The graph represents a network of 3,936 Twitter users whose tweets in the requested range contained "#iot", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 11 August 2020 at 21:01 UTC. The requested start date was Tuesday, 11 August 2020 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 1-day, 1-hour, 6-minute period from Sunday, 09 August 2020 at 22:54 UTC to Tuesday, 11 August 2020 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.
Machine Learning doesn't have to be a black box anymore. What use is a good model if we cannot explain the results to others. Interpretability is as important as creating a model. In his book'Interpretable Machine Learning', Christoph Molnar beautifully encapsulates the essence of ML interpretability through this example: Imagine you are a Data Scientist and in your free time you try to predict where your friends will go on vacation in the summer based on their facebook and twitter data you have. Now, if the predictions turn out to be accurate, your friends might be impressed and could consider you to be a magician who could see the future.
Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them. However, existing machine learning approaches to diagnosis are purely associative, identifying diseases that are strongly correlated with a patients symptoms. We show that this inability to disentangle correlation from causation can result in sub-optimal or dangerous diagnoses. To overcome this, we reformulate diagnosis as a counterfactual inference task and derive counterfactual diagnostic algorithms. We compare our counterfactual algorithms to the standard associative algorithm and 44 doctors using a test set of clinical vignettes. While the associative algorithm achieves an accuracy placing in the top 48% of doctors in our cohort, our counterfactual algorithm places in the top 25% of doctors, achieving expert clinical accuracy. Our results show that causal reasoning is a vital missing ingredient for applying machine learning to medical diagnosis. In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them, while existing diagnostic algorithms are purely associative. Here, the authors reformulate diagnosis as a counterfactual inference task and derive new counterfactual diagnostic algorithms.
The amount of attention paid to Kubernetes has increased substantially over the past couple of years. What started out as a relatively obscure container management system open sourced by Google has turned into the must-have technology for running machine learning and advanced analytics applications, among other workloads. But is Kubernetes the real deal? Will K8s deliver on the hype, or turn into just another once-shiny thing that lost its luster? Kubernetes certainly seems to be the right technology for the right time.
Bosses don't often play down their products. Sam Altman, the CEO of artificial intelligence company OpenAI, did just that when people went gaga over his company's latest software: the Generative Pretrained Transformer 3 (GPT-3). For some, GPT-3 represented a moment in which one scientific era ends and another is born. Mr Altman rightly lowered expectations. "The GPT-3 hype is way too much," he tweeted last month.
Artificial Intelligence (AI) has promised much so far, but is yet to deliver on its full potential. While many organizations have done few successful POCs (proof-of-concepts), very few organizations have been able to implement AI at scale and derive the promised benefits. One of the key reasons for this anomaly is that most organizations do not seem to have yet discovered the right implementation rhythm for AI programs. AI is neither a pure-play'language' based technology (example, Java) nor a'function / modules' based technology platform (example, HR or Finance ERP) and hence organizations cannot use these implementation rhythms for implementing AI. AI is more a'purpose' based technology (example, voice recognition, document processing) and doing the right experiments at the right stage can help organizations discover their own unique right AI implementation rhythm and achieve three critical success factors for implementing AI in their organizations – (1) create the right AI launch pad (2) maximize scalability and adoption (3) optimize maintainability and maximize Return on Investment (ROI).
When it comes to dimensionality reduction, the Singular Value Decomposition (SVD) is a popular method in linear algebra for matrix factorization in machine learning. Such a method shrinks the space dimension from N-dimension to K-dimension (where K N) and reduces the number of features. SVD constructs a matrix with the row of users and columns of items and the elements are given by the users' ratings.
Many tasks in Machine Learning are setup as classification tasks. The name would imply you have to learn a classifier to solve such a task. However, there are many popular alternatives to solving classification tasks which do not involve training a classifier at all! For the purpose of illustrating these alternative methods, let's use a very simple, yet visual classification problem. Our dataset shall be a random image from the internet.
Tech advances coupled with AI can help insurers manage risk, improve underwriting and boost customer experience. In the wake of the pandemic, people have dramatically changed how they live, communicate, work and shop. COVID-19 has also changed how they interact with critical services such as healthcare and insurance. In a time of change and uncertainty, customers are seeking reassurance and easy transitions to the "new normal." Insurers that take advantage of the new data and customer insights this global digital shift has provided can better assess customer claims and applications, and deliver a better experience.
Computational learning theory, or statistical learning theory, refers to mathematical frameworks for quantifying learning tasks and algorithms. These are sub-fields of machine learning that a machine learning practitioner does not need to know in great depth in order to achieve good results on a wide range of problems. Nevertheless, it is a sub-field where having a high-level understanding of some of the more prominent methods may provide insight into the broader task of learning from data. In this post, you will discover a gentle introduction to computational learning theory for machine learning. A Gentle Introduction to Computational Learning Theory Photo by someone10x, some rights reserved.