Samsung employees protest and threaten strike, demanding share of profits amid AI boom
Record profits but angry workers: thousands of Samsung workers threatening strike over bonus dispute.
Thousands of Samsung Electronics workers protested on Thursday at its chip complex in South Korea, demanding higher bonuses and threatening to strike as the company sees record profits due to artificial intelligence driving up memory chip demand.
Holding signs and waving banners, the workers gathered at a factory compound in Pyeongtaek, amid a heavy police presence, shouting “make compensation transparent and remove maximum limits on bonuses!”
Union representatives put attendance at around 40,000 members, though police did not state an official count.
The protest unfolded the same day that Samsung's main competitor, SK Hynix, reported its best-ever quarterly results — record revenue and operating profit for the first three months of the year, which the company credited to soaring global investment in data centres and AI infrastructure that drove up the demand for its memory chips.
Samsung, which together with SK Hynix produces about two-thirds of global memory chips, forecast earlier this month that its first-quarter operating profit would reach a record 57.2 trillion won (€33 billion).
Samsung’s union, which represents about 74,000 workers, says the company has failed to offer adequate compensation despite its strong performance. It has rejected the management’s proposal for bonuses of restricted stock and called for removing caps on bonuses.
If talks with management break down, the union has threatened an 18-day strike beginning May 21, estimating it would cost the company over 1 trillion won (€578 million) per day.
“We won’t stop this fight until our fair demands are met,” Choi Seung-ho, a union leader, said through a loudspeaker from atop a crane-mounted structure.
South Korea’s semiconductor makers have benefited from the AI boom but the war in the Middle East has clouded the future outlook, disrupting supplies of key materials such as helium that are crucial to chipmaking and pushing up energy costs.
But in a conference call on Thursday, Woo Hyun Kim, SK Hynix’s chief financial officer, said the company is closely monitoring the conflict but does not expect a meaningful impact on production.
April 22, 2026
By Kelley M. Sayler
Congressional Research Service (CRS).
On February 27, 2026, President Donald J. Trump directed federal agencies to “IMMEDIATELY CEASE all use of [American AI company] Anthropic’s technology.” Secretary of Defense Pete Hegseth (who is now using “Secretary of War” as a “secondary title” under Executive Order (E.O.) 14347 dated September 5, 2025) subsequently directed the Department of Defense (DOD, now using “Department of War” as a secondary designation under E.O. 14347) to designate Anthropic a supply-chain risk to national security; bar defense contractors, suppliers, and partners from working with Anthropic; and describe an up-to-six-month period of transition away from Anthropic products.
This designation follows a reportedly months-long dispute between DOD and Anthropic over DOD use of Anthropic products, including Claude, the company’s generative AI model. On March 9, Anthropic filed a civil complaint in the U.S. District Court for the Northern District of California and a petition for reviewin the U.S. Court of Appeals for the D.C. Circuit challenging these directives. While the district court issued a preliminary injunction in favor of Anthropic on March 26, the court of appeals denied Anthropic’s motion for a stay on April 8, thus undoing the lower court’s injunction.
Some lawmakers have called for a resolution to the disagreement and for Congress to act to set rules for the department’s use of AI and/or autonomous weapon systems.
Background
In July 2025, DOD announced that it had awarded contracts to Anthropic, Google, OpenAI, and xAI for up to $200 million each “to accelerate Department of Defense (DoD) adoption of advanced AI capabilities to address critical national security challenges.” Although DOD has not publicly outlined the full range of use cases for these companies’ AI models, Anthropic has stated that Claude “is reportedly the Department’s most widely deployed and used frontier AI model.” Anthropic has further statedits models are used “across the Department of War and other national security agencies for mission-critical applications, such as intelligence analysis, modeling and simulation, operational planning, cyber operations, and more.” Although Anthropic’s usage policy prohibits use of its models to incite violence or to develop or design weapons, reports indicate that Claude was used in the January 2026 operation to capture Venezuelan President Nicolás Maduro.
