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Neural Networks


Building a Chess Engine: Part 2

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Hi everyone, this will be the second instalment in my tutorial series for building a chess engine. This lesson will focus on building an AI agent that we can play. This lesson is going to be more technical than part 1, so please bear with me. I try to supply both equations and diagrams to help make things a little easier. Now that we have finished building our chess game, we can begin designing an AI that plays it.


Landing AI: Unlocking The Power Of Data-Centric Artificial Intelligence

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Artificial intelligence (AI) has been hugely transformative in industries with access to huge datasets and trained algorithms to analyze and interpret them. Probably the most obvious examples of this success can be found in consumer-facing internet businesses like Google, Amazon, Netflix, or Facebook. Over the last two decades, companies such as these have grown into some of the world's largest and most powerful corporations. In many ways, their growth can be put down to their exposure to the ever-growing volumes of data being churned out by our increasingly digitized society. But if AI is going to unlock the truly world-changing value that many believe it will – rather than simply making some very smart people in Silicon Valley very rich – then businesses in other industries have to consider different approaches.


Supervised, Semi-Supervised, Unsupervised, and Self-Supervised Learning

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The exponential number of research and publications have introduced many terms and concepts in the domain of machine learning, yet many have degenerated to merely buzzwords without many people fully understanding their differences. The most common, and perhaps THE type that we refer to when talking about machine learning is supervised learning. In simple words, supervised learning provides a set of input-output pairs such that we can learn an intermediate system that maps inputs to correct outputs. A naive example of supervised learning is determining the class (i.e., dogs/cats, etc) of an image based on a dataset of images and their corresponding classes, which we will refer to as their labels. With the given input-label pair, the current popular approach will be to directly train a deep neural network (i.e., a convolutional neural network) to output a label prediction from the given image, compute a differentiable loss between the prediction and the actual correct answers, and backpropagate through the network to update weights to optimise the predictions.


What you should know about developing GPT-3 applications

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Last week, OpenAI removed the waitlist for the application programming interface to GPT-3, its flagship language model. Now, any developer who meets the conditions for using the OpenAI API can apply and start integrating GPT-3 into their applications. Since the beta release of GPT-3, developers have built hundreds of applications on top of the language model. But building successful GPT-3 products presents unique challenges. You must find a way to leverage the power of OpenAI's advanced deep learning models to provide the best value to your users while keeping your operations scalable and cost-efficient.


Artificial intelligence, hardware innovations boost confocal microscope's performance

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Since artificial intelligence pioneer Marvin Minsky patented the principle of confocal microscopy in 1957, it has become the workhorse standard in life science laboratories worldwide, due to its superior contrast over traditional wide-field microscopy. They boost resolution by imaging just one, single, in-focus point at a time, so it can take quite a while to scan an entire, delicate biological sample, exposing it light dosages that can be toxic. To push confocal imaging to an unprecedented level of performance, a collaboration at the Marine Biological Laboratory (MBL) has invented a "kitchen sink" confocal platform that borrows solutions from other high-powered imaging systems, adds a unifying thread of "Deep Learning" artificial intelligence algorithms, and successfully improves the confocal's volumetric resolution by more than 10-fold while simultaneously reducing phototoxicity. Their report on the technology is published online in Nature. "Many labs have confocals, and if they can eke more performance out of them using these artificial intelligence algorithms, then they don't have to invest in a whole new microscope. To me, that's one of the best and most exciting reasons to adopt these AI methods," said senior author and MBL Fellow Hari Shroff of the National Institute of Biomedical Imaging and Bioengineering.


AI Innovations That Made Headlines In 2021

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AI has, by now, proven its power and impact. The artificial intelligence space is constantly evolving and improving with every passing day. Tech companies and researchers are investing big in bringing out innovations due to the massive potential the impact of AI can hold on the world's biggest problems. As we head towards the end of 2021, let us look back at some of the major AI innovations and incidents that took centre stage this year. OpenAI released DALL·E, a 12-billion parameter version of GPT-3 trained to generate images from text descriptions, using a dataset of text-image pairs.


Israel's D-ID Uses AI To Give A Voice To Victims of Domestic Violence

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A chilling video featuring the faces of five Israeli women who were murdered by their husbands has gone viral in an eerie social media campaign that has brought them back to life after death. With artificial intelligence and animation capabilities from Israeli "creative reality" startup D-ID, the videos use the voice of each victim -- as well as realistic facial features and gestures -- to convey the message that someone living in the reality of domestic abuse can and should get out before its too late. The project, dubbed Listen To Our Voices, was created in response to a global and local surge in domestic violence since the start of the pandemic, and in honor of International Day for the Elimination of Violence Against Women on November 25. With deep learning technology, AI startup, D-ID captured the faces, voices, and gestures of the late Michal Sela, the late Esther Aharonovitch, the late Marin Haj Yechieh, the late Esther Barhani, and the late Sagit Ozeri, as they described their own marital difficulties which led to verbal and physical abuse from their spouses. The five victims also encouraged other women who experience similar relationships to talk to experts who know how to deal with these situations.


Building a QABot using Deep Learning

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In this case study, we will make a chatbot/QA Bot Question and answers. Basically, we would be dealing with a CSV file consisting of questions With their corresponding answers. This is a deep learning based problem. In the below two graphs I tried to plot the counts'DifficultyFromQuestioner' and'DifficultyFromAnswerer' to see how the data vary. The plot depicts that the'DifficultyFromAnswerer' in this the too easy and too hard are really less in the count so need to properly divide the data.


explainable-artificial-intelligence-for-human-machine-interaction-in-brain-tumor-localization

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Primary malignancies in adult brains are globally fatal. Computer vision, especially recent developments in artificial intelligence (AI), have created opportunities to automatically characterize and diagnose tumor lesions in the brain. AI approaches have provided scores of unprecedented accuracy in different image analysis tasks, including differentiating tumor-containing brains from healthy brains. AI models, however, perform as a black box, concealing the rational interpretations that are an essential step towards translating AI imaging tools into clinical routine. An explainable AI approach aims to visualize the high-level features of trained models or integrate into the training process.


Learning About Neural Networks

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Approximately billions of nerve cells make up the human brain in the form of a neural network. Neurons process small tasks before activating the next one in order to carry on processing. Learning from the environment is one of the main characteristics of neural networks. Our brains operate in this way. By increasing or decreasing synaptic connections among neurons, the learning process is spread throughout the network so that the more relevant information gets stronger synaptic connections, while less relevant information gets weaker.