTI5aQqI1wV825rxHmeuPI4c7Z3ptzeCVEm3q81S0khhmPdOgSQdGQ8gPED0I8x69aR3JPb4N3wxyewNKzvRmSOvTlM8hNJwLHvhylPFTE/i4903Uccx7F7Iqb0SnuET01JDKZ+8cU6cffpGy96OTcpeDKWf7YFT8wGxNGjRoDXvbf9ilJ90E/FyaNHbf8AYpSfdBPxcmjQFs2h9idl+51N+KXTbSnaH2J2X7nU34pdNtAGvFsUMQ/uiSTc6gyM7eDk3dqBZ5RyPXHXyAP60kes69pHy14tWSUf3RSCP0iXuiJXEJh9zLC0v4xJ8PUDjny669H4d/7fyS9D57/yBpPZb/3oep7SGjWtfbl26Dy7EbOR6j7cVH//AC6Pbn26/EfZv/GK/wBl15x9CbK1r3t3qBS9nVfM9VV0iBkDVVNFHM1OCcczHI6LIOvHBJ6sDg40qvW+/ogaKz11Zb+wmzz1UFNJJBEN3By8gUlV4imBbJwMZGfhGos++u0GbsOi3p2n7cHZtuLgslZTUlVFd/Y/xYDcsKj5BwV6heR6tx5aA1nuXedkjjv9vpZtw2a5WmsppbjUzJDVrRiash7hGT0od73nBgG5Ex8+WRxGJSXA01HGc7lpu9ulp5hhSycHApVWn+ugWMndqBK3JkzlWUBAku574rbdeN1xrvy921bXPRRpFHtxKgWo1FYi8Y26+lGoyFIHIw8gcDIzZLduSrloUgoty18LW+rtdFNHHQ08xp++9EApD48+696njbDIrh/ekEga22bejX7e7w3K/wBaabcQpQ0NNTwqjrDMnseeVWSXUE5qRgtj1DwlnarhWQ9q9FRzTbgmFZVVskFAsNPHLVd3U3BXhEwrMRmBzJmQYaXHUnLNIz2Xvid9tGKXel0qWN6SzoJrLFTkcYpiLYoZgXVQpIqRksAWzjJDnbm/KebtHqbTU3u7XCpuVa1JTq1uSFGFLNX95SGQHj7kRkPy5OYmHXDhQLv2F10ly2eK2WuuNe0kr/p2uSON6kh3BcIkjhAD4cZGSpbHiydj6ovZBe/bFtdLx7NS3cVBOK+SmSnM+HcZ7tCQoGOPy8c+vV60Aar9d9n1m+49z/HUWrBqv132fWb7j3P8dRaAsGjRo0BqP6LFBJ9D/uqM88MKIeBip+vYPIjy0p+guWJPofNvrBJJJH3lTxaQnLDvWw3Xyz8Hq8umNOPorVduwPdCxjxH0LH8tg1SPoXLrvS1/Q2bVqdpbZi3PWzTT9/6bXi2EoWZu9IMT4YnGU4jHI/BjXWtpwlsr2bNz82aumqq/E8uXw/an8SXxBQfJKDhm6M2a67a1PSOjWtPbl27fEhZv/GK/wBl0e3Lt2+JCzf+MV/suuQ9Q2XrzB26XiipO1Wjo7jW3mOnlpWMsFPDAwiDUN2jWrRpJwHXk/AwvHxzGTjEjd5eantG+iQi7Q9q7fX6Hy2Hbl0FUb3do91xyG2Khh7uTiYV5k85AIwCWxnKhSTXe2zdsto7QbTTnd9xt0NqQ3GWOK1xTRUYejuwNWZnOMsI2Tu3KqFieTyBIArPaxehQRX64Gr3DYAscatUVENNXC3L3NViUp6XyqC3enCMWUZ6KPFz0z2eVwj+iF7XXeS9Uvc3GodgncSyURW40zBZC9RibkQPECWHLwkEB19F9qO+JrRT3dot53SyS2cJWGWGzw1z2pXp6rFR3IYvMH4v0I4+F8HKPx032c3Ot279ER2rXOpvlXao5KurSCRKOKVqfvbpAgjAZsSF3PH1tlgqgnGenA2jCwcPEhiSSclS63fQedtvw/adrx8HGwIOUcOTcml+FVVvhr2ejvFBPdrjZqWpoKjcCxUVypzWJ3FO/BPRKCIUIY1g8bO0beknkV58AFAATPeL1KDuOGKt3Eqtda+neJo6dVDeiVrGikK1R5cOXeGYDk3DiS2SHnrvWrhttjpxvK80/d3WCzvAlijkwHoKSQWxmz4UfmJBVNjHNVB6rl1u/e8FXS3uko79cUf0ivs06tQRMKmWOnuMpowwPRYlUHvVyT3DA9eeOY9E9C6NGjQBqv7G/UWp+7F2/wB4VGrBqv7G/UWp+7F2/wB4VGgLBo0aNAeL/ojYYX+jD7OZGkqBKDawqI7cGAq5MkgHpjpk+RyAc5A17QGvF30R8k8f0YvZoEqJkjke2I0aw8klPpMpHJ8eDGCR5cvLrr0ld91dsdLdKqms3ZFarhQxystPVSbpWBpowejmP0duBI9WTj4dejtr/ZYP5T5/4L+87X/yehf9Gtae3Lt2+JCzf+MV/suj25du3xIWb/xiv9l15x9AWLtQmNPsS7VAade6jR+cABdMSKeYBdA3HzK8hkAj160JX3620VoqLeG3HBWR7VSr9JkWmdVpVSlHpxRakDgD1NOMK2QAo46vm0t9dsG5+y6+Xvtd7M4+z+50VYVokpLglyaanSUcKruwq8D0yImJJAHly4iv3HfMv6ali3vco0k2pHelMdohmDAClxdAysQWXkMU4yG5DpoCoWa7Jc9vT97c9wTrV7aDvPFS0yARtJV4rgj1TZWTmcU7B1THvTlueGu3H3lfupJL1fab0SSiLr6HBKbdzrSyly1YfSO/JwB/0WQRg+I2+xb9qItu1NZLvW8Smh27JfJpDYY0lRC1WxuHdZDc5BG/6XIGCjADIIGGu3pXWa67ppqjfd2oXtM9P4fYGJ1tpnrWQlHJxU+kMCpwSIjkDqMaAWbkr6ih3QJZJdxUEU1dQwGKWKnmaOUexz+j8/SxzMoMRErZMfLowKju9m9gVxmudLepJqq61Hc1rwA1wjX0fgFHoq8JZOZjAAMhxy5DzxgV+t33TWzeMlNWXq58YForatNHQRuKKWoegxCZFJ5d9zAJbBTvVxjKZuPYxe3vVFcy94qbiaGtlo8zUiU/ovAJ+lgFJ5hM/VD77lnqNAbJ0aNGgNe9t/2KUn3QT8XJo0dt/wBilJ90E/FyaNAWzaH2J2X7nU34pdNtKdofYnZfudTfil020AHy14tjEI/uica+hN3jM8nfd0eAAtEi458cc+vlyzgA69pa8TwiL6Y6hCSl8N4uXuX6kS+Qxjn5Z6548cevXo/DnXK/kl6Hz/x9W9l/5oep7Y0aB5aNecj6ANUztbWc7MqPRop2dZEPKnJEydcZTCP19R6DwljkY1c9UHtwmuEPZ5XvbaC71kwK4is8zRVxGevcFUY8vhAAPHl1GgNN3CS6w3bdKdxfoDTSUzBaGmMiUvKqX6xPoDd8XxiZfHxGPCucNZqG3Xl7dSslpWICW34FPUuxwWpowmPR2xESQXwFAQciCCzSViPcO6bLuvdde2xd23SOCahSKmpq7nSu0lUqs1txTqzBSQ1VyPEKvQe+Bnir3KlHD3O1twHFztKk0d0m4uD6KC0YanIFGBx7/hhcK+QW7wsBH2St1qttTOLfXz93dFhzXBoGUBJR3EYFInOjH2rkPlctyJHNZFmW7N2stS+gXCUs8maeoRko3AlqiEkl9DHua5Ji8XibkMHhxiWbBG7K7b8vKx7ojEF8LFrndREO6WCfjwCU4D2w9FjBDMSyk+Ic1y2Gt3S/a3Ej7c3T3ZrKsMtXcD6AAJq7i1Q3o3P0Y+L0b1ZK9McAoG6OyJahdut6TBLG5c+Kdy0zjkwy+UTB6YHQ+EKcnPS9a132GzXGfZscl0t12opuTDurxO0tdgSPgzFkU5/WjB8HDr16bE0Aar9d9n1m+49z/HUWrBqv132fWb7j3P8AHUWgLBo0aNAaj+iwCt2AbqDxiQEUWVKls/p2DpgAk/xA6V/QYqi/Q+7f7uneBHkqJBHIhV1DSk+NSAQ3XqCM5zpl9FqQPofN2FlDALRHB8j+nIOmk/0EvD/4ddtiMTBcz9JjmX6ofqmQDz/XZGc51ssHF5HlsyyXVVrfHNvqujvON7Xsy2tbK4S5XLmzZnly3VZN13rm31ob20aNGsTsDXnLtvFw/RKsEdPS3HjI7lfR0Ywt+lLipcuKdu7m6lSBKvuY59OHOL0brzL281V/j7S6NKOybpqaZKcMj22sZIJWNHdsinCws61oAYN3bZ7swnoxQqBL7TEu1HDVVBt1dRBZ1XvLQWnqYcw1GfR4zSOMkN1PAecmWIjHdad7OLXeaT6ITtQmai495NWyrHSysxgAuMWRDiAk5Knwkdc+9YNwbZnarU7riO4Kum29u/kKKlhji25cS0vOOO4uXtoem4elP3/GU/BBF0XAY0bbdp3Hbe2ftPuA2zfeU9f4PQa+ReQappHzRMsGWiDH3TI48hJyBTkDKhiSVwaSW+1bfY+h9ZnPFwMNqOLGTk9ItScUn/Mv4lXRxLvFJdls23h6Fd4zJcEgxDEzJIPRaf3CT9JHhRHGTKOOW4nm3PMjXciXFYN1GS2VIVJ6xTksUjX0eq9zX9LDnTjOQST4s9VCAxqLjFum22rbEiWrcdUtbc6aU+xt37yKKI0NKCsuabKW0FR3gXxd6TkkFi3e/Vm53fcIl27uVUa616M0lxdokj9CrzyINOCbfnPd8iB3vd4wOHGDQ9TaNGjQBqv7G/UWp+7F2/3hUasGq/sb9Ran7sXb/eFRoCwaNGjQHjD6I0Qj6MPs450LSvI1rCyrGWVAtVI3jbiQo6dMkHPl69ezxrxR9EkI/wD4zuzPnHKT/e0qVbEefS3+qADr6+OSBnPrxr2vr0dt+Vg/lPn/AIN+87X+f0DRo0a84+gK32iCT2nXAxRPI4EZAjGX+qL1QcHyw8wOLZIAxrUN2qK+Cgrbd7X6mOZLGlUamlaSVgAlMBIrGlcNVg5KplieRyG6a232ltUpsi6PRwTzTKiFI6dwkzHvFx3bFWAk/W+E+