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How do the big tech giants compare to entire countries? Here’s how Apple, Microsoft, and Amazon’s market caps stack up against national GDP.
Experts are excited by the concept of microreactors because nuclear facilities have historically relied on economies of scale — a paradigm this tech could reverse.
“Microreactors promise to turn this paradigm on its head by approaching cost competitiveness through technological learning,” Alex Gilbert from the nuclear power think tank Nuclear Innovation Alliance, told CNBC.
Oklo’s “fast reactor” plant uses energy from already-spent nuclear reactor fuel, technology that has been around since the 1950s, according to CNBC.
Livestreaming is a trillion-dollar industry in China, where social networking and entertainment meet marketing and e-commerce. China’s retail economy has become influencer-driven, with almost all online consumers buying products based on recommendations by their idols. That trend has enabled livestreaming and video platforms like Kuaishou and Douyin, which cultivated a roster of hugely popular influencers, to participate in the e-commerce market through partnerships with the online sales platforms.
Many people and businesses would not have survived the pandemic if it were not for the gig economy, says the co-founder of Asia Innovations Group, the start-up behind the live-streaming platform Uplive.
Ut ohh.
The middle and working classes have seen a steady decline in their fortunes. Sending jobs to foreign countries, the hollowing out of the manufacturing sector, pivoting toward a service economy and the weakening of unions have been blamed for the challenges faced by a majority of Americans.
## GENERAL FUSION (VANCOUVER) • JUN 16, 2021.
# General Fusion to build its Fusion Demonstration Plant in the UK, at the UKAEA Culham Campus.
*Unlike conventional nuclear power, which involves fission or splitting atoms, the emerging fusion technology promises clean energy where the only emission would be helium, and importantly, no radioactive waste.*
If you walk down the street shouting out the names of every object you see — garbage truck! bicyclist! sycamore tree! — most people would not conclude you are smart. But if you go through an obstacle course, and you show them how to navigate a series of challenges to get to the end unscathed, they would.
Most machine learning algorithms are shouting names in the street. They perform perceptive tasks that a person can do in under a second. But another kind of AI — deep reinforcement learning — is strategic. It learns how to take a series of actions in order to reach a goal. That’s powerful and smart — and it’s going to change a lot of industries.
Two industries on the cusp of AI transformations are manufacturing and supply chain. The ways we make and ship stuff are heavily dependent on groups of machines working together, and the efficiency and resiliency of those machines are the foundation of our economy and society. Without them, we can’t buy the basics we need to live and work.
Smart strategies like this can help workers learn to embrace technological change. If the government helps people plan their next move if and when they’re no longer needed in their current job, workers will be able to roll with the economy’s punches more easily. Combined with national health insurance, education and retraining assistance — and a robust unemployment insurance system — it could make terror of job loss a thing of the past.
The U.S. government must assuage people’s anxiety about technology upending their working lives, in part by helping them forge new career paths.
Machine learning techniques have contributed to progress in science and technology fields ranging from health care to high-energy physics. Now, machine learning is poised to help accelerate the development of stronger alloys, particularly stainless steels, for America’s thermal power generation fleet. Stronger materials are key to producing energy efficiently, resulting in economic and decarbonization benefits.
“The use of ultra-high-strength steels in power plants dates back to the 1950s and has benefited from gradual improvements in the materials over time,” says Osman Mamun, a postdoctoral research associate at Pacific Northwest National Laboratory (PNNL). “If we can find ways to speed up improvements or create new materials, we could see enhanced efficiency in plants that also reduces the amount of carbon emitted into the atmosphere.”
Mamun is the lead author on two recent, related journal articles that reveal new strategies for machine learning’s application in the design of advanced alloys. The articles chronicle the research outcomes of a joint effort between PNNL and the National Energy Technology Laboratory (NETL). In addition to Mamun, the research team included PNNL’s Arun Sathanur and Ram Devanathan and NETL’s Madison Wenzlick and Jeff Hawk.
Taiwan Semiconductor Manufacturing Co. ’s chips are everywhere, though most consumers don’t know it.
The company makes almost all of the world’s most sophisticated chips, and many of the simpler ones, too. They’re in billions of products with built-in electronics, including iPhones, personal computers and cars—all without any obvious sign they came from TSMC, which does the manufacturing for better-known companies that design them, like Apple Inc. and Qualcomm Inc.
TSMC has emerged over the past several years as the world’s most important semiconductor company, with enormous influence over the global economy. With a market cap of around $550 billion, it ranks as the world’s 11th most valuable company.
