In the latest version(0.1.10) of OptimalFlow, it added a Flask-based'no-code' Web App as a GUI. Users could build Automated Machine Learning Models all by clicks, without any coding (Documentation). OptimalFlow was designed highly modularized at the beginning, which made it easy to continue developing. And users could build applications based on it. The web app of OptimalFlow is a user-friendly tool for people who don't have coding experience to build an Omni-ensemble Automated Machine Learning workflow simply and quickly.
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.
Dictation software has come a long way in recent years. It used to be a bit of a gimmick, but today it is changing the way companies do business. Dictation software makes it easier to take notes in meetings, keep track of important conversations, or transcribe documents while on the go. It can also empower persons with disabilities who are unable to type using conventional methods. As the software continues to improve, the number of business applications of this technology is rapidly increasing.
Machine learning is simply a computer learning from data instead of following a recipe. It's meant to mimic how people (and perhaps other animals) learn while still being grounded in mathematics. This post is meant to get you started with a basic machine learning model. Now, we're not re-creating Alexa, Siri, Cortana, or Google Assistant but we are going to create a brand new machine learning program from scratch. This course is meant to be easy assuming you know a bit of Python Programming.
Harries, Luke, Clarke, Rebekah Storan, Chapman, Timothy, Nallamalli, Swamy V. P. L. N., Ozgur, Levent, Jain, Shuktika, Leung, Alex, Lim, Steve, Dietrich, Aaron, Hernández-Lobato, José Miguel, Ellis, Tom, Zhang, Cheng, Ciosek, Kamil
Efficient software testing is essential for productive software development and reliable user experiences. As human testing is inefficient and expensive, automated software testing is needed. In this work, we propose a Reinforcement Learning (RL) framework for functional software testing named DRIFT. DRIFT operates on the symbolic representation of the user interface. It uses Q-learning through Batch-RL and models the state-action value function with a Graph Neural Network. We apply DRIFT to testing the Windows 10 operating system and show that DRIFT can robustly trigger the desired software functionality in a fully automated manner. Our experiments test the ability to perform single and combined tasks across different applications, demonstrating that our framework can efficiently test software with a large range of testing objectives.
To succeed in a machine learning and data science career, there are a lot of different elements you have to know quite well to be effective at your job. In this post, we'll go over the top 3 skills you should master as a data scientist! Data scientists are like engineers, but instead of coding a web app as a frontend engineer would do, they are responsible for architecting data processing pipelines, designing and implementing models, and developing infrastructure for system evaluation and metrics computation. As you can imagine, performing these tasks requires a reasonable amount of fluency with a high-level programming language (think Python, R, Matlab, or Julia), as well as data science specific libraries (think Pandas, Scikit-learn, Matplotlib, or Tensorflow). Developing this skill alone is something that can make up a year or more of an undergraduate computer science degree.
Behavior Trees (BTs) were invented as a tool to enable modular AI in computer games, but have received an increasing amount of attention in the robotics community in the last decade. With rising demands on agent AI complexity, game programmers found that the Finite State Machines (FSM) that they used scaled poorly and were difficult to extend, adapt and reuse. In BTs, the state transition logic is not dispersed across the individual states, but organized in a hierarchical tree structure, with the states as leaves. This has a significant effect on modularity, which in turn simplifies both synthesis and analysis by humans and algorithms alike. These advantages are needed not only in game AI design, but also in robotics, as is evident from the research being done. In this paper we present a comprehensive survey of the topic of BTs in Artificial Intelligence and Robotic applications. The existing literature is described and categorized based on methods, application areas and contributions, and the paper is concluded with a list of open research challenges.
Deep learning, a subset of machine learning and artificial intelligence (AI), has been there since a while, but became an overnight "sensation" when in 2016, Google's AI program, a robot player beat human grandmaster Lee Seedol in the famed game of AlphaGo . Since then, deep learning training and learning methods became widely acknowledged for "humanizing" machines. Many of the advanced automation capabilities now found in enterprise AI platforms are due to the rapid growth of ML and deep learning technologies, as researchers predict deep learning to provide formidable momentum for the adoption and growth of AI, even though most of these experiments are in their infancy. By definition, deep learning is a powerful tool for enterprises looking to gain actionable insights and enable automated responses to a flood of data, especially unstructured data, from all kinds of devices, Internet of Things (IoT), social media and – of course – from corporate data systems. From that perspective deep learning works incredibly well with unstructured data, such as images, sound, time-series of events and so on.