LGtFvd9zUdb6Z7TN1VMcO04qg0/sjK0cjr6IRG/KDmbofJQTnBkyc6Ay2Frn7W5K5rZdapYrHNVN36SJUSBTUsZO7jpUYVjDIZECNyJUYKgR4qiO8Q3XclN6Pdg1LURqTBGe7hzVHPojCiAl59e9OWxkjpjgMUN83NdKK5XNtj7ppBV2Jqo0sVwljaJnmrCYYRHBkV65IkCHkBw6+9x0pIN4XW/7ooo7TuqnANNNHMl4WGDw1Tu4oGFMMv4SKkHqcgDOQugG24I7rDu2nja2VcBZaYd3Q8pYACaXwBjSNxjbHumCuPCMeMiXYPYqtWKO4NVUskWKh1UysSRjj4EzFH7iv2h65BPX1nVu463dK7w4w7Z3Lhai3L/AHuubGmYF6HkYeVP9ajw+k8cDo3+M5bI7BprpNSXn2Stl0pOFwlWM3CpMpkXC9afMaAUvl3WM58WcHOQNraNGjQGve2/7FKT7oJ+Lk0aO2/7FKT7oJ+Lk0aAtm0PsTsv3OpvxS6ba13tTZG5X2vZ3Xtf3dGrUFOQi01pwvua9BmiJx85Ost62zum1UfpMfa3vOYlgoRKC2SMSf3Mdvdj97p5nUN0rZKVukX/AFqEfQ+xjt4Ttu9tsmUlaT2L9CHHJo2pvqvPPk3L3vqx8ustdb+0uGaSG37p35WYeWOJz7CRLIVxhvFRZVTlup6+HIBBB1MksnaJBahcJN4b+nqS5X0Km9gGlC5xyLPSpH8PQMeny9Na4eNPCvI6tU+xnJtWxYG2ZOXjeSSkt+kluen+DZo6DGjWoTF2j8o4lvPac8xJM8apt0CJMDi3NqcI5LchxViQFJbjyQNloaPtKqK409beO0ujpyAUqHO23APXoypAWBzgdAw65JHXWZ1G2dLr7HVNQmWjlkSSI8/coRK7DBGFUkDPXVKexbyjjeR9/domEBJAprCxIAz0ApiT83npjT7L3RPBHM3a7vOIuoYo9NZ+Sn4DiiIyPkJ0Bni23eqNxMu4XPdvMqBLepKd86tlfEegPmWz6/IdNFLZb1bqWCmF2lIpAlEpW3qealIxyHiOEyMknqCD1x59faNubH/4x7w/k1o/sOuPaNuf4493/wAmtH9h0B1t22b1QW1LY17kkBhjoC5oE6JGrqCQH94cgjryGRk+eO0e3L2t+muzX2VxWRRUhia3pwUQPM4LEMDxPeYGOvlknPQ9o25/jj3f/JrR/YdHtG3P8ce7/wCTWj+w6AfWOmq44pKquneSWcjIeERsoXIwQpI/jHTGPn0z1q20WbtJrtxXi2XHfG8rfbqER+gVzew0hrCeXPMYoPcwMLx8TEgnIXGDlise/HuRoJO0bfaRq7L6S1HYe6IBbDDEBYg8fLjkchkDVYzjO8runXf/AG6evQhO1Zf71cZLXQPWRQd6VZQQTgKCerH5BpFT1slx3dt+tlh7ppbNczwyf2ei69QOh8x8+sXtG3N8cW78fwa0f2HSKt2TuQb5tEX6Lu7SzWq4sHNNaeSgTUeQP0ljByM5GegxjrmxJs3Rqn+0bc/xx7v/AJNaP7DpPUWHeUVbUUdP2m72qTCY1HCktaci3Unk9vEZAGScOfIDoSBqG6JSbHnapsJe03YV02O11e2i5dz+mkhEpj7uZJeikgHPDHn69YeyPs6Xsr2RSbMW8NdPRZJZPSmgERfm5bHEM2MZx56qtlo+0+7x0U0m5d+0S1LqJlqfYRHplMauGZTQ5b3/ABIXJDKwI6al1ds3/TVlfTLurtHeCj4d3VCPbxjqgVyzRqKcyYU9DyRWyfCGHXWyxprD5K+bd95yPYsB7Stsy/tFHLeu67rhvNo6Najnpu0o2+pq7dfu0ipkVc0kcq7fp/SMrlWbnTcogScEOoZcdVz01Otlp35XUENXV7t7SbfPLnnSTx7daSLBIAZo4GQkgZHFj0YZwcgZHUbO1S937fuVwuVtulPfZ6SLb9TLdFgShSQTs9NUQcAzEeId6xAPTqAR1BWDS7e3dPVikm7Ru0Gmz5yyUljMY6Eg8kpW8+JHydM4yM4rv2Lx7grILhfe0G/3Gopl4wyVVts0pjGc+HlQnHUA6rLNXN3l4KDfPdLq19UKtwdoG2KTctqsUnaJTmsjrlaFKGkSq5TkSIYWCMzIxEoyWHHHRcNnEu2XjdVxu1muMtxjEN2j7unho6AE04ZUnZ2d3Ikj8Pds3FSrFVAyeWnKdml1jWJE7Vd0KsDc4gKGzgI2CMr+kehwSPmJ1ipOyqsoa6pudF2m7kp6yswaioit9mWSbHlzYUOW/j1io42a5SVdS+78joc9mUKhB3W9tPju0VdHS91bmZ6Lal9pbbS2tNySN7H03sd3nsco7xGEXllveAKAceLofFkZJdtt3u9wXahe9VEC3SCS1u/oEfgQrIQ/v+qYk4jyPwknqM3tG3L8ce8P5NaP7DpRuzbu+7FYKq52PtF3peq6EL3VEkdmiMhLAEl2ocAKCWPmcKcAnA10Npat0jjbpWbH0a12Ns739h4bhJ2n70jqWhEktIlNZpHVsZKKTRqD16A9PmHqyWjau8rjAZ6vtT3tRnOBHNS2QPjA6+ClcY6/CD0PTVYTjiRU47mSWea+1MV/jtAoeUbkDmCeRHHJcDGOIPTz+HWDY36i1P3Yu3+8KjS72jbn+OLd/wDJrR/YdI9mbK3JJaKhk7Xd2xD2Wui8UprSRkV04J8VETkkZPqyTgAYGrA2bo1R7htDdNFRy1S9r+8HMS8uIpLUxPzBaAsT8gBJ1Xquk7Q6d0ak3Z2g11MUR2migssZ6qGKrHLRK5IB+2VRnIzy6ai9aJp1Zj392AJvntWsfac27HojZfRP0iKISCbuJmkGZOY455Y96cefXy1t0a1ZR2ftNa1z11w3ZvmKpiWHjRU72CWWRmIDgM1IiAJnOSwyAcAnAMYx9oYqGpvZ3tP5nj3XuO3SrjDFyWFOVUL4BgnkxkHEEK5XWeNPEUYydpKkcuBsmDs05zwo05u3v1fE25o1p+lj7UKi7x0U1x7TaWikCg1kp2yRG5bByiQklQviyMnpjjp7Bt/eMtRHC/aL2gQK5wZXpLEUTofPjSk+r1A+eszpLteKSWst7xwzGJ0eOZWEfeHKOHGFyMnw6Rew97khnqGvMi98IKnBt681ZAvkA3vz3fUe96jA+HD7R90fHJu/+S2j+w6BsfdHxybv/kto/sOgBLLfrTSyr7Nz1Ap2mrOUVuQtI8jSMQihurjl8HE5GB6gRWG+0Mryi+TSdzNK6BLehx38vIlPF145Oc5PngDoNHtG3N8cW8P5NaP7Do9o25vji3h/JrR/YdAdqiw3v06CtS9yA0cJoRHHb0wVlMILAls8RwDNnJ88HpgtNu26uoYzHW1jTdwq06cqdY/CoHVcMcqfVnr550p9o+5vji3h/JrR/YdVx7P2kLvePbyb23obMaQzPeG9hQomyMRLF6CWIxnLEjBwADnOqznHDWabrd5ukQ2bT1gr6h6Sinqo4TM0SFwg82IHlrXtTYN7QV/oUfabvmZWJxMlHY+7ABA68qYHPXy49cHGcabx7I3QyKzdsm7ySOuKW0Y/qOrEld7ULrLd9j01VLAsZF1VBxJKsBFJ1BPq64/i0aTds2ztxU+16V5e1fdVSDXoOMtNawB7nJ18FGpz/Ho0BtXaKp7VLLlV/U6m9Q/Yl0bluNJa6OOSoneATzR0yPHHyYPI6ovTB+2YDJGBnJwBnVS2xJUe1u1YrKoAUMHQVDgD3Nfl1YLS8HKpkuNdJ3aCMAzVDcQSSPMn19NTRNVqQ6+okS1TUQ3HUU1RQ8BUVqQws+SPMoVKjOQfejGM+WdQtx7noqCOWonv9Xa0tXcmsC0iy8xM4SM4KsTlhjKeWTny6WvlYycmuhz/AAn/AJ6pnaBcO0Ojq7YvZhQbSuFM6zG4teLtNTtEwMfdd0I1YNkGXlnGMLjPUaWLJ26biIDUN7bKizC3UZrawRRQSFIPETLiRWPTu3HT7xyNRdwb2s9tv8FrqL5PSTRGJ5YEgR0kVw5QFyDxDcHGQR1UAHr1gWy5dqZSrqLzbto8o5YRTU9NcpeUyHue9dpGOEIzUgDic8Iycciohis7Q7ldKeG67E2clBNUotXUDcrtOkGEzIoEPicHmOBIGFU8/EQqxYwXd1vul7u8Ns3hc0exS0aV9vhtySCI1CqYlDGIs4cEHwMSOXUgDAYXavFyM1bbt9vZoqSY0M6GKnKLMviYMZVOG4svrxjB6jObRGLDHGkcdbTqqDCgVAwPm667crHkEV0GR5fpny/26WLFjUdzudkgW2X+YO6q63CBaeQzLjzwQUIOc9B6tdRuK303d009S8koqRQM3d+c+PI4GBn4R06403E1lAx7IQ/yn/nrFDHt2nDrT1NLEJHaRwk4XkzHLMcHqSfM+vSxZLFLL+uj+/8A8tLq+x3OqkL0l9lo1IxxjVWGevXxKfhH+iPlzN9JtP7aJ/Kv+ej0m0/tqn8r/wCeliyNcbRcaulSGiu5oZFYMZkQOxwCMYYEYyQT83q8xCptt7gipHgqN31FRIY+CzPTwBlPh8WFjAJ6H1Y8R6abek2n9tU/lf8Az11ea3yxukFx5yFWKhKkk9B8h0sWMOCetF+9qv1qp7frMOK49iLn6v8AHUWlqSVJVSa6r6gf9Yf/AI6UVjT+2+0v6ZU8vY24DPfvnHe0frzpQo2TwT9Yv3hqm3ncFBbrjdLhU3qppqPb9OZ7jTCmDJ3Zjdw4PEuxCqThCclcYz0I8lTxOK6rHT/CH/46zWa2pdHuD1U1XK8TQrEDWzIBypomOeLetmJJx6zqreVrrKSlka69PK/Qxtc1orrXzVV2neGnohVPRmOMJBGC