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) …
Boosting refers to any Ensemble method that can combine several weak learners into a strong learner. The general idea of most boosting methods is to train predictors sequentially, each trying to correct its predecessor. There are many boosting methods available, one of the most popular is AdaBoost (Adaptive Boosting). The way for a new predictor to correct its predecessor is to pay a bit more attention to the training instances that the predecessor underfitted. This is the technique used by AdaBoost.
Imagine you have visited a store to buy something, and a sales guy comes to you to know the requirements and help you in finding the right stuff, don't you think it will be helpful. Especially, the newcomers will benefit from such service. Personalization has a magnetic effect. Every user wishes to be treated personally and knows about their requirement. And, the good thing is businesses fulfilling those requirements satisfactorily are said to have accomplished personalization marketing effectively. Automation technology has sprung up to execute enhancive personalization.
With the withdrawal of the U.K. from the European Union, MHRA as part of its new Brexit freedoms is moving to update the country's regulations for software and AI as a medical device without the burden of accommodating the regulatory approaches of EU members. "These measures demonstrate the U.K.'s commitment, following our exit from the European Union, to drive innovation in healthcare and improve patient outcomes," states MHRA's announcement. "Regulatory measures will be updated to further protect patient safety and take account of these technological advances." AI and SaMD technologies have the potential for better diagnosing and treating a wide variety of diseases, but FDA has yet to finalize a regulatory framework for machine learning-based software as a medical device. The agency is considering a total product lifecycle-based regulatory framework for adaptive or continuously learning algorithms.
According to Statista, the total enterprise data volume will double by 2022 worldwide, reaching more than 2 petabytes. And about 80% of this data will be unstructured (think: email, imaging, and other data that cannot be analyzed as is). Undeniably valuable sources of insight, the ever-growing volumes of unstructured data present a problem, though: handling it is less than rewarding for the human workforce. So, how do enterprises make the data they have drive real business value, and how do they do it without overburdening the employees? Intelligent document processing, or IDP for short, might be an answer.
Machine learning can pinpoint "genes of importance" that help crops to grow with less fertilizer, according to a new study published in Nature Communications. It can also predict additional traits in plants and disease outcomes in animals, illustrating its applications beyond agriculture. Using genomic data to predict outcomes in agriculture and medicine is both a promise and challenge for systems biology. Researchers have been working to determine how to best use the vast amount of genomic data available to predict how organisms respond to changes in nutrition, toxins, and pathogen exposure-;which in turn would inform crop improvement, disease prognosis, epidemiology, and public health. However, accurately predicting such complex outcomes in agriculture and medicine from genome-scale information remains a significant challenge. In the Nature Communications study, NYU researchers and collaborators in the U.S. and Taiwan tackled this challenge using machine learning, a type of artificial intelligence used to detect patterns in data.
Usually when we think of computers, we probably imagine glowing displays, interconnected networks sharing digital information, and more software applications than anyone one person could ever come close to using -- but that's only part of computing's story. Analog computers, and later mechanical computers, were an integral part of humanity's pursuit of scientific discovery, fueled by our desire to anticipate future events and outcomes. For a species that conquered the entire world thanks to our larger brains and toolmaking prowess, it's no surprise that we've been using artificial tools to augment and enhance our intelligence as far back as our history goes -- and probably even longer than that. From the careful positioning of stones in England, to the soaring water clocks of China's Song Dynasty to the precise arrangement of mechanical gears in the visionary inventions of Blaise Pascal and Charles Babbage, analog and mechanical computers have served our forebearers well and helped them not just survive but thrive by transcending the bounds of our biology. In Salisbury Plain in the south of England, a collection of about 100 massive and roughly even-cut stones form a pair of standing rings whose purpose is lost to history, but whose construction began before the invention of the wheel and took at least 1,500 years to complete, and possibly even longer.
Word embedding is the approach of learning word and their relative meanings from a corpus of text and representing the word as a dense vector. The word vector is the projection of the word into a continuous feature vector space, see Figure 1 (A) for clarity. Words that have similar meaning should be close together in the vector space as illustrated in see Figure 1 (B). Word2vec is one of the most popular words embedding in NLP. Word2vec has two types, Continuous Bag-of-Words Model (CBOW) and Continuous Skip-gram Model , the model architectures are shown in Figure 2. CBOW predicts the word according to the given context, where Skip-gram predicts the context according to the given word, which increases the computational complexity .
In multi-agent reinforcement learning (MARL), the environments are highly stochastic due to the partial observability of each agent and the continuously changing policies of the other agents. One of popular research directions is to enhance the training procedure of fully cooperative and decentralized agents. In the past few years, a number of MARL researchers turned their attention to centralized training with decentralized execution (CTDE). Among these CTDE approaches, value function factorization methods are especially promising in terms of their superior performances and data efficiency. Value function factorization methods introduce the assumption of individual-global-max (IGM) , which assumes that each agent's optimal actions result in the optimal joint actions of the entire group. Based on IGM, the total return of a group of agents can be factorized into separate utility functions for each agent.