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) …
Deep Learning and Machine Learning are two subfields of Artificial Intelligence (AI) that use algorithms to learn patterns and make predictions based on data. Machine Learning algorithms, on the other hand, can have various structures, including decision trees, support vector machines, and more. Machine Learning algorithms, on the other hand, are typically designed for simpler problems with smaller data sets. Machine Learning algorithms, on the other hand, can be trained on smaller data sets and with less computational power. Machine Learning algorithms, on the other hand, are faster and easier to implement on simpler problems.
Large Language Models (LLM) are on fire, capturing public attention by their ability to provide seemingly impressive completions to user prompts (NYT coverage). They are a delicate combination of a radically simplistic algorithm with massive amounts of data and computing power. They are trained by playing a guess-the-next-word game with itself over and over again. Each time, the model looks at a partial sentence and guesses the following word. If it makes it correctly, it will update its parameters to reinforce its confidence; otherwise, it will learn from the error and give a better guess next time.
Accuracy is crucial for success in machine learning, but how do developers measure it? Several mathematical testing methods can reveal how accurate a machine learning model is and what types of predictions it is struggling with. The foundation of machine learning accuracy is the confusion matrix. The confusion matrix is used to compare the predictions of a machine-learning model with reality. True positives and true negatives are predictions that match reality, while false negatives and false positives are incorrect predictions.
Only 35% of projects today are completed successfully. One reason for this disappointing rate is the low level of maturity of technologies available for project management. This is about to change. Researchers, startups, and innovating organizations, are beginning to apply AI, machine learning, and other advanced technologies to project management, and by 2030 the field will undergo major shifts. Technology will soon improve project selection and prioritization, monitor progress, speed up reporting, and facilitate testing. Project managers, aided by virtual project assistants, will find their roles more focused on coaching and stakeholder management than on administration and manual tasks. The author show how organizations that want to reap the benefits of project management technologies should begin today by gathering and cleaning project data, preparing their people, and dedicating the resources necessary to drive this transformation.
Originally published on Towards AI. In recent years, air traffic has become a serious issue in the world. Delays in air traffic are caused by factors such as air system delays, security delays, airline delays, late aircraft delays, and weather delays. Air Traffic Control (ATC) will become more complex in the future decades as aviation grows and becomes more complex, and it must be improved to ensure aviation safety. Nowadays, Artificial Intelligence (AI) plays an important role in data management and ATC decision-making.
Purpose: The purpose of this study is to predict dropouts in two runs of the same MOOC using an explainable machine learning approach. With the explainable approach, we aim to enable the interpretation of the black-box predictive models from a pedagogical perspective and to produce actionable insights for related educational interventions. The similarity and the differences in feature importance between the predictive models were also examined. Design/Methodology/Approach: This is a quantitative study performed on a large public dataset containing activity logs in a MOOC. In total, 21 features were generated and standardized before the analysis. Multi-layer perceptron neural network was used as the black-box machine learning algorithm to build the predictive models.
With January coming to an end, we here in the Northeast let out a collective sigh of relief as the month ends without any major snowstorms that tend to happen in the first month of the year. Weather forecasting is a centuries-old practice that has its roots in divination and other less-than-scientific prediction methods, but as we move into the future, our tools allow us to more accurately predict what the future holds. Our historical approach to weather prediction is based on viewing patterns and creating physics models within the collected data, or Numerical Weather Prediction (NWP). Other methods, such as Deep Learning Weather Prediction (DLWP) that uses historical weather data, are gaining traction as they outperform NWP over longer timeframes. Google's MetNet-2 uses deep learning algorithms and live satellite inputs to create "nowcasts," or weather probability predictions in the immediate future.
AI will form a key component of cyber defense strategies in 2023, allowing companies to move to an entirely new approach to cybersecurity. Because of this, companies look to innovative tools to respond to threats and--even better--prevent them in the first place. Previously, Gartner outlined its top seven cybersecurity trends for last year. With each one, it becomes more apparent that humans will need the support of artificial intelligence and machine learning tools to stay ahead of the curve. These predictions for 2022 are becoming even more potent for this year.
We seem to be in the golden era of AI. Every week there is a new service that can do anything from creating short stories to original images. These innovations are powered by machine learning. We use powerful computers and vast amounts of data to train these models. The problem is, this process leaves us with a poor understanding of how they actually work.