+ZQ4AY57txjkfe+XXJxWW7xR11TRT7gqK6Z6SK5xw1EEcXdU0hcKwZVUMMocgnK+HljkMzJLDco7dBL6OgrBIhqFW7VXdrHn3RkbzLAe9BABPQkA50ruVtvDZSxqJJIqkKfS7rVRL3XdqSwKhstybAA6EZ6g9NWbaJcpJWZdqXaOoghqm3VNd47nQw19GJ44In9HI+qqiKrkHmmcjwniOmTqAm9rNBtG8X+feE01FRUstfNXpTIJKSm7rmJBEFLMAoLDKtkgjr5ahXGvv9FXVcFPaZXpi0qUkor7kzvIqkL3ipCwRS3EFgzYUEjlgZw2Ot3nPWVEF/tMNOlMSrtTXC7SDnkgcGenRXHl4lJABz69acnKrTXiV5WPFD+1X6mipq2aq3JV1cMdPBI1RPTRQpAJS/AqwQKzHI8JywHd8h4gW5tAqqe/JQVO/wB66ZF4C2zx0kc0nFW5MQoVyeobKgDw+WCdVEXffMtXTwQbfhWCWreBy1xu4ZeJ8TFfRQFBLLhiePiOT4Wxbu+tcCSmVb+rRp3rCMVpDdRkLjqxyfIdfM+QJ1SpE8on0obzU9dbrl7JV14nWimK06Ubxw92sjMArBgvPOcDBYjr5HUq23Gnuk1RBSt4qWQxSchjDAAkeXXzGq3KdvVNattqYb87pOOBlhrmiEikFWEmOHQ4IYNptFaaQqJY57kvNQRmtqFOD8ILZHzaakrEUtE0O5KKV1KiRV+UHH/ppaNv3ZYKmFdxz85omjimaOMtAxBAcDjxYjIOGBGR5Y6aw+xUX+GXP8IT/l6PYqL/AAy5/hCf8vTUtzji47Yv1XPFNRbzrKFEUq8UcEDq5ycNl4yQeuD6vCuAOuWtvttXS1VRNU3E1Mc3Hu4mRQIsDBC4AJz59c/e6aV+xUX+GXP8IT/l6wVaywLSpDW1irmYHNVIScFMZJOT5nUajXcy18E/WL94ar+yFQ2apyi59mLt5gfthPpd3lT/AIdV/wAof/jpPtZ6gW+sVayqUC73Xos7gfX0/wAulCi93qto7Vaau51Z7uCjheoldIyxVEHJiFAJOAD0AJOkdHVpJH6DHemnqJoTVQzMkYYQ5AzgAAgdOuPXpfc6uupLNeKyCvqe+pbXU1ETNKzhZEXKnBJBwfh1ZauhrEnhFDTpLAUkMxkqnRlfA4cQAQQfFnJGOnnq2VpJ/fQUU+e4cEn437FYuF8pKS0wxSboqYZRTSVvpiU0btNDGMs3EIVxhgQFGTjoD11nmuUN2obPHa90yUs1zhFRSyJFFyq0CgseMi9OjBiAAR/ERpstPXpSCS4QJTztlikFU8qEjHrKgn72fm1R6q9dodPZZdwKLZNTNBHLBHDS1s87sxUBWjhEj4GWJZVboASAMkUcmpKO+zaMbVt14+ifAeXHcNtitFvVN1SRNVwiaKvSKOUzxqFDP0Xuxnmp6AAdcAAHEW/7utD3pdv0m757fXi0zXzuqWlSfvaONlVpQxRlIDOg4g8jkYBGdV6Pem9JaBqoQxJVGBQKeaz3aItUYHKMBoQ7Rqc5kCHA4kquenZN471NclPULGIOHOWopLHdauMk+97tljEcqnD+IPgAKeobpdxmlfqvcusNfWvP2Ldca5rhEtJQ7qltNTTUkdZMzQRcjFLzEbyLKvhyY36dD0IOOmp9pjqrnYm9B3Kbg0pcJcYPR24nPqCDgceXl8/w6V0z7jkFKk90iimqCQAtqqSqsF5HLGQcRg9C2Mnp55GpFVDuOlgeolvtOyRLyIjts0jYHqCrKST8gGdUzS4foHCK3zXn7Eg3mksVJUwXm8Gea2pG9XK8QVgsrMIyQgC9cY6fBk+en8dPLIiuvHqM4z5ap8MV2WoqJ4bjEk5ISaQWSoVpOHIDr3njAw2CMjBGPMZ7R1t9Z44lv/FpCQqtaKpSAPWQZPCvXzOB59eh1Ny4foUvC/3I+fsWKvs1yqiDSXKSkIBBKcWzkjrhgfl+/wDNru1orzQGlS4Ms5Xj6TxBcH9dgjGf4sartLU7grJHhp9xRF48cw1sqE45AODylGDgjp8upfou6f8A9xUv8jl/Pai5cC0YQmrU15+xKo9u7gpixqt11FXkNhZKeFApIIGOKA4BIIzny6k6eUsBip44pnEsiLhnKjLH4dVcDcVNVUnpd5gnhlnWN0SnkRiCD5MZSB5D1HXQvUiSRTX1fSRwP0w/lyPy6J5iJ4eWtUxb23qg2pSeED++Cer/ABcmjVf7VTK+3KcSVNQ49NTo8zN/0cnwnRoZ0PtsfY3av4FB+LXUi6fWMf3XtH9cTUfbH2N2r+BQfi11nun1lF92LR/XE10YPzI9qMNs+RPsLlU201FV6UtwrIjxjXu0kHDwuHzgg9TgqfhUkfARDuEcsBiiWV5cBQWdvFjJ+AdfV/t66c6UXoAzRZViPD5Y6dW6nPq+br5awRtE1hbb9fn3TPSVDXQw+nrGql6wxKveDyPscqlfPzlK9T48eIdto3q53C6ol3rNw0rV1KogjEdTLDHIeXLk0tDEEIUL4nbBLYxkZNj3Lc6jbdtluVduangV2KoZzBAiYSRgAZCAxJABy2AFz0AYmuLvy4CGiCXykkrJwh9DepoDI8RVWWXpOB4wwxjPrIUZGowMFJNSmr4O78rObBrCuEm29/3VmwaK3vRySyNcKup71Y1KzMpVSoI5DAGC2evqyOgGpePn1q2HtEiilkPt6oqqJ65oY5RWW54xyV2jjCxvzGe76Zyx5MTgDwu7fv8ApZaoW+O62+53CUMUoILhRrUdDgEIJTkHB9fQg/ML1R0couD8GXfHz6MfPqtT7yjSmmqaOgFWKed4ZlStgTuggUszF3AUgMMqTyHrA1J27uWPcDVsQphTyUMkaOnpMUx8cYdT7mzYBVgRnzz00J5RZsmt9jHmPn0Y+fXXJ+HRk/DoXO2Pn1il6TU/+VJ+KfXfJ+HWKQnv4Bn1yfin1DAqT6mn+SNKqz7LLT9zbh+No9NU+poP3I0hrpnG/bNTZHBrRc3I+UTUIH851IHz+9PzHTTaY90uZ/xtP/VIdK396fmOmm0/f3Mf42n/AKpDrOe9dvozHF3w7fRjyo+t5f8AIb+bVfpGRqqrCvyZZuLDK+E93H06AerB65PXzxgCwVH1vL/kN/NpBEXFbUAwlFLFlcKfFhYx8Jz1J+D3p6dMmyNEKL3ZbLbrbVTUlhpi9ZUo86RCOJqh5JAHJLDjyYMwycE8j4lJ5CibdayXK4VVhrbPZ3ttPFHJKgraOfNWiiNWKqBjMaRBDwTj6OenXA2LuiOSex1ESUlZVBinOCkcpNInNeQUiSPBxn7cdM9G96adFQX1q6nlrKK7c5KeNGSFKiFPCGVsha0x8+IUcm8WSjBm4eG0YxSpHNiYdPmKuz/KGFypuz+qk43K+2+RaWNZTDK0OII5eKjkrA8Q/MLnpnmPLprtY4uz+32ysttnv1oW1ipFSlPSmCOOAsynJ4jGTLg8sDrx82yzcS2q61qzSPR3aESH3NDVzCVIyDyTmlZ74+jwkFSAO8ycl5Md7bPcbZSVUtq29fWjre6niFexqZVd0Unl39VzUAZUp4OJB8/M3dE5ZxeZX5/1DSOs2NJVpfqastLVMlP3aVUciFzAzu2OX60vHIfg5I3rB04t9Xb6ylWW2VEM9OpMatEwZcr0IBHTp5aRWq/X+pr1gr9sXGnikY4kkgiCKDyIJKVDkYARfLOQfIMOOWa93tFAhtFZPIeMLFaRUWKT7ZzzlXkmCmOBPUN1PkKZUtxpGUlvXl/csOjVZq71uGgpI55LXU1ZlKNimt3jjBbBR1M+eXUeIeEYJzjWKh3BuKpuFVHNYq6KKCNZVjkoFVnVuWAsonMbMOGGAwRyU4wQRNF874MtePk0uuP/AFYDy5T/AM6ahx7ir2oWqX2je0lWVYvR2FPzYFQTIMTFeIJx77OR5EddZ55WqKWhnenlgaVZXMUuOcZIjJVuJIyPI4JGR0J1V7yXvRi0o2t9YVn3Yuv9en0389KNrfWFZ92Lr/Xp9SWJd7+x6/8A3FrP6Gr+PLWv739j1/8AuLWf0NbAHlqZ/Lj2v0OVfvEuyP6yF95x6P1C+9f3xwPL16pi0lnuGwaCiv1BDWUVRHSRPDM6ojM0iBMliB0YqceZIwASQDc7y5SnBDYPF8fe+TrqqW5KWXZlugrDVCOWCni/S07wycmZQuHRlYdSM9R01Rb/AL6js3YMnfSvUq9t292ZpQrBQbGpo6cuuKeGqpe6kkyQgx34Vj54BH/rqdTUHZtNcjcYLPT1E5kR3uHp0MrF+8Ij5MZi5LNGGGQc9PWCAy9FtEdCKynqt0U696FcNLWSyEYOV4yciFIbqygEEDDAgazUNtsENc8kHs/30kCczPUV7oVHugHujFeQPTA69WU+ZGtNL1OJOW54nkKbv7QbvRI9ys9FUQUAWfglZTr3cnPio+qhQwMrEMSMc+h5NjWWhptj2e2VdtoLBFBAhaKoppayBgUICNzzKehxxOepI6jzOsXsvsqqpoI5rhfVc1PdRxJcKtZmdznjiOTLqOOMHOPnbriro9idy5rL/uXuI5iolhvNyjTlkjiGikHIZQ9SSPl66dbIblJc6fkMaaq2rXLVy0dlWc1UsXpPo1XTkySurEgkTDqMsD5Zz05DqMVXX7EoEtiVltpYu7kEkQmq6VRTszDxEtL196D4eXQdMnRDa7JLJPHFeN2vHTMUYrW1sivGGGY1cEs5D+Llky4yobuxx1Ko7fYYEejg9s7LPFIjGaquLtxKnlxd2LI3TwsCGBxxOdLKOH8yvsXEzrt60PC0T7Ql7us4x1Cu8ZPEHoGPeHKjAPQnz8vPUmkpKVJg8O16