Over the past 100 years the financial industry has largely excluded people in retirement. Even today tech entrepreneurs are ignoring financial inclusion for people over 60, who make up the wealthiest part of the financial system, and instead, are developing financial products for younger people. The most valuable and capable client demographic in terms of purchasing power are the citizens of the 7th Continent which is made up of 1 billion people over 60. The global spending power of this demographic is expected to be $15 trillion this year. Who will serve this market?

"Ensuring that human rights are strengthened and not undermined by artificial intelligence is one of the key factors that will define the world we live in", says Council of Europe Commissioner for Human Rights, Dunja Mijatović, in a Recommendation published today. This Recommendation – entitled "Unboxing artificial intelligence: 10 steps to protect human rights" – provides a number of steps which national authorities can take to maximise the potential of artificial intelligence systems and prevent or mitigate the negative impact they may have on people's lives and rights. The Recommendation also contains an annexed checklist to help implement the measures recommended in each key area. "Artificial intelligence driven technology is entering more aspects of every individual's life, from smart home appliances to social media applications, and it is increasingly being utilised by public authorities to evaluate people's personality or skills, allocate resources, and otherwise make decisions that can have real and serious consequences for the human rights of individuals. Finding the right balance between technological development and human rights protection is therefore an urgent matter", says Commissioner Mijatović.

The Indian technology industry is facing its biggest-ever HR challenge with the need to recruit and skill more than 2 million professionals, as growing demand for'exponential tech professionals' puts extreme pressure on it to remain globally competitive, according to a report. The increasing competition has not left organisations with much of an alternative. They have to either embrace the challenge or perish, according to the report titled'AI & Future Of Work: Redefining Future Of Enterprise' by BML Munjal University. Employability with technology continues to be a problem despite India having a large number of higher academic institutions, it added. "There is an expected supply of 7 million people for the Indian technology industry that consists of graduates, PGs (postgraduates), diploma holders and PhDs (but) overall employability is 18 per cent only," the report said.

This week, DARPA shared footage of an experimental new program that uses large drones swarms to locate targets and gather situational intelligence in urban raid missions. Part of DARPA's Offensive Swarm-Enabled Tactics (OFFSET) program, the test featured a coordinated group of 250 autonomous air and ground vehicles. Those vehicles were sent into to a simulated urban environment, providing live information about sight lines, enemy positioning, environmental hazards, and general layout as part of a simulated military raid. The test was conducted at DARPA's Camp Shelby Joint Forces Training Center, a facility in Hattiesburg, Mississippi. The missions tasked the drone swarm with finding several AprilTags, a kind of QR code, that had been placed inside buildings in the training compound, which was designed to approximate a city block.

This paper is an effort to complement the contributions made by researchers working toward the inclusion of non-English languages in natural language processing studies. Two novel Hindi language resources have been created and released for public consumption. The first resource is a corpus consisting of nearly thousand pre-processed fictional and nonfictional texts spanning over hundred years. The second resource is an exhaustive list of stop lemmas created from 12 corpora across multiple domains, consisting of over 13 million words, from which more than 200,000 lemmas were generated, and 11 publicly available stop word lists comprising over 1000 words, from which nearly 400 unique lemmas were generated. This research lays emphasis on the use of stop lemmas instead of stop words owing to the presence of various, but not all morphological forms of a word in stop word lists, as opposed to the presence of only the root form of the word, from which variations could be derived if required. It was also observed that stop lemmas were more consistent across multiple sources as compared to stop words. In order to generate a stop lemma list, the parts of speech of the lemmas were investigated but rejected as it was found that there was no significant correlation between the rank of a word in the frequency list and its part of speech. The stop lemma list was assessed using a comparative method. A formal evaluation method is suggested as future work arising from this study.

