Meta has announced a significant update regarding using AI labels across its platforms, replacing the ‘Made with AI’ tag with ‘AI info’. This change comes after widespread complaints about the incorrect tagging of photos. For instance, a historical photograph captured on film four decades ago was mistakenly labelled AI-generated when uploaded with basic editing tools like Adobe’s cropping feature.
Kate McLaughlin, a spokesperson for Meta, emphasised that the company is continuously refining its AI products and collaborating closely with industry partners on AI labelling standards. The new ‘AI info’ label aims to clarify that content may have been modified with AI tools rather than solely created by AI.
The issue primarily stems from how metadata tools like Adobe Photoshop apply information to images, which platforms interpret. Following the expansion of its AI content labelling policies, daily photos shared on Meta’s platforms, such as Instagram and Facebook, were erroneously tagged as ‘Made with AI’.
Initially, the updated labelling will roll out on mobile apps before extending to web platforms. Clicking on the ‘AI info’ tag will display a message similar to the previous label, explaining why it was applied and acknowledging the use of AI-powered editing tools like Generative Fill. Despite advancements in metadata tagging technology like C2PA, distinguishing between AI-generated and authentic images remains a work in progress.
Key internet technical bodies, including the Internet Engineering Task Force, World Wide Web Consortium, Internet Research Task Force, and the Internet Society’s Board of Trustees, have signed an open letter to the UN arguing against a centralised governance of the internet, which they argue is being proposed in the UN’s Global Digital Compact (GDC). The letter states that some of the proposals in the latest version of the GDC, released on 26 June 2024, can be interpreted as mandating more centralised internet governance, which the technical bodies believe would be detrimental to the internet and global economies and societies.
The GDC aims to create international consensus on principles for an ‘inclusive, open, sustainable, fair, safe and secure digital future’. However, the technical bodies argue that the GDC is being developed through a multilateral process between states, with very limited engagement of the open, inclusive, and consensus-driven methods used to develop the internet and web to date.
Specifically, the GDC proposes the establishment of an international scientific panel on AI to conduct risk assessments, an office to facilitate follow-ups on the compact, and calls on the UN to play a key role in promoting cooperation and harmonisation of data governance initiatives. The technical bodies view these proposals as steps towards more centralised internet governance, which they believe would be detrimental.
Mary Meeker, renowned for her annual ‘Internet Trends’ reports, has released her first study in over four years, focusing on the intersection of AI and US higher education. Meeker’s previous reports were pivotal in analysing the tech economy, often spanning hundreds of pages. Her new report, significantly shorter at 16 pages, explores how the collaboration between technology and higher education can bolster America’s economic vitality.
In her latest report, Meeker asserts that the US has surpassed China in AI leadership. She emphasises that for the US to maintain this edge, technology companies and universities must work together as partners rather than see each other as obstacles. The partnership involves tech companies providing GPUs to research universities and being transparent about future work trends. Simultaneously, higher education institutions must adopt a ‘mindset change,’ treating students as customers and teachers as coaches.
Meeker highlights the historical role of universities like Stanford and MIT in driving tech innovation, initially through government funding, now increasingly through industry support. She underscores the critical nature of the coming years for higher education to remain a driving force in technological advancement. Echoing venture capitalist Alan Patricof, Meeker describes AI as a revolution more profound than transistors, PCs, biotech, the internet, or cloud computing, suggesting that AI is now ready to optimise the vast data accumulated over the past decades.
Meeker’s new report was shared with investors at her growth equity firm, BOND, and published on the firm’s website, aiming to inform and guide the next steps in integrating AI with higher education to sustain America’s technological and economic leadership.
California lawmakers are poised to vote on groundbreaking legislation aimed at regulating AI to prevent potential catastrophic risks, such as manipulating the state’s electric grid or aiding in the creation of chemical weapons. Spearheaded by Democratic state Sen. Scott Wiener, the bill targets AI systems with immense computing power, setting safety standards that apply only to models costing over $100 million to train.
Tech giants like Meta (Facebook) and Google strongly oppose the bill, arguing that it unfairly targets developers rather than those who misuse AI for harmful purposes. They contend that such regulations could stifle innovation and drive tech companies away from California, potentially fracturing the regulatory landscape.
