The flourishing assessments of fairness measure in machine learning algorithms have shown that dimension reduction methods such as PCA treat data from different sensitive groups unfairly. In particular, by aggregating data of different groups, the reconstruction error of the learned subspace becomes biased towards some populations that might hurt or benefit those groups inherently, leading to an unfair representation. On the other hand, alleviating the bias to protect sensitive groups in learning the optimal projection, would lead to a higher reconstruction error overall. This introduces a trade-off between sensitive groups' sacrifices and benefits, and the overall reconstruction error. In this paper, in pursuit of achieving fairness criteria in PCA, we introduce a more efficient notion of Pareto fairness, cast the Pareto fair dimensionality reduction as a multi-objective optimization problem, and propose an adaptive gradient-based algorithm to solve it. Using the notion of Pareto optimality, we can guarantee that the solution of our proposed algorithm belongs to the Pareto frontier for all groups, which achieves the optimal trade-off between those aforementioned conflicting objectives. This framework can be efficiently generalized to multiple group sensitive features, as well. We provide convergence analysis of our algorithm for both convex and non-convex objectives and show its efficacy through empirical studies on different datasets, in comparison with the state-of-the-art algorithm.

Abstract-- Logic synthesis requires extensive tuning of the synthesis optimization flow where the quality of results (QoR) depends on the sequence of optimizations used. Efficient design space exploration is challenging due to the exponential number of possible optimization permutations. Therefore, automating the optimization process is necessary. In this work, we propose a novel reinforcement learning-based methodology that navigates the optimization space without human intervention. We demonstrate the training of an Advantage Actor Critic (A2C) agent that seeks to minimize area subject to a timing constraint. Using the proposed methodology, designs can be optimized autonomously with no-humans in-loop. Evaluation on the comprehensive EPFL benchmark suite shows that the agent outperforms existing exploration methodologies and improves QoRs by an average of 13%. Logic synthesis transforms a high-level description of a design into an optimized gate-level representation. Modern logic synthesis tools represent a given design as an And-Inverter Graph (AIG), which encodes representative characteristics for optimizing Boolean functions.

About 87% of marketing organizations have already started using some level of personalization. By 2024, AI identification of emotions is expected to influence more than 50% of online advertisements globally. Gartner has confirmed that AEI is among the key technology trends that are expected to witness tremendous growth in the next five years. Computer vision allows AI to interpret and manipulate physical environments, which is one of the key technologies used for emotion recognition. Artificial Emotional Intelligence (AEI) will sense customer emotions, based on which companies can influence buying decisions.

The latest report titled global Artificial Intelligence Platform Software market includes the comprehensive study of the present market scope and based on the research that is being carried out the analysts at The Research Insights state that the newest developments that are presently affecting the changing scenario products and services that have high rankings and great feedback are described wisely. The Artificial Intelligence platform provides tools and technologies to build applications with AI-rich capabilities. The algorithms used for formulating the AI platform provide logical models for application developers to fabricate various innovative applications with capabilities, such as speech and voice recognition, text recognition, and predictive analytics. The factors likely to drive the Artificial Intelligence platform market are the substantial increase in data generation, high demand for AI-based solutions, the need to enhance customer experience, and the increasing operational efficiency & reduced cost that AI platforms offer. Among end users, the BFSI segment is projected to have the largest share, while healthcare is expected to have the highest growth rate during the forecast period.

The FinChatBot and IBM team are pleased to announce the official launch of our inaugural Hackathon Challenge @ IBM in Sandton. To participate, please register your details on the ChatBot link below: https://registration.finchatbot.com/ All details are on the event image above. Once you have completed the registration process, you will receive a confirmation email with all required information. One of the main reasons for this is due to lack of education about insurance and how to access it.

