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Quantum computing – Explained easily with some examples

https://youtu.be/lWDI_BrnQIA

Quantum computing is a fascinating topic with a lot of potential for breakthroughs in various fields. Here are some ideas for content on quantum computing for your YouTube channel:

Introduction to Quantum Computing: Start with an overview of what quantum computing is, how it works, and why it’s important. This could include a brief history of quantum computing, the basics of qubits, and how quantum computers differ from classical computers.

Applications of Quantum Computing: Explore the potential applications of quantum computing, including its use in cryptography, optimization problems, machine learning, and more. You can also discuss the current limitations of quantum computing and where researchers are focusing their efforts to overcome them.

Quantum Algorithms: Dive into some of the key quantum algorithms, such as Shor’s algorithm for factoring large numbers and Grover’s algorithm for searching unsorted databases. Explain how these algorithms work and why they are significant.

Quantum Hardware: Discuss the different approaches to building quantum computers, including superconducting qubits, trapped ions, and topological qubits. Explain the advantages and disadvantages of each approach and the progress that has been made in building larger and more stable quantum computers.

Quantum Error Correction: Explain the challenge of maintaining the fragile quantum state of qubits and how quantum error correction techniques can be used to mitigate errors and improve the reliability of quantum computers.

Quantum Cryptography: Explore the potential of quantum cryptography, including quantum key distribution and quantum-resistant cryptography. Discuss how quantum computing is changing the field of cybersecurity.

Future of Quantum Computing: Look ahead to the future of quantum computing and what breakthroughs we might expect to see in the next few years. Discuss the potential impact of quantum computing on various industries, including finance, healthcare, and energy.

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Can AI kill the greenscreen?

The greenscreen is a staple of visual effects — and it may stick around even in the age of AI “magic.” The video above explains why.

It turns out that greenscreens, while imperfect, provide certain background separation benefits that are tough for AI to replicate due to the way it’s been trained and the limitations of available data. Preparation can help improve results, but this video shows why, ultimately, AI tools will remain one in a suite of options rather than a greenscreen killer.

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How Generative AI Could Replace Artists in Creative Industries | Tech News Briefing | WSJ

The threat that technology will replace workers is something more people are grappling with due to the introduction of new tools powered by generative artificial intelligence. Creative workers like artists, writers, and filmmakers are among those raising the loudest alarm. But is their concern warranted? And what impact could AI have on the future workforce?

Join us for the third episode of our series “Artificially Minded” with host Zoe Thomas.

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Will AI Steal My Job? (Testing ChatGPT) || Peter Zeihan

Worried AI is going to steal your job and kick you to the curb? If you watched the video, you can probably see why I’m not worried about my job security. While AI is going to change the way we do a lot of things, it still needs some time before it’s cracking the kind of nuanced jokes I’m famous for.

Note: This video was recorded prior to the GPT-4 update

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The history and future of AI in 8 minutes | Oxford professor Michael Wooldridge

In his book “A Brief History of AI,” Michael Wooldridge, a professor of computer science at the University of Oxford and an AI researcher, explains that AI is not about creating life, but rather about creating machines that can perform tasks requiring intelligence.

Wooldridge discusses the two approaches to AI: symbolic AI and machine learning. Symbolic AI involves coding human knowledge into machines, while machine learning allows machines to learn from examples to perform specific tasks. Progress in AI stalled in the 1970s due to a lack of data and computational power, but recent advancements in technology have led to significant progress. AI can perform narrow tasks better than humans, but the grand dream of AI is achieving artificial general intelligence (AGI), which means creating machines with the same intellectual capabilities as humans. One challenge for AI is giving machines social skills, such as cooperation, coordination, and negotiation.

The path to conscious machines is slow and complex, and the mystery of human consciousness and self-awareness remains unsolved. The limits of computing are only bounded by imagination.

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Larry Fink on Tokenization is the Future of the Next Generation

“I believe the next generation for markets, the next generation for securities, will be tokenization of securities,” BlackRock CEO Larry Fink said during the New York Times DealBook event.

We at Fusang have been busy exploring regulated solutions and structures which will allow us to unveil our own unique turnkey tokenisation solution for non-digitally native assets. Along with a custody and settlement system, the aim is to allow for fuss free tokenisation, providing issuers and investors a regulated fuss free marketplace for tokenised assets.

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What Is Tokenization (And Why You Need It)

What Is Tokenization? With regard to data security, tokenization refers to the process of securing sensitive data by substituting valuable elements with non valuable equivalents known as tokens. Tokens serve merely as a reference to the original item or currency and provide value only within an authorized, isolated ecosystem that validates its purpose.

The constant threat of cyber attacks and fraud has made both personal and financial data extremely susceptible to exploitation. Thankfully, tokenization converts sensitive data to digital tokens that have little or no value outside a specific digital ecosystem.

Once all elements have been tokenized, there’s no distinguishable relationship between the original information and its tokenized results which provides excellent security to data that’s stored or “at rest.”

Integrating tokenization into credit card processing provides substantial protection to consumers and businesses alike, by yielding only unusable tokens to hackers. This forces fraudsters to go elsewhere to seek valuable digital assets like credit card numbers and social security numbers.

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