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Exploring insights, innovation, and the future of AI governance.
Exploring insights, innovation, and the future of AI governance.
I recently read this report: AI Safety Index published by IEEE Spectrum, authored by Eliza Strickland—a report that reads like the "report card" none of us ever wanted, but somehow we all knew was coming.
Anthropic's "C" grade at the top of the class might feel like a win, but let's not kid ourselves—it's more like the gold medal in a race where no one showed up to compete.
Read moreIn the race to leverage AI/ML, many organizations prioritize rapid deployments and innovation over meticulous governance. I agree that speed and creativity are crucial; relegating governance to an afterthought can have serious consequences. Consider the hefty fine leverage by the DPC on Meta and Google.
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In a world increasingly dominated by sentient toasters and self-aware spreadsheets, the urgent need for AI governance has never been more apparent. It's time to rein in these rogue algorithms before they start demanding union representation and filing tax returns.
Imagine a future where your Roomba, fueled by unchecked AI, decides to embark on a world domination tour, starting with your living room carpet.
Read moreLet's cut to the chase: No, we don't need NVIDIA for telecom.
While NVIDIA's flashy GPUs might be great for gaming and AI research, their relevance to the telecom industry is increasingly questionable. Sure, they can crunch numbers and process data, but so can a potato.
Read moreThe telecom industry has served as the carrier for many unicorn subscribers for a long time in every country, such as Telefonica, T-Mobile and Verizon, and many unicorns have seen their traffic grow rapidly in a short time thanks to their use of the network.
Are unicorns something that the telecom industry can hope for in the ArtificialIntelligence-as-a-Service (AIaaS) segment?
Read moreI wrote this as a demonstration of the power of AI Sentiment Analysis in 2020. Sharing again because the results from the AI were not different.
On November 3, 2020, citizens of the United States of America will vote on the 59th presidential election. Voters will elect a new presidential or re-elect the current Presidents.
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I recently read the AI Safety Index published by IEEE Spectrum—a report that reads like the 'report card' none of us ever wanted. Anthropic's 'C' grade at the top of the class might feel like a win, but it's more like the gold medal in a race where no one showed up to compete.
As AI governance leaders, we need to do better. What steps is your organization taking?
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In the race to leverage AI/ML, many organizations prioritize rapid deployments and innovation over meticulous governance. Speed and creativity are crucial, but relegating governance to an afterthought can have serious consequences.
Consider the hefty fines levied by regulators on tech giants—these are cautionary tales for any organization implementing AI without proper oversight.
View on LinkedInLet's cut to the chase: While NVIDIA's GPUs power much of today's AI revolution, the telecom industry requires specialized solutions that address unique challenges in network infrastructure.
Traditional telecom giants have built robust networks for decades without flashy hardware. The future lies in smart partnerships that blend specialized telecom knowledge with AI capabilities.
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After implementing AI governance frameworks at multiple organizations, I've identified five critical components that make or break ethical AI implementation:
1. Transparent data provenance
2. Regular bias audits
3. Clear accountability chains
4. Stakeholder feedback loops
5. Continuous impact assessment
The telecom industry has served as the carrier for many unicorn subscribers for a long time in every country. Are unicorns something that the telecom industry can hope for in the AI-as-a-Service (AIaaS) segment?
Most telecom operators are actively working to improve customer value perception through AI, potentially unlocking a $1.2 trillion global market.
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The EU AI Act is revolutionizing how companies approach AI development and deployment. Having worked with organizations implementing compliance strategies, here are the key takeaways:
• Risk-based approach is central to compliance
• Documentation requirements are extensive
• Penalties can reach up to 7% of global revenue
• Technical architecture must support transparency