If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
'Lost to be found' started as a public intervention project, where commonly found notebooks were filled with abstract signs and symbols as part of my cognitive drawing practice, and then anonymously left in different parts of the world. Till date some 70 notebooks have been lost in various public places, not bearing any marks of artist, so that drawings may be the only source of interpretation. There are no whereabouts of the original notebooks, if they remain, modified or are destroyed, and no stories of what happened when someone must have found them. Some notebooks from this project were exhibited as part of Open Sessions 10 group exhibition'Marginalia', at The Drawing Center in New York in 2017. In this series of works, I am curating pages from these scanned notebooks to be interpreted and reconstructed by ai algorithm trained on public ImageNet dataset using variational auto encoder and clip.
Author summary Healthcare systems around the world are struggling to accommodate high numbers of the most severely ill patients with COVID-19. Moreover, the pandemic creates a pressing need to accelerate clinical trials investigating potential new therapeutics. While various biomarkers can discriminate and predict the future course of disease for patients of different disease severity, prognosis remains difficult for patient groups with similar disease severity, e.g. patients requiring intensive care. Established risk assessments in intensive care medicine such as the SOFA or APACHE II show only limited reliability in predicting future disease outcomes for COVID-19. In this study we hypothesized that the plasma proteome, which reflects the complete set of proteins that are expressed by an organism and are present in the blood, and which is known to comprehensively capture the host response to COVID-19, can be leveraged to allow for prediction of survival in the most critically ill patients with COVID-19. Here, we found 14 proteins, which over time changed in opposite directions for patients who survive compared to patients who do not survive on intensive care. Using a machine learning model which combines the measurements of multiple proteins, we were able to accurately predict survival in critically ill patients with COVID-19 from single blood samples, weeks before the outcome, substantially outperforming established risk predictors.
Did you miss a session from the Future of Work Summit? Cyberattacks are happening faster, targeting multiple threat surfaces simultaneously using a broad range of techniques to evade detection and access valuable data. A favorite attack strategy of bad actors is to use various social engineering, phishing, ransomware, and malware techniques to gain privileged access credentials to bypass Identity Access Management (IAM) and Privileged Access Management (PAM) systems. Once in a corporate network, bad actors move laterally across an organization, searching for the most valuable data to exfiltrate, sell, or use to impersonate senior executives. IBM found that it takes an average of 287 days to identify and contain a data breach, at an average cost of $3.61M in a hybrid cloud environment.
Deep Learning, which is based on the use of neural networks, can be applied to very different types of information, which call for the use of particular networks better suited to achieving specific objectives. The Artificial Neural Networks (ANN), on which Deep Learning is based, are computational models that mimic the functioning of biological neurons. An ANN is made up of nodes (artificial neurons), single processing units that work in parallel, organized in layers or layers: an input layer, multiple hidden layers and an output layer. The nodes "weigh" the input data by categorizing its aspects, and by connecting to other nodes, they transfer them to the next layer until the output is obtained. The weight is the strength of the connection between nodes and represents the influence, positive or negative, of each input on the specific characteristic that must be identified.
The artificial-intelligence industry is often compared to the oil industry: once mined and refined, data, like oil, can be a highly lucrative commodity. Now it seems the metaphor may extend even further. Like its fossil-fuel counterpart, the process of deep learning has an outsize environmental impact. In a new paper, researchers at the University of Massachusetts, Amherst, performed a life cycle assessment for training several common large AI models. They found that the process can emit more than 626,000 pounds of carbon dioxide equivalent--nearly five times the lifetime emissions of the average American car (and that includes manufacture of the car itself). It's a jarring quantification of something AI researchers have suspected for a long time.
Using artificial intelligence (AI) researchers have found that between 2007 and 2016 online sentiments around climate change were uniform, but this was not the case with vaccination. Climate change and vaccinations might share many of the same social and environmental elements, but that doesn't mean the debates are divided along the same demographics. A research team from the University of Waterloo and the University of Guelph trained a machine-learning algorithm to analyze a massive number of tweets about climate change and vaccination. The researchers found that climate change sentiment was overwhelmingly on the pro side of those that believe climate change is because of human activity and requires action. There was also a significant amount of interaction between users with opposite sentiments about climate change.
To develop a convolutional neural network (CNN)–based deformable lung registration algorithm to reduce computation time and assess its potential for lobar air trapping quantification. In this retrospective study, a CNN algorithm was developed to perform deformable registration of lung CT (LungReg) using data on 9118 patients from the COPDGene Study (data collected between 2007 and 2012). Loss function constraints included cross-correlation, displacement field regularization, lobar segmentation overlap, and the Jacobian determinant. LungReg was compared with a standard diffeomorphic registration (SyN) for lobar Dice overlap, percentage voxels with nonpositive Jacobian determinants, and inference runtime using paired t tests. Landmark colocalization error (LCE) across 10 patients was compared using a random effects model.