The declarative visual animation language decouples The adoption of a standard declarative specification of planning the visualisation engine in the same way PDDL decouples tasks through PDDL (McDermott et al. 1998), fostered models from solvers. PDDL modelers can extend their problems by International Planning Competitions (McDermott 2000), with a single animation profile and visualize the plans has boosted the development of solvers by the research community.

Robots are extending their presence in domestic environments every day, being more common to see them carrying out tasks in home scenarios. In the future, robots are expected to increasingly perform more complex tasks and, therefore, be able to acquire experience from different sources as quickly as possible. A plausible approach to address this issue is interactive feedback, where a trainer advises a learner on which actions should be taken from specific states to speed up the learning process. Moreover, deep reinforcement learning has been recently widely utilized in robotics to learn the environment and acquire new skills autonomously. However, an open issue when using deep reinforcement learning is the excessive time needed to learn a task from raw input images. In this work, we propose a deep reinforcement learning approach with interactive feedback to learn a domestic task in a human-robot scenario. We compare three different learning methods using a simulated robotic arm for the task of organizing different objects; the proposed methods are (i) deep reinforcement learning (DeepRL); (ii) interactive deep reinforcement learning using a previously trained artificial agent as an advisor (agent-IDeepRL); and (iii) interactive deep reinforcement learning using a human advisor (human-IDeepRL). We demonstrate that interactive approaches provide advantages for the learning process. The obtained results show that a learner agent, using either agent-IDeepRL or human-IDeepRL, completes the given task earlier and has fewer mistakes compared to the autonomous DeepRL approach.

Semantic clone detection is the process of finding program elements with similar or equal runtime behavior. For example, detecting the semantic equality between the recursive and iterative implementation of the factorial computation. Semantic clone detection is the de facto technical boundary of clone detectors. This boundary was tested over the last years with interesting new approaches. This work contributes a semantic clone detection approach that detects clones with 0% syntactic similarity. We present Semantic Clone Detection via Probabilistic Software Modeling (SCD-PSM) as a stable and precise solution to semantic clone detection. PSM builds a probabilistic model of a program that is capable of evaluating and generating runtime data. SCD-PSM leverages this model and its model elements to finding behaviorally equal model elements. This behavioral equality is then generalized to semantic equality of the original program elements. It uses the likelihood between model elements as a distance metric. Then, it employs the likelihood ratio significance test to decide whether this distance is significant, given a pre-specified and controllable false-positive rate. The output of SCD-PSM are pairs of program elements (i.e., methods), their distance, and a decision whether they are clones or not. SCD-PSM yields excellent results with a Matthews Correlation Coefficient greater 0.9. These results are obtained on classical semantic clone detection problems such as detecting recursive and iterative versions of an algorithm, but also on complex problems used in coding competitions.

Reinforcement learning has been applied to human movement through physiologically-based biomechanical models to add insights into the neural control of these movements; it is also useful in the design of prosthetics and robotics. In this paper, we extend the use of reinforcement learning into controlling an ocular biomechanical system to perform saccades, which is one of the fastest eye movement systems. We describe an ocular environment and an agent trained using Deep Deterministic Policy Gradients method to perform saccades. The agent was able to match the desired eye position with a mean deviation angle of 3:5+/-1:25 degrees. The proposed framework is a first step towards using the capabilities of deep reinforcement learning to enhance our understanding of ocular biomechanics.

The goal of universal machine translation is to learn to translate between any pair of languages, given a corpus of paired translated documents for \emph{a small subset} of all pairs of languages. Despite impressive empirical results and an increasing interest in massively multilingual models, theoretical analysis on translation errors made by such universal machine translation models is only nascent. In this paper, we formally prove certain impossibilities of this endeavour in general, as well as prove positive results in the presence of additional (but natural) structure of data. For the former, we derive a lower bound on the translation error in the many-to-many translation setting, which shows that any algorithm aiming to learn shared sentence representations among multiple language pairs has to make a large translation error on at least one of the translation tasks, if no assumption on the structure of the languages is made. For the latter, we show that if the paired documents in the corpus follow a natural \emph{encoder-decoder} generative process, we can expect a natural notion of ``generalization'': a linear number of language pairs, rather than quadratic, suffices to learn a good representation. Our theory also explains what kinds of connection graphs between pairs of languages are better suited: ones with longer paths result in worse sample complexity in terms of the total number of documents per language pair needed. We believe our theoretical insights and implications contribute to the future algorithmic design of universal machine translation.

