Beijing now controls the largest navy in the world and is attempting to double the size of its nuclear warhead stockpile over the next decade; reaction from Fox News senior strategic analyst Gen. Jack Keane, chairman of the Institute for the Study of War. New attack drones, 5th-generation stealth fighter jets, reconfigured cargo planes and Russian-built air defenses are making China's Air Force even deadlier. In fact, all of these advances present a great concern to U.S. war planners. The size of the People's Liberation Army Air Force is reported to include a total of 2,500 aircraft, making it the third-largest in the world, according to the Pentagon's 2020 China Military Power report. U.S. threat assessors are not merely concerned about the size of the Chinese Air Force but the increasing technical sophistication and multi-mission tactics with which it operates.

Lawrence Livermore National Laboratory (LLNL) and AI company Cerebras Systems today announced the integration of the 1.2-trillion Cerebras' Wafer Scale Engine (WSE) chip into the National Nuclear Security Administration's (NNSA) 23-petaflop Lassen supercomputer. The pairing of Lassen's simulation capability with Cerebras' machine learning compute system, along with the CS-1 accelerator system that houses the chip, makes LLNL "the first institution to integrate the AI platform with a large-scale supercomputer and creates a radically new type of computing solution, enabling researchers to investigate novel approaches to predictive modeling," according to the lab. Work on initial AI models began last month. Lassen is the "unclassified companion," according to LLNL, to the IBM/Nvidia system Sierra (ranked no. 3 on the Top500 list of the world's most powerful supercomputer) and is no. Funded by the NNSA's Advanced Simulation and Computing program, the platform aims to accelerate solutions for Department of Energy and NNSA national security mission applications.

The history of Artificial Intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen. The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain. The field of AI research was founded at a workshop held on the campus of Dartmouth College during the summer of 1956.Those who attended would become the leaders of AI research for decades.

Human race suffers from the God Complex. Art and science strive to achieve recreate the human form, thought pattern, aesthetics, and ethics. Can we replicate the human intellect by making machines think for themselves? Artificial intelligence does not face the moral dilemma of making choices that fall in the grey area, it is binary in its output. The concept of GIGO – garbage in, garbage out holds in the case of AI too.

It has a transforming impact on International trade and affects the international trade in the number of ways, it increases the Economic growth and provides new opportunities for international trade, it affects the type and quality of economic growth with international trade implications. Artificial intelligence changing global value chains and international trade patterns, the World trade organization will play a critical role in further shaping how is developed and deployed globally. Artificial Intelligence has an impact on global value chains, which can be used to improve predictions of future trends – changes in consumer demand, and how to manage risk along the supply chains. Stocks in the warehouse can be managed more efficiently to improve the accuracy of just in time manufacturing and delivery, Robotics aid in packing and inventory inspections. AI has a great impact on the digital platform used for international trade marketing.

As wildfires grow in size and frequency, more resources are needed to keep them in check. But experts say a Trump administration directive halting the purchase of new drones jeopardizes the rise of cutting-edge technology, curtailing the ability to manage wildfires and potentially putting more lives in danger. In October 2019, the US Department of the Interior grounded its fleet of more than 800 drones and put a freeze on buying new ones due to concerns of Chinese spying. Many of the devices were used in wildfire fighting and prevention, including starting prescribed burns, a key tool in controlling wildfire. The interior department carries out more than 10,000 drone flights a year on average, according to federal documents.

Will Thursday 13 August 2020 be remembered as a pivotal moment in democracy's relationship with digital technology? Because of the coronavirus outbreak, A-level and GCSE examinations had to be cancelled, leaving education authorities with a choice: give the kids the grades that had been predicted by their teachers, or use an algorithm. They went with the latter. The outcome was that more than one-third of results in England (35.6%) were downgraded by one grade from the mark issued by teachers. This meant that a lot of pupils didn't get the grades they needed to get to their university of choice.

Until now building machine learning (ML) algorithms for hardware meant complex mathematical mode s based on sample data, known as " training data," in order to make predictions or decisions without being explicitly programmed to do so. And if this sounds complex and expensive to build, it is. On top of that, traditionally ML related tasks were translated to the cloud, creating latency, consuming scarce power, and putting machines at the mercy of connection speeds. Combined, these constraints made computing at the Edge slower, more expensive, and less predictable. Tiny Machine Learning (TinyML) is the latest embedded software technology that moves hardware into an almost magical realm, where machines can automatically learn and grow through use, like a primitive human brain.

Owing to the global lockdowns that resulted from the COVID-19 pandemic, fuel demand plummeted and the price of oil futures went negative in April 2020. Robust fuel demand projections are crucial to economic and energy planning and policy discussions. Here we incorporate pandemic projections and people’s resulting travel and trip activities and fuel usage in a machine-learning-based model to project the US medium-term gasoline demand and study the impact of government intervention. We found that under the reference infection scenario, the US gasoline demand grows slowly after a quick rebound in May, and is unlikely to fully recover prior to October 2020. Under the reference and pessimistic scenario, continual lockdown (no reopening) could worsen the motor gasoline demand temporarily, but it helps the demand recover to a normal level quicker. Under the optimistic infection scenario, gasoline demand will recover close to the non-pandemic level by October 2020. The COVID 19 pandemic and consequent lockdown has had a substantial impact on mobility and therefore fuel demand and it is not clear when demand will recover. Ou et al. use a machine learning model that integrates health recovery scenarios to project the near-term future of gasoline demand.

For all of human history, people have spied on one another. To find out what others are doing or planning to do, people have surveilled, monitored, and eavesdropped--using tools that constantly improved but never displaced their human masters. Artificial intelligence (AI) and autonomous systems are changing all of that. In the future, machines will spy on machines in order to know what other machines are doing or are planning to do. Intelligence work will still consist of stealing and protecting secrets, but how those secrets are collected, analyzed, and disseminated will be fundamentally different.