Goto

Collaborating Authors

Results


Ambra Health-Arterys Partnership Accelerates AI

#artificialintelligence

Cloud-based medical image management company Ambra Health announced Tuesday it will partner with the vendor neutral artificial intelligence (AI) platform Arterys. It's a move that will streamline interoperability and accelerate the use of AI applications, the companies said. "We're making AI real by improving the physician experience," said John Axerio-Cilies, chief executive officer of Arterys. "We are increasing diagnosis, treatment accuracy, and ultimately outcomes that matter to patients and providers." This partnership brings together Arterys' seven AI solutions that have been cleared by the U.S. Food & Drug Administration, including Cardio AI, Lung AI, and Neuro AI, with Ambra's interoperable, customizable cloud platform that consolidates multiple imaging systems that allows for secure access to imaging data anywhere, anytime.


Exact Clustering in Tensor Block Model: Statistical Optimality and Computational Limit

arXiv.org Machine Learning

High-order clustering aims to identify heterogeneous substructure in multiway dataset that arises commonly in neuroimaging, genomics, and social network studies. The non-convex and discontinuous nature of the problem poses significant challenges in both statistics and computation. In this paper, we propose a tensor block model and the computationally efficient methods, \emph{high-order Lloyd algorithm} (HLloyd) and \emph{high-order spectral clustering} (HSC), for high-order clustering in tensor block model. The convergence of the proposed procedure is established, and we show that our method achieves exact clustering under reasonable assumptions. We also give the complete characterization for the statistical-computational trade-off in high-order clustering based on three different signal-to-noise ratio regimes. Finally, we show the merits of the proposed procedures via extensive experiments on both synthetic and real datasets.


Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges

arXiv.org Artificial Intelligence

As we make tremendous advances in machine learning and artificial intelligence technosciences, there is a renewed understanding in the AI community that we must ensure that humans being are at the center of our deliberations so that we don't end in technology-induced dystopias. As strongly argued by Green in his book Smart Enough City, the incorporation of technology in city environs does not automatically translate into prosperity, wellbeing, urban livability, or social justice. There is a great need to deliberate on the future of the cities worth living and designing. There are philosophical and ethical questions involved along with various challenges that relate to the security, safety, and interpretability of AI algorithms that will form the technological bedrock of future cities. Several research institutes on human centered AI have been established at top international universities. Globally there are calls for technology to be made more humane and human-compatible. For example, Stuart Russell has a book called Human Compatible AI. The Center for Humane Technology advocates for regulators and technology companies to avoid business models and product features that contribute to social problems such as extremism, polarization, misinformation, and Internet addiction. In this paper, we analyze and explore key challenges including security, robustness, interpretability, and ethical challenges to a successful deployment of AI or ML in human-centric applications, with a particular emphasis on the convergence of these challenges. We provide a detailed review of existing literature on these key challenges and analyze how one of these challenges may lead to others or help in solving other challenges. The paper also advises on the current limitations, pitfalls, and future directions of research in these domains, and how it can fill the current gaps and lead to better solutions.


Making AI, Machine Learning Work for You!

#artificialintelligence

Most data organisations hold is not labeled, and labeled data is the foundation of AI jobs and AI projects. "Labeled data, means marking up or annotating your data for the target model so it can predict. In general, data labeling includes data tagging, annotation, moderation, classification, transcription, and processing." Particular features are highlighted by labeled data and the classification of those attributes maybe be analysed by models for patterns in order to predict the new targets. An example would be labelling images as cancerous and benign or non-cancerous for a set of medical images that a Convolutional Neural Network (CNN) computer vision algorithm may then classify unseen images of the same class of data in the future. Niti Sharma also notes some key points to consider.


Making AI, Machine Learning Work for You!

#artificialintelligence

Most data organisations hold is not labeled, and labeled data is the foundation of AI jobs and AI projects. "Labeled data, means marking up or annotating your data for the target model so it can predict. In general, data labeling includes data tagging, annotation, moderation, classification, transcription, and processing." Particular features are highlighted by labeled data and the classification of those attributes maybe be analysed by models for patterns in order to predict the new targets. An example would be labelling images as cancerous and benign or non-cancerous for a set of medical images that a Convolutional Neural Network (CNN) computer vision algorithm may then classify unseen images of the same class of data in the future. Niti Sharma also notes some key points to consider.