mJ4mhQSSPExIVWVWBEhJ4gkdcHxdMnOkRsdjhhWSKs3vCtT1ceyFylbjnxKQ7sU8vfKFbHvW69ZUG2rRWvGZ6vdpmKqMvdq6DgjLyAYRyKhKhQhbq2R4iWYsTNowgno/Jew/neWQ0DTQGF/TEBUsG/XdcjpqG/1WXr/0j/0jrIluhti2+mgnq5EWrTBqaqSd/tvt5CWPn6ydY2+rS/vj/wBI6zX4mdktIRKb2p/Y5B/DU/FyaNc9qf2OQfw1PxcmjRmTHu2PsbtX8Cg/FrqRdPrKP7r2j+uJqPtj7G7V/AoPxa6z3T6yj+69o/ria3wfmR7UYbZ+7z7C/wCk9761EKgP9qcjOOhPngj/ANfm040mvUaPWQsyklU6dM/bfMdYLebROAMevz9WqFe0nmuMgEV0jhR5freqqSzEyN58JVABwcA4wMAeHV/DDHUnWr71ZUlvjQRbdhaCtrZBM3dx4lZnY85ENE4OGJAJbHmxY+JjeJljpVr6epOqlnmpKoqLk7GtxK9PVVMYGC7B4lEoIXHIMoIz4T1A465NRPRx1lsq6q4GJlMkUiSVJYOASG5NLyCluXhUjkMeLUWz7ehtqy2+jslPRCerDTmnpqeP0in4DAkC0Sq5CNIOHUgsw59fDiuG1dt1JmgrtpUNbBHR80pjbKeRHZIm4jxUgI4A4XqfMjifI23mPN36eXuM6eKtjmqIJbhVSIe9QyJU1PQKPEVHenBJwBxIIxkHqQvPpVwgrKlZTX1DxKUCQvOvLioh5IO9Ic5IYg8SerAkrloVi2xR0cNNJDtO2UExEtNBUU9LCJVBVcIcUShEDE+riBgnkOgUDaqUU8lXDsi1h548T1UUNOJoo++AJQLbwXJ+A5yCx9SnUUTzVu9PcfLXXM1MlZMteI4pIykXeVCPzxyPKMy8WQcVXBIyWPzM1s8MtXRUlSbtNDKhbKzvMhky78iUaU8h4go5ZA49MjiBWL5Z5kuImh2xS1rd5HWemTQQ9/zw2WIFA68i5byJfOD0DEa4qtsWqgrbV6Fsq3TQ0zRozzUsAk5gguGApHAGctlHUFmYj4dSIyit/p7lwo9v3ChWStttzM9Q3vRU1lRJC4A8ipchTkAcgCfn8tNIEui1UXp89LIhL8O6iZCPcW88s2euf9nz6q71N9tiR0NnpGiiZ5aUIHESxy8y/NeFIR1LYycIR16tk6e26eWSvjSS41tWFV8GeFUX6m4zkRJliQemSPPpqrWlm0cSMXlivNe4J7xB+5GkdbT53xZ6vl7y1XKPjj4ZqI5/2aeJ71f8kaVVf2W2r7nXD8bR6g3Gr+8Y/ADqx2mihp6JamnQiWpjikk8RwzCNVHn5dFA6fBquP7xvmOrXbP1NpP3iP8AojVZLpKySdWQJarcYtsUxtNH6UzoJ4fTCY44yfdGD934yq9QvEcj0yucivX6qhaSnpqm2UVYXqngjFTH0XFM0xYe5MAfDjrhcZ8RYBDdajrTyj9w382qVd6q52yUS0kD4qa1w5jgjfwejYVm5SqesqRrkAnqAVVOUqlqVatCx5rNUQuLPtmy1dXHMYzC0aoOIYDlyEDH1r9rjrnPTUOG8WOSijnG2LOzojvVqlOT3QCkqUHo+ZOQBP2vycjqXT3nelRQ3GreL0SdAy0lNNTUpZioDZylYysGwUHJk6sCRgZ1jtO5d3SM63G1VAHdOArQUkcisGBV+S1ckbBlLLxyGDJk8Qy5u0qMssuv/qENdZamphjptu2Z4ZVlZ3EGGVVGMcfR/Mv4cMR5qRyLAaLbXWW4zSyLtqyJQRqmJwgaXkSvLlEYAFUDmc8j70eQJxlp7tu404qZ2blBOxenFPSrJPHxQhUPpRUdRIORIOWXw4BLdKC871aKvqK5R4Y0NNCtHTRvk4JAPprqxHUHkVGTkEjpquiNYYdq2m++KIkd1tweI1G17CYFgaSqeGIuY5FL+FQKYhgQgIJIJLYA8i3V7xbY4Wml2jZB3kxip+KE8xkY54pvczgTdDke5rgnn4ZduvW+O+jNxRDHLyYKKWmTuurALIRVtnyVsr5qxHhIxrNNet2mkoIoY5EqpnAqZjBSlIl5MPFH6VkDDKcq7nwHplsBoX5JdKl/9Q9jirnscUlQtLtu0uYUZkEtLw5eIBSSIDgEHPTPq9XXWe0XKgfcclDZbLbIoRDKVq4gFkLRsqSpxEIXCuyg4kJ8jjBGubLuu8PQVUl527XipgklKqppEMqAkoEAqXBPEL1LAEn1eQf0N0iq6SKoqIzRu6hjDNLGXjJAPFijMuRnBwSMg4JHXQmWDFK9V3xf6IXTXHe6y8Ydv2t4snDtcyrDzx4fRzn1ev1nzx1m15kaC3yTRLHK6StIityAbKZAOBn58D5tTBWUpIAqoiScAcxqPdxj0IfJP/SXTQyjDK7u/D2IPq0k2hPFU2qoqYHDxy3W5ujD1qa6cg/e07+AarHZt02jCD/hlf8A1ubUmo6vf2PX/wC4tZ/Q1sDVNFs9mYK+0d93Xp1vnpu848uHMBeWOmcZ8s6stXdaegqaejmjnL1Ico0cEkiDgATyZVITz6ciM+Qyempk7gkuL9DnUWsaUuhpeTl7mC/yLHBEzFQOR99jHl8oOkFhpKWu2pQUlbTxVEEtJGskUqB0cYHQg9CNPbhNBVAxvFI3csQw8Ser1EjxdD6vl1rh6fflJEljptz2tKVXFLAfa1XlwMJxJmiqlBGHXLjiuQ/lxYDNp9Cs6HKHJuMmlqt/eSty7ev5rJarblkoZBDGIqZDU00CshjCshLUcjIvhUYDHIHwALpfVba3Tc6mFZNr0FHT92sUoSoo5O4KSN3bwF6NiwWMqoDccYwACOb4oaLe57y1R7voIxFIkf6gXaNQegAST03HEHHvTx/i1jFr3i1fU95ui3STIGSRpNu3ZkAVwDxLVnHOcEMvXAyDgZ0SbXOiZ/spLnSj99w6g29VU3oEknZxt2pni90kqe/iWWGYS5DKBTgHCgPyBU8umPttTKWw+lq1Hd+zvb8NLTSk0ipMkq4Z8s/AwqEPUtgFuvr9eq7RDedohhmpd0W7DZpoxJte7SkBcHBV6s4AyME9PfAHORpyH7TWgjqF3XtspJwx/wDK9YD48Yypq8jz65HT141buNFKD3TXn7FntNvgo4ndLRQ2+aXiZRTYIZsZPi4qThmcAkdfPpnGp3EfCNUCO69o0qRyruiyBJXaMctn3BSCoBJINSCB4hg+R6geRxnWp7SSkT+2rb2JQpXO1K4ebhBkelZHVhnOMDLHoCQ14EKWG/415+xeCM9Sw0Y/dDVCS5dosqzMu6bEvo8fevnaNePD8C/prxt+5XLfJrhLn2jSUi1a7osgjfy5bPrw/vmAyhquQ94fMeWD6xlrwGfD+tefsXSt6TUPUfXafzHUFvq0376/9I6RWCq3hdL5QRXTcVvnpVmLvHDtaupHcqrEASyzMqjI8ypz5eZGnr/Vpf3x/wCkdQk7bLucZRSi77Cl9q8sUO26dppUjU1yDLHAz3cnTRqrfRONx7P6HC5PsxD6/wDET6NGZs2Ttj7G7V/AoPxa6Zm11V2ganpDGHhrrfVNzJA4RVAkYdB58VOPlx5aW7YB9rdqOP8AqUH4tdWjbfSeqz61j/nbVlJweZFcSCxIOD3MZS3Gmgm9HnngSXCng0wDYZuK9PPqxAHwkgagXFo6mVJgYjhPc27w4z169Bg6ay0lLO3OamikYFTlkBPhYMv3mAI+UA6WXSJI3ijij4qoUBVyABk/ANVRKK7Ne56duE+5tuI2M4cMDjJGcd58II/iOk1Ttq0vcGrKu4WQVbSFyWmqFJc59XfAdCSQMdD1GDqdVUttjh7qWTccilM844JpSEBIC5CE+rPrb/SOebnHYpqplmmvkFRl4i0Fvnbq3LyYRFR77owOfLr5alwjLeYTlGS5zT7RVXbJs9XK9JWVtrMgqWq2iFRVKRMy5ZsCcYyozjywD6tcXDau36+kSnrrlZhT9SvCqqogSQASSs4z0bzz9t8umUlqt4uBhW7bkWQkuTHTMIuqchiQxcQMDAAbAzxx6hnucdpcCoqGv0KtGY+EFHPJxAwc8RGxDZbz8z8oXpOU0U4t6ySE9JsayPLS1lLU2tzSQiKBlnq2UR8mYLjvyCMu/nnz+DU+gtUVEnoNDebVil7xDGZqh2QSYVlOZiQCePQ+vqOvXUq309mwJaGrvaCGHlxkpZgWXGPeyR8iRnyHw+WsKwW+GqWvhuG5Ye9qmjKi3yYLllPiBh5CPOAD73z+A4OOhbNB1c0RLnt2hq6yW8XW6WhpBCEleSoqAndIynDL3/HAJTOR6xnz1ZqSotVop4rXFPSxJCCiRvWEsMYyPECfWPMnzGlFTFapfS0ev3GFjh90RKOpK8SU94BFhm6L73LAcj+u10porPGKJmq7/KVYycpLZMrTNgEmTEAI65bA4jJIx5AMozQ6JotIdjgiBT6wRKT/AP50COSaRHCKoi5u3jJJHBl6eEfCNLxd7dRU4iaO4cacmHrQzl3KcQeICZf3wOVBBAYjIViGNvq4qsVHdRzpxiP1WB485APTkBn+Ly1DTSGeL0sTJ71f8kaVVn2WWn7nXD8bR6lXIXD0H+9o92HHoAMkevHLpn59LIRXG/2T2Rx6SbZcTJjH7NSfB0zjHloXHz+9PzHVrtn6m0n7xH/RGqo4PBjj1HVqt5YWum4Y5CnTAPlniPPR7g9xKIBGCOmtV3Lt67LKCppLffaK6081WXNOpsU9UCoaQBmaBJFiz3TeGQqykhGCvlRfmn3GlBDIaS3mq5p6QgqH7tU+3KtwyzAdQCACehI89L6+5boJaOz01E7R1ARvSmkjUw