For all their differences, big tech companies agree on where we're heading: into a future dominated by smart machines. Google, Amazon, Facebook, and Apple all say that every aspect of our lives will soon be transformed by artificial intelligence and machine learning, through innovations such as self-driving cars and facial recognition. Yet the people whose work underpins that vision don't much resemble the society their inventions are supposed to transform. WIRED worked with Montreal startup Element AI to estimate the diversity of leading machine learning researchers, and found that only 12 percent were women. That estimate came from tallying the numbers of men and women who had contributed work at three top machine learning conferences in 2017.

Kingsoft Corp. cloud's computing brand is the world's leading cloud computing service provider and China's Top 3 cloud computing company. Founded in 2012, it has established data centers and operations in Beijing, Shanghai, Chengdu, Guangzhou, Hong Kong and North America. At present, Kingsoft has reached a valuation of 2.373 billion US dollars, becoming the independent cloud service provider in China with the highest market capitalization. Kingsoft cloud products include cloud service solutions for side industries such as games, video, government, healthcare, and finance. Kingsoft has been conducting research and practical applications of artificial intelligence, launching the four layered IaaS, Paas, SaaS industry solutions, which are applicable to various combined AI solutions and services in various industries. In 2018, Kingsoft launched the blockchain ecosystem plan, "Project-X", making full use of the advantages of the cloud to promote the development and application of blockchain technology. Bottos is an infrastructure that focuses on artificial intelligence. It possesses both an underlying public chain designed specifically for data property and a data flow platform for the entire artificial intelligence and its derivatives. A consensus-based, scalable, easy-to-develop, and collaborative one-stop application platform for data, models, computing power and storage of multi layered shared services through data mining and smart contracts.

We present the Network-based Biased Tree Ensembles (NetBiTE) method for drug sensitivity prediction and drug sensitivity biomarker identification in cancer using a combination of prior knowledge and gene expression data. Our devised method consists of a biased tree ensemble that is built according to a probabilistic bias weight distribution. The bias weight distribution is obtained from the assignment of high weights to the drug targets and propagating the assigned weights over a protein-protein interaction network such as STRING. The propagation of weights, defines neighborhoods of influence around the drug targets and as such simulates the spread of perturbations within the cell, following drug administration. Using a synthetic dataset, we showcase how application of biased tree ensembles (BiTE) results in significant accuracy gains at a much lower computational cost compared to the unbiased random forests (RF) algorithm. We then apply NetBiTE to the Genomics of Drug Sensitivity in Cancer (GDSC) dataset and demonstrate that NetBiTE outperforms RF in predicting IC50 drug sensitivity, only for drugs that target membrane receptor pathways (MRPs): RTK, EGFR and IGFR signaling pathways. We propose based on the NetBiTE results, that for drugs that inhibit MRPs, the expression of target genes prior to drug administration is a biomarker for IC50 drug sensitivity following drug administration. We further verify and reinforce this proposition through control studies on, PI3K/MTOR signaling pathway inhibitors, a drug category that does not target MRPs, and through assignment of dummy targets to MRP inhibiting drugs and investigating the variation in NetBiTE accuracy.

The ever-increasing size of modern datasets combined with the difficulty of obtaining label information has made semi-supervised learning of significant practical importance in modern machine learning applications. Compared with supervised learning, the key difficulty in semi-supervised learning is how to make full use of the unlabeled data. In order to utilize manifold information provided by unlabeled data, we propose a novel regularization called the tangent-normal adversarial regularization, which is composed by two parts. The two terms complement with each other and jointly enforce the smoothness along two different directions that are crucial for semi-supervised learning. One is applied along the tangent space of the data manifold, aiming to enforce local invariance of the classifier on the manifold, while the other is performed on the normal space orthogonal to the tangent space, intending to impose robustness on the classifier against the noise causing the observed data deviating from the underlying data manifold. Both of the two regularizers are achieved by the strategy of virtual adversarial training. Our method has achieved state-of-the-art performance on semi-supervised learning tasks on both artificial dataset and FashionMNIST dataset.

