This paper proposes a new architecture of incremen-tal fuzzy inference system (also called Evolving Fuzzy System-EFS). In the context of classifying data stream in non stationary environment, concept drifts problems must be addressed. Several studies have shown that EFS can deal with such environment thanks to their high structural flexibility. These EFS perform well with smooth drift (or incremental drift). The new architecture we propose is focused on improving the processing of brutal changes in the data distribution (often called brutal concept drift). More precisely, a generalized EFS is paired with a module of anticipation to improve the adaptation of new rules after a brutal drift. The proposed architecture is evaluated on three datasets from UCI repository where artificial brutal drifts have been applied. A fit model is also proposed to get a "reactivity time" needed to converge to the steady-state and the score at end. Both characteristics are compared between the same system with and without anticipation and with a similar EFS from state-of-the-art. The experiments demonstrates improvements in both cases.

Graphs are an essential part of many machine learning problems such as analysis of parse trees, social networks, knowledge graphs, transportation systems, and molecular structures. Applying machine learning in these areas typically involves learning the graph structure and the relationship between the nodes of the graph. However, learning the graph structure is often complex, particularly when the graph is cyclic, and the transitions from one node to another are conditioned such as graphs used to represent a finite state machine. To solve this problem, we propose to extend the memory based Neural Turing Machine (NTM) with two novel additions. We allow for transitions between nodes to be influenced by information received from external environments, and we let the NTM learn the context of those transitions. We refer to this extension as the Conditional Neural Turing Machine (CNTM). We show that the CNTM can infer conditional transition graphs by empirically verifiying the model on two data sets: a large set of randomly generated graphs, and a graph modeling the information retrieval process during certain crisis situations. The results show that the CNTM is able to reproduce the paths inside the graph with accuracy ranging from 82,12% for 10 nodes graphs to 65,25% for 100 nodes graphs.

Washington D.C. [USA], July 14 (ANI): Researchers developed a new artificial intelligence (AI) tool for detecting unfair discrimination such as race or gender. Preventing unfair treatment of individuals on the basis of race, gender or ethnicity, for example, been a long-standing concern of civilized societies. However, detecting such discrimination resulting from decisions, whether by human decision-makers or automated AI systems, can be extremely challenging. This challenge is further exacerbated by the wide adoption of AI systems to automate decisions in many domains including policing, consumer finance, higher education, and business. "Artificial intelligence systems such as those involved in selecting candidates for a job or for admission to a university are trained on large amounts of data. But if these data are biased, they can affect the recommendations of AI systems," said Vasant Honavar, one of the researchers of the study presented at the meeting of The Web Conference.

Emirates NBD will also use AWS data analytics, Internet of Things, Natural Language Processing, alongside other innovative technology as part of its continuing efforts to better engage with clients and simplify banking. A front runner in retail banking creation, Emirates NBD is working with AWS due to its wide and deep portfolio of cloud solutions and the increased security and controls Emirates NBD can reach from the cloud, and is continued to purchase AWS as its preferred supplier for machine learning workloads. With AWS, Emirates NBD will take additional advantage of AWS artificial intelligence and system learning solutions including Amazon SageMaker, a totally managed machine learning service for construction, training, and deploying system learning models to provide pertinent real-time banking experiences. To create a more profitable and customer centric banking expertise, Emirates NBD can also be leveraging Amazon Personalize, an AWS system learning service which allows the evolution of personalized recommendations to establish new personalized retail banking software. Among the first of those software is a personal finance manager which uses an automated, self learning system to deliver an extremely personalized banking experience to clients so as to predict what each individual client needs as well as match this with the most suitable solution.

A team of researchers at the University of North Carolina at Chapel Hill and the University of Maryland at College Park has recently developed a new deep learning model that can identify people's emotions based on their walking styles. Their approach, outlined in a paper pre-published on arXiv, works by extracting an individual's gait from an RGB video of him/her walking, then analyzing it and classifying it as one of four emotions: happy, sad, angry or neutral. "Emotions play a significant role in our lives, defining our experiences, and shaping how we view the world and interact with other humans," Tanmay Randhavane, one of the primary researchers and a graduate student at UNC, told TechXplore. "Perceiving the emotions of other people helps us understand their behavior and decide our actions toward them. For example, people communicate very differently with someone they perceive to be angry and hostile than they do with someone they perceive to be calm and contented."

