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neural network


Technical Challenges of AI in Moderating Hate Speech Even in 2021

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The spread of misinformation and hate speech is increasing on multiple social media platforms affecting a certain group of people. Celebrities and politicians are experiencing the most as primary targets but that is affecting the minds of common people as well. The malicious digital content also contains hate speech regarding different ethnicity and minorities like LGBTQ. Hate speech travels faster than light on social media platforms. This can develop violence, riots, or other dangerous impacts in society. It is seen that AI models and deep learning algorithms are advancing as per time but it is still struggling in moderating hate speech.


Artificial Intelligence in Radiology: Current Applications and Future Technologies

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For lung nodules, CNN have been shown to distinguish between benign and malignant classifications at a higher performance than traditional CADx systems due to their ability to function at higher degrees of noise tolerance (Hosny et al. 2018; Nasrullah et al. 2019). Furthermore, in a study done on patients with non-small cell lung cancer, AI CADx algorithms were able to use CT images to significantly predict which cancers contained EGFR mutations, informing on potential treatment with Gefitinib (Bi et al. 2019). Deep learning algorithms have also been trained to accurately classify prostate cancer on Magnetic Resonance Imaging (MRI), which can promote early treatment as well as decrease the number of unnecessary prostate biopsies and prostatectomy procedures performed (Bi et al. 2019). An additional study reported an AI system that was able to use MRI imaging to accurately generate brain tumour classification differentials at a level that exceeded human performance. The algorithm generated the correct diagnosis in one of its top three differentials 91% of the time, outperforming academic neuroradiologists (86%), fellows (77%), general radiologists (57%), and radiology residents (56%) (Rauschecker et al. 2020).


IBM Research Engineer Abhishek Bhandwaldar On His Machine Learning Journey

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Analytics India Magazine got in touch with Abhishek Bhandwaldar, Research Engineer at IBM to understand his machine learning journey. Abhishek has a Master's in Computer Science from the University of North Carolina. "It is important to have a basic understanding of the different topics in the field to make sure you end up in the area you feel most passionate about," says Abhishek. Abhishek: My introduction to AI was through video games. Then, I read about how'Deep Blue' devised long-term strategies and beat an expert opponent in chess.


Radiology: Artificial Intelligence

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Although AI has generated excitement for the future of radiology, hopes for an automated radiological future have been dashed by reports of poor generalization of deep learning models. Models trained on images from one hospital can perform poorly when tested on images from a different one, often related to differences in disease prevalence between hospitals. Perhaps more concerning, deep learning models trained on chest radiographs (CXRs) with an underrepresentation of females have been shown to be biased for a variety of thoracic diseases; not surprisingly, these models performed better on CXRs of male patients. Biases and underrepresentation in datasets was one of several topics covered at this year's Conference on AI, Ethics, and Society, organized by the Association for the Advancement of Artificial Intelligence (AAAI) and the Association for Computing Machinery (ACM). Because AI models can reflect biases in the datasets used to develop them, detecting the presence of biases and addressing them is an important task.


A Bot that Bird Watches so You Don't Have To

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If you have ever tried to watch a live nest camera hoping to observe a falcon or other interesting bird, you may have had the experience of opening the live stream and seeing an empty nest. Not sure how long you should wait for the bird to return? In this article, I will describe my final project for the 12-week Metis Data Science Bootcamp that I attended January–March 2021. My project was aimed at automating nest monitoring for the Nottingham Trent University Falcon Cam. Using deep learning and automation tools, I designed a method for 24-hour bird detection that serves as an infrastructure upon which a Twitter bot or other notification system can be built to notify users when the bird enters or leaves the nest.


Deep Probabilistic Decision Learning Returns Perfect Flow to Operations

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FlowOps enables optimal experience, predictions and decisions in the operations of factories and supply chains. In human brains, there are three key learning functions related to how we sense, predict and decide. Findings in computational neuroscience [1, 2] suggest that different parts of brain areas play a distinct but connected role in each function. These can be equated with the three Explainable AI (XAI) engines in Noodle.ai's The interplay between deep learning and probabilistic learning are similar to a human brain's thinking fast and slow like in Kahneman's System 1 and System 2. System 1 is a fast, intuitive, heuristic, deterministic, differentiable, and more affective mind, whereas System 2 is a slow, deliberate, logical, probabilistic, integrating, and more cognitive mind. Deep learning (Sentinel) enables fast, scalable, and associative pattern detections from high-dimensional, noisy and temporally correlated data, using differential optimizations on flexible functions with deterministic model parameters.


Importance of Loss functions in Deep Learning and Python Implementation

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We know in neural networks, neurons work with corresponding weight, bias and their respective activation functions. The weights get multiplied with the inputs and then activation function is applied to the element before going to the next layer. Finally, we get the predicted value (yhat) through the output layer. But prediction is always closer to the actual (y), which we term as errors. So, we define the loss/cost functions to capture the errors and try to optimize it though backpropagation.


Pointer Networks with Transformers

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The original Pointer Networks paper[1] was originally accepted to NeurIPS 2015, making it quite old in deep learning years. Nonetheless, it has amassed over 1700 citations to date and continues to be integrated into modern solutions[2, 3], has received many improvements [4, 5], and has inspired alternative architectures[6]. It even plays a small, but important role in a state-of-the-art model for playing StarCraft II created by Tencent AI Lab [7]. What is it about pointer networks that makes them so applicable even today? This simple and elegant architecture addresses a subtle complication in sequence prediction problems.


Guest Editorial: Active Learning for Optimal Experiment Design in High Energy Physics

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This entry is a part of the NYU Center for Data Science blog's recurring guest editorial series. Irina Espejo Morales is a CDS Ph.D. student in data science and also a DeepMind fellow. Kyle Cranmer is a CDS professor of data science and professor of physics at the NYU College of Arts & Science. Lukas Heinrich is a staff scientist at CERN working with the ATLAS experiment at the LHC and former NYU graduate student. Gilles Louppe is an associate professor in artificial intelligence and deep learning at the University of Liège (Belgium) and former Moore Sloan fellow.


AI & Law: Legal Stockpiling

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Artificial Intelligence (AI) is gradually and inexorably entering into the legal profession. There is the use of Natural Language Processing (NLP), which we already experience in everyday ordinary interaction with Alexa and Siri and has been increasingly added into various LegalTech systems such as used for contract management, e-Discovery, and the like. Another avenue of AI consists of Machine Learning and Deep Learning. These computational pattern matching techniques are being used to predict court rulings and are also employed to ferret out prior relevant cases amongst a large-scale corpus of online court records. One of the most fascinating and likely law-disruptive AI technologies involves AI-based legal reasoning systems. The notion is that the AI simulates the legal argumentation precepts of human attorneys and essentially carries out a limited form of legal reasoning. Initially, these AI-based legal reasoners would be used as an aid for lawyers and jurists seeking to craft legal arguments. In this semi-autonomous mode, the AI works hand-in-hand with the human legal expert and they jointly establish a robust legal argument or legal posture. Some assert that this capability by the AI will inevitably be further advanced and we will have available fully autonomous AI-based legal reasoning systems that can act in lieu of needing any human legal guidance.