WASHINGTON – Tech giants Microsoft, Amazon and Google are joining forces with international organizations to help identify and head off famines in developing nations using data analysis and artificial intelligence, a new initiative unveiled Sunday. Rather than waiting to respond to a famine after many lives already have been lost, the tech firms "will use the predictive power of data to trigger funding" to take action before it becomes a crisis, the World Bank and United Nations announced in a joint statement. "The fact that millions of people -- many of them children -- still suffer from severe malnutrition and famine in the 21st century is a global tragedy," World Bank Group President Jim Yong Kim said in a statement. "We are forming an unprecedented global coalition to say, 'no more.' " Last year more than 20 million people faced famine conditions in Nigeria, Somalia, South Sudan and Yemen, while 124 million people currently live in crisis levels of food insecurity, requiring urgent humanitarian assistance for their survival, the agencies said. Over half of them live in areas affected by conflict.

BENGALURU: pi Ventures, an early stage venture fund focused on startups leveraging AI and IoT, has closed its maiden fund with a corpus of Rs 225 crore, higher than its initial target of Rs 195 crore. UK's development finance institution CDC Group has also invested Rs 40 crore in pi Ventures. CDC has a portfolio of $5.3 billion, and has financed 715 and 338 businesses in Africa and South Asia respectively. Its objective is to invest in startups with deep-tech capabilities that have the potential to change the business landscape. A number of funds have been raised in recent times with the objective of investing mainly in India based ventures and in areas related to new age digital tech.

Artificial Intelligence (AI) has been the talk of the town for a while, but it seems it can walk that talk too, with AI being potentially able to influence gender parity in the workplace. What AI can bring to the workplace was one of the topics explored and debated during'The Future of Work - Accelerating Gender Parity' Conference held by Harvard Business School (HBS) Club of India at Taj Mahal Palace Hotel in Mumbai on September 21. HBS faculty, industry leaders and other experts spoke at the conference to encourage the strategic importance of gender diversity in a changing environment. "Technology by itself is neutral, so technology can't favour women. But it can be utilized to mitigate the inherent biases against women. What we have started to see in organisations is that AI is being used to look at patterns, along with algorithms, to see if we are biasing some of our decisions, be it decisions concerning entry, promotions or the jobs that they get, against women," said Rekha Menon, Chairman and Senior Managing Director at Accenture in India, during the conference.

Diagnosis of such problems before the baby is born, allowing for prompt treatment within a week after birth, is known to markedly improve the prognosis, so there have been many attempts to develop technology to enables accurate and rapid diagnosis. However, today, fetal diagnosis depends heavily on observations by experienced examiners using ultrasound imaging, so it is unfortunately not uncommon for children to be born without having been properly diagnosed. In recent years, machine learning techniques such as deep learning have been developing rapidly, and there is great interest in the adoption of machine learning for medical applications. Machine learning can allow diagnostic systems to detect diseases more rapidly and accurately than human beings, but this requires the availability of adequate datasets on normal and abnormal subjects for a certain disease. Unfortunately, however, since congenital heart problems in children are relatively rare, there are no complete datasets, and up until now, prediction based on machine learning was not accurate enough for practical use in the clinic.

In this News Insight from HIT Consultant, Dale Van Demark discusses how we may be on the brink of a revolution in healthcare driven by AI. For more insights on how healthcare providers can enhance the patient experience through technology today, download our in-depth guide. An Accenture report was released at HIMSS18 with a bold prediction: the healthcare artificial intelligence (AI) market may hit $6.6 billion in the next three years. In 2014, that number was just $600 million, meaning the AI healthcare market could see an eleven fold increase in value in less than a decade. The survey showed that as of 2018, one in five U.S. consumers have already used healthcare services "powered by artificial intelligence," and many are open to AI clinical services, like home-based diagnostics (cited by 66 percent of respondents) and virtual health assistants (61 percent).

