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New Advances Bring the Era of Quantum Computers Closer Than Ever

From the article:

” home new advances bring the era of quantum computers closer than ever

Quantum computing New Advances Bring the Era of Quantum Computers Closer Than Ever By Charlie Wood April 3, 2026

Two research groups say they have significantly reduced the amount of qubits and time required to crack common online security technologies.

Kristina Armitage/Quanta Magazine Introduction Some 30 years ago, the mathematician Peter Shor(opens a new tab) took a niche physics project — the dream of building a computer based on the counterintuitive rules of quantum mechanics — and shook the world.

Shor worked out a way for quantum computers to swiftly solve a couple of math problems that classical computers could complete only after many billions of years. Those two math problems happened to be the ones that secured the then-emerging digital world. The trustworthiness of nearly every website, inbox, and bank account rests on the assumption that these two problems are impossible to solve. Shor’s algorithm proved that assumption wrong.

For 30 years, Shor’s algorithm has been a security threat in theory only. Physicists initially estimated that they would need a colossal quantum machine with billions of qubits — the elements used in quantum calculations — to run it. That estimate has come down drastically over the years, falling recently to a million qubits. But it has still always sat comfortably beyond the modest capabilities of existing quantum computers, which typically have just hundreds of qubits.

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The Race to Harness Quantum Computing’s Mind-Bending Power | The Future With Hannah Fry

Get “The AI Career Survival Guide” here: https://technomics.gumroad.com/l/ai-survival-guide.
What happens when human labor becomes mathematically obsolete? For thousands of years, the global economy has run on the biological engine of human workers. But a new era has arrived: The Physical Singularity.
In this video, we break down the brutal thermodynamics of the labor inversion, revealing how major AI companies are mass-producing humanoid robots that operate for just 57 cents an hour. We expose the massive industry shift from digital generation to “World Models,” and how China’s manufacturing miracle is driving hardware costs to zero. With 10 billion robots projected by the 2040s, experts like Geoffrey Hinton are warning of a hive-mind “alien intelligence.” The digital era is over. The physical agent era has begun.
Welcome to Technomics. If you want to stay ahead of the curve and understand the real impact of the AI revolution, hit that subscribe button.
Sources & Research Links:
The 57¢ / Hour Labor Inversion Math: https://www.ark-invest.com/articles/valuation-models/ark-pub…oid-robots.
Unitree G1 Official $16,000 Pricing: https://www.unitree.com/g1/
China’s 2024 Robotics Dominance (IFR Report): https://ifr.org/ifr-press-releases/news/china-dominates-industrial-robot-market.
Elon Musk’s 10 Billion Robot Prediction: https://www.youtube.com/watch?v=ODsjGOGX_oM
Geoffrey Hinton on AI Hive Mind (“Immortality, but it’s not for us”): https://www.youtube.com/watch?v=qpoRO378qRY
Geordie Rose on Alien Intelligence (“The same way you don’t care about an ant”): https://www.youtube.com/watch?v=1pd4i2YlGmc.
DeepSeek AI Cost Efficiency Breakthroughs: https://www.deepseek.com/
Timestamps:
00;00 — The 57¢ Workforce & The Great Deception.
02;48 — The Math of the Labor Inversion.
05;01 — Why OpenAI Killed Sora (World Models)
09;16 — The Manufacturing Miracle: China’s Hardware Collapse.
12;53 — 10 Billion Robots & Alien Intelligence.
15;58 — How to Survive the Singularity.
Disclaimer:
The content in this video is for educational and informational purposes only and does not constitute financial or investment advice. The views and opinions expressed in this video are based on current research and industry trends, which are subject to rapid change. We do not guarantee the accuracy or completeness of the projections discussed. Copyright Disclaimer under section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, comment, news reporting, teaching, scholarship, education, and research.
#PhysicalSingularity #HumanoidRobots #ArtificialIntelligence #OpenAI #FutureOfWork #TechTrends

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Space Updates / Overmedicated Children

In the first half, space historian and author Rod Pyle discussed the renewed U.S. lunar ambitions under NASA’s Artemis program, along with other space news. Describing the recent Artemis rocket launch as “smooth as silk,” he praised the Space Launch System (SLS), though he acknowledged its high cost and reliance on shuttle-era technology. He explained that the SLS was built under NASA’s traditional cost-plus contracting model, contrasting it with private-sector efforts like SpaceX and Blue Origin, which assume more financial risk. Comparing Artemis to the Apollo-era Saturn V, Pyle noted both rockets are “remarkable machines” suited to their missions, but highlighted that Artemis cannot carry both the lunar module and capsule in a single launch as Saturn V did.

