IFTTT can tell you when to change your home's air filter: Last month on our IoT Podcast, we mentioned the new Ecobee service that sends you air filters for your HVAC system. Thanks to IFTTT, if you use 3M Filtrete Smart filters, you can set up an IFTTT recipe when it's time to replace that filter, even if you don't have a smart thermostat. You can't set up an automatic order based on the sensor in the filter, but with IFTTT, you could change the color of a light, add a task on your to-do list, or create some other action that tells you it's time to buy a clean filter replacement. The new IFTTT integration is one of several the company debuted this month. Covariant raises $40M to build better AI for robots: Covariant, a robotics startup that was initially born as an academic research project, raised $45 million in Series B funding this week.

Wei Li is vice president in the Software and Services Group and general manager of Machine Learning and Translation at Intel Corporation, responsible for several areas of software systems, including machine learning, binary translation, and emulation. His team works with industry and academia to enable the software ecosystem, and collaborates with Intel hardware teams designing future processor products. Since joining Intel in 1998, Wei has led teams that contributed to Intel data center, client/mobile, Internet of Things, and artificial intelligence businesses. He holds 11 U.S. patents, and has served as an associate editor for ACM Transactions on Programming Languages and Systems. Wei earned a Ph.D. in computer science from Cornell University, completed the Executive Accelerator Program at the Stanford Graduate School of Business, and he taught computer science at Stanford University.

When Camille François, a longstanding expert on disinformation, sent an email to her team late last year, many were perplexed. Her message began by raising some seemingly valid concerns: that online disinformation -- the deliberate spreading of false narratives typically designed to sow mayhem -- "could get out of control and become a huge threat to democratic norms". But the text from the chief innovation officer at social media intelligence group Graphika soon became rather more wacky. Disinformation, it read, is the "grey goo of the internet", a reference to a nightmarish, end-of-the world scenario in molecular nanotechnology. The solution the email proposed was to make a "holographic holographic hologram". The bizarre email was not actually written by François, but by computer code; she had created the message -- from her basement -- using text-generating artificial intelligence technology.

If diversity breeds innovation, and innovation is predictive of success, do underrepresented scientists generate more novel innovations? To compare minority and majority scholars' rates of scientific novelty, Hofstra et al., used machine learning to analyze a population of 1.2 million U.S. doctoral recipients to identify scientific innovations, investigate the rates at which these innovations get taken up by others, and examine the impact these innovations have on scientific careers. Results show that rewards are similar for low-impact innovation; however, as impact novelty increases, minority contributions become devalued and are taken up at lower rates. This study reveals a stratified system in which minorities need to innovate at higher levels to achieve similar levels of career success, further stressing the critical need to address biases in research evaluation and publication.

Everyone's giving away games these days and while Square Enix's new "Stay Home and Play" bundle isn't free, it might as well be. The breadth and scope of the deal is pretty amazing: More than 50 games for under $40. It's the sort of anthology bundle we usually see from publishers during the Steam Summer and Winter Sale, but now available on a random weekend in May. And sure, less than $1 per game makes sense in some cases. Does anyone really want to replay the original Just Cause?

Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) are nature-inspired, swarm-based optimization algorithms respectively. Though they have been widely used for single-objective optimization since their inception, they suffer from premature convergence. Even though the hybrids of GSA and PSO perform much better, the problem remains. Hence, to solve this issue we have proposed a fuzzy mutation model for two hybrid versions of PSO and GSA - Gravitational Particle Swarm (GPS) and PSOGSA. The developed algorithms are called Mutation based GPS (MGPS) and Mutation based PSOGSA (MPSOGSA). The mutation operator is based on a fuzzy model where the probability of mutation has been calculated based on the closeness of particle to population centroid and improvement in the particle value. We have evaluated these two new algorithms on 23 benchmark functions of three categories (unimodal, multi-modal and multi-modal with fixed dimension). The experimental outcome shows that our proposed model outperforms their corresponding ancestors, MGPS outperforms GPS 13 out of 23 times (56.52%) and MPSOGSA outperforms PSOGSA 17 times out of 23 (73.91 %). We have also compared our results against those of recent optimization algorithms such as Sine Cosine Algorithm (SCA), Opposition-Based SCA, and Volleyball Premier League Algorithm (VPL). In addition, we have applied our proposed algorithms on some classic engineering design problems and the outcomes are satisfactory. The related codes of the proposed algorithms can be found in this link: Fuzzy-Mutation-Embedded-Hybrids-of-GSA-and-PSO.