This paper proposes an exploration method for deep reinforcement learning based on parameter space noise. Recent studies have experimentally shown that parameter space noise results in better exploration than the commonly used action space noise. Previous methods devised a way to update the diagonal covariance matrix of a noise distribution and did not consider the direction of the noise vector and its correlation. In addition, fast updates of the noise distribution are required to facilitate policy learning. We propose a method that deforms the noise distribution according to the accumulated returns and the noises that have led to the returns. Moreover, this method switches isotropic exploration and directional exploration in parameter space with regard to obtained rewards. We validate our exploration strategy in the OpenAI Gym continuous environments and modified environments with sparse rewards. The proposed method achieves results that are competitive with a previous method at baseline tasks. Moreover, our approach exhibits better performance in sparse reward environments by exploration with the switching strategy.

It has been shown that injecting noise into the neural network weights during the training process leads to a better generalization of the resulting model. Noise injection in the distributed setup is a straightforward technique and it represents a promising approach to improve the locally trained models. We investigate the effects of noise injection into the neural networks during a decentralized training process. We show both theoretically and empirically that noise injection has no positive effect in expectation on linear models, though. However for non-linear neural networks we empirically show that noise injection substantially improves model quality helping to reach a generalization ability of a local model close to the serial baseline.

We propose a general framework for solving statistical mechanics of systems with a finite size. The approach extends the celebrated variational mean-field approaches using autoregressive neural networks which support direct sampling and exact calculation of normalized probability of configurations. The network computes variational free energy, estimates physical quantities such as entropy, magnetizations and correlations, and generates uncorrelated samples all at once. Training of the network employs the policy gradient approach in reinforcement learning, which unbiasedly estimates the gradient of variational parameters. We apply our approach to several classical systems, including 2-d Ising models, Hopfield model, Sherrington--Kirkpatrick spin glasses, and the inverse Ising model, for demonstrating its advantages over existing variational mean-field methods. Our approach sheds light on solving statistical physics problems using modern deep generative neural networks.

This document is designed to be a first-year graduate-level introduction to probabilistic programming. It not only provides a thorough background for anyone wishing to use a probabilistic programming system, but also introduces the techniques needed to design and build these systems. It is aimed at people who have an undergraduate-level understanding of either or, ideally, both probabilistic machine learning and programming languages. We start with a discussion of model-based reasoning and explain why conditioning as a foundational computation is central to the fields of probabilistic machine learning and artificial intelligence. We then introduce a simple first-order probabilistic programming language (PPL) whose programs define static-computation-graph, finite-variable-cardinality models. In the context of this restricted PPL we introduce fundamental inference algorithms and describe how they can be implemented in the context of models denoted by probabilistic programs. In the second part of this document, we introduce a higher-order probabilistic programming language, with a functionality analogous to that of established programming languages. This affords the opportunity to define models with dynamic computation graphs, at the cost of requiring inference methods that generate samples by repeatedly executing the program. Foundational inference algorithms for this kind of probabilistic programming language are explained in the context of an interface between program executions and an inference controller. This document closes with a chapter on advanced topics which we believe to be, at the time of writing, interesting directions for probabilistic programming research; directions that point towards a tight integration with deep neural network research and the development of systems for next-generation artificial intelligence applications.

Abstract--The automated wafer inspection and quality control is complex and time consuming task, which can be speed up using neuromorphic memristive architectures, as a separate inspection device or integrating directly into sensors. This paper presents the performance analysis and comparison of different neuromorphic architectures for patterned wafer quality inspection and classification. The application of nonvolatile memristive devices in these architectures ensures low power consumption, small on-chip area scalability. We demonstrate that Long-Short Term Memory (LSTM) outperforms other architectures for the same number of training iterations, and has relatively low on-chip area and power consumption. I. INTRODUCTION With the increase of density and complexity of semiconductor devices on the wafer, wafer surface inspection becomes increasingly complex, important, and time consuming task.