He outlined the Artemis timeline, with Artemis III originally planned for a Moon landing next year, now delayed to Artemis IV in 2028. Pyle also commended NASA chief Jared Isaacman for navigating budgetary challenges and advancing the Artemis program despite delays. Reflecting on the historic Apollo 8 mission as “a remarkably daring and dangerous mission” driven by Cold War geopolitics, he recalled the iconic “reading of Genesis from lunar orbit” and the transformative Earthrise photo. Elon Musk’s pivot from Mars to the Moon was driven by financial incentives and NASA funding delays, he suggested, noting that lunar missions are “a few days away, instead of seven or eight months,” making the Moon a more achievable target.

Addressing current spacefaring nations, the guest identified the U.S., Russia, China, Japan, and India as major players, with China rapidly advancing. China’s lunar program is “very steady and consistent,” Pyle said, and is aiming for a 2029–2030 landing that will replicate Apollo 11’s short visit, with longer-term plans for a lunar base. He raised the question of whether the U.S. and China can coexist on the Moon if both establish bases. On technology, he cited AI’s role in rover autonomy despite hardware limitations, noting successful AI-driven test drives on Mars. Looking further ahead, he projected human Mars missions in the mid-2030s, contingent on nuclear propulsion and necessary infrastructure.

Google DeepMind’s Research Lets an LLM Rewrite Its Own Game Theory Algorithms — And It Outperformed the Experts

Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.

Targeting metabolism to combat anticancer and antibacterial drug resistance

Combating anticancer and antibacterial drug resistance by metabolic targeting.

Bacteria and cancer cells activate defense mechanisms driven by central carbon and amino acid metabolism to overcome drug-induced stress.

Drug tolerance and persistence are driven by a dormant state in bacteria, whereas cancer cells upregulate energy metabolism to withstand prolonged drug exposure.

Biofilms, granulomas, and the tumor microenvironment share hypoxic and acidic conditions, where cells rely on anaerobic and lipid metabolism for survival.

Macrophage immunometabolism influences disease progression in tuberculosis and cancer. Common approaches for overcoming drug resistance include blocking metabolic targets that enhance drug lethality and synergistic drug combinations.

Drug repurposing, dietary interventions, and immunotherapy have shown use in cancer, but their antibacterial potential remains underexplored. sciencenewshighlights ScienceMission https://sciencemission.com/Targeting-metabolism-to-combat-anticancer

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Beyond the AI Hype: When Will We Know We’ve Reached AGI?

When NVIDIA founder and CEO Jensen Huang told podcaster Lex Fridman in a recent interview that he thinks we have already achieved AGI, I understood why the statement landed with such force. Today’s systems are impressive, useful, and often psychologically persuasive. They can create the feeling that the threshold has already been crossed. But my answer is no: we have not achieved AGI just yet. In my 2026 book, SUPERALIGNMENT: The Three Approaches to the AI Alignment Problem — How to Ensure the Arrival of Benevolent Artificial Superintelligence Aligned with Human Goals and Values, I argue that AGI should not be declared based on hype, surprise, or market excitement. It should be recognized only when three far more meaningful benchmarks are met.

In fact, one of the reasons this debate keeps spiraling into confusion is that we have been trapped for years in the “moving goalposts” problem. By practical conversational standards, machines passed the Turing test long ago. But every time AI masters a previously “human-exclusive” capacity—dialogue, strategy, writing, even emotional style—many observers simply redefine that achievement as mere automation. That is precisely why I reject unstable, psychology-based thresholds. If our benchmark is just whatever still makes humans feel uniquely special, then AGI will always remain one step away by definition.

That is why, in SUPERALIGNMENT, I start with operational definitions of AGI and ASI. For me, AGI is not merely a system that performs well across many cognitive tasks. It is a system that can generalize knowledge across domains, reason abstractly, adapt to open and uncertain environments, transfer learned knowledge to novel contexts, and introspect on its own reasoning. In other words, AGI is not just impressive breadth. It is flexible, self-reflective generality at par with or above human capabilities. That is a much higher bar than what most people mean when they casually say, “AI is already general.”

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