Facebook's AI can generate MRI images in minutes instead of an hour

Engadget

Magnetic Resonance Imaging (MRI) has been providing physicians with vital insights into patients' insides since their development in the 1970s. However, the machines operate at a glacially slow pace and require the patient remain perfectly still. This makes them ill-suited for use with small children (who'd have to be sedated) and people experiencing time-critical medical emergencies such as strokes. Now, after two years of research, teams from Facebook AI and NYU Langone Health have developed a neural network that can cut the amount of time people have to spend in an MRI machine from more than an hour to just a few minutes. The network, dubbed fastMRI, shortens the scanning time because it only requires a quarter as much data to resolve the image.


Tensor denoising and completion based on ordinal observations

arXiv.org Machine Learning

Higher-order tensors arise frequently in applications such as neuroimaging, recommendation system, social network analysis, and psychological studies. We consider the problem of low-rank tensor estimation from possibly incomplete, ordinal-valued observations. Two related problems are studied, one on tensor denoising and another on tensor completion. We propose a multi-linear cumulative link model, develop a rank-constrained M-estimator, and obtain theoretical accuracy guarantees. Our mean squared error bound enjoys a faster convergence rate than previous results, and we show that the proposed estimator is minimax optimal under the class of low-rank models. Furthermore, the procedure developed serves as an efficient completion method which guarantees consistent recovery of an order-$K$ $(d,\ldots,d)$-dimensional low-rank tensor using only $\tilde{\mathcal{O}}(Kd)$ noisy, quantized observations. We demonstrate the outperformance of our approach over previous methods on the tasks of clustering and collaborative filtering.


Artificial Intelligence for Social Good: A Survey

arXiv.org Artificial Intelligence

Its impact is drastic and real: Youtube's AIdriven recommendation system would present sports videos for days if one happens to watch a live baseball game on the platform [1]; email writing becomes much faster with machine learning (ML) based auto-completion [2]; many businesses have adopted natural language processing based chatbots as part of their customer services [3]. AI has also greatly advanced human capabilities in complex decision-making processes ranging from determining how to allocate security resources to protect airports [4] to games such as poker [5] and Go [6]. All such tangible and stunning progress suggests that an "AI summer" is happening. As some put it, "AI is the new electricity" [7]. Meanwhile, in the past decade, an emerging theme in the AI research community is the so-called "AI for social good" (AI4SG): researchers aim at developing AI methods and tools to address problems at the societal level and improve the wellbeing of the society.


What is Deep Learning? Cybiant Knowledge Centre Cybiant

#artificialintelligence

In the previous article, 'What is Machine Learning?" Deep learning has advanced side-by-side with the digital era, which has led to a massive increase of data in all types. This data, also known as big data, is generated from sources like social media, internet search engines, e-commerce websites, among others. However, this data is normally generated as unstructured and because of the sheer quantity of it, it could take many years to sort and analyse all of it. This is where Deep learning and machine learning come into play. Deep Learning is part of a broader family of machine learning methods based on artificial neural networks. Much like machine learning, deep learning can be supervised, semi-supervised, and unsupervised. Deep learning architectures such as deep belief networks, recurrent neural networks, deep neural networks and convolutional neural networks have been applied to various fields like social network filtering, computer vision, natural language processing, and medical image analysis just to name a few. Machine learning is the most common technique in artificial intelligence. From what we explained in the previous article, machine learning is a self-adaptive algorithm that is continually improved through continual analysis of patterns and new information. Deep learning is a subset of machine learning. Let's delve deeper into the definition of Deep Learning and gain a better understanding of why it has become a subset of machine learning. Artificial intelligence is a set of algorithms and intelligence to try to mimic human intelligence. Machine learning is one of them, and deep learning is one of those machine learning techniques."


The virtual assistant Penn Today

#artificialintelligence

The doggie raincoat was cute the first time around, modeled by an adorable mutt and found through an intentionally clicked link. But then, like an Internet phantom, the canine outerwear kept showing up, in ads along the right-hand side of an email browser, in Facebook, and in several news articles. A product seen on a website visited once reappeared as if multiplying. It's an experience that just about anyone who does anything on the web these days has likely had: Click on a link or visit a website and suddenly, that item follows your electronic path. The strategy is called ad remarketing, and it's intended to capture the 98% of would-be consumers who view a product but don't buy it.