It really doesn't matter whether you are a retail or institutional investor. Both need the skill to predict a bit the future to make the right choices in their portfolios. Of course, assuming all investors have the core goal to have a higher growth rate than the market average, as otherwise, it would be sufficient to go with an S&P 500 ETF. The only way to achieve this high yield is by making the right choices when selecting a stock. Based on this result, the ones who want to outperform the market need to allocate capital accordingly.

We demonstrate how advancements in satellite imagery and machine learning can help ameliorate these data and inference challenges. In the context of an expansion of the electrical grid across Uganda, we show how a combination of satellite imagery and computer vision can be used to develop local-level livelihood measurements appropriate for inferring the causal impact of electricity access on livelihoods. We then show how ML-based inference techniques deliver more reliable estimates of the causal impact of electrification than traditional alternatives when applied to these data. We estimate that grid access improves village-level asset wealth in rural Uganda by 0.17 standard deviations, more than doubling the growth rate over our study period relative to untreated areas. Our results provide country-scale evidence on the impact of a key infrastructure investment, and provide a low-cost, generalizable approach to future policy evaluation in data sparse environments.

Rep. August Pfluger joins'Fox & Friends First' and calls out Biden's handling of border crisis The feds have turned to cutting-edge cameras developed by a virtual reality wunderkind to help them monitor the southern border -- by creating an invisible border wall. The high-tech watch poles known as Autonomous Surveillance Towers are powered by solar energy and use artificial intelligence to detect movement along a two-mile radius, sending the information in real-time to agents patrolling the area. And they're now being installed at different points along the nearly 2,000 miles of the US-Mexico border. "The ASTs are in remote locations that are difficult to reach," Border Patrol agent Joel Freeland recently told The Post. "They operate 24-hours a day and are environmentally friendly because they rely entirely on solar power." The ASTs were developed by Palmer Luckey, the 28-year-old founder and designer of Oculus VR and Oculus Rift.

Considerable research has been devoted to deep learning-based predictive models for system prognostics and health management in the reliability and safety community. However, there is limited study on the utilization of deep learning for system reliability assessment. This paper aims to bridge this gap and explore this new interface between deep learning and system reliability assessment by exploiting the recent advances of physics-informed deep learning. Particularly, we present an approach to frame system reliability assessment in the context of physics-informed deep learning and discuss the potential value of physics-informed generative adversarial networks for the uncertainty quantification and measurement data incorporation in system reliability assessment. The proposed approach is demonstrated by three numerical examples involving a dual-processor computing system. The results indicate the potential value of physics-informed deep learning to alleviate computational challenges and combine measurement data and mathematical models for system reliability assessment.

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Digital technologies – AI in particular – can become an essential enabler for the energy transition. A new report, Harnessing AI to Accelerate the Energy Transition, defines the actions needed to unlock AI's potential in this domain. This entry was posted on Thursday, September 2nd, 2021 at 4:21 PM and is filed under Energy and resources, Science technology and innovation. You can follow any responses to this entry through the RSS 2.0 feed. Both comments and pings are currently closed.

The use of artificial intelligence (AI) is growing in almost every area of business, from banking to transport to healthcare, as the true potential of the technology becomes increasingly recognised. Within our industry, AI has the potential to transform the way solar projects are operated and managed. When aggregated, enormous datasets from thousands of solar farms can help predict output by analysing trends in cloud cover, radiance and more, while more sophisticated imaging and assessment techniques are improving our understanding of module performance and degradation over time. Although still nascent in the solar space, AI approaches will almost certainly change the industry as we know it. Those who embrace the technology early may well gain a competitive advantage.

Spiking neural networks (SNNs) with leaky integrate and fire (LIF) neurons, can be operated in an event-driven manner and have internal states to retain information over time, providing opportunities for energy-efficient neuromorphic computing, especially on edge devices. Note, however, many representative works on SNNs do not fully demonstrate the usefulness of their inherent recurrence (membrane potentials retaining information about the past) for sequential learning. Most of the works train SNNs to recognize static images by artificially expanded input representation in time through rate coding. We show that SNNs can be trained for sequential tasks and propose modifications to a network of LIF neurons that enable internal states to learn long sequences and make their inherent recurrence resilient to the vanishing gradient problem. We then develop a training scheme to train the proposed SNNs with improved inherent recurrence dynamics. Our training scheme allows spiking neurons to produce multi-bit outputs (as opposed to binary spikes) which help mitigate the mismatch between a derivative of spiking neurons' activation function and a surrogate derivative used to overcome spiking neurons' non-differentiability. Our experimental results indicate that the proposed SNN architecture on TIMIT and LibriSpeech 100h dataset yields accuracy comparable to that of LSTMs (within 1.10% and 0.36%, respectively), but with 2x fewer parameters than LSTMs. The sparse SNN outputs also lead to 10.13x and 11.14x savings in multiplication operations compared to GRUs, which is generally con-sidered as a lightweight alternative to LSTMs, on TIMIT and LibriSpeech 100h datasets, respectively.

Agent-based systems have the capability to fuse information from many distributed sources and create better plans faster. This feature makes agent-based systems naturally suitable to address the challenges in Supply Chain Management (SCM). Although agent-based supply chains systems have been proposed since early 2000; industrial uptake of them has been lagging. The reasons quoted include the immaturity of the technology, a lack of interoperability with supply chain information systems, and a lack of trust in Artificial Intelligence (AI). In this paper, we revisit the agent-based supply chain and review the state of the art. We find that agent-based technology has matured, and other supporting technologies that are penetrating supply chains; are filling in gaps, leaving the concept applicable to a wider range of functions. For example, the ubiquity of IoT technology helps agents "sense" the state of affairs in a supply chain and opens up new possibilities for automation. Digital ledgers help securely transfer data between third parties, making agent-based information sharing possible, without the need to integrate Enterprise Resource Planning (ERP) systems. Learning functionality in agents enables agents to move beyond automation and towards autonomy. We note this convergence effect through conceptualising an agent-based supply chain framework, reviewing its components, and highlighting research challenges that need to be addressed in moving forward.

Scientists have long aimed to discover meaningful equations which accurately describe data. Machine learning algorithms automate construction of accurate data-driven models, but ensuring that these are consistent with existing knowledge is a challenge. We developed a methodology combining automated theorem proving with symbolic regression, enabling principled derivations of laws of nature. We demonstrate this for Kepler's third law, Einstein's relativistic time dilation, and Langmuir's theory of adsorption, in each case, automatically connecting experimental data with background theory. The combination of logical reasoning with machine learning provides generalizable insights into key aspects of the natural phenomena.