According to reporting, Anthropic inquired about DOD’s use of Claude, generating concerns within the department that Anthropic might not approve of certain use cases and, therefore, might attempt to limit DOD use of its models. As a result, the Pentagon reportedly requested that Anthropic—and other AI companies—allow use of AI models for “all lawful purposes.” While Anthropic was reportedly “willing to adapt its usage policies for the Pentagon,” the company was, given its assessment of “what today’s technology can safely and reliably do,” unwilling to allow two use cases: mass domestic surveillance and fully autonomous weapon systems. In explaining his decision to deny the Pentagon’s request for “full, unrestricted access to Anthropic’s models,” Anthropic CEO Dario Amodei stated that autonomous weapon systems “may prove critical for our national defense. But today, frontier AI systems are simply not reliable enough to power fully autonomous weapons.”
DOD is not publicly known to be using Claude—or any other frontier AI model—within autonomous weapon systems. DOD Directive (DODD) 3000.09, “Autonomy in Weapon Systems,” outlines the approval process for developing and deploying autonomous weapon systems and identifies requirements for their use.
What Are Autonomous Weapon Systems?
DODD 3000.09 defines autonomous weapon systems as “weapon system[s] that, once activated, can select and engage targets without further intervention by [a human] operator.” This concept of autonomy is also known as human out of the loop or full autonomy. The directive contrasts such systems with human-supervised, or human on the loop, autonomous weapon systems, in which operators have the ability to monitor and halt a weapon’s target engagement. Another category is semiautonomous, or human in the loop, weapon systems that “only engage individual targets or specific target groups that have been selected by [a human] operator.”
DODD 3000.09 requires all systems, including autonomous weapon systems, be designed to “allow commanders and operators to exercise appropriate levels of human judgment over the use of force.” Such judgment does not require manual human “control” of the weapon system but rather broader human involvement in decisions about how, when, where, and why the weapon will be employed (i.e., a human must assess the operational environment and decide to deploy the weapon, which can then operate autonomously). This involvement includes a human determination that the weapon will be used “with appropriate care and in accordance with the law of war, applicable treaties, weapon system safety rules, and applicable rules of engagement.” The requirement for “human judgment over the use of force” does not mean that such systems are operating with a human in the loop.
Related Legislation and Issues for Congress
The department updated DODD 3000.09 in January 2023 and later that year, Congress passed the National Defense Authorization Act for Fiscal Year 2024 (NDAA; P.L. 118-31). Section 251 requires that the Secretary notify the defense committees of any changes to DODD 3000.09 within 30 days. The Secretary is directed to provide a description of the modification and an explanation of the reasons for the modification. Section 1066 of the FY2025 NDAA (P.L. 118-159) additionally requires the Secretary to submit to the committees, annually through December 31, 2029, a “comprehensive report on the approval and deployment of lethal autonomous weapon systems by the United States” through December 31, 2029. Congress has not legislated on the department’s use of AI models or their reliability; some Members have introduced related legislation (e.g., S. 1394 and H.R. 2894, 118th Congress; S. 4113, 119th Congress).
Should Congress decide that more oversight is needed, it may codify the requirements of DODD 3000.09 or consider additional notification requirements for DOD’s use of autonomous weapon systems or AI models. Congress may also restrict funds for the development and/or use of autonomous weapon systems, or for certain use cases of AI models by DOD, should Congress deem such uses pose an unacceptable level of risk at the current stage of technological development.
Source: This article was published by the Congressional Research Service (CRS).
The Congressional Research Service (CRS) works exclusively for the United States Congress, providing policy and legal analysis to committees and Members of both the House and Senate, regardless of party affiliation. As a legislative branch agency within the Library of Congress, CRS has been a valued and respected resource on Capitol Hill for nearly a century.
April 23, 2026
UN News
By Elma Okic
If AI is “a very fast car with no steering wheel” then regulation must provide one, insists Nobel laureate and Artificial Intelligence pioneer Geoffrey Hinton, the visionary scientist widely known as the “godfather” of the self-learning tech.
Speaking at the Digital World Conference (DWC): AI for Social Development – co-organized by the UN Research Institute for Social Development (UNRISD) – Professor Hinton stressed that rapid advances in AI must be guided more carefully to serve societies – rather than undermine them.