8ASykIctyOMeWAeufKr0KN0rKr2edr3Zt2k3RLDZ9u3ehuclAtz9GuW35qYCnZIXDd6U7nJWph8AfkMsCAVYDYvsRav2spP9Qv/DVCr9870p5q1aSy08kdJPLEGamuGWXqIyAtI3PLAcihZQGyC2Blxar9us1Ygv1JSxAwIS1Is8mJeI5KC0QHHks2DnOBGSPdABbLpaafegpRldPcWUWi0j/8rpP9Qv8Aw0G0Wn9q6Qf5hf8AhpFW7zprfOKapS6l2jMi9zaaqYFQ3E9Y4mGcjy88YOMEHWWn3TFUxU88PpxjqUSSMtb516OQF5BkBU9RkMAQOpwOuoplqHHsRaf2roz/AJhf+Gj2ItP7VUf+oX/hrD6XVfrz97R6XVfrz97ShRm9iLV+1lJ/qV/4aPYi0/tZSf6hf+GsPpdV+vP3tHpdV+vP3tKFGYWm1qQy22lBHUEQr/w0r3GFWopFUADjL0Hzpqd6XVfrz97S28u8ponc5YiYf7V0qhVC/wCDSja/1hWfdi6/16fWSVL57MRiE/pMkFshePHHUfruWf4sY1j2v9YVn3Yuv9en1YsWaw/qp/mH/pLqyEA+YGq3YulzyfLuG/pLqx5HwjVWVYvvQHoxz08D/APUPh6ff1r/AHZetqW6SnW/09v5PVMKcy3aCmLuFiYsA8i5bPHoMkcVPTI1sK71droqdaq7XSGhhDcBJNMkalj5DLdM9PLS2aislLN3024mp3nZIwTNCpck4Rclcnq2APl+XROiHqtHRSdxXbZlstKw3200MNKqr3onuVNCqMilODu0oJZcFPX6x5ahGt7NVu0VrltNugroaWGKaJ7rTLLTYHdLC0ffcgVXpnGMHAJJxrZq2OCVOS3mtdTkZDxkfAftdcR7cpoY0hhulZHHGoVEUxgKo8gAE6DVnJWQ1L6n5exr++Xza1tgpae7WOmR6dwaOGW500Ld31BkXnKpwvEA+vqPMDQLttNqVaShsMMqS92DFDdKYe6OOXAkTYLAk/Dk5xnPXYXsBGfO713+lH+Trn2Bix+rFd/pR/kai1wNozaVZ5eK9jXFHctgXG5JALXbPTD3kbhbpTu0aJH0ZlWUnqCw6DPRicdTrtLedhuaarehtb000wphU+ylOI4myRjBkHiGWIAGehPQ41sT2vxZz7LVwP8AlR/ka4bb8Lgq90rSpGCC0fX/AOzS0Tyr+uXivYqVDSdnDQNT26rtUsb8pCIqwN0RupyGyAp6H1AdD06as8EMVNGIYECICSFHkMnJxrN7X4s59la3Pl76Py/0NdvYJf23rv8ATj/J1F0MTE5R72+3U5o8Gpj6es/zHSF/qsv74/8ASOrJSWxKSXvfTqiYjyErKQPvAfDqrVwqe6qvQ8CfMnd8vLlk41PSZ9JU+1P7HIP4cn4uTRqDvz2S9qUXspnvTcVKBuPIJ3b4zx6Zzn/Zo1DRDRZtq9ivYzNtizzTdkuzpJJKCnZ3aw0pLExrkk931Osl77EOyY0fG2dkezBOWAB9haOPA+UtC4/+06te0qulXallBqYgRb6bI5j9jXXe/wBWGowlLTCuLSIrxpKoIQsAzdSB0GWxnrjHr1Vq1QTp2a2rOwzack7ih7OOzGGBpJAnPbMDOsfQLnwgF/M58vVg+Yy1fYXtFbQkdu2D2WrdBOWkmqNoRPAYuuFCK6sG8vEWI8+nXpaamjpKi3T2eax1UtHRKvcL6SoNT4TkK3ecsjJU95xB+UddR9yLLPEWTbFTcXoO6NKsFasJk5uFcZ7xeigBjy8wOmTjNtBoUv8AQNoiqInZ12OB1d2ZztVSHQhQi8cjjjDknJzyUALxPLLbOwy1xXEVF47O+yCqoiDygg2osMmcYHGQlgBnBOVb4Bjz1c90UtPXS1An2xW3P0OiaogaCpjjWpk8X6WXlMvjPFT4wI/EMtkaLxLJNd6d32rX1i0QSaOphqYkUO3PK8GlXkVKJnIK+6DBOGw0Gguo+xjsqacrcOxvs9jh4nBhtNO7E9MZBhAx771/Bqf+gp2IH/8ASXZRz/2DS/kajPTNdNwXaSs2fUx+gtSmjrpa9RFX8gOZREkJTu8BTzUcvVkddZrnQUV7mnqrtte4PJSzGmhaOrRDPEFVxKnCYYXk7Jh+LZVunEgloNDuOxTsPB5foS7J+f2BpfyNc/oKdiHxR7J/ANL+Rpr7DWi52WG3VkDRUxVWFPJMwkjx5Asjk5+ZiNRqS7VKo9NT2SpWOlrfQVLOByhVR7uOZBK9fVyJx6z000FIh/oKdiPq7JNlD/uGl/I1jbsa7ClYhuyrYysOuDY6QEf/AGauXdU3+Er98agV23rLcZTNVTOXKhDxnKAgZxkAgH3x+/poNDXNk7Btkw7pvdxvex9gVlkqxELVQw7Vo4zR8eXImXiWlL5UnOAOIwOpOpcXYj2denn0jsn7PDR8mKkWKES8eRwCCpXIHHJ9ZJ6DHW/XCyWi60ooq+QSRKwYDvCpyPI5Ug6iQ7O2tTU70sEXGF0KFfSHI4kg4GW6DKr0Hljp69UhHK2227fV4Kq0X93btkJRSpCn9A/sU+KHZn4ApfzekdZ2K9jY3xaKZeybZwie1XF3T2CpgrMs1GFJHDqRybB+U/DrZwrKMAAVUX+mNIK2rpTv2zN6TFgWi5DPMfs1FqwFv6CHYr8UOy/wBS/m9JKjsR7M/TqlqTsp2KkXKMRCeyUbJxHVzxWEMD9r1Y+ecdMHZnptJ/hUP+sGqnenkjrq+/U23prhWW2nZreIKlUNYTG5MQDOqAk+HL4GWBz00rUlOim2jsJ213VE9+7Puy8SK4asSm2rAVdDGmQhYZBEneYYjBXjkZzrJUdh22zcbjJS9nfZOaBmQ26FtpRrMi8fGJZORViW8iqAY8wdW/uEo7rX3Sks8r1MlGGMwkGJ2UviEKXwreFTnAHjHU4OsdrhW1XCpFHYJ4UqaWOqaVZwyNPyflCEZ/Aw8J5DCnl1IwNToNCk1HYXb5YahIezzshpZJeQib2qJKYMjw5zgOQcZOFz8A8gxsvYrtGG1wR7i7KOyytuI5Gomotvx0sB8R48Y3SVhheOSX6nPQdBp/tiBLb3TU+1q22tX0MdRUiSeORaeVcYpm92Y8x3jdUUxni3jzxzBpJqyk2tdfRuz+5JIaV6pbZLXQO9ZK0WTTrJ37BGJHDLEIPMNjTQaHSl7GezM1SrW9jnZytP5mSK0wFyMHyUwgefH7b4fm0yHYv2IeQ7J9k9euPYKl/I10t8ctsoa2ppdqVYLRwR+iiqjeSoznkB3koRQnNuRLAsQ5APQtktdhsdtvyPQ2CsgaPkq1YqM06KA2AEMuQPEQAEx1HwZDQaHI7F+xI9R2S7K/ANL+Ro/QX7Evil2V+AaX8jU64UlutVal9p6KWsraiWKkcwFiwSSQAsQTxCrnkT8Cnz6DU+3VMlVNPHV0706xuVjZnX3QDHiGCcA9ehwdNBoIW7GOxBRybsm2SAPWbDS/kaR7y7Eezi4bbraPZHZ/2fW29SqopqufbVJULD4hzbu+IDNw5ccnHLGcjWyWp6R1KtOMH90NLW2vYWp6qlaSUw1kTQzJ6QwBQgggYPh6E+WNOx0/vjp4kNKSplPj7Eey9bNBA/ZhsOe5xwgSSNYKZI5JAuORCp0yepA+XWay9iHZQKc+y/ZNsJ6gkdKewU/ADA6eJSc5z16erVmr9m7Vuc0VRW0qPLCCqOsrIQpJOPCRkDJxnyycYydTqOgs1rqpqqCRI5aoKJC02eXHy6E+rPq1SEckVG7riS6K5+gh2K/FBsz8AUv5vSPZvYv2OT2mpkqOyfZ8ri7XRAz2KmY8Vrp1UZ4eQAAHwADWzfTaP/AAuH/WDSDZNVTLZqlWqIgfZi7ebj/D59WIEdz7Eex8UMvoXZDs3vyuI8WOjTr87RED7x+Y6r9b2HbTdo3tfZn2ZRKFjBjqtuwSksB48uqLkE9BhRj33X3utn3etUWyqNEsdVOsLPHAsoBlYDouc9Mnpn1Z0hp6OmRGsa2aqioaiJppJe+HBZCesWQ/MH1jiOOAfF6tEtbLXpTKfF2DbQSy1cEmw+zCe6ssYpZZdnwGnQgDmZI1Ks+fFjDLjI6HBzCk7CLccLH2c9jpPeI3enaaDChW5LwyerMUweXhCkYYtlbfcQTZ1t67TqaiGnpDUQU/pKxuJlXww8xJkOckcuXEes4yRmq4oLrSWq33Cw1Jhnh5TOtQo9BYKuFdhKHYnJUFOXkeRAOdToRoUuDsKtguVNNUdn/ZB6CHUVECbQQO6ZyxRy/hY+WSCB8B0//QZ7NQ3i7GOzgL8lqgOf/wCgY1MuEr1dntdHJsu4PD3CzvSLWQo9K6hQIWKzBXOGb3rMvgOT1BOO8c7xflpK3ZtXNSi1vUCvatjjjWfkAKUokpbmQzMJACgwfFnA00GhkoOxfsgamVrn2PbCinPvkgstNIg+ZjEpP3hqR+gr2IfFJsj8A0v5Gut3paS65orptmpqYaOnikglhqVXvHk5K8SESq+UCKSWCqea8SSCB2tIo5qT2vybYuVDQzcg7VM8TKBjl1ZZ2fqenT1/f00GgfoKdiPxS7K/ANL+b0foKdiPxSbK/ANL+b1mS4GzrWWiz2OpnhtqQmAiQYqO8LFlRpGAPHAzlvXjpqyRileNXaoUFgCRyA00FIqTdjPYapw3ZRshT8tipB//AI1Wz2DbIffkd1XZHZ/7VVomh9jE2rR949QSD3zTFeXTBAQADBJPXGtkV9ns1zCrWTFuIIHGfj0OM9VI+Afe12ltVnmt7WuWQNTMnAp3v2vwZznVZxzKk2t26uN1re/p6aIcYurKFUdh/Zubhml7KOzz0InrzsMHejy+BePwn