While highlighting California’s role as a leader in AI adoption, Governor Gavin Newsom has not publicly endorsed the bill. His administration is concurrently exploring rules to combat AI discrimination in employment and housing, underscoring the dual challenges of promoting AI innovation while safeguarding against its misuse.
The proposed legislation has garnered support from prominent AI researchers and would establish a new state agency to oversee AI development practices and enforce compliance. Proponents argue that California must act swiftly to avoid repeating past regulatory oversights in the social media sector, despite concerns over regulatory overreach and its potential economic impact.
The Japanese Defence Ministry has unveiled its inaugural policy to promote AI use, aiming to adapt to technological advancements in defence operations. Focusing on seven key areas, including detection and identification of military targets, command and control, and logistic support, the policy aims to streamline the ministry’s work and respond to changes in technology-driven defence operations.
The new policy highlights that AI can enhance combat operation speed, reduce human error, and improve efficiency through automation. AI is also expected to aid in information gathering and analysis, unmanned defence assets, cybersecurity, and work efficiency. However, the policy acknowledges the limitations of AI, particularly in unprecedented situations, and concerns regarding its credibility and potential misuse.
The Defence Ministry plans to secure human resources with cyber expertise to address these issues, starting a specialised recruitment category in fiscal 2025. Defence Minister Minoru Kihara emphasised the importance of adapting to new forms of battle using AI and cyber technologies and stressed the need for cooperation with the private sector and international agencies.
Recognising the risks associated with AI use, Kihara highlighted the importance of accurately identifying and addressing these shortcomings. He stated that Japan’s ability to adapt to new forms of battle with AI and cyber technologies is a significant challenge in building up its defence capabilities. The ministry aims to deepen cooperation with the private sector and relevant foreign agencies by proactively sharing its views and strategies.
Anthropic is launching a new program to fund the creation of new benchmarks for better assessing AI model performance and its impact. In its blog post, Anthropic stated that it will offer grants to third-party organisations developing improved methods for evaluating advanced AI model capabilities.
Urging the AI research community to develop more rigorous benchmarks that address societal and security implications, Anthropic advocated for revising existing methodologies through new tools, infrastructure, and methods. Highlighting how they aim to develop an early warning system to identify and assess risks, it specifically called for tests to evaluate a model’s ability to conduct cyberattacks, enhance weapons of mass destruction, and manipulate or deceive individuals.
Moreover, Anthropic also aims for its new program to support research into benchmarks and tasks that explore AI’s potential in scientific study, multilingual communication, bias mitigation, and self-censorship of toxicity. In addition to grants, researchers will have the chance to consult with the company’s domain experts. The company also expressed interest in potentially investing in or acquiring the most promising projects, offering various ‘funding options tailored to the needs and stage of each project’.
Why does this matter?
Benchmarks are the process of evaluating the quality of an AI system. The evaluation is typically a fixed process of assessing the capability of an AI model, usually in one area, while AI models like Anthropic’s Claude and Open AI’s ChatGPT are designed to perform a host of tasks. Thus, developing robust and reliable model evaluations is complex and is riddled with challenges. Anthropic’s initiative to support new AI benchmarks is commendable, with their stated objective of the program serving as a catalyst for progress towards a future where comprehensive AI evaluation is an industry-standard. However, given their own commercial interests, the initiative may raise trust concerns.
The UN General Assembly has adopted a resolution on AI capacity building, led by China. This non-binding resolution seeks to enhance developing countries’ AI capabilities through international cooperation and capacity-building initiatives. It also urges international organisations and financial institutions to support these efforts.
The resolution comes in the context of the ongoing technology rivalry between Beijing and Washington, as both nations strive to influence AI governance and portray each other as destabilising forces. Earlier this year, the US promoted a UN resolution advocating for ‘safe, secure, and trustworthy’ AI systems, gaining the support of over 110 countries, including China.
China’s resolution acknowledges the UN’s role in AI capacity-building and calls on Secretary-General Antonio Guterres to report on the unique challenges developing countries face and provide recommendations to address them.
