We propose a series of data-centric heuristics for improving the performance of machine learning systems when applied to problems in quantum information science. In particular, we consider how systematic engineering of training sets can significantly enhance the accuracy of pre-trained neural networks used for quantum state reconstruction without altering the underlying architecture. We find that it is not always optimal to engineer training sets to exactly match the expected distribution of a target scenario, and instead, performance can be further improved by biasing the training set to be slightly more mixed than the target. This is due to the heterogeneity in the number of free variables required to describe states of different purity, and as a result, overall accuracy of the network improves when training sets of a fixed size focus on states with the least constrained free variables. For further clarity, we also include a "toy model" demonstration of how spurious correlations can inadvertently enter synthetic data sets used for training, how the performance of systems trained with these correlations can degrade dramatically, and how the inclusion of even relatively few counterexamples can effectively remedy such problems.

As a substantial amount of multivariate time series data is being produced by the complex systems in Smart Manufacturing, improved anomaly detection frameworks are needed to reduce the operational risks and the monitoring burden placed on the system operators. However, building such frameworks is challenging, as a sufficiently large amount of defective training data is often not available and frameworks are required to capture both the temporal and contextual dependencies across different time steps while being robust to noise. In this paper, we propose an unsupervised Attention-based Convolutional Long Short-Term Memory (ConvLSTM) Autoencoder with Dynamic Thresholding (ACLAE-DT) framework for anomaly detection and diagnosis in multivariate time series. The framework starts by pre-processing and enriching the data, before constructing feature images to characterize the system statuses across different time steps by capturing the inter-correlations between pairs of time series. Afterwards, the constructed feature images are fed into an attention-based ConvLSTM autoencoder, which aims to encode the constructed feature images and capture the temporal behavior, followed by decoding the compressed knowledge representation to reconstruct the feature images input. The reconstruction errors are then computed and subjected to a statistical-based, dynamic thresholding mechanism to detect and diagnose the anomalies. Evaluation results conducted on real-life manufacturing data demonstrate the performance strengths of the proposed approach over state-of-the-art methods under different experimental settings.

Last November, at Fort Campbell, Tennessee, half a mile from the Kentucky border, a single human directed a swarm of 130 robots. The exercise was part of DARPA's OFFensive Swarm-Enabled Tactics (OFFSET) program. If the experiment can be replicated outside the controlled settings of a test environment, it suggests that managing swarms in war could be as easy as point and click for operators in the field. "The operator of our swarm really was interacting with things as a collective, not as individuals," says Shane Clark, of Raytheon BBN, who was the company's main lead for OFFSET. "We had done the work to establish the sort of baseline levels of autonomy to really support those many-to-one interactions in a natural way."

This article was published as a part of the Data Science Blogathon. According to the Bureau of Labor Statistics, the job outlook for computer and information research scientists, data scientists is projected to grow by at least 19 per cent by 2026. Data is collected and processed in every company regardless of the domain. Data scientists dive into the data to find valuable insights beneficial to the company. Most companies store and manage their data with Relational Database Management System (RDBMS).

Surgalign Holdings has announced that it has received US Food and Drug Administration (FDA) 510(k) clearance for its HOLO Portal surgical guidance system for use within lumbar spine procedures. According to Surgalign, the HOLO Portal system is the world's first artificial intelligence (AI)-driven augmented reality (AR) guidance system for spine and the first clinical application of Surgalign's HOLO AI digital health platform. Terry Rich, president and chief executive officer of Surgalign, said: "Receiving the initial clearance for the HOLO Portal system is a significant milestone and represents a critical step toward building the foundation of the digital surgery of the future. This system is designed to improve patient outcomes by delivering intelligent solutions to our customers, and we believe it is truly revolutionary. "With clearance in hand for our guidance application, our near-term focus is getting the platform into the hands of surgeons as we work towards a market release.

Intel Corp. is planning to invest investment more than $20 billion in two computer chip plants in central Ohio to help address a global semiconductor shortage. Intel Corp. is planning to invest investment more than $20 billion in two computer chip plants in central Ohio to help address a global semiconductor shortage. COLUMBUS, Ohio -- Intel will invest $20 billion in a new computer chip facility in Ohio amid a global shortage of microprocessors used in everything from phones and cars to video games. After years of heavy reliance on Asia for the production of computer chips, vulnerability to shortages of the crucial components was exposed in the U.S. and Europe as they began to emerge economically from the pandemic. The U.S. share of the worldwide chip manufacturing market has declined from 37% in 1990 to 12% today, according to the Semiconductor Industry Association, and shortages have become a potential risk.

An enormous robotic yak, strong enough to carry up to 352 pounds, and able to sprint along at up to 6 miles per hour, has been developed by Chinese scientists. The robot can deal with all sorts of road and weather conditions, according to the Chinese state run People's Daily, which shared a video of the yak on a road. When deployed, it will join soldiers from the Chinese army on logistics and reconnaissance missions across complex environments including snowfields, deserts and mountains. The missions will include working in remote border regions, as well as in high risk combat zones, according to reports by Chinese state media. The robot comes with multiple sensors, giving it a high degree of situational awareness that analysts say can be fed into commanders in a battlefield environment. The robot can deal with all sorts of road and weather conditions, according to the Chinese state run People's Daily, that shared a video of the yak on a road The full details of the Chinese robot yak haven't been revealed, but it can carry up to 352lb of goods.

"SOUTH OF THE Huai river few geese can be seen through the rain and snow." In classical Chinese this verse is a breakthrough--not in literature but in computing power. The line, composed by an artificial intelligence (AI) language model called Wu Dao 2.0, is indistinguishable in metre and tone from ancient poetry. The lab that built the software, the Beijing Academy of Artificial Intelligence (BAAI), challenges visitors to its website to distinguish between Wu Dao and flesh-and-blood 8th-century masters. Anecdotal evidence suggests that it fools most testers.

Mustafa Suleyman, a co-founder of artificial intelligence research company DeepMind, has left Google to join venture capital firm Greylock Partners. Suleyman has brought to an end an eight-year run at Google, where he was most recently the company's vice president of AI product management and policy. He joined Google when it bought DeepMind in 2014 and became the latter's head of applied AI. Suleyman was reportedly placed on administrative leave in 2019 following allegations that he bullied employees. Suleyman, who moved to Google at the end of that year, said on a podcast with Greylock partner Reid Hoffman this week that he "really screwed up" and that "I remain very sorry about the impact that that caused people and the hurt that people felt there."

Gibson Dunn lawyers examine a number of the most significant developments from 2021 affecting companies as they navigate the evolving AI and autonomous systems landscape.