1eQHryHcfYj2LGNc9kWyycftBS/kaa0uz9q0jMaaPhzzkelOc5DAnqx6kM2T8vyDTimlt9JBHSw1MQSJQigyA4A6Y89WJNL9snY92R0G2KWah7LdpU8hr0UvFZKZCR3chxkJ5dBo1bO22pppNq0gSeNz7IJ0Vgf+jk0aUKDbKIdt2klF+sYPUP2NdZ7oiegY4Lg3Wz56f8A10esW2PsatP8Bg/FrrPdPrH/AL1s/wDXo9bYPzI9qMdr+RPsZabhZ6uqeQ0V1NEHiVFCU0T8GDglxyU5JXK4OR1zjWCtp/RhHHIyyPxUF+AXJz54Axp5pTeSRLHhiM8QcevqflH/AL9Wsr6DVGv9/V942lYTXQ7iBkqatY0edYYe6XhIxCkQOGPQdCv2vn55SWvfF4vlHBLT19NHJcKpkX0eeKURKqBiI8wAsepyHAAAB5DxY2JuG4zWuiSphqooAC/J5VVlGInK++dPtwnkST5dMllSW/dF2qUSoeut0sOGkbuqUAtGoywB9IIUnqAeoHmRjWkW4qlXfT/UzeIsN5Um/P8AVlLtO/NxXK5xU1NdrdUq9QqsEq4SBGqMXIxTDl5E+fUAEEZwb1cd9bYs1Z3V8uFtt8PcmQyTT4YMCOnAr5YIPLPzZ1HpN31tYk1RDX0TxxusJVaUc0fugxLH0gjB6kesBTnOOWssu6K4Kp9OoqNpZIggqoF8MZDcnIE4JHhHXPQepuuErlqxy6f8LGMe4rNW0VNX2eopayKpmSNGDFVILAEg8TnAOfg+EgZIXDe9pS2LcqhKKNWk7n65ULzwhIDMBn3/AMHkM6W1PaTS0lLG1ZuSywVD8vcX7pXJOSpCmp8sFfX14k9AemebfUvcSSm/2SjPfMirVRJ7mroHgVsVOOTASEHpyAyAOJzCVbzKWLNvmtruXuNX3PRRyNHLSxIFpDWczKvExhC2c4wB0IycYx8GMlDuyyz0AuNc1PSQu5jRjKHDEY8io8jkEfOM4PTS6TfDE+mxXi1+xgCJJVtGncxSOOKF5PSAAO8PljqMqDnrqdDu23NTipbfO2ZIokVJ5I3XgJWGFOe9PEFg2FJJPlnoTqdCYSxJO034L3HVPV22qleGmkjkeN3RgF8mQgMPL1FgP49ZXijE8HgXzfyH+LfVbl3ZQW1MXvfe1oJe8ePJZYQOnFVw8x8QkBz16+9wD11Y4pUmNK6TJKDyPNCCDmJjkY1SRupNrVffiKIUTul8C+Q9Q0qq409t1q8A/U24er/G0em0P1NfmGldX9l1p+5tw/G0mhoNHjj4N4B5H1atVtjVrXTAKoJgTB4jp4Rqrv7xvmOrVa/1NpP3iP8AojUMhkGe13JaaLF8kLQeKVjTxZmUdSD4fDnGMjHmfkIVVEVdXyzQUt1eiMNQMtHHG5Kd2h4EMpwCWz6z8vqFpqPreX97b+bSClA9Lqzk9ZfInyPdp5DJwPvfN1yY/FoyKzKmapl7TL3BuC+24XShkSlqpoYI3rlDRcJcYKikBHTHQs5Az18m1KbfN7mq7iGvkVNGlXPTRIKin8HcpHnHKAkMSTzUl1XPgdverbavctXDVSKl1t0KwTvFIZYkKxLzUAue/BBwfWPXnA6Ax6HeNVUxU6yXC3PJU0oqo5EhQxiNlXixUVDHB8T8gSvB069CTspSW6vBexjy6i6yvwIW0t811zopbjeJbckKSiCMxV4lRmbgF5sIU4NykhHHr9VP63Gm9n7R9lXineSm3BQNNCMVEUchcxvniR5AkcugbHUYOscu66yJRFLNBzcSordzGFDIiPzwZ+qhCxIyTgqeg6tjl3jJC1wrXvdrFvoahIz4EDBeoOZDUBclvIlVx5YJOquLDx/5WMU3ZRTT0ppfR5aaqcBJhMQSpijkQgcfWJB0JHTHUk41FG/KE3KqtqJSyS0pl5IlUC/gQsRxI8/CfWRjrnSiz9p1tud0qKWHdFlr0pgO/mppKcxxBWKszEVJYKzFQMr0wQck6kT7yuFvcSXO8WfuqkuKVVp0jfClSzktVEOvEOMrjBGSMLgzSMniTttN+CGtTvCOKKKanp6aZZoXkUCpGSwdVAGFII98SfMYwAeuGFLuK1zyvRy1UCVkLLHPAGz3cjA4XOBn3rYPrxnSSHeMMUlTTXncdkttTSIROlXwhYMVyjce/JVcEEhupB8xnWek3B6Q8Mce9Ns1DRrDJMIU6skvJomX3bwB4+JUnIPEkZGAIdF8OWLv1fcvcssc0M8feQkMuSucEdQSD/tGoV3VHFEzKCcT+r90ullu3ZZqyp4rvOwViyzMkMdNURlsMR3aZ7w8nyHHQDORgAjq0u3QUXzT/wBJdUZ0Jt7yAUT1oPvaUbXRDQVmUB/vxdfV/wDXT6c6UbW+sKz7sXX+vT6ksWWwxx+yeOC/UH9X7pdZ9wWmSsqKeansNHWtHHLCZJqkwtHHIAHCkKfPA+DyHXWKwfqp/mH/AKS6sequ+hkXTsqZtd4oLFFBatr28VCvg0i3ApCqkeIiQxMeh6AcR0+Dy1St9U1fRT0FdcNrwRssNRM0qtPVgTgpGsZECrkMjucupXIHkRkbh1W97tALePSC3Hi3vWAx1Xr1dPL58/zGsraqy0MRQlmavqINp2JtW42WhnltkRSaNKtYgyvHG7qSSnmMHm/UdDyJ650zh2Nt6CoFXFSkTA5DnBKnGDjI6Z9ePPJ+HTa1q6W2lWUOHEKBg5YtnAznkWOfnJPynz1J0aveZyqW8gew8P7PL95PydHsPD+zy/eT8nU/Rq1sm2QPYeH9nl+8n5Oj2Hh/Z5fvJ+Tqfo0ti2QPYeH9nl+8n5Oj2Hh/Z5fvJ+Tqfo0ti2Q4bZDDIJO8d8Z6MFx/sGqw0cYeTwL9Uf1fujq56pz++k/fH/pHRMJlN7UUQbcgIUD9Ox+r/FyaNc9qP2OQfw6P8XJo0YY92x9jVp/gMH4tdZ7p9Y/962f+vR6wbY+xq0/wGD8Wus90+sf+9bP/AF6PW2D8yPajDa/kT7GX7Se9Fu/hClh1XOB6sn5D/wCmnGk16OKqHy96PPH675TrFbzaIl3TVwUNsWeqt8VZGr+8kp5JsNwbiQsaO3vsAnj0BJ+AGtDdG1WMscNpsJeBGaoPuojwSR7m/o2JcgdQvw/MTdLndLXZqQ193uFNRU4PEy1Eqxpk+rLEDPTy1qGO33S2JSVtHujY9M8bLDDVtXoTJwAJQExdejHIznx/KNVc2pUn9+JMYKbac6fQrSLY29trW1qymgobbCVlMMZggndZGGRl+MAAHFj1BYHJGfXqFTb+2NWTR0sdkt6oA0cne0U0YEKjl4eUGD0U4UkDPEZBbpW6iCrrnnuB3JsWSniMMdUVqwY1cRhVLMIcIxERx5fAPtcW2jrO0O8yzyWfcG2aiIRr4YKzveBYZR8iDyPUjPQ+ry0Up9P6f3NVgcZea9iN7edmRSW2mqLHaFjqlSUcIJmEEbKCMj0bCtjOVcoQQAeumtiq7fua31E9q21t2pFLVGnKM8iKp4BjyDU+Q2Gj6AEEHORjGslDS9qkEAjrprJVSDOZBUtGT06dBTkdD1/95012vabxQSXesvRpu/udw9LVKeUusaCGKNV5FFyfcvg9Y1ZSkJYEVFvN5p8OrvOPY2pENRCm2rGFaMBB354uwI4hx3PQDGc9eoHT1jgWmVZIWXbFhHQM57w5iboTx9x8WDy6njnp5afcD8B0cD8B1azDI/qf33ChLHQTPis27aQiMwQqgclQRwPVBjpnI646YJ9TBIooZII4Y0jUFwAqgAYiYDoPkAH8Ws/A/AddHGJ4Afhf8W+oZaqQlh+pL8w0rq/sutP3NuH42k00h+pL8w0lrqynTfdlt5c9/LaLnMq4PvVmogevzuupLDx/eN8x1arX+ptJ+8R/0Rqqv7xvmOrVa/1Npf3iP+iNQ9xD3Gap+t5f8hv5tV6kJeoq5OTFXmPEMCMYRFPmB6wT6/POdWGo+t5f8hv5tVOxFWkuAQKMV0+QvDz8Pnx9fz9fh1CIQplrrUtZXpJt6nZYJh6Wxt855h3Kh1buCsreZIGcZyWA8RxW64Ur85BtS3QBaSGeMJT1Abu5Q7cTyplIOPNMclyeQBIBmbmrdr320XGyVG5bVCI2SOq754ZREQ/RXRzjOVIGfWNV2zw2azSVdF+iXt2WKlZ4WpWpqVPRpRjlz4uCrZUZBx1U9PghSzaJmKWbSM9e4Z1O5rTUJDONu+k08BPNZLXVd5C4IRzGDT+Ijko6AZAc5Co2MdFe7bcaOdKPa1vhaQyiBKikniQ903RpQ0C8DyKlV68hkqTg4VRW+g2p6FI/adabfVTryeVqGnikq4WwFDAt5chy5Y/XeQzq6m1bk9DenG5/djwCzNQpyXCAN4c4PJgW6+WceQ1dN9JooOtWVTbm5tv3ysSnpdlR0jvI3MzWirgDAdVIMtKnU4fIPwL1PLpZHtMomk7jae3yn2rtKQzZ88gQkDzYeZ88/JpnSUdziaJ6u7Go4KFkUQIiuRzyenUZ5L0z9oPhOpvHSxkf1Py9hPVUFTUvMzWKzOZIRh5JC2ZAvQMO76rnAznOOuPVrDR2fwl6za9jhc4VlgfmOBB5eIxLnzxjHUdemcafcf8A3nRx/wDedLGTrf33C2Kw2hVj7yzW4NDL3sfCnUhGDHg4yOjAYOR5Hy1luvvKH5p/6S6m8dQrsMLRD5J/6S6hlyFpRtb6wrPuxdf69PpvpHsypirLNNWQ57ue6XOVMjB4tWzEZ/iOpJLZYf1T/wAw/wDSXVj1XLB+qn+Yf+kumtfdRQVEUBt9bOJY5ZO8gh5onAA8Wx1DNnw9OpBHwZq9WVZO1Xt5xVE1CFppCjcW6hWb1r+tVv8A3/snx3kywLP6BPCSWBjnHFhj4MZB/iOkt43Bb5yKW5bbq6tORjHCAyqM48XQHAzjr6uOemM6NNEUP7MVFpol5liKaPqyhSfCOpAVMfNwX/JHkJZkjX3zqPL1/D5aocVu2Xd0oXn2Ch9DVYKdqmj4tFGGOFJZclMknByOucZ0xn2psuqVlqNk2KQNknNJFnPw54ZB6Dr8g1C524LnbixXe7W6w2qsvl4q0paG308lVUzvnjFEilnY49QAJ1xZ7tbr/aaO+WirSqobhTx1VNOmeMsTqGVhn1EEHWgfoqN11V8pds9hVFTVEEnaFc4aWqrELcI7dFIr1IDKMh8CNcEAYkPXzxvSglprZQ01tt9DDBTUkSQQxI+FjjUAKoGOgAAGsMPFeJjTgt0a8XrXcq8T1tp+HLZdgwNonefFcml0ZE1FPtclJdWXrG2jUD2Tf9hT/Wf8tHsm/wCwp/rP+WuimeVlZP0ageyb/sKf6z/lo9k3/YU/1n/LSmKZP1Tm99J++v8A0jqywV7TTLEYkHLPUPn1fNqtP76T98f+kdEEU7tR+xyD+HR/i5NGjtR+xyD+HR/i5NGjDHu2PsatP8Bg/Frp7bbbS3T0inq1YoklLULg4Ikjk7xD/Eyqf4tItsfY1af4DB+LXVn239Xq/wDJj/nbVra1QlFSVMmVMt3iqxFT0hlp+MZM3fop5GQBhx4+QQls564x686jVslQvBqhjG7ADiWU4JPlnGnelV3DGVOIz0XPzZPyj/1+bVSEVCv3oltqFjr5aKnhZGbnJWBXzmTgOBQZzwHr6Hl58cnmXesNtkij3E8Ft7zmfFVI3hHQFRxHIkgjA/267y3Pc0lWlNb6F0Rm4STSUScFct745qFJUKR1UNnBwT5DrX3jdcEyNBbnNNJx5YpElaIMR1bFSC3HPUIrE4OM6ukjPnJ3r/1Fp7Sbek0yVc1uiq6ac0pQ16FkOWDhj3fh8SoMevI+AZn3fej2acQVa0iSCIyOJq5YypB971Tzx1PwZHw67R3rdT1ZVrDUQUxcRorQQEoOJHIstScryGeig8SBxzk64uVy3rA0kNBTRytFlu8WiRklBxxVA1UhBHXJbAPqx6zLwUm9bffFfqjPJvS2KIHW50ZWdXdS9SqZUZKlcr1yBn1AfLqAO0OmDLAZKI1AYwyxCuX3OYMy8CSnl0Xr+68vhm+yG7patVSlWCBmLe6UqMVQN70lan3xHrC4+T1a60N43U8bNWWeUGOPqvcxL3kneDqP0w2BwOMH1gnPkpg15Nvol4x9jms3vbqUVDCvpCtOnNy1Sq48J8/CcePgnw+PPqwcdNvqGroIailmo556uVY6SGOuRhPkAniwXqcHOADkY+Hple9blp6iCoez1E9K65kp4oIu9ViowC5qOI8R88EdD1x101pbq9Q6IbRW04IXJl7vCErnHhc+XkSMjPrOlotydq2mu+PsYGuG4Go46mltMM7yKrcBWKBgqpOG4depcDoMhQemcCTbJLtUVJFztccCJC8iulSJMPjHHARfUW66m81/Xj7+stOc96Qc+5N5HRvQwyvfb8vYr8P1JfmGqzcaSobtQsFaseYI7BeImfPkzVFuIH3lb72rND9SX5hpXV/ZdafubcPxtJoaDZ/eN8x00sNRWVK1sXpD8aQwRxIAoGDTxPjJB+2Y6Vye8b5jpptMe63Q/wCNpv6pDrOe+Pb6GOLvj2+jMks9/WghmagPfu6rPF6ShWJD75g3DxYHqwM/Jqtbj3eu3qiGmlPeNNLJGOU8UQAWJXzlwMsWdF4jPQls9MavtT9by/5Dfza17uCjpJbrTVtRUSxzQ1M8VOIKlYs8qVWblmQc/DG3T1ZDcfD3gtv3I0hGF850iBL2mRtSVM1JSGaakDSSxenUo4QqCzOxGSPCrHGPVrrZO09NwNFT2yhlqKkRtJUxRVdO3dKG4qVP24JwPVjz+DPNBU26NK2ooKuunpaYtI1R7JwyxNIvFygLVB4kKOWDxUKD5A4PS1+w0UbyU15rgoEkgaW6pPh/CHQMZ2xgd23EkKAwPmTqaroJUMH6/wBf6Tk9pEFJPJTrQqkFIzCpK1tMDDkqoIUZ85XCHOMefya72XtJF7aWamt0pookRjMKun5DIBbkn2oVSxLE48Pq64jRUlqiRKx6i7PHHVcppprysiU3vT3jFqkgIS4HEZ8z4cMOWWKmtPo1ZEKm59xTQ83la7I3JZADkP6SWBx15NjopwfLNafD78Syw8O7zeT/AKTvbu0WtuFfFb1skneiFmqIlrYO+ikBbKBCoJwAM5wQTjHTOuh7S5lp6mqa1HhTuqZFxpiMM0YViQPDkSE9fgHXxZBTCy11ZDMlbXiSZO7jdbjGVKYwCVjqCD58Q2C2VHXPE6m017prNbLdQ0lXO8JVKaBTPSscLyXiXebDMOK9ORY81wDhuKpff+S/J4XF+f8AQR27SoZqOouVvo5pqWmHiZqmnQ++ABI64BBz85A+HGUdoKtcUp1onFLPD30EzVtKHlUKSxEYJ6DB65+1b4Ora3X6iulPLPTbnpSsJYSYlgfhgkZJRiAPC3mc9DnGp9MJayFamkvcc8TgMjxojKQR5gj5DqNfv/Jd4ODvbfn/AElTHaFcJbiluhsnJpKkUYPsjTnjMyF0RgqnBKjlj4MnrjVqrJJ5IaI1CFJAJQVyDx950yAAfn1ke31EpQT3AuiSJKVEYGSpyOvzjXFxOfRz+7n/AJ01KvpRhPDhBpwd/fYiL6jqudn1PLSbZFJOvGWGvuEbjOcMKyYH/aNWPSja31hWfdi6/wBen1cFnsHS6f5hv6S6aXK0Jc3idrhXU/dZ6U05jDZ/XfDjSuwfqp/mH/pLqx6pJJ7yLcXaFFdt302h9BW+3al91EpmgqQsp6HwciD4eucD4Bqn752NXVgtZoLiKx6YtHwutK9dGS0sZDlUxgjjjJyOOQRjJ1sfVc3kYhHRtNw4rOhwxjGfdE8ufT+b1YIOCIpVQU5ReZbzW8fZ7uM09d3tFtHme6P2Ny++7qDlw8Q6Z554jGeXhBJVelx7PtyrV3kxUO0yvscOHDbcwBkxUYEYDEB+idUJb3uQcDlcYZ4ZKOr7yUGGQU5A7yBxwMNL1HjYHzPvufn0fBGsF2lpUuG4c8Qxs4yVanJKhav5Sx8j0bmv7lfFqMqNP9RicfJCSPs8vvsnQwy27ZJ4UmZpPavIRyLgEZ5gE4Hwqfk9eltr7Pd2cLL6TR7QWTu5BLz27M2EzFjnkgM3n77i3vsMepF/p5KX2dtsipGC9tAXxwZA7zy6HkR/ksy/JnSixVNC8W2nREHuUiJmSmznNPn3r9M9OiFfVmPyAZIj/UYnHyRVbv2f7kS1Tmno9q+GWUxd3tudZOHdzZ+2JHkuAcjPHBB453Ht6lnobDbqOpl7yaCkhjkfr4mVACeoHrHwD5hqi7hlolsdZGVgBE068i9PhGMVThgCwA9fqUkZ9/1zsC0srWujZTlWgjI6g9OI9YLA/wARPznz0UVHVFZYspqpErRo0asZhqnN76T98f8ApHVx1TmHik/fH/pHUolGpPol6qqpNhUMlJUTwsbvEpaJypI7mbpkerRq1dqQztyD+HR/i5NGjDMO2+0LZybdtaNeMFaKAEejy+fdj9zqx2LtM2PTz1BlvfEOqY/S0x8uX7j5dGjSxY4/RW2D+3v/AJWb8jSm89uXZRaquiorpffdbgzRwD0CdweOCc+Dp56NGoIIc3an2HQV9PbGloTPUlggFokx4R1ye7+Uff8An0wj3v2PyIJENvKsAR/et+v/APXo0aA7e3Tsg+C3/gt/zeuPbp2Qf9n/AILf83o0aA59unZD/wBn/gt/zej26dkOMf3v/Bb/AJvRo0Ae3Tsh/wCzvwW/5vR7dOyH4bf+C3/N6NGgD26dkPw2/wDBb/m9Zo+0fs0o6eSKhuMFOrK3hioZUBJHyJ82jRoCpXHe2z7hb/RVvojJ4nrTSlTj1Eceo0tp977Qob/Zab2dMohttwUuaeUdTLSkdOPl0P3tGjU2TY/ftD2cVYezHqP/AFeX8nTOz9omyqBJZhuVM1XdSMjUU54FYUjxkL195n+PRo1D13lZJSq+gnv2p7LdGQ7liwwI+sZ/X/8Ax0or94bDr+BbdkcTRuZAVtjsSxXjnxo3XiSMjHQkaNGgohxX7YMMMlNFvQJDNkSRraAFfIIPIdzg5BPn8OsaXXs4iXjFuyJBknC2VR1OMn6j68D7w0aNNRRkS/bBjjMSbzCozFyotAALEFScdz54JHzE64S+bAjjeJN5KqSgK6izgBgPIEdz1GjRoSrW5gt77PkKlN4qpQYUizgcepPT3Hp1JP8AHo9m+z7EY9uK4iOY/wC848BznI9x6devz6NGhOaXFnVbx2drFLAu7oxHPkyoLKvF8+fIdz1zj16lRbt2fAgjg7QZ40UBQq2wgAAYAAEXqAA0aNA23vbMib02ojqx7QqhwDkq1ubB+Q4izrm5domyudOsd9EmO9ZiKWZQM8enVfkOjRoVSoQS7q2jJeUuo3FhUIPEUs3L3uOOePvfXjHnrjbHaFs9aCrzePfXa5sP0vL5GtmI+1+AjRo1ay9ljs3aVsqnuHey3rC9ywz6NL58l/c/Jp5+itsH9vv/ACs35GjRqGQw/RW2D+33/lZvyNI90dqmyGSlMN8bCzIWYU9QOI7xOvh4k/7fmb3pNGoIFa9q2y2pLh/fxySYst3dU3M91B4kzggefrjORnic5PW6dru0O/vSLfTJGlszEBS1HujYqMgHkMnovQiPz9+ftTRoCXD2qbIF6o2G42ZDQBefcVfFz3nqyfP50z+79Wl1q7V9mMLCz7iIIhlVz3FWApzD4COTdffAAiQdD41z4jRoDpee1TZ/sRLFDfZHkEswWMxVYPWKfAJ5jHIlR75h1HRcckt9m7XtiVVrpZpLy8TmJeaPTzMQcdckKc/f0aNATP0Vtg/t9/5Wb8jR+itsH9vv/KzfkaNGgD9FbYP7ff8AlZvyNVGt39s6rp6inW+GPvjIA4p5cjJPX3ujRqUEUfe+8No0G04qUXwSM1xWXC00qqoMbjABXy/46NGjSybP/9k=)
