Contrary to common fears around how robots will impact jobs, leaders across the globe are reporting increased adoption of artificial intelligence (AI) and robots at work and many are welcoming it with love and optimism. According to the second annual "AI at Work" study of 8,370 employees, managers and HR leaders across 10 countries, including the UAE, conducted by Oracle and research firm Future Workplace, 64% of the people trust a robot more than their managers and half have turned to a robot instead of their manager for advice. Rahul Misra, vice-president for applications at Oracle Lower Gulf, told TechRadar Middle East that 82% of people think robots can do things better than their managers. In the UAE, respondents said robots are better at maintaining work schedules (42%), problem-solving (34%) and providing unbiased information (32%) while the top three tasks where managers are better than robots were understanding feelings (46%), coaching them (32%) and evaluating team performance (25%). "UAE is building a future based on tech innovation. Anything where the managers' role does not have an emotional quotient, people believe they can work with a fact-based model," he said.

People around the world marched for climate change on September 20, 2019, with protests taking place across 4,500 locations in 150 countries, all inspired by Swedish climate activist Greta Thunberg. It is obvious the call for a healthier planet is being demanded by more and more people internationally. But what is the answer? Millions of people across the globe marched on September 20, 2019 to demand urgent action on climate change. One of the questions being posed: Can Artificial Intelligence (AI) and tech companies help address climate change and save the planet?

In this work we present an adversarial training algorithm that exploits correlations in video to learn --without supervision-- an image generator model with a disentangled latent space. The proposed methodology requires only a few modifications to the standard algorithm of Generative Adversarial Networks (GAN) and involves training with sets of frames taken from short videos. We train our model over two datasets of face-centered videos which present different people speaking or moving the head: VidTIMIT and YouTube Faces datasets. We found that our proposal allows us to split the generator latent space into two subspaces. One of them controls content attributes, those that do not change along short video sequences. For the considered datasets, this is the identity of the generated face. The other subspace controls motion attributes, those attributes that are observed to change along short videos. We observed that these motion attributes are face expressions, head orientation, lips and eyes movement. The presented experiments provide quantitative and qualitative evidence supporting that the proposed methodology induces a disentangling of this two kinds of attributes in the latent space.

FISHDBC is a flexible, incremental, scalable, and hierarchical density-based clustering algorithm. It is flexible because it empowers users to work on arbitrary data, skipping the feature extraction step that usually transforms raw data in numeric arrays letting users define an arbitrary distance function instead. It is incremental and scalable: it avoids the $\mathcal O(n^2)$ performance of other approaches in non-metric spaces and requires only lightweight computation to update the clustering when few items are added. It is hierarchical: it produces a "flat" clustering which can be expanded to a tree structure, so that users can group and/or divide clusters in sub- or super-clusters when data exploration requires so. It is density-based and approximates HDBSCAN*, an evolution of DBSCAN.

The transcriptions used to train an Automatic Speech Recognition (ASR) system may contain errors. Usually, either a quality control stage discards transcriptions with too many errors, or the noisy transcriptions are used as is. We introduce Lead2Gold, a method to train an ASR system that exploits the full potential of noisy transcriptions. Based on a noise model of transcription errors, Lead2Gold searches for better transcriptions of the training data with a beam search that takes this noise model into account. The beam search is differentiable and does not require a forced alignment step, thus the whole system is trained end-to-end. Lead2Gold can be viewed as a new loss function that can be used on top of any sequence-to-sequence deep neural network. We conduct proof-of-concept experiments on noisy transcriptions generated from letter corruptions with different noise levels. We show that Lead2Gold obtains a better ASR accuracy than a competitive baseline which does not account for the (artificially-introduced) transcription noise.

Employees have more trust in robots than they do their human managers, a global study has revealed. A survey across 10 countries have found that 64 percent prefer to seek advice or guidance from artificial intelligence over their boss and 82 percent feels it does a better job. The majority of workers are also optimistic, excited and grateful about having robot co-workers and nearly a quarter reported having a loving and gratifying relationship with the intelligent-style software. The study was conducted by the US technology company Oracle and research firm Future Workplace. The team surveyed 8,370 employees, managers and HR leaders and'found that AI has changed the relationship between people and technology at work and is reshaping the role HR teams and managers need to play in attracting, retaining and developing talent.'

Take, for example, pork prices in China, which have more than doubled this year. The retroactive explanation seems simple. An outbreak of African swine fever has dramatically reduced the supply of Chinese pigs, driving more price-sensitive buyers out of the market. But price forecasting is seldom so simple, particularly over long-term periods. To predict Chinese pork prices over several years, you would first need to solve several component problems which are all interrelated.

Farmers in Colombia's maize-growing region of Córdoba had seen it all: too much rain one year, a searing drought the next.

Workers in India (89%) and China (88%) are more trusting of robots over their managers, followed by Singapore (83%), Brazil (78%), Japan (76%), UAE (74%), Australia/New Zealand (58%), the U.S. (57%), the U.K. (54%), and France (56%). More men (56%) than women (44%) have turned to AI over their managers.

IMAGE: This is a maize field in Colombia. Farmers in Colombia's maize-growing region of Córdoba had seen it all: too much rain one year, a searing drought the next. Yields were down and their livelihoods hung in the balance. The situation called for a new approach. They needed information services that would help them decide what varieties to plant, when they should sow and how they should manage their crops.