This is the Part 6 of a short series of posts introducing and building generative adversarial networks, known as GANs. In this post we will develop a system for testing a GAN using controllable synthetic data. Too often GANs are tested against datasets which are very varied and this makes assessing the GAN very difficult. We'll also do some experiments with some of the many GAN design options to see if they help or hinder. Using controlled and simpler synthetic image data makes this assessment easier. Output from a conditioned GAN learning four classes of synthetic image. Previously: Part 1 introduced the idea of adversarial learning and we started to build the machinery of a GAN implementation. Part 2 we extended our code to learn a simple 1-dimensional pattern 1010. Part 3 we developed our code to learn to generate 2-dimensional grey-scale images that look like handwritten digits Part 4 extended our code to learn full colour faces, and also developed convolutional networks to encourage learning localised image features Part 5 developed a conditional GAN that can be trained to output images of a desired class.

Opportunities for digital technologies implementation, including implementation of artificial intelligence, are being implemented in the mining sector. Technologies help to save money and to solve problems that humans can't solve. McKinseyestimates that by 2035, the use of data analysis and digital technologies will help coal, iron ore, and copper producers save between $290 billion and $390 billion annually. Digital technologies and artificial intelligence enable companies to extract minerals in hard-to-reach places and under extreme weather conditions. This article first appeared in Mining Review Africa Issue 10, 2019 Read the full digimag here or subscribe to receive a print copy here This means that in an environment when mineral resources are becoming increasingly scarce, it is possible to develop deposits that used to be inaccessible, to do it without endangering lives of employees and to minimize human errors that often lead to costly mistakes.

Artificial Intelligence (AI) has been put forward as a potential solution for many of the gravest problems facing society, from the opioid crisis to poverty and famine. But although technology clearly has the potential to do a great deal of good, there's a sound business reason that tech companies often pour large amounts of resources into social projects that don't seem to align with their core business of selling software and services. This is down to the fact that tackling social issues often involves developing solutions to problems very similar to those faced by businesses. Additionally, working with governments or NGOs on building these solutions can often mean access to new datasets. Learning derived from these datasets can later be developed into products and services to offer to clients (even if the data itself isn't).

We study a noisy symmetric tensor completion problem of broad practical interest, namely, the reconstruction of a low-rank symmetric tensor from highly incomplete and randomly corrupted observations of its entries. While a variety of prior work has been dedicated to this problem, prior algorithms either are computationally too expensive for large-scale applications, or come with sub-optimal statistical guarantees. Focusing on "incoherent" and well-conditioned tensors of a constant CP rank, we propose a two-stage nonconvex algorithm --- (vanilla) gradient descent following a rough initialization --- that achieves the best of both worlds. Specifically, the proposed nonconvex algorithm faithfully completes the tensor and retrieves all individual tensor factors within nearly linear time, while at the same time enjoying near-optimal statistical guarantees (i.e. minimal sample complexity and optimal estimation accuracy). The estimation errors are evenly spread out across all entries, thus achieving optimal $\ell_{\infty}$ statistical accuracy. The insight conveyed through our analysis of nonconvex optimization might have implications for other tensor estimation problems.

Deep neural networks have gained remarkable success over a l arge variety of applications, including computer vision [ 1 ], natural language processing [ 2 ], speech recognition [ 3 ] and Go games [ 4 ]. But the reason why deep networks perform well over various tasks is still not exactly understood. The optimization performance of deep networks is one of the subj ects which requires an involved theoretical study, given that gradient descent can achieve zero training loss even for random labels [ 5 ], and the loss of deep networks is highly non-convex. There are different lines of works investigating the optimization of deep networks from different perspec tives. For example, a large number of works consider the optimization landscape correspondin g to different activation functions [ 6 - 11 ], whereas some others [ 12 - 15 ] ensure global convergence by imposing some restrictions o n the input distribution. In the recent years, there has been considerably many papers providing convergence guarantees for over-parameterized two-layer and deep networks. It is s hown in [ 16 ] that gradient descent can find the near-global minima of a single hidden layer network i n polynomial time with respect to the accuracy and sample size.