In this paper, we propose exact passive-aggressive (PA) online algorithms for learning to rank. The proposed algorithms can be used even when we have interval labels instead of actual labels for examples. The proposed algorithms solve a convex optimization problem at every trial. We find exact solution to those optimization problems to determine the updated parameters. We propose support class algorithm (SCA) which finds the active constraints using the KKT conditions of the optimization problems. These active constrains form support set which determines the set of thresholds that need to be updated. We derive update rules for PA, PA-I and PA-II. We show that the proposed algorithms maintain the ordering of the thresholds after every trial. We provide the mistake bounds of the proposed algorithms in both ideal and general settings. We also show experimentally that the proposed algorithms successfully learn accurate classifiers using interval labels as well as exact labels. Proposed algorithms also do well compared to other approaches.

It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly speaking, we equate the plausibility of a model with the likeliness that a user accepts it as an explanation for a prediction. In particular, we argue that, all other things being equal, longer explanations may be more convincing than shorter ones, and that the predominant bias for shorter models, which is typically necessary for learning powerful discriminative models, may not be suitable when it comes to user acceptance of the learned models. To that end, we first recapitulate evidence for and against this postulate, and then report the results of an evaluation in a crowd-sourcing study based on about 3.000 judgments. The results do not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then relate these results to well-known cognitive biases such as the conjunction fallacy, the representative heuristic, or the recogition heuristic, and investigate their relation to rule length and plausibility.

Google has pledged not to use its powerful artificial intelligence (AI) to create weapons of war, illegal surveillance or to cause "overall harm". On Thursday, Sundar Pichai, CEO for Alphabet Inc.'s Google, set out a series of principles about AI at the company. The announcement follows more than 4,500 Google employees having written a letter in April, calling on the company to get out of the "business of war" and cancel Pentagon work. Pichai said in Thursday's post that Google recognizes that its powerful technology "raises equally powerful questions about its use". AI is being used for good, he said, citing use cases such as machine-learning sensors being built by higher schoolers to predict the risk of wildfires; farmers using it to monitor their cows' health; and doctors who are using it to diagnose breast cancer and to prevent blindness.

Princeton Univ, $29.95 (288p) ISBN 978-0-691-18351-0 The Greeks thought of everything, including sci-fi tropes such as androids and artificial intelligence, according to this lively study of mythology and technology. Stanford classicist Mayor (The Amazons) surveys myths from ancient Greece (with excursions to India and China) about bio-techne, life crafted by artifice. She finds a trove of them, including those of the bronze warrior-robot Talos, who patrolled Crete, hurling boulders at ships and roasting soldiers alive; statues by the legendary engineer Daedalus, so lifelike that they had to be tethered to stay put; and marvels by the blacksmith god Hephaestus, including automated rolling tripods that served Olympian feasts and talking robot servants to help at his forge. Taking a more organic approach, the witch Medea, after defeating Talos with sweet talk and trickery, invented herbal drugs to reverse aging. Mayor also looks at real-life automata in ancient Alexandria--mechanical beasts and people that moved, vocalized, and dispensed milk to bemused onlookers.

An issue across every sector, from market research to employee engagement and government relations, is how to truly understand large groups of people across political, geographical, and cultural divides and amplify their collective voice. This problem is intensified when challenging issues arise and sending out a survey doesn't provide the opportunity to discover what you don't know to ask. On the other hand, focus groups don't represent enough people to justify action. We've come a long way in learning how to better understand massive groups of people utilizing artificial intelligence (AI). This piece outlines key advantages of AI in market research resulting from my work with a number of organizations facing the challenge of transitioning from traditional market research to modern representative intelligence; that is intelligence capable of engaging, understanding and authentically representing massive groups of stakeholders (customer, employees, citizens, etc.).

Many enterprises are stopping to WAIT rather than fully applying the strategic direction offered by big data analytics.Adrian Bridgwater There's an old saying in business: if don't ask the right questions, then how can you expect to get the right answers? For any given product or service, customer expectations, aspirations and requirements are often locked up in the customer's mind. Finding out what makes people tick is the key. In traditional terms, this is the last mile of business. This foundational truth applies as much to traditional negotiations, sales and general commerce as it does to the electronic business.