In particular, our ecosystem has the satisfying the set of idiosyncratic requirements that this ecosystem following set of requirements: has for AutoML solutions. Our framework was piloted and deployed in numerous applications and performed at the level of - AutoML system should be able to work with different types the experienced data scientists while building high-quality ML of data collected from hundreds of different information models significantly faster than these data scientists. We also compare systems and often changes more rapidly than these systems the performance of our system with various general-purpose can be fully documented using metadata and painstakingly open source AutoML solutions and show that it performs better for preprocessed by data scientists for the ML tasks using ETL most of the ecosystem and OpenML problems. We also present the tools.
In the age of big data, data processing and analytics are fundamental, ubiquitous, and crucial to many organizations which undertake a digitalization journey to improve and transform their businesses and operations. Data analytics typically entails other key operations such as data acquisition, data cleansing, data integration, modeling, etc., before insights could be extracted. Big data can unleash significant value creation across many sectors such as health care and retail. However, the complexity of data (e.g., high volume, high velocity, and high variety) presents many challenges in data analytics and hence renders the difficulty in drawing meaningful insights. To tackle the challenge and facilitate the data processing and analytics efficiently and effectively, a lot of algorithms and techniques have been designed and numerous learning systems have also been developed by researchers and practitioners such as Spark MLlib, and Rafiki. To support fast data processing and accurate data analytics, a huge number of algorithms rely on rules that are developed based on human knowledge and experience. For example, Shortest-job-first is a scheduling algorithm that chooses the job with the smallest execution time for the next execution. However, without fully exploiting characteristics of the workload, it can achieve inferior performance compared to DRL-based scheduling algorithm .
This report from the Montreal AI Ethics Institute covers the most salient progress in research and reporting over the second quarter of 2021 in the field of AI ethics with a special emphasis on "Environment and AI", "Creativity and AI", and "Geopolitics and AI." The report also features an exclusive piece titled "Critical Race Quantum Computer" that applies ideas from quantum physics to explain the complexities of human characteristics and how they can and should shape our interactions with each other. The report also features special contributions on the subject of pedagogy in AI ethics, sociology and AI ethics, and organizational challenges to implementing AI ethics in practice. Given MAIEI's mission to highlight scholars from around the world working on AI ethics issues, the report also features two spotlights sharing the work of scholars operating in Singapore and Mexico helping to shape policy measures as they relate to the responsible use of technology. The report also has an extensive section covering the gamut of issues when it comes to the societal impacts of AI covering areas of bias, privacy, transparency, accountability, fairness, interpretability, disinformation, policymaking, law, regulations, and moral philosophy.
Artificial intelligence (AI) is continuing its migration out of the research lab and into the world of business. Leading companies across hundreds of industries are harnessing its power -- from banks analyzing countless data points in seconds to detect fraud, to call centers deploying chatbots to improve customer interactions. These early uses are still fairly limited, but huge advances in deep learning (a subset of machine learning) are starting to impact AI in ways that will soon help society and business tackle a wider set of more general problems. Such advances will also make it possible to automate more complex physical tasks that require adaptability and agility. At Salesforce, we believe AI has tremendous potential for improving the way organizations operate (and you can learn how AI is built into our entire Salesforce Customer 360 here).
There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.
App builders commonly use security challenges, a form of step-up authentication, to add security to their apps. However, the ethical implications of this type of architecture has not been studied previously. In this paper, we present a large-scale measurement study of running an existing anti-fraud security challenge, Boxer, in real apps running on mobile devices. We find that although Boxer does work well overall, it is unable to scan effectively on devices that run its machine learning models at less than one frame per second (FPS), blocking users who use inexpensive devices. With the insights from our study, we design Daredevil, anew anti-fraud system for scanning payment cards that work swell across the broad range of performance characteristics and hardware configurations found on modern mobile devices. Daredevil reduces the number of devices that run at less than one FPS by an order of magnitude compared to Boxer, providing a more equitable system for fighting fraud. In total, we collect data from 5,085,444 real devices spread across 496 real apps running production software and interacting with real users.
Artificial Intelligence and Machine Learning have been making our lives easier for quite some time. Today, we're going to talk about Python For AI & Machine Learning. Though the community keeps discussing the safety of its development, at the same time it is working relentlessly to grow the capacity and abilities of AI and ML. The demand for AI is at its peak, as it is highly used in analysing and processing large volumes of data. Due to the high volume and intensity of this work, it cannot be handled and supervised manually. AI is used in analytics for data-based predictions that enable people to come up with more effective strategies and strong solutions. FinTech applies AI in investment platforms to conduct market research and make predictions about where to invest funds for greater profits. The travel industry utilises AI to launch chatbots and make the user journey better. Python Web App Examples are proof of that. Due to such high processing power, AI and ML are absolutely capable of providing a better user experience, that is not only more apt but also more personal, making it more effective than ever.
This paper reviews the current state of the art in Artificial Intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically Machine Learning (ML) algorithms, is provided including Convolutional Neural Network (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement Learning (DRL). We categorise creative applications into five groups related to how AI technologies are used: i) content creation, ii) information analysis, iii) content enhancement and post production workflows, iv) information extraction and enhancement, and v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, machine learning-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of machine learning in domains with fewer constraints, where AI is the `creator', remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human centric -- where it is designed to augment, rather than replace, human creativity.
Computational intelligence in finance has been a very popular topic for both academia and financial industry in the last few decades. Numerous studies have been published resulting in various models. Meanwhile, within the Machine Learning (ML) field, Deep Learning (DL) started getting a lot of attention recently, mostly due to its outperformance over the classical models. Lots of different implementations of DL exist today, and the broad interest is continuing. Finance is one particular area where DL models started getting traction, however, the playfield is wide open, a lot of research opportunities still exist. In this paper, we tried to provide a state-of-the-art snapshot of the developed DL models for financial applications, as of today. We not only categorized the works according to their intended subfield in finance but also analyzed them based on their DL models. In addition, we also aimed at identifying possible future implementations and highlighted the pathway for the ongoing research within the field.
Value-at-Risk (VaR) and Expected Shortfall (ES) are widely used in the financial sector to measure the market risk and manage the extreme market movement. The recent link between the quantile score function and the Asymmetric Laplace density has led to a flexible likelihood-based framework for joint modelling of VaR and ES. It is of high interest in financial applications to be able to capture the underlying joint dynamics of these two quantities. We address this problem by developing a hybrid model that is based on the Asymmetric Laplace quasi-likelihood and employs the Long Short-Term Memory (LSTM) time series modelling technique from Machine Learning to capture efficiently the underlying dynamics of VaR and ES. We refer to this model as LSTM-AL. We adopt the adaptive Markov chain Monte Carlo (MCMC) algorithm for Bayesian inference in the LSTM-AL model. Empirical results show that the proposed LSTM-AL model can improve the VaR and ES forecasting accuracy over a range of well-established competing models.