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Monte Carlo Tree Search: A Review of Recent Modifications and Applications

arXiv.org Artificial Intelligence

Monte Carlo Tree Search (MCTS) is a decision-making algorithm that consists in searching large combinatorial spaces represented by trees. In such trees, nodes denote states, also referred to as configurations of the problem, whereas edges denote transitions (actions) from one state to another. MCTS has been originally proposed in the work by Kocsis and Szepesvári (2006) and by Coulom (2006), as an algorithm for making computer players in Go. It was quickly called a major breakthrough (Gelly et al., 2012) as it allowed for a leap from 14 kyu, which is an average amateur level, to 5 dan, which is considered an advanced level but not professional yet. Before MCTS, bots for combinatorial games had been using various modifications of the min-max alpha-beta pruning algorithm (Junghanns, 1998) such as MTD(f) (Plaat, 2014) and hand-crafted heuristics. In contrast to them, MCTS algorithm is at its core aheuristic, which means that no additional knowledge is required other than just rules of a game (or a problem, generally speaking). However, it is possible to take advantage of heuristics and include them in the MCTS approach to make it more efficient and improve its convergence. Moreover, the given problem often tends to be so complex, from the combinatorial point of view, that some form of external help, e.g.


PayScale and Payfactors Join Forces to Create Compensation Technology and Data Powerhouse

#artificialintelligence

Today PayScale, the industry leader in compensation data and technology, and Payfactors, a leading compensation data management company with deep industry expertise, announced that they have merged. Together, the combined company will become one of the largest providers of its kind in North America to help job seekers, employees and businesses get pay right. "Compensation and pay equity strategies are shifting even further on to the C-Suite agenda given the accelerated shift to remote and hybrid work and the overwhelming importance of the social justice movement," said Scott Torrey, CEO, PayScale. "Together, the PayScale leadership team's experience running SaaS businesses at scale combined with Payfactors' deep compensation expertise creates the optimal set of capabilities to offer faster paced innovation to get pay right today and in the future for even the largest of organizations." Both companies have built deep relationships with organizations and individuals by helping people navigate the increasingly complex compensation landscape.


Artificial Intelligence in the Creative Industries: A Review

arXiv.org Artificial Intelligence

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.


How Diffblue uses AI to automate unit testing for Java applications

#artificialintelligence

Developers are critically important to every business, and they're not cheap. According to the Bureau of Labor Statistics, the median annual salary for a software developer in the US is close to $110,000. In San Francisco, it's closer to $145,000, where entry-level developers can command $100,000. Meanwhile, developer tools are typically priced using the size of development team (seats) as a proxy. Production platforms, on the other hand, are typically charged by the size of the environment.


5 Innovative AI Software Companies You Should Know

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With AI often thrown around as a buzzword in business circles, people often forget that machine learning is a means to an end, rather than an end in itself. For most companies, building an AI is not your true goal. Instead, AI implementation can provide you with the tools to meet your goals, be it better customer service through an intuitive chatbot or streamlining video production through synthetic voiceovers. To help shed light on some real-world applications of machine learning, this article introduces five innovative AI software that you should keep on eye on throughout 2020. Scanta is an AI startup with a very interesting history.


Why Racial Bias Still Haunts Speech-Recognition AI

#artificialintelligence

When you ask Siri a question or request a song through Alexa, you're using automated speech recognition software. Companies use AI services to screen job applicants. Court reporters use speech recognition tools to produce records of depositions and trial proceedings. Physicians use software by Nuance and Suki to dictate clinical notes. If you have a physical impairment, you might use speech recognition software to navigate a web browser. YouTube uses it to create automatic captions, whose malaprops inspired a parody series called Caption Fail.


5 Innovative AI Software Companies You Should Know - KDnuggets

#artificialintelligence

With AI often thrown around as a buzzword in business circles, people often forget that machine learning is a means to an end, rather than an end in itself. For most companies, building an AI is not your true goal. Instead, AI implementation can provide you with the tools to meet your goals, be it better customer service through an intuitive chatbot or streamlining video production through synthetic voiceovers. To help shed light on some real-world applications of machine learning, this article introduces five innovative AI software that you should keep on eye on throughout 2020. Scanta is an AI startup with a very interesting history.


Facial recognition software company reveals it was security breach exposing its entire client list

Daily Mail - Science & tech

Facial recognition software provider Clearview AI has revealed that its entire client list was stolen by someone who'gained unauthorized access' to company documents and data. According to a notice sent to its customers, Cleaview AI said that in addition to its client list, the intruder had gained access to the number of user accounts associated with each client, as well as the number of searches conducted through those accounts. The company didn't specify how the security breach had occurred nor who might have been responsible, and it claimed its servers and internal network hadn't been compromised. Facial recognition software company Clearview AI has revealed a security breach that exposed it's client list and number of searches those clients made'Unfortunately, data breaches are part of life in the 21st century,' Clearview attorney Tor Ekeland told The Daily Beast, who broke the story. 'Our servers were never accessed.


The 84 biggest flops, fails, and dead dreams of the decade in tech

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The world never changes quite the way you expect. But at The Verge, we've had a front-row seat while technology has permeated every aspect of our lives over the past decade. Some of the resulting moments -- and gadgets -- arguably defined the decade and the world we live in now. But others we ate up with popcorn in hand, marveling at just how incredibly hard they flopped. This is the decade we learned that crowdfunded gadgets can be utter disasters, even if they don't outright steal your hard-earned cash. It's the decade of wearables, tablets, drones and burning batteries, and of ridiculous valuations for companies that were really good at hiding how little they actually had to offer. Here are 84 things that died hard, often hilariously, to bring us where we are today. Everyone was confused by Google's Nexus Q when it debuted in 2012, including The Verge -- which is probably why the bowling ball of a media streamer crashed and burned before it even came to market.


Dimension Five Technologies Inc. Enters into a Share Exchange Agreement with Digital Cavalier Technology Services Inc. - Dimension Five Technologies Inc

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VANCOUVER, BC / ACCESSWIRE / December 12, 2019 / Dimension Five Technologies Inc. (CSE:DFT) (the "Company"), is pleased to announce that it has entered into a share exchange agreement dated December 11, 2019 (the "SEA") with Digital Cavalier Technology Services Inc. doing business as Youneeq ("Youneeq") to acquire all of the issued and outstanding securities of Youneeq (the "Transaction"). The Company and Youneeq have signed the SEA and signatures are being gathered from Youneeq shareholders in order to obtain a fully executed version. The proposed transaction is dependent on all Youneeq shareholders signing the SEA. Youneeq is an award-winning personalization and recommendation engine powered by artificial intelligence (AI). The company is poised to become a leading multi-channel AI personalization engine focused on the anonymous audience, the single biggest segment for marketers.