Lifeboat Foundation

Physicists at CERN’s Large Hadron Collider (LHC) have made the first ever direct observation of neutrinos in a particle accelerator.

Neutrinos are tiny, near massless and chargeless particles. They are among the elementary particles that make up the Standard Model of particle physics. Of all the particles in the Standard Model, neutrinos are among the least understood.

Even seeing a neutrino is extremely difficult, despite the fact they are among the most numerous particles in the universe. An estimated 100 trillion (100 million million) neutrinos pass through your body every second!

We may have become the most complex living creature in part by accident and replication of error.

Quantum computers, technologies that perform computations leveraging quantum mechanical phenomena, could eventually outperform classical computers on many complex computational and optimization problems. While some quantum computers have attained remarkable results on some tasks, their advantage over classical computers is yet to be conclusively and consistently demonstrated.

Ramis Movassagh, a researcher at Google Quantum AI, who was formerly at IBM Quantum, recently carried out a theoretical study aimed at mathematically demonstrating the notable advantages of quantum computers. His paper, published in Nature Physics, mathematically shows that simulating random quantum circuits and estimating their outputs is so-called #P-hard for classical computers (i.e., meaning that is highly difficult).

“A key question in the field of quantum computation is: Are quantum computers exponentially more powerful than classical ones?” Ramis Movassagh, who carried out the study, told Phys.org. “Quantum supremacy conjecture (which we renamed to Quantum Primacy conjecture) says yes. However, mathematically it’s been a major open problem to establish rigorously.”

Diagonal Arguments are a powerful tool in maths, and appear in several different fundamental results, like Cantor’s original Diagonal argument proof (there exist uncountable sets, or “some infinities are bigger than other infinities”), Turing’s Halting Problem, Gödel’s incompleteness theorems, Russell’s Paradox, the Liar Paradox, and even the Y Combinator.

In this video, I try and motivate what a general diagonal argument looks like, from first principles. It should be accessible to anyone who’s comfortable with functions and sets.

Continue reading “What A General Diagonal Argument Looks Like (Category Theory)” »

Experts at Berkeley Lab finished winding more than 2000 kilometers of superconducting wire into cables for new magnets that will help upgrade the Large Hadron Collider and the search for new physics.

AAV development for cell and gene therapy in 2023 is being impacted by manufacturing and regulation challenges, however advancing technologies offer opportunity, according to leaders in the field.

As proven by recent regulatory approvals sweeping the cell and gene therapy industry, particularly within Europe and the US, these pioneering treatments have demonstrated great capacity in helping to resolve hard-to-treat diseases.

We can initiate AI with text prompts or by clicking a camera shutter. Why is the output of only one of them eligible for copyright?

The adoption of artificial intelligence (AI) and generative AI, such as ChatGPT, is becoming increasingly widespread. The impact of generative AI is predicted to be significant, offering efficiency and productivity enhancements across industries. However, as we enter a new phase in the technology’s lifecycle, it’s crucial to understand its limitations before fully integrating it into corporate tech stacks.

Large language model (LLM) generative AI, a powerful tool for content creation, holds transformative potential. But beneath its capabilities lies a critical concern: the potential biases ingrained within these AI systems. Addressing these biases is paramount to the responsible and equitable implementation of LLM-based technologies.

Prudent utilization of LLM generative AI demands an understanding of potential biases. Here are several biases that can emerge during the training and deployment of generative AI systems.

Lack of skills and training is a big issue. Some employees may simply not have the necessary skills or training to perform their tasks effectively. Investing in employee development can improve performance and solve lots of heartaches. Let us share Jenny’s story: Why Jenny’s Promotion Changed the Way We View Training: A Dive into Neuroscience “Guess who got the promotion?” Jenny burst in one Monday morning, her face beaming with a mix of surprise and elation. Most of us knew Jenny from her early days—an employee with lots of enthusiasm but, frankly, a bit lost in the intricacies of the industry.