“If you ever went out with a car that had no brake, boy, you are in trouble if you go down a hill,” he told delegates. “But you’re in even more trouble if there’s no steering wheel.”
His remarks came during a busy week for AI policymaking, as governments and UN panels stepped up discussions on governance, inclusion and risk management, amid the growing integration of artificial intelligence across the global economy and society.
The haves and the have-nots
The pace of AI’s growth is staggering. According to UN Trade and Development (UNCTAD)’s Technology and Innovation Report 2025, the global AI market is projected to grow from $189 billion in 2023 to $4.8 trillion by 2033, an economy larger than Japan’s, built in a single decade.
Yet the capacity to build and shape it remains in the hands of just a few economies and firms, UNCTAD Acting Secretary‑General Pedro Manuel Moreno warned at the Commission on Science and Technology for Development (CSTD), also meeting this week.
That concentration risks deepening global inequalities. Doreen Bogdan‑Martin, Secretary‑General of the UN International Telecommunication Union (ITU), pointed out that generative AI adoption in the industrialized ‘Global North’ is growing nearly twice as fast as in the developing ‘Global South’.
“Left unaddressed, this is a second great divergence – widening the gap between countries shaping artificial intelligence and those merely consuming it” – Ms. Bogdan‑Martin said, adding that gaps in infrastructure, investment and capacity cannot be closed by any single country or organization alone.
This week’s flurry of international activity on AI and digital technology in Geneva and beyond, reflects the international push to ensure that all countries can benefit and regulate Artificial Intelligence as it increasingly shapes our economies, societies and daily lives.
Distinct areas of discussion are becoming clear.
While the focus of the Commission on Science and Technology for Development is on global‑level digital policymaking, discussions at the AI For Social Development Conference underscored the need for transparent, accountable and rights‑based AI governance to address risks such as bias, opaque algorithms and having large volumes of data concentrated in the hands of just a privileged few massive corporations.
Participants at the World Conference – convened by UNRISD and international NGO, the World Digital Techology Academy – examined AI’s growing role in social protection, labour markets, education and the green energy transition, while stressing the importance of protecting vulnerable groups and ensuring the benefits of technological change are shared more fairly.
Data-driven approach
Any proposals for AI governance must be data-driven and this is the fundamental work of the UN’s Independent International Scientific Panel on AI, which convened its first in‑person meeting in Madrid on Wednesday.
Opening the Scientific Panel’s first in‑person meeting in Madrid, co-chair Maria Ressa explained the group’s mandate to provide an independent, scientific and authoritative assessment of how AI systems are shaping societies.
Ressa, a Nobel Peace Prize laureate and campaigning Philippines journalist, warned that increasingly powerful AI tools are accelerating the undermining of democratic systems using “narrative warfare” in which falsehoods are manufactured and amplified at scale; the weakening of institutions such as the media and courts; and, ultimately, strategic corruption once accountability erodes.
Its findings will inform the discussions of another key UN AI initiative – the UN’s Global Dialogue on Artificial Intelligence Governance – which meets in July, also in Geneva.A worldwide discussion
The Scientific Panel’s findings which Ms. Ressa co-chairs with renowned Canadian computer scientist Yoshua Bengio will inform the discussions of another key UN AI initiative – the UN’s Global Dialogue on Artificial Intelligence Governance – which meets in July, also in Geneva.
The Global Dialogue brings together all 193 United Nations Member States, the private sector, civil society, academia and the tech world to share best practices and build common approaches to AI governance.
“The policy conversation will be science and evidence-based, pooled perspectives, scientific perspectives from a multidisciplinary lens from across the world,” says the UN Special Envoy for Digital and Emerging Technologies, Amandeep Gill.
“This is how policy discussions should be, and the UN is very proud to facilitate this first ever such confluence of science and policy in a fast-paced emerging technology.”
By Eurasia Review
Synthetic voices are increasingly a part of our lives, from digital assistants like Siri and Alexa to automated telemarketers and answering machines. With the expansion of generative AI, a new type of synthetic voice has been developed: voice clones, which can recreate a facsimile of a person’s voice from only a few seconds of recorded speech.