The Incremental K-means (IKM), an improved version of K-means (KM), was introduced to improve the clustering quality of KM significantly. However, the speed of IKM is slower than KM. My thesis proposes two algorithms to speed up IKM while remaining the quality of its clustering result approximately. The first algorithm, called Divisive K-means, improves the speed of IKM by speeding up its splitting process of clusters. Testing with UCI Machine Learning data sets, the new algorithm achieves the empirically global optimum as IKM and has lower complexity, $O(k*log_{2}k*n)$, than IKM, $O(k^{2}n)$. The second algorithm, called Parallel Two-Phase K-means (Par2PK-means), parallelizes IKM by employing the model of Two-Phase K-means. Testing with large data sets, this algorithm attains a good speedup ratio, closing to the linearly speed-up ratio.

Frequency is one of the major factors for training quality word embeddings. Several work has recently discussed the stability of word embeddings in general domain and suggested factors influencing the stability. In this work, we conduct a detailed analysis on the stability of concept embeddings in medical domain, particularly the relation with concept frequency. The analysis reveals the surprising high stability of low-frequency concepts: low-frequency (<100) concepts have the same high stability as high-frequency (>1000) concepts. To develop a deeper understanding of this finding, we propose a new factor, the noisiness of context words, which influences the stability of medical concept embeddings, regardless of frequency. We evaluate the proposed factor by showing the linear correlation with the stability of medical concept embeddings. The correlations are clear and consistent with various groups of medical concepts. Based on the linear relations, we make suggestions on ways to adjust the noisiness of context words for the improvement of stability. Finally, we demonstrate that the proposed factor extends to the word embedding stability in general domain.

Canonical correlation analysis (CCA) is a classical and important multivariate technique for exploring the relationship between two sets of continuous variables. CCA has applications in many fields, such as genomics and neuroimaging. It can extract meaningful features as well as use these features for subsequent analysis. Although some sparse CCA methods have been developed to deal with high-dimensional problems, they are designed specifically for continuous data and do not consider the integer-valued data from next-generation sequencing platforms that exhibit very low counts for some important features. We propose a model-based probabilistic approach for correlation and canonical correlation estimation for two sparse count data sets (PSCCA). PSCCA demonstrates that correlations and canonical correlations estimated at the natural parameter level are more appropriate than traditional estimation methods applied to the raw data. We demonstrate through simulation studies that PSCCA outperforms other standard correlation approaches and sparse CCA approaches in estimating the true correlations and canonical correlations at the natural parameter level. We further apply the PSCCA method to study the association of miRNA and mRNA expression data sets from a squamous cell lung cancer study, finding that PSCCA can uncover a large number of strongly correlated pairs than standard correlation and other sparse CCA approaches.

Motion synthesis in a dynamic environment has been a long-standing problem for character animation. Methods using motion capture data tend to scale poorly in complex environments because of their larger capturing and labeling requirement. Physics-based controllers are effective in this regard, albeit less controllable. In this paper, we present CARL, a quadruped agent that can be controlled with high-level directives and react naturally to dynamic environments. Starting with an agent that can imitate individual animation clips, we use Generative Adversarial Networks to adapt high-level controls, such as speed and heading, to action distributions that correspond to the original animations. Further fine-tuning through the deep reinforcement learning enables the agent to recover from unseen external perturbations while producing smooth transitions. It then becomes straightforward to create autonomous agents in dynamic environments by adding navigation modules over the entire process. We evaluate our approach by measuring the agent's ability to follow user control and provide a visual analysis of the generated motion to show its effectiveness.