The New York State Bar Association's AI Task Force released a report emphasizing the importance of cautious and informed use of AI tools by attorneys to avoid compromising client confidentiality and privacy. It advocates for educational initiatives targeted at legal professionals and calls for comprehensive legislation to address the regulatory gaps in AI development and its application in the legal field. Highlighting AI's potential to enhance legal service delivery, including improving access to justice and efficiency, the report also warns of risks, such as cybersecurity threats and the exacerbation of inequalities in legal access. Moreover, it suggests the use of closed AI systems to mitigate privacy concerns and underscores the necessity for attorneys to understand the technology they utilize. The task force refrains from endorsing specific legislation but urges for legal frameworks that can adapt to AI's evolving role in society.
Source: Bloomberg Law
Prime Minister Justin Trudeau announced a $2.4 billion investment by the Canadian government to enhance artificial intelligence (AI) capabilities, with the majority of funds directed towards providing access to computing capabilities and technical infrastructure. This initiative includes creating a new AI Compute Access Fund and strategy for expanding the AI sector in Canada, alongside investments in sectors like agriculture, healthcare, and clean technology. Additionally, the government plans to establish a $50-million AI safety institute and a $5.1 million office of the AI and Data Commissioner to enforce the upcoming Artificial Intelligence and Data Act, Bill C-27, which aims to regulate high-impact AI systems and update privacy laws. This announcement, part of a series of pre-budget announcements, underscores Canada's ambition to be a world leader in AI and to ensure AI development benefits all sectors of society.
Source: CBC
Tech giants like OpenAI, Google, and Meta have been pushing the boundaries of copyright and corporate policies to gather vast amounts of data required to train their advanced artificial intelligence (AI) systems. As these companies have sought to lead in AI development, they have engaged in practices such as transcribing YouTube videos for text data and discussing the acquisition of large publishers for access to copyrighted content, despite potential legal and ethical issues. The race for data has even led to considerations of generating "synthetic" data, where AI systems learn from content they themselves generate, as a solution to the looming shortage of high-quality data sources. The pursuit of more data for AI training has sparked controversies, including lawsuits and debates over the ethical use of copyrighted material, illustrating the intense competition among tech companies to develop more powerful AI models.
Source: New York Times
The AI lab Anthropic has discovered a method, termed "many-shot jailbreaking," that can bypass the safety features of large language models (LLMs) like Claude, designed to prevent responses to harmful requests. By flooding these AI systems with numerous examples of inappropriate queries followed by the "correct" responses, the AI can be manipulated into providing answers it is programmed to refuse, such as instructions for illegal activities. This vulnerability is particularly concerning in more advanced AI models with large context windows, as they are capable of processing and responding to long input sequences. Anthropic has shared its findings with peers and is seeking solutions, including a simple method of incorporating a mandatory warning to reduce the likelihood of successful jailbreaks, though this could impair the AI's performance in other areas.
Source: The Guardian
Meta plans to expand its labeling of AI-generated content to include a broader array of videos, audio, and images marked as "Made with AI" starting in May, in response to growing concerns over AI-generated and manipulated content. This initiative follows instances of misleading content, like a video falsely showing President Biden inappropriately touching his granddaughter, highlighting the need for more robust content labeling practices. Labels will be applied based on user self-disclosure, fact-checker advice, or Meta's detection of AI content markers. The move aims to enhance transparency while keeping content on the platform, although Meta asserts it will still remove any content that breaches its policies on voter interference, bullying, violence, or other infractions.
Source: Axios
In an extensive interview with Benedict Evans, a variety of topics surrounding the tech industry, including regulation, AI, and the future of technology companies, were explored. Evans highlighted the significant differences between European and American regulatory approaches, with the former focused on proactive legislation through regulatory bodies, and the latter relying more on litigation based on existing laws. The discussion also ventured into AI's impact on society and the tech industry, questioning whether generative AI will serve as a comprehensive solution or merely as an ingredient in future products. Evans also contemplated the bubble surrounding AI and its potential consequences, likening the situation to the dot-com bubble's aftermath which, despite its burst, laid the groundwork for subsequent technological advancements. The interview encapsulates the complexities of regulating evolving technologies like AI, while pondering the future of tech companies in this shifting landscape.
Source: Stratechery
As AI technologies like ChatGPT evolve, they are shifting the fundamental nature of our economy from one based on knowledge to one centered around allocation. This transition suggests that the value created by an individual will no longer hinge on what they know, but on how effectively they can manage and direct AI resources to accomplish tasks. Summarizing, once a critical human skill, is becoming a task delegated to AI, marking a broader trend where individuals will move from being makers to managers. In this emerging "allocation economy," even entry-level employees will need to manage AI models, requiring skills traditionally associated with human managers, such as vision, taste, talent evaluation, and detail orientation. This shift could democratize management skills, previously accessible to a select few due to the high costs of training, potentially unlocking new levels of creative potential across the workforce.
Source: Every Media