The deep learning revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. These new methods provide analysis of data from sensors, cameras, and acoustic recorders, even in real time, in ways that are reproducible and rapid. Off-the-shelf algorithms can find, count, and classify species from digital images or video and detect cryptic patterns in noisy data. Using these opportunities requires collaboration across ecological and data science disciplines, which can be challenging to initiate. To facilitate these collaborations and promote the use of deep learning towards ecosystem-based management of the sea, this paper aims to bridge the gap between marine ecologists and computer scientists. We provide insight into popular deep learning approaches for ecological data analysis in plain language, focusing on the techniques of supervised learning with deep neural networks, and illustrate challenges and opportunities through established and emerging applications of deep learning to marine ecology. We use established and future-looking case studies on plankton, fishes, marine mammals, pollution, and nutrient cycling that involve object detection, classification, tracking, and segmentation of visualized data. We conclude with a broad outlook of the field's opportunities and challenges, including potential technological advances and issues with managing complex data sets.

Nonnegative matrix factorization (NMF) has found many applications including topic modeling and document analysis. Hierarchical NMF (HNMF) variants are able to learn topics at various levels of granularity and illustrate their hierarchical relationship. Recently, nonnegative tensor factorization (NTF) methods have been applied in a similar fashion in order to handle data sets with complex, multi-modal structure. Hierarchical NTF (HNTF) methods have been proposed, however these methods do not naturally generalize their matrix-based counterparts. Here, we propose a new HNTF model which directly generalizes a HNMF model special case, and provide a supervised extension. We also provide a multiplicative updates training method for this model. Our experimental results show that this model more naturally illuminates the topic hierarchy than previous HNMF and HNTF methods.

"The use of deep learning and visual experiences has been a key focus for us," … leader of artificial intelligence and head of data engineering at KPMG.

Knowledge graphs have been around for almost half a century – as the term was first coined in 1972! For a long time, they simply languished in the academic world until Google announced their knowledge graph in 2012. Since then, knowledge graphs have evolved quite dramatically, and now there is no turning back. The last 10 years have seen a meteoric rise in machine learning (ML) and artificial intelligence (AI). Because of their ability to drive intelligence into data and add context, knowledge graphs are used to make ML and AI more reliable, robust, trustworthy, and explainable.

China, in the midst of a significant upgrade of its military forces and purported arms race with the U.S., showed off a new drone that has a wingspan of nearly 70 feet and can fly continuously for almost a full day. Known as the CH-6 drone, it is'aimed at high-end arms and dual-use markets,' according to Chinese news outlet, Global Times. The drone - built by CH UAV - can fly continuously for 20 hours and reach speeds of 435 miles per hour, the news outlet added. It can also reach heights of almost 50,000 feet. It is the latest step in a growing arms race between China and the US, as the Chinese seek to overhaul their military.

The UK government has published its much-awaited National AI Strategy in pursuit of "global science superpower" status. The document talks of plans for a "new national programme and approach to support research and development" plus a government white paper on the governance and regulation of AI [PDF]. Details of the strategy were trailed back in January when the AI Council published its "AI Roadmap" including 16 recommendations to the government. Among the headlines from today's announcement is the recognition that to make any progress, ministers must ensure the right skills are in place – and that means starting at school age right up to postgraduate learning. The government also plans to launch a National AI Research and Innovation Programme to – you guessed it – improve research.

The US military has started using AI to guide its air strikes, according to Secretary of the Air Force Frank Kendall. Speaking at the Air Force Association's Air, Space & Cyber Conference in National Harbor, Maryland, on 20 September, Kendall said that the US Air Force (USAF) had recently "deployed AI algorithms for the first time to a live operational kill chain". He didn't give details of the strike, such as whether it was by a drone or piloted aircraft, or if there were civilian casualties.

This article is the first in my series of articles aimed at'Commodity Price Forecasting' which will cover the business aspects and need for commodity price forecasting. Commodity price forecasting tends to play a critical role in the strategic plan of procurement and pricing of products that rely on these commodities as raw materials. Forecasting empower the planners to make data-driven decisions, reduce pricing-related risks, and proactively manage suppliers while minimizing disruption in the supply chain because of price volatility. For manufacturing heavy companies, raw material bulk procurement can be upwards of 50% of cost of running the entire business. Hence, the need for accurate price forecasting for business to make more informed pricing decisions has become more important.

Law enforcement and private businesses have used face recognition for years, but use of the technology in distributing government aid has expanded rapidly during the pandemic. States and federal agencies have turned to face recognition as a contactless, automated way of verifying the identity of people applying for unemployment and other public benefits. Experts and activists worry that failures of this technology could prevent people from getting benefits they desperately need--and that it could be even more dangerous if it works as designed. The pandemic accelerated the use of many biometric data collection tools--temperature checks at doors, thermal cameras in schools, face scans at airports. When it comes to benefits such as unemployment, state governments are turning in particular to facial recognition, to verify people's identity before releasing money they are entitled to.

China on Tuesday showed off its increasingly sophisticated air power including surveillance drones, with an eye on disputed territories from Taiwan to the South China Sea and its rivalry with the United States. The country's biggest airshow, in the southern coastal city of Zhuhai, comes as Beijing pushes to meet a 2035 deadline to retool its military for modern warfare. China still lags the United States in terms of tech and investment in its war machine, but experts say it is narrowing the gap. On Tuesday, the air force aerobatic team left colourful vapour trails as it manoeuvred in formation, while visitors inspected new jets, drones and attack helicopters on the tarmac. The CH-6, a prototype drone with a wingspan of 20.5 metres (67 feet), was among the domestic tech unveiled.