In order to create effective machine learning and deep learning models, you need copious amounts of data, a way to clean the data and perform feature engineering on it, and a way to train models on your data in a reasonable amount of time. Then you need a way to deploy your models, monitor them for drift over time, and retrain them as needed. You can do all of that on-premises if you have invested in compute resources and accelerators such as GPUs, but you may find that if your resources are adequate, they are also idle much of the time. On the other hand, it can sometimes be more cost-effective to run the entire pipeline in the cloud, using large amounts of compute resources and accelerators as needed, and then releasing them. The major cloud providers -- and a number of minor clouds too -- have put significant effort into building out their machine learning platforms to support the complete machine learning lifecycle, from planning a project to maintaining a model in production.

We are providing an Artificial Intelligence and Cognitive Computing Market report for the forecast period 2020 – 2026. The aim of this document is to educate the reader and provide an in-depth analysis of this industry along with the conditions. By going through this report, there is an emphasis on gathering information about product/service of interest. The reader will obtain a complete explanation of the product/service, resolving any queries which may arise while reading this document. We make it a point to provide the valuation of the industry according to the current conditions.

Artificial intelligence technology has reached the point where it's less of a trend than a business necessity across virtually every business. In the next four years, it is expected that AI's industry growth will start to explode and its impact on business and society will begin to emerge. Businesses looking for an effective AI development partner often face difficulties connected with an oversaturated market and a multitude of vendors offering similar services as choosing the right service provider you should consider many factors, starting from the location of the company, it's size, rates, technologies they use, their expertise, and so on. The companies mentioned below are at the forefront of digital transformation. And some of them provide best-in-class AI solutions, products, and consulting services that will unleash even more innovations in the years to come.

It's no secret that the legal field is a competitive environment. Junior lawyers are undeterred by (and perhaps even attracted to) the cutthroat nature of the business, and one-upping the competitor is necessary to get a job in the legal field. Firms turn to the latest and greatest tech development to compete with each other and "keep up with the [legal] Joneses." In 2019 alone, investments in B2B legal tech soared past $1 billion. Still, some legal professionals fear that cutting-edge technology, such as artificial intelligence (AI), will eliminate the role of junior lawyers in the future. It's clear to many, however, that law firms must incorporate new legal tech developments in order to attract top talent, remain a top competitor, and mold their junior lawyers to be better than the next.

Three friends were having morning tea on a farm in the Northern Rivers region in New South Wales (NSW), Australia, when they noticed a drilling rig setting up in a neighbor's property on the opposite side of the valley. They had never heard of the coal seam gas (CSG) industry, nor had they previously considered activism. That drilling rig, however, was enough to push them into action. The group soon became instrumental in establishing the anti-CSG movement, a movement whose activism resulted in the NSW government suspending gas exploration licenses in the area in 2014.2 By 2015, the government had bought back a petroleum exploration license covering 500,000 hectares across the region.3 Mining companies, like companies in many industries, have been struggling with the difference between having a legal license to operate and a moral4 one. The colloquial version of this is the distinction between what one could do and what one should do--just because something is technically possible and economically feasible doesn't mean that the people it affects will find it morally acceptable. Without the acceptance of the community, firms find themselves dealing with "never-ending demands" from "local troublemakers" hearing that "the company has done nothing for us"--all resulting in costs, financial and nonfinancial,5 that weigh projects down. A company can have the best intentions, investing in (what it thought were) all the right things, and still experience opposition from within the community. It may work to understand local mores and invest in the community's social infrastructure--improving access to health care and education, upgrading roads and electricity services, and fostering economic activity in the region resulting in bustling local businesses and a healthy employment market--to no avail. Without the community's acceptance, without a moral license, the mining companies in NSW found themselves struggling. This moral license is commonly called a social license, a phrase coined in the '90s, and represents the ongoing acceptance and approval of a mining development by a local community. Since then, it has become increasingly recognized within the mining industry that firms must work with local communities to obtain, and then maintain, a social license to operate (SLO).6 The concept of a social license to operate has developed over time and been adopted by a range of industries that affect the physical environment they operate in, such as logging or pulp and paper mills. What has any of this to do with artificial intelligence (AI)?