In JASA, published on behalf of the Acoustical Society of America by AIP Publishing, a pair of researchers from University College London and the University of Roehampton evaluated the intelligibility of humans and voice clones. They found that voice clones are easier than humans to understand in noisy environments.
Voice clones differ from traditional synthetic voices in the amount of sampling they require. Synthetic voices like Siri require a voice actor to spend hours in a recording booth. In contrast, a voice clone can be made from as little as 10 seconds of speech, significantly expanding the number of potential voices as well as the number of potential applications.
Researchers Patti Adank and Han Wang specialize in studying human perception of unclear speech and were fascinated by the idea of machine-replicated speech. A key question they were looking to answer was just how easy voice clones are for the average person to understand. They suspected that voice clones would simply be poor representations of actual human voices and that people would struggle to understand them. What they found could not be more different.
“I thought initially that voice clones would be less intelligible because they were unfamiliar,” said Adank. “I found they were up to 20% more intelligible, which was quite shocking. A small part of our paper is talking about that experiment, and then a large part is me and my collaborator frantically trying to find out what it is that makes those voice clones more intelligible.”
The duo initially presented volunteers with human voices and voice clones, asking them to rate their intelligibility. After finding that voice clones were consistently rated easier to understand, they repeated the experiment with elderly volunteers to determine if being hard-of-hearing alters the effect; with American volunteers — the original cohort was British — to judge if the accent plays a role; and with a filter designed to mimic cochlear implants. In every case, voice clones emerged victorious.
After examining over 100 acoustic measurements, Adank believes the only way to solve the mystery is to work with collaborators who specialize in text-to-speech systems to adapt an existing open-source cloning system.
“I am now going to try and recreate [the effect] by studying how synthesizers work and how they use digital signal processing to generate those voices, just to get a bit of a handle on this,” said Adank.
Meet ACE: The AI robot can beat human table tennis pros
A new robot developed by Sony can now take on top table tennis players, highlighting how quickly artificial intelligence is advancing into complex human skills.
At the start of this week, a robot beat human runners in a half-marathon in Beijing. Now, another one can apparently outplay table tennis professionals. Is this how it begins - machines quietly overtaking us, one task at a time?
The answer is yes - and no. In a new study, a robot built by Japanese electronics giant Sony has beaten professional players. But the features that make this possible are anything but human-like. The robot, called “Ace,” has a single arm with eight joints and uses its nine camera eyes to track the ball’s logo and detect its spin.
How did it get so good?
One thing humans and robot arms have in common is the need for training. Simply programming a robot to play table tennis is not enough, Sony AI researcher Peter Dürr, co-author of the study published Wednesday in Nature, explains. “You have to learn how to play from experience.”
Ace was trained using an AI method known as reinforcement learning. According to Sony, the study shows how advances in artificial intelligence can not only help to make robots faster but also much more agile
Sony set up a full-size Olympic table tennis court at its Tokyo headquarters, where official rules were applied, Dürr said. Several athletes said they were impressed by how good Ace was.
The experiment was conducted on a standard-sized court, and official table tennis rules were applied.
The outcome shows that a machine can achieve human, expert-level play in a common competitive sport, interacting with skilled human athletes, “a longstanding milestone for AI and robotics research”, according to Sony.
The technology behind the speed and agility
The goal wasn’t just speed. Researchers could have built a machine that catches the ball and plays it back faster than a human can react to. But the idea was to build a robot that would actually play the game - and be on as level a playing field as possible, said Michael Spranger, president of Sony AI.
The speed, reach, and performance of the machine are compared to those of a skilled athlete who trains at least 20 hours a week. “The goal is to have some level of comparability, some level of fairness to the human, and win really at the level of AI and the level of decision-making and tactics and, to some extent, skill”, Spranger said.
After submitting their paper for review before it was published in Nature, Sony’s team kept improving the robot. They said Ace became faster, played longer rallies, and moved more aggressively closer to the table. In December, it faced four highly skilled players and beat all but one.
Another professional player, Kinjiro Nakamura, who competed in the 1992 Barcelona Olympics, said he saw Ace make a shot that seemed impossible for a human. But now that the robot has done it, he added, it suggests a human might be able to do it too.
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