Safeguarding artificial intelligence with security and trustworthiness is becoming increasingly complex, particularly with the growth of remote work. The enterprise network has effectively become much larger, more dispersed, and more difficult to secure.
Security and trustworthiness are critical not only when using an external LLM (like ChatGPT, Claude, or Bard) for content generation, summaries, translations, etc., but even more so when using internal fine-tuned LLM.
But the AI promises of today may become the cybersecurity perils of tomorrow:
An AI analyzing medical scans might miss a rare condition due to limited training data. This could be a mistake in the training data and an error in the AI's capabilities.
An AI resume screener trained on past data (mostly male applicants in tech) might downplay resumes from qualified women. This is both a mistake (biased training data) and a bad choice (perpetuating gender bias).
A facial recognition software might misidentify someone due to poor lighting or a bad angle in a surveillance photo. This is an error because the AI wasn't designed for perfect recognition in all situations.
AI plays a crucial role in enhancing cyber defenses, detecting and responding to threats more effectively by analyzing vast amounts of data in real-time and identifying malicious activity.
A wrong AI decision is a combination of several factors. For instance, an AI recommending higher bail amounts for minorities might be due to biased training data (mistake) leading to a discriminatory outcome (bad choice).
‘Cyber-physical attacks’ fueled by AI are a growing threat, experts say.1 They can happen very quickly, and they are sophisticated and complex to detect, mitigate, and lead to wrong decisions.
What do you think? Is a wrong AI decision a mistake, a bad choice, or an error?
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🚨❓ Is a wrong AI decision a mistake, a bad choice or an error?
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Safeguarding artificial intelligence with security and trustworthiness is becoming increasingly complex, particularly with the growth of remote work. The enterprise network has effectively become much larger, more dispersed, and more difficult to secure.
Security and trustworthiness are critical not only when using an external LLM (like ChatGPT, Claude, or Bard) for content generation, summaries, translations, etc., but even more so when using internal fine-tuned LLM.
But the AI promises of today may become the cybersecurity perils of tomorrow:
An AI analyzing medical scans might miss a rare condition due to limited training data. This could be a mistake in the training data and an error in the AI's capabilities.
An AI resume screener trained on past data (mostly male applicants in tech) might downplay resumes from qualified women. This is both a mistake (biased training data) and a bad choice (perpetuating gender bias).
A facial recognition software might misidentify someone due to poor lighting or a bad angle in a surveillance photo. This is an error because the AI wasn't designed for perfect recognition in all situations.
AI plays a crucial role in enhancing cyber defenses, detecting and responding to threats more effectively by analyzing vast amounts of data in real-time and identifying malicious activity.
A wrong AI decision is a combination of several factors. For instance, an AI recommending higher bail amounts for minorities might be due to biased training data (mistake) leading to a discriminatory outcome (bad choice).
‘Cyber-physical attacks’ fueled by AI are a growing threat, experts say.1
They can happen very quickly, and they are sophisticated and complex to detect, mitigate, and lead to wrong decisions.
What do you think? Is a wrong AI decision a mistake, a bad choice, or an error?
Looking forward to your answers and comments,
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Is a wrong AI decision a mistake, a bad choice or an error?
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‘Cyber-physical attacks’ fueled by AI are a growing threat, experts say on CNBC