I've invested a considerable amount of time taking numerous courses, so I dug into my emails to collect some of the suggestions I've doled out. First, it's worth addressing the extent to which a product manager even needs to understand how AI works in order to be effective. There is an endless stream of business articles about what AI is, what it does and how it is going to disrupt this and that, all of which is great, but I am talking about understanding how it works (e.g. As Marty Cagan pointed out in Inspired (a must-read), product managers can come from a variety of different vertical disciplines, including those that are not necessarily technical, such as marketing or sales. Can these individuals, or even product managers who come from engineering but don't necessarily have a background in AI, be successful managing AI products?

I came across all kinds of advice when I was looking for a data science internship. But surprisingly, not many people talk about how to land that internship. My learning journey during my internship with Analytics Vidhya was equal parts challenging and fulfilling. I realized how vast and complex data science is and how unprepared I was for a full-time role. My path to become a data scientist would have been far more arduous and difficult one if I hadn't first interned. Even for experience people – internships are a very effective way to break into data science. We have now seen so many successful transitions enabled by internships. If you are looking for tips to prepare yourself for a data science internship, then you've come to the right place! In this article, I've drawn on my experience on the key aspects you need to know to land your first internship in data science. Each section is filled with plenty of tips, tricks, and resources. It won't be easy – but you would know what needs to be done. If you are looking for a guided journey with mentorship – check out our Certified Program: Data Science for Beginners (with Interviews) .

UNESCO has published the Beijing Consensus on Artificial Intelligence (AI) and Education, the first ever document to offer guidance and recommendations on how best to harness AI technologies for achieving the Education 2030 Agenda. It was adopted during the International Conference on Artificial Intelligence and Education, held in Beijing from 16 – 18 May 2019, by over 50 government ministers, international representatives from over 105 Member States and almost 100 representatives from UN agencies, academic institutions, civil society and the private sector. The Beijing Consensus comes after the Qingdao Declaration of 2015, in which UNESCO Member States committed to efficiently harness emerging technologies for the achievement of SDG 4. Ms Stefania Giannini, Assistant Director-General for Education at UNESCO, stated that ''we need to renew this commitment as we move towards an era in which artificial intelligence – a convergence of emerging technologies – is transforming every aspect of our lives (…) we need to steer this revolution in the right direction, to improve livelihoods, to reduce inequalities and promote a fair and inclusive globalization.'' The Consensus affirms that the deployment of AI technologies in education should be purposed to enhance human capacities and to protect human rights for effective human-machine collaboration in life, learning and work, and for sustainable development. The Consensus states that the systematic integration of AI in education has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards SDG 4. In summary, the Beijing Consensus recommends governments and other stakeholders in UNESCO's Member States to: The Consensus also details its ambitions for UNESCO to act as a support system for the capacity building of education policy-makers to implement the recommended measures, and to act as a convener for financing, partnership and international cooperation together with other international organizations and partners active in the field of AI in education.

One of the questions that arises when designing models that learn to solve multiple tasks simultaneously is how much of the available training budget should be devoted to each individual task. We refer to any formalized approach to addressing this problem (learned or otherwise) as a task selection policy. In this work we provide an empirical evaluation of the performance of some common task selection policies in a synthetic bandit-style setting, as well as on the GLUE benchmark for natural language understanding. We connect task selection policy learning to existing work on automated curriculum learning and off-policy evaluation, and suggest a method based on counterfactual estimation that leads to improved model performance in our experimental settings.

Deep learning had been used in program analysis for the prediction of hidden software defects using software defect datasets, security vulnerabilities using generative adversarial networks as well as identifying syntax errors by learning a trained neural machine translation on program codes. However, all these approaches either require defect datasets or bug-free source codes that are executable for training the deep learning model. Our neural network model is neither trained with any defect datasets nor bug-free programming source codes, instead it is trained using structural semantic details of Abstract Syntax Tree (AST) where each node represents a construct appearing in the source code. This model is implemented to fix one of the most common semantic errors, such as undeclared variable errors as well as infer their type information before program compilation. By this approach, the model has achieved in correctly locating and identifying 81% of the programs on prutor dataset of 1059 programs with only undeclared variable errors and also inferring their types correctly in 80% of the programs.