Existing simulations designed for cultural and interpersonal skill training rely on pre-defined responses with a menu option selection interface. Using a multiple-choice interface and restricting trainees' responses may limit the trainees' ability to apply the lessons in real life situations. This systems also uses a simplistic evaluation model, where trainees' selected options are marked as either correct or incorrect. This model may not capture sufficient information that could drive an adaptive feedback mechanism to improve trainees' cultural awareness. This paper describes the design of a dialogue-based simulation for cultural awareness training. The simulation, built around a disaster management scenario involving a joint coalition between the US and the Chinese armies. Trainees were able to engage in realistic dialogue with the Chinese agent. Their responses, at different points, get evaluated by different multi-label classification models. Based on training on our dataset, the models score the trainees' responses for cultural awareness in the Chinese culture. Trainees also get feedback that informs the cultural appropriateness of their responses. The result of this work showed the following; i) A feature-based evaluation model improves the design, modeling and computation of dialogue-based training simulation systems; ii) Output from current automatic speech recognition (ASR) systems gave comparable end results compared with the output from manual transcription; iii) A multi-label classification model trained as a cultural expert gave results which were comparable with scores assigned by human annotators.

The task automatic music composition entails generative modeling of music in symbolic formats such as the musical scores. By serializing a score as a sequence of MIDI-like events, recent work has demonstrated that state-of-the-art sequence models with self-attention work nicely for this task, especially for composing music with long-range coherence. In this paper, we show that sequence models can do even better when we improve the way a musical score is converted into events. The new event set, dubbed "REMI" (REvamped MIDI-derived events), provides sequence models a metric context for modeling the rhythmic patterns of music, while allowing for local tempo changes. Moreover, it explicitly sets up a harmonic structure and makes chord progression controllable. It also facilitates coordinating different tracks of a musical piece, such as the piano, bass and drums. With this new approach, we build a Pop Music Transformer that composes Pop piano music with a more plausible rhythmic structure than prior arts do. The code, data and pre-trained model are publicly available.\footnote{\url{https://github.com/YatingMusic/remi}}

--Graph neural networks are currently leading the performance charts in learning-based molecule property prediction and classification. Computational chemistry has, therefore, become the a prominent testbed for generic graph neural networks, as well as for specialized message passing methods. In this work, we demonstrate that the replacement of the underlying networks with hypernetworks leads to a boost in performance, obtaining state of the art results in various benchmarks. A major difficulty in the application of hypernetworks is their lack of stability. We tackle this by combining the current message and the first message. A recent work has tackled the training instability of hypernetworks in the context of error correcting codes, by replacing the activation function of the message passing network with a low-order T aylor approximation of it. We demonstrate that our generic solution can replace this domain-specific solution. I NTRODUCTION The field of learning-based prediction of molecule properties holds the promise of delivering accurate predictions at a fraction of the complexity that is required by the Density Functional Theory (DFT) models, while not being tied to the assumptions and approximations of this theory.

We consider the problem of sampling from a strongly log-concave density in $\mathbb{R}^{d}$, and prove an information theoretic lower bound on the number of stochastic gradient queries of the log density needed. Several popular sampling algorithms (including many Markov chain Monte Carlo methods) operate by using stochastic gradients of the log density to generate a sample; our results establish an information theoretic limit for all these algorithms. We show that for every algorithm, there exists a well-conditioned strongly log-concave target density for which the distribution of points generated by the algorithm would be at least $\varepsilon$ away from the target in total variation distance if the number of gradient queries is less than $\Omega(\sigma^2 d/\varepsilon^2)$, where $\sigma^2 d$ is the variance of the stochastic gradient. Our lower bound follows by combining the ideas of Le Cam deficiency routinely used in the comparison of statistical experiments along with standard information theoretic tools used in lower bounding Bayes risk functions. To the best of our knowledge our results provide the first nontrivial dimension-dependent lower bound for this problem.

Mitotic count is a commonly used method to assess the level of progression of breast cancer, which is now the fourth most prevalent cancer. Unfortunately, counting mitosis is a tedious and subjective task with poor reproducibility, especially for non-experts. Luckily, since the machine can read and compare more data with greater efficiency this could be the next modern technique to count mitosis. Furthermore, technological advancements in medicine have led to the increase in image data available for use in training. In this work, we propose a network constructed using a similar approach to one that has been used for image fraud detection with the segmented image map as the second stream input to Faster RCNN. This region-based detection model combines a fully convolutional Region Proposal Network to generate proposals and a classification network to classify each of these proposals as containing mitosis or not. Features from both streams are fused in the bilinear pooling layer to maintain the spatial concurrence of each. After training this model on the ICPR 2014 MITOSIS contest dataset, we received an F-measure score of 0.507, higher than both the winners score and scores from recent tests on the same data. Our method is clinically applicable, taking only around five min per ten full High Power Field slides when tested on a Quadro P6000 cloud GPU.