Major Developments

In the third quarter of 2024, significant legislative, regulatory, and litigation activities related to artificial intelligence (AI) and connected and automated vehicles (CAVs) unfolded, offering numerous opportunities for industry participation and commentary. Source

Federal Legislative Developments

A notable bipartisan push to advance federal AI legislation persisted, although legislative passage remained elusive. Nevertheless, several targeted bills with bipartisan backing showed potential for progression during the lame duck period. The Senate Commerce, Science, and Transportation Committee advanced several AI-related bills, including the Validation and Evaluation for Trustworthy (VET) Artificial Intelligence Act (S.4769), which mandates the National Institute of Standards and Technology (NIST) to develop voluntary guidelines for AI systems. The Promoting United States Leadership in Standards Act of 2024 (S.3849) and the Future of Artificial Intelligence Innovation Act of 2024 (S.4178) also moved forward, emphasizing the importance of U.S. leadership in AI standards and innovation, respectively.

Additionally, the Senate Homeland Security and Governmental Affairs Committee advanced the PREPARED for AI Act (S.4495), aiming to establish a risk-based framework for AI use by federal agencies. Furthermore, an amendment proposed to the National Defense Authorization Act for Fiscal Year 2025 (S.4638) sought to introduce transparency and training requirements related to AI within federal operations.

Federal Executive and Regulatory Developments

The federal executive branch, under the White House's direction, continued to focus on AI objectives, launching a Task Force on AI Datacenter Infrastructure to streamline policy coordination across the government. The Federal Communications Commission (FCC) took steps to address AI-generated robocalls and proposed amendments to its rules under the Telephone Consumer Protection Act (TCPA). Meanwhile, the Federal Trade Commission (FTC) launched "Operation AI Comply," targeting companies misusing AI for deceptive practices. The U.S. Patent and Trademark Office (USPTO) and the U.S. Copyright Office issued new guidance and reports addressing AI-related patent eligibility and copyright issues, respectively.

State Legislative Developments

States actively enacted laws impacting AI development and usage. Illinois's HB 3773 mandated employer notification for AI use in employment decisions and prohibited discriminatory AI practices. Colorado sought public input on refining its AI Act, SB 205, to align with other jurisdictions. Several states, including Hawaii, California, and New Hampshire, enacted laws restricting AI-generated content during election periods to prevent misinformation and manipulation.

Industry Impact

The impact of artificial intelligence (AI) is being felt across a multitude of industries, revolutionizing traditional processes and redefining workforce dynamics. A notable 77% of businesses have either integrated AI into their operations or are actively exploring its potential, demonstrating the widespread adoption of this transformative technology. Source

This integration is not uniform across all sectors; larger enterprises, for instance, are twice as likely to adopt AI compared to their smaller counterparts, due to their financial capacity for research and development. In terms of specific industries, marketing and advertising lead with a 37% adoption rate of generative AI technologies, followed closely by the technology sector at 35% and consulting at 30%. These figures highlight the diverse applications of AI across different professional domains. Even sectors like teaching and healthcare are experiencing AI integration, albeit at lower rates of 19% and 15%, respectively.

IT professionals are at the forefront of AI utilization, with 54% incorporating these technologies into their daily tasks. This trend extends to other roles as well, with data engineers, developers, and data scientists employing AI at rates of 35%, 29%, and 29%, respectively. The adoption of AI is not only transforming the nature of these roles but also enhancing efficiency, as reported by 61% of office workers surveyed, who credit AI with improving their productivity and decision-making abilities.

Despite these advancements, the integration of AI also raises concerns about job displacement. Recent data indicates that 14% of workers have already experienced job loss due to AI and automation. The tech sector, in particular, has been significantly impacted, with 136,831 job losses reported this year, marking the most substantial layoffs since 2001. Moreover, companies like British Telecom (BT) have announced plans to reduce their workforce by utilizing AI-driven technologies, further illustrating the shifting employment landscape.

AI's influence on wages is another critical consideration. Since 1980, automation and early AI have contributed to significant wage reductions, affecting blue-collar workers most notably. However, the current trend is shifting towards impacting white-collar professionals, particularly those in roles requiring programming and writing skills. This evolution signifies a paradigm shift