Connecticut is spearheading efforts by developing what could be the nation’s first Citizens AI Academy. The free online resource aims to offer classes for learning basic AI skills and obtaining employment certificates.
Democratic Senator James Maroney of Connecticut emphasised the need for continuous learning in this rapidly evolving field. Determining the essential skills for an AI-driven world presents challenges due to the technology’s swift progression and varied expert opinions. Gregory LaBlanc from Berkeley Law School suggested that workers should focus on managing and utilising AI rather than understanding its technical intricacies to complement the capabilities of AI.
Several states, including Connecticut, California, Mississippi, and Maryland, have proposed legislation addressing AI in education. For instance, California is considering incorporating AI literacy into school curricula to ensure students understand AI principles, recognise its use, and appreciate its ethical implications. Connecticut’s AI Academy plans to offer certificates for career-related skills and provide foundational knowledge, from digital literacy to interacting with chatbots.
Despite the push for AI education, concerns about the digital divide persist. Senator Maroney highlighted the potential disadvantage for those needing more basic digital skills or access to technology. Marvin Venay of Bring Tech Home and Tesha Tramontano-Kelly of CfAL for Digital Inclusion stress the importance of affordable internet and devices as prerequisites for effective AI education. Ensuring these fundamentals is crucial for equipping individuals with the necessary tools to thrive in an AI-driven future.
The surge in US stock prices, driven by enthusiasm for AI, draws comparisons to the dot-com bubble two decades ago, sparking concerns over inflated valuations. The S&P 500 has reached new records, climbing more than 50% from its October 2022 low, while the Nasdaq Composite has surged over 70% since the end of 2022. A few massive tech stocks, including Nvidia, are leading this rally, reminiscent of the ‘Four Horsemen’ tech stocks of the late 1990s.
Despite the impressive gains, some analysts caution that today’s tech stocks are more financially robust than their dotcom-era counterparts. However, fears persist that the AI-driven surge might end in a crash similar to the dotcom bust, which saw the Nasdaq Composite plummet nearly 80% from its March 2000 peak, and while some companies like Amazon thrived post-bubble, many did not recover.
Current tech stock valuations, while high, are more grounded in solid earnings prospects rather than speculative growth, a key difference from the dot-com era. For instance, Nvidia trades at 40 times forward earnings estimates, far lower than Cisco’s 131 times in 2000. Although the S&P 500’s price-to-earnings ratio of 21 is above its historical average, it remains below the peak levels of the late 1990s. Nonetheless, investors remain cautious, wary of metrics becoming overly stretched if economic growth continues and tech stocks keep climbing.
In the evolving landscape of marketing and advertising, the integration of generative AI presents both promise and challenges, as highlighted in a recent Forrester report. Key obstacles include a lack of AI expertise among agency employees and concerns over job obsolescence. Also, the human factor poses a significant hurdle that the industry must address urgently to fully harness the potential of genAI.
The potential economic impact of genAI on agencies is profound. Seen as a transformative force akin to the advent of smartphones, genAI promises to redefine creativity in marketing by combining data intelligence with human intuition. Agency leaders overwhelmingly recognise it as a disruptive technology, with 77% acknowledging its potential to fundamentally alter business operations. However, the fear of job displacement among employees remains palpable, exacerbated by recent industry disruptions and the rapid automation of white-collar roles.
To mitigate these concerns and fully embrace genAI, there is a pressing need for comprehensive AI literacy and training within agencies. While existing educational programmes and certifications provide a foundation, they are insufficient to meet the demands of integrating AI into everyday creative processes. Investment in reskilling and upskilling initiatives is crucial to empower agency employees to confidently navigate the AI-driven future of marketing and advertising.
Industry stakeholders, including agencies, technology partners, universities, and trade groups, must collaborate to establish robust training frameworks. In addition, a concerted effort will not only bolster agency capabilities in AI adoption but also ensure that creative workforce remains agile and competitive in an increasingly AI-centric landscape. By prioritising AI literacy and supporting continuous learning initiatives, agencies can position themselves at the forefront of innovation, delivering enhanced value to clients through AI-powered creativity.
