FINAL WORD
In turn , cyber security teams are also using AI to continuously learn and adapt to new threats . AI algorithms can analyse patterns of attacks and predict vulnerabilities before they are exploited , offering proactive protection against adversarial attacks . The same approach can be taken to protect LLMs .
Paraphrasing algorithms could also generate new training data that captures the essence of source material without infringing on copyright . AI systems could also automate the process of licensing and rights management , matching content with its copyright status and negotiate or execute licensing agreements .
John Costello , CTO , Publicis Sapient
AI-driven systems can be implemented to track data provenance and perform integrity checks , ensuring that data is from trusted sources and has not been tampered with . Advanced monitoring and anomaly detection algorithms could identify unusual patterns or biases in the model ' s outputs , which might indicate that it had been compromised . AI systems can then be
4 . Skills gap
The rise of AI has created demand for new roles and skill sets . The World Economic Forum estimates that tens of millions of jobs will be created , changed and destroyed . Given the existing skills shortage , organisations are racing to catch up with demand and reskill and upskill workers to cope in this new environment .
But AI can also play a pivotal role in bridging this gap . From AI-powered personalised learning platforms to GenAI simulations and learning environments , AI can create learning programs as well as monitor results and provide feedback and mentoring . It can also make learning more accessible through natural language processing and translation services .
As a new era of technology dawns , the call for ethical considerations , sustainable practices and a balanced approach to AI deployment is both timely and imperative . The journey ahead will require collaboration between industry , government and society to ensure that AI ’ s potential benefits are harnessed and that it develops itself as a force for good . p designed to initiate automated re-training processes if poisoning or corruption is detected .
3 . Intellectual property and copyright issues
From Getty to the New York Times , everyone is starting to sue over GenAI . Artists claim their images have been used without their knowledge or consent to train image-generation engines . Text articles , music , images and video are all potentially being scraped . There are warnings this could lead to a ‘ legal doomsday ’.
But if western democracies simply slam the brakes on GenAI , will states with less observance of intellectual property rights simply continue with the technology ?
Ensuring that the sourced data respects copyright and IP rights is crucial for ethical and legal compliance . AI itself can assist in developing sophisticated content attribution and source tracking mechanisms . Privacy is another issue : federated learning and differential privacy can allow AI systems to learn from data without directly accessing or exposing individual data points .
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