Last year, Ted Serbinski called his accelerator, Techstars Detroit, the "'comeback city's' startup ecosystem." Since 2015, the accelerator had supported and mentored 54 transportation-related companies, with funding from some big transportation names, like Ford, Honda, AAA, and Nationwide. Success stories included Cargo, a startup that helps ride-hail drivers make supplemental income by running rider-friendly "convenience stores" out of their cars; Splt, a ride-share company acquired by Bosch in 2018; and Acerta, which applies machine learning to automotive manufacturing. Instead, it's shutting down, as earlier reported by TechCrunch. "Right now, there is no funding," says Serbinski, the accelerator's managing director.

Ladder system in corporate world: pros and cons'Make in India' 2.0: Here's why the time is ripe for PM Narendra Modi's flagship project Taxpayer's charter brings focus on taxpayers' rights: what it must include'Make in India' 2.0: Here's why the time is ripe for PM Narendra Modi's flagship project Taxpayer's charter brings focus on taxpayers' rights: what it must include Computing has moved on from information organisation and manipulation. Digitisation is turning everything and everyone into data, and machines can convert data into vision, hearing, text, speech, movement, patterns and decisions. With cognition coming to machines, modern computing is about autonomous learning and action by machines. Artificial intelligence (AI) is here and is evolving fast. Businesses and managers must keep up.

The White House's proposed budget for fiscal year 2021 includes sizable increases in federal funding for AI and quantum computing projects. All told, the overall increase in federal R&D funding is 6% compared to fiscal year 2020, reaching $142.2 billion. Although much of the funding is aimed at R&D and infrastructure investments like $25 million to begin research for a quantum internet, the tenor of the briefing included defense tones. In espousing the need to invest in quantum information science (QIS), an official on the call said the U.S. needs to stay ahead of China and Europe, which are investing in their own quantum computing projects. U.S. CTO Michael Kratsios was clear about American values as they pertain to AI. "I think with regards to some of our adversaries and others around the world [that] utilize this technology, it's imperative that the U.S. continues to lead in technologies like AI," he said.

Amazon wants Donald Trump to submit to questioning over the tech company's losing bid for a $10bn military contract. The Pentagon awarded the cloud computing project to Microsoft in October. Amazon later sued, arguing that Trump's interference and bias against the company harmed Amazon's chances. Amazon was considered an early frontrunner for a project that Pentagon officials have described as critical to advancing the US military's technological advantage over adversaries. The project, known as Joint Enterprise Defense Infrastructure, or Jedi, will store and process vast amounts of classified data, allowing the US military to improve communications with soldiers on the battlefield and use artificial intelligence to speed up its war planning and fighting capabilities.

Machine Learning (ML) algorithms have been used for assessing soil quality parameters along with non-destructive methodologies. Among spectroscopic analytical methodologies, energy dispersive X-ray fluorescence (EDXRF) is one of the more quick, environmentally friendly and less expensive when compared to conventional methods. However, some challenges in EDXRF spectral data analysis still demand more efficient methods capable of providing accurate outcomes. Using Multi-target Regression (MTR) methods, multiple parameters can be predicted, and also taking advantage of inter-correlated parameters the overall predictive performance can be improved. In this study, we proposed the Multi-target Stacked Generalisation (MTSG), a novel MTR method relying on learning from different regressors arranged in stacking structure for a boosted outcome. We compared MTSG and 5 MTR methods for predicting 10 parameters of soil fertility. Random Forest and Support Vector Machine (with linear and radial kernels) were used as learning algorithms embedded into each MTR method. Results showed the superiority of MTR methods over the Single-target Regression (the traditional ML method), reducing the predictive error for 5 parameters. Particularly, MTSG obtained the lowest error for phosphorus, total organic carbon and cation exchange capacity. When observing the relative performance of Support Vector Machine with a radial kernel, the prediction of base saturation percentage was improved in 19%. Finally, the proposed method was able to reduce the average error from 0.67 (single-target) to 0.64 analysing all targets, representing a global improvement of 4.48%.

In Financial Signal Processing, multiple time series such as financial indicators, stock prices and exchange rates are strongly coupled due to their dependence on the latent state of the market and therefore they are required to be jointly analysed. We focus on learning the relationships among financial time series by modelling them through a multi-output Gaussian process (MOGP) with expressive covariance functions. Learning these market dependencies among financial series is crucial for the imputation and prediction of financial observations. The proposed model is validated experimentally on two real-world financial datasets for which their correlations across channels are analysed. We compare our model against other MOGPs and the independent Gaussian process on real financial data.

The prediction of absorption, distribution, metabolism, excretion, and toxicity (ADMET) of small molecules from their molecular structure is a central problem in medicinal chemistry with great practical importance in drug discovery. Creating predictive models conventionally requires substantial trial-and-error for the selection of molecular representations, machine learning (ML) algorithms, and hyperparameter tuning. A generally applicable method that performs well on all datasets without tuning would be of great value but is currently lacking. Here, we describe Pareto-Optimal Embedded Modeling (POEM), a similarity-based method for predicting molecular properties. POEM is a non-parametric, supervised ML algorithm developed to generate reliable predictive models without need for optimization. POEMs predictive strength is obtained by combining multiple different representations of molecular structures in a context-specific manner, while maintaining low dimensionality. We benchmark POEM relative to industry-standard ML algorithms and published results across 17 classifications tasks. POEM performs well in all cases and reduces the risk of overfitting.

Weight pruning is a powerful technique to realize model compression. We propose PCNN, a fine-grained regular 1D pruning method. A novel index format called Sparsity Pattern Mask (SPM) is presented to encode the sparsity in PCNN. Leveraging SPM with limited pruning patterns and non-zero sequences with equal length, PCNN can be efficiently employed in hardware. Evaluated on VGG-16 and ResNet-18, our PCNN achieves the compression rate up to 8.4X with only 0.2% accuracy loss. We also implement a pattern-aware architecture in 55nm process, achieving up to 9.0X speedup and 28.39 TOPS/W efficiency with only 3.1% on-chip memory overhead of indices.

Contextual bandit algorithms are applied in a wide range of domains, from advertising to recommender systems, from clinical trials to education. In many of these domains, malicious agents may have incentives to attack the bandit algorithm to induce it to perform a desired behavior. For instance, an unscrupulous ad publisher may try to increase their own revenue at the expense of the advertisers; a seller may want to increase the exposure of their products, or thwart a competitor's advertising campaign. In this paper, we study several attack scenarios and show that a malicious agent can force a linear contextual bandit algorithm to pull any desired arm $T - o(T)$ times over a horizon of $T$ steps, while applying adversarial modifications to either rewards or contexts that only grow logarithmically as $O(\log T)$. We also investigate the case when a malicious agent is interested in affecting the behavior of the bandit algorithm in a single context (e.g., a specific user). We first provide sufficient conditions for the feasibility of the attack and we then propose an efficient algorithm to perform the attack. We validate our theoretical results on experiments performed on both synthetic and real-world datasets.

Machine learning tasks entail the use of complex computational pipelines to reach quantitative and qualitative conclusions. If some of the activities in a pipeline produce erroneous or uninformative outputs, the pipeline may fail or produce incorrect results. Inferring the root cause of failures and unexpected behavior is challenging, usually requiring much human thought, and is both time-consuming and error-prone. We propose a new approach that makes use of iteration and provenance to automatically infer the root causes and derive succinct explanations of failures. Through a detailed experimental evaluation, we assess the cost, precision, and recall of our approach compared to the state of the art. Our source code and experimental data will be available for reproducibility and enhancement.