By Seraina Kull
Welcome to the ninth bi-weekly Tech News Digest, provided by the GISA Technology and Security Initiative (TechSec). Our goal here is to give you an easy-to-read update on what has been happening lately in the world of technology and security. To do so, we pick the top news stories from the last two weeks and present a short summary. Should you be interested in knowing more, just follow the links below the respective paragraphs.
Chinese Tech Stocks Fell After Antitrust Fines
China’s State Administration for Market Regulation issued a new wave of fines for violations of its anti-monopoly law. All of the 28 violations involved companies failing to report merger deals for antitrust review, some of them dating back to 2011. Tech giants Tencent and Alibaba were on the receiving end of more than half of the antitrust activity accusations. With 12 cases against Tencent and five cases against Alibaba, their share prices dropped 2.9% and 5.8%, respectively.
And while the fines preceded plunging stocks, activities violating China’s Anti-Monopoly Law could have even more dire consequences in the future. As it stands, the authorities did not order the involved companies to undo the deals, but issued a fine of 500’000 yuan (roughly USD $74’600) for each case. This amount is the maximum under current law. However, this low maximum fine will increase to 5 million yuan starting August 1st. Updates on the Anti-Monopoly Law reflect a campaign against companies’ monopolistic behaviour, namely by Big Tech, that began in late 2020. Ant Group, an Alibaba affiliate, was the first to experience the more stringent investigations, when its high-profile IPO was suspended. What followed was a wave of 98 fines in 2021. Among the fined firms were major platform-based and internet companies like Baidu, JD, Meituan, and the recently hit Alibaba and Tencent. Experts believe that the amended law will close regulatory loopholes and consequently ensure fair market competition by discouraging large companies from employing illegal activities and abusing their market dominance.
Read more about this on Forbes.
The Large Hadron Collider Now Smashes Together Particles with Record Energy Levels
On more local news, CERN’s Large Hadron Collider has once again started experiments to unlock the secrets of our universe. After over three years of maintenance work and upgrading, the LHC’s record-breaking high energy collisions on July 5th mark the beginning of the third cycle of particle collisions planned to last four years. The higher-intensity beams compared to the previous experiment cycles allow for collisions with increased energy. This means that the accelerated protons nearly reach the speed of light in the 27-kilometre loop with a record energy of 13.5 trillion electronvolts.
As the world’s most powerful particle accelerator, the LHC has contributed to discoveries in physics since 2010. One of the breakthroughs was finding evidence of the Higgs Boson, also called ‘God particle’, in 2012. Finding the Higgs Boson was key to confirming the Standard Model, which describes what matter is made of. In the future, the scientists at the CERN particle physics laboratory near Geneva will continue researching the Higgs mechanism and other, lesser-known physics phenomena using data from LHC experiments.
Thanks to the upgrades over the past three years, the LHC now operates with higher collision energy, increased collision rates, new detector systems, and an upgraded computer infrastructure. Hopefully, this will lead to larger data samples and higher-quality data, providing new insights into the composition of the universe, ‘dark matter’, and the Big Bang.
Can AI Improve Wealth Distribution?
The United States’ wealth is concentrated at the very top and the inequality this produces outpaces that of many other HICs (high income countries). And because the current, human-made political system perpetuates this trend, AI researchers are asking themselves whether machine learning is better equipped to create an equitable society. A recent paper by a research team at Google’s DeepMind shows that this might just be the case. At least, the study participants in a series of experiments prefer the AI Human Centered Redistribution Mechanism to redistribution schemes based on already existing human economic systems.
During the experiment, the participants played an online public goods game, where they chose to either keep a monetary endowment to themselves or put part of it in a public fund. The fund was then allocated back to the participants based on three different human-made redistribution systems and the AI-created one. A deep neural network was given the task to distribute resources in a way humans would prefer. The outcome was a system that consideried the advantages and disadvantages the participants had at the start of the game. Compensating players based on their contribution relative to the size of the endowment they had received did not maximise efficiency, but it did convince the participants. A majority of them preferred the AI scheme to the strict libertarian and egalitarian alternatives. The results come with the usual caveats of such experiments and the researchers pointed out that they were not proposing AI-based governance, but the study nevertheless contributes to research on how AI can assist public policy.
Read more about this on VICE.
If you would like to hear more tech news, participate in events related to technology and security or learn practical technology skills, consider following us on Instagram, LinkedIn or join our Initiative as a member!