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cover of episode How Policing Content Fell Out of Fashion at Meta

How Policing Content Fell Out of Fashion at Meta

2025/1/9
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WSJ Tech News Briefing

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Alexa Corse
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Brian Gormley
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Brian Gormley: 我认为医疗AI初创公司成功的关键在于找到合适的要素组合。这包括拥有足够的数据来训练你的算法,具备深厚的技术专长,同时拥有对医疗保健领域的专业知识和对市场机会的深刻理解。许多公司缺乏这些要素中的一个或多个。有些公司缺乏足够强大的数据来训练他们的算法;有些公司缺乏足够的医疗专业知识来理解市场动态;还有些公司可能对市场机会和其技术的最佳应用场景缺乏足够的了解。风险投资家们正在寻找那些已经弄清楚如何在不同市场中有效应用其技术的公司。 拥有专有数据是这些公司脱颖而出的关键。在医疗保健领域,获得能够回答特定问题的数据并非易事,因为许多医疗系统信息分散在保险公司、医疗系统和制药公司中。因此,初创公司需要找到一种方法来获取或自己生成这些数据。生物技术公司可以通过开发其他公司无法复制的生物数据,并利用这些数据来训练算法,从而发现其他公司无法找到的药物,从而在竞争中脱颖而出。 当然,这些初创公司也面临着来自现有科技公司的竞争。这些公司拥有自己的AI战略、既有的关系网络,并且已经融入医疗系统。但是,如果一家初创公司能够发现现有公司尚未完全解决的弱点,那么他们就有了机会。风险投资家们正在寻找那些能够利用技术来充分解决现有公司尚未解决的问题的公司。 此外,拥有医疗和技术双重专长的人才对于医疗AI初创公司的成功至关重要。获得这种人才并非易事,因为AI专家和医疗系统专家都非常抢手。你需要将这些人才整合到一家公司中,融合两种文化,组建一个能够解决特定问题的团队。风险投资家们正在寻找这样的公司。

Deep Dive

Key Insights

Why is Meta ending third-party fact-checking on its platforms in the U.S.?

Meta is ending third-party fact-checking to simplify its content moderation approach and focus on free speech. CEO Mark Zuckerberg cited concerns about political bias among fact-checkers and the perception of censorship, particularly among conservatives. The company aims to rely more on community-based systems, like X's Community Notes, to provide context to posts.

What are the risks and benefits of Meta's shift to community-based content moderation?

The benefits include increased trust from users who prefer peer-based moderation over professional fact-checkers. However, risks include delays in attaching context to viral posts, as community-based systems take time to work. Additionally, the system relies on user consensus, which may not always be accurate or timely.

How does Meta's decision to end fact-checking align with Elon Musk's approach at X?

Meta's decision aligns with X's approach by adopting a community-based moderation system similar to X's Community Notes. Mark Zuckerberg specifically credited X for this feature, where users vote on contextual notes appended to posts, aiming to reduce reliance on professional fact-checkers.

What challenges do healthcare AI startups face when seeking venture capital funding?

Healthcare AI startups often lack a strong combination of data, technological expertise, and healthcare market understanding. Many struggle to acquire proprietary data, which is crucial for training algorithms. Additionally, competition from incumbents with established AI strategies and relationships in the medical system poses significant challenges.

What factors make healthcare AI startups stand out to investors?

Startups that generate proprietary data and have a clear understanding of the healthcare market stand out. Companies with technologies that produce unique biological data or address unmet needs in the healthcare system are particularly attractive to venture capitalists.

How might Meta's decision to scale back content moderation affect advertisers?

Advertisers are not planning dramatic changes in response to Meta's decision, as the platform remains dominant in the advertising space. Meta's strong return on investment and ability to weather past controversies have reassured advertisers, though the long-term impact remains to be seen.

What are the potential international implications of Meta's decision to end fact-checking?

Meta's decision could clash with the European Union's stricter regulations on social media content moderation. The EU has been pushing for stronger oversight of illegal and harmful content, and Meta's shift toward less moderation may conflict with these efforts, particularly as the changes initially roll out only in the U.S.

What role does talent acquisition play in the success of healthcare AI startups?

Talent acquisition is critical for healthcare AI startups, as they need a mix of technological and healthcare expertise. The competition for AI specialists and professionals with medical system knowledge is intense, and startups that successfully integrate these skill sets are more likely to secure funding and succeed.

Chapters
Many healthcare AI startups fail to secure funding due to a lack of crucial elements: insufficient data for algorithm training, inadequate healthcare expertise, and a poor understanding of market opportunities. Investors prioritize companies possessing a blend of data, technological prowess, healthcare knowledge, and market awareness.
  • Insufficient data for algorithm training is a major pitfall.
  • Lack of healthcare expertise hinders market understanding.
  • Investors seek companies with a strong combination of data, technology, healthcare knowledge, and market understanding.
  • Proprietary data generation is a key differentiator for successful startups.

Shownotes Transcript

Meta) will no longer use third-party fact checking on its social-media platforms in the U.S., following X’s example in using community-based notes instead. WSJ’s technology reporter Alexa Corse joins host James Rundle to discuss the reasons behind Meta’s decision and what users can expect next. Plus, how investors are selecting the winners in the crowded field of artificial intelligence-powered healthcare) startups.

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