Cryptomonnaies

AI Will Help Crypto Get To The Next Level

Even as blockchain and cryptoasset firms find themselves facing a barrage of legal and regulatory actions, and a much-discussed crypto winter, they are also facing down the ever-present conversation around AI. Spurred on by the rapid adoption and implementation of ChatGPT (and ChatGPT-like solutions), venture capitalists and other investors have begun to shift interest and funds toward AI projects at an accelerating rate. According to CB Insights, AI start-ups attracted over $5 billion in venture capital in Q1 2023 alone, after raising over $40 billion in 2022. While tokenization, Web3, and other blockchain-related projects are certainly being developed and built out, AI and crypto are being framed by some as adversaries.

This misses the broader point.

The reality is the mega trends of blockchain-based transactions, tokenized financial assets, and the integration of AI into virtually every business are not going away. Rather, and evidenced by recent moves and actions to further promote bitcoin and/or crypto investing options as well as the fervor around AI projects (and constructive policy), these trends are only set to accelerate moving forward. Equally as real, however, are the concerns and problems that have arisen in the cryptoasset space. A lack of trust, past-collapses, legal action taken against alleged fraudsters, and a general lack of transparency continue to prove difficult obstacles for firms in the space to overcome.

AI is not a cure-all for the ills of the crypto space, but there are some ways that AI-based tools can – and will – improve the understandability, transparency, and comfort with which firms and investors can utilize crypto where effective.

Source

Mots-clés : cybersécurité, sécurité informatique, protection des données, menaces cybernétiques, veille cyber, analyse de vulnérabilités, sécurité des réseaux, cyberattaques, conformité RGPD, NIS2, DORA, PCIDSS, DEVSECOPS, eSANTE, intelligence artificielle, IA en cybersécurité, apprentissage automatique, deep learning, algorithmes de sécurité, détection des anomalies, systèmes intelligents, automatisation de la sécurité, IA pour la prévention des cyberattaques.

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