Predictive prefetching can also apply to other scenarios, such as forecasting pieces of content or widgets that users are most likely to view or interact with and personalizing the experience based on that information. Language Models for Dialog Application, or LaMDA for short, is the newest model and is used to enable Google to have fluid and natural conversations. This helps Google understand how queries relate to pages by looking at the content on a page, or a search query, and understanding it within the context of the page content or query.
real-world machine learning applications
Tuberculosis is more common in developing countries, which tend to have older machines. The machine learning program learned that if the X-ray was taken on an older machine, the patient was more likely to have tuberculosis. It completed the task, but not in the way the programmers intended or would find useful. Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.
Generative AI: is its output protectable by intellectual property rights? – Lexology
Generative AI: is its output protectable by intellectual property rights?.
Posted: Thu, 08 Jun 2023 20:43:05 GMT [source]
The challenge is ensuring that the machine-learning system and the environment coevolve in a way that lets the system make appropriate decisions. Each of these models has its own strengths and weaknesses, and choosing the right model for a given task will depend on the specific requirements of the task. OpenAI provides resources and documentation on each of these models to help users understand their capabilities and how to use them effectively. The challenge for artificial intelligence in disciplines like robotics is that these systems need to ingest, interpret, and respond to visual information. As such, advancements in areas like advanced visual and acoustic sensors, environmental navigation systems and mobility capabilities sought to keep up with the time.
- Each drink is labelled as a beer or a wine, and then the relevant data is collected, using a spectrometer to measure their color and a hydrometer to measure their alcohol content.
- Google didn’t get into the specifics of that at all, In fact, it wasn’t even mentioned during the formal discussions and little more was revealed in talks during breaks than has already been released.
- Today research is ongoing into ways to offset bias in self-learning systems.
- There are four key steps you would follow when creating a machine learning model.
- Medical machine learning systems will learn from data and help patients save money by skipping unnecessary tests.
- During training, the model tries to learn the patterns in data based on certain assumptions.
Significantly, computer vision isn’t necessary in many applications of machine learning. A machine learning system managing a manufacturing line or modeling digital twins for shipping tankers doesn’t have much use for computer vision capabilities. The information these systems need to learn and operate are available as numerical representations. Furthermore, computer vision could be defined as a subset of deep learning. Instead of processing simulated data or statistics, however, computer vision breaks down and interprets visual information. Interset augments human intelligence with machine intelligence to strengthen your cyber resilience.
![how machine learning 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)
Classical, or «non-deep», machine learning is more dependent on human intervention to learn. Human experts determine the set of features to understand the differences between data inputs, usually requiring more structured data to learn. Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. When companies today deploy artificial intelligence programs, they are most likely using machine learning — so much so that the terms are often used interchangeably, and sometimes ambiguously. Machine learning is a subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed. In machine learning, you manually choose features and a classifier to sort images.
What is the life cycle of a ML project?
The ML project life cycle can generally be divided into three main stages: data preparation, model creation, and deployment. All three of these components are essential for creating quality models that will bring added value to your business.
How is machine learning programmed?
In Machine Learning programming, also known as augmented analytics, the input data and output are fed to an algorithm to create a program. This yields powerful insights that can be used to predict future outcomes.