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) …
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.
The graph represents a network of 1,583 Twitter users whose tweets in the requested range contained "#selfdrivingcars", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 19 January 2022 at 13:47 UTC. The requested start date was Wednesday, 19 January 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 14-day, 1-hour, 46-minute period from Tuesday, 04 January 2022 at 19:34 UTC to Tuesday, 18 January 2022 at 21:20 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.
We have accepted the use of artificial intelligence (AI) in complex processes--from health care to our daily use of social media--often without critical investigation, until it is too late. The use of AI is inescapable in our modern society, and it may perpetuate discrimination without its users being aware of any prejudice. When health-care providers rely on biased technology, there are real and harmful impacts. This became clear recently when a study showed that pulse oximeters--which measure the amount of oxygen in the blood and have been an essential tool for clinical management of COVID-19--are less accurate on people with darker skin than lighter skin. The findings resulted in a sweeping racial bias review now underway, in an attempt to create international standards for testing medical devices.
Researchers have created a method to help workers collaborate with artificial intelligence systems. In a busy hospital, a radiologist is using an artificial intelligence system to help her diagnose medical conditions based on patients' X-ray images. Using the AI system can help her make faster diagnoses, but how does she know when to trust the AI's predictions? Instead, she may rely on her expertise, a confidence level provided by the system itself, or an explanation of how the algorithm made its prediction -- which may look convincing but still be wrong -- to make an estimation. To help people better understand when to trust an AI "teammate," MIT researchers created an onboarding technique that guides humans to develop a more accurate understanding of those situations in which a machine makes correct predictions and those in which it makes incorrect predictions.
The goal of training an artificial neural network is to achieve the lowest generalized error in the least amount of time. In this article I'll outline a brief description of some common methods of optimizing training. Feature scaling, is the process of scaling the input features such that all features occupy the same range of values. This ensures that the gradient of the cost function is not exaggerated in any particular dimension, which reduces oscillation during gradient descent. Oscillation during gradient descent means the training is not maximally efficient, as it's not taking the shortest path to the minimum of the cost function.
Entering 2022, the world continues to endure the pandemic. But the security industry has, no doubt, continued to shift, adapt, and develop in spite of things. Several trends have even accelerated. Beyond traditional "physical security," a host of frontiers like AI, cloud computing, IoT, and cybersecurity are being rapidly pioneered by entities big and small in our industry. By all appearances, the security industry is in a stage of redefining itself.
The adoption of artificial intelligence in the workplace was greatly accelerated by the pandemic, and as we inch back toward the office setting, these tools are only expected to grow in value. Fifty-six percent of companies improved their relationship with AI during 2021, up 45% from 2020, according to a recent survey conducted by consulting firm, McKinsey. Within the world of HR, recruiting and hiring have seen a particular uptick when it comes to embracing AI tools. "The traditional 9-to-5 culture is gone forever," says Sunny Saurabh, CEO at AI recruiting platform, Interviewer.AI. "A lot of the workforce is going to go remote in the next three to four years and some of the biggest challenges that we'll find in remote settings is collaboration and how to bring in productivity. In some worlds it was only thought possible in a face-to-face setup -- but now you're seeing a lot of AI tools come in and take over that space."
Many algorithms, whether supervised or unsupervised, make use of distance measures. These measures, such as euclidean distance or cosine similarity, can often be found in algorithms such as k-NN, UMAP, HDBSCAN, etc. Understanding the field of distance measures is more important than you might realize. Take k-NN for example, a technique often used for supervised learning. As a default, it often uses euclidean distance. However, what if your data is highly dimensional?
Significant hurdles leaders face this year include managing talent, formulating strategies, operational plans, and organizing employee tasks in ways that ensure everyone accesses growth opportunities. These challenges emphasize the importance of good strategy, and are essential for organizational survival. Vijay Pereira, Professor and head of department of people and organizations, at NEOMA Business School in France, believes artificial intelligence (AI) can help leaders undertake these challenges. For example, his recent work concludes that evolutionary computation and data mining can explore large databases or social media to locate potential talented individuals for recruitment purposes. In addition, machine learning helps reanalyze and recognize patterns from data collected from existing decision support systems to help organizations improve their strategic planning processes.