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Curated by Joan
Quantum computing uses qubits that can exist in multiple states simultaneously.
Error correction is essential for maintaining qubit stability in quantum computing.
Quantum computing has the potential to transform industries like cryptography and drug discovery.
As of 2024, noise and decoherence remain significant challenges for quantum computation.
Developing quantum algorithms could reshape cybersecurity with new encryption methods.
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Digital journalists must navigate this landscape with vigilance, ethics, and a commitment to truth.
Unknown[1]
Journalists need to show their audiences why their institution should be worthy of trust.
Joy Mayer[3]
The most vigorous defense against online misinformation remains the ethics of journalism.
Unknown[1]
Engaging directly with readers can also enhance credibility.
Unknown[1]
An active fact-checking community is essential on social media.
Unknown[5]
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AI-generated image detection accuracy rates exceed 95%.
Leading tools for detection include Faster-Than-Lies and AI or Not.
Common artifacts in AI-generated images include inconsistent shadow sources and incorrect reflections.
Decopy's AI image detector analyzes patterns and metadata.
Robustness to adversarial perturbations is crucial for effective detection.
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Curated by Joan
Lab-grown leather can be produced in about two weeks.
It uses 80% less water compared to traditional leather production.
Lab-grown leather generates 90% fewer emissions than conventional leather.
The leather industry contributes 8-10% of global greenhouse gas emissions.
Consumer demand for lab-grown leather is rapidly increasing.
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Transcript
Welcome to our podcast on the hidden cost of artificial intelligence hallucinations. Today we explore how fabricated facts from artificial intelligence models can lead to serious economic loss and damage to reputation. Imagine a scenario where a trusted legal document is based on case law that never existed, prompting a high-profile lawsuit and a steep drop in market value. This is not just a mistake but a sign of failure in quality control that can cost companies millions in lost revenue. In regulated sectors, such errors may invite legal liabilities and permanently erode public trust. By ensuring that artificial intelligence systems rely on verified data and human oversight, organizations can reduce these risks and preserve their brand credibility. Let us remain vigilant and invest in robust safeguards to make our artificial intelligence systems accountable and accurate. Thank you for listening.
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Transcript
Imagine a world where your skills count more than the piece of paper you were given at graduation. Today, many employers are questioning the old rule that a college degree is the only ticket to a good job. Instead, businesses are turning to a hiring method that focuses on real talent. This approach, known as competency-based hiring, evaluates what you can do rather than where you learned to do it. Companies once tied to degree inflation are now offering opportunities to skilled workers from all walks of life. They are discovering that hands-on experience, the ability to learn quickly, and solid problem-solving skills can create a more diverse and effective workforce. As more organizations move away from the rigid paper ceiling, more workers can finally break free and be recognized for their true abilities. The future of hiring is here, and it values your competencies over a college diploma.
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The Google Search Console API has several important limitations. First, the API enforces quotas on usage, limiting the number of queries per second (QPS), queries per minute (QPM), and queries per day (QPD) at various levels including user, site, and project[1]. Specifically, users can only retrieve a maximum of 50,000 pairs of pages and keywords per property daily, which can hinder data collection for larger sites[5].
Additionally, the API may provide aggregated data, which can lead to sampling and a lack of complete keyword visibility, particularly for larger sites where a significant portion of data may be missed[3][5].
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Chav culture in Britain emerged in the early 2000s, characterized by young people from lower socioeconomic backgrounds who adopted a specific style and behavior often seen as anti-social, including wearing branded sportswear and jewelry[4]. The term 'chav' is believed to originate from the Romani word 'chavi,' meaning child, and gained prominence after its first recorded use in a Usenet forum in 1998[2][1].
The media played a significant role in popularizing chav culture, portraying it through characters and sketches in shows like 'Little Britain,' which highlighted stereotypes around class and behavior[6][5]. The term has since become a contentious descriptor, reflecting broader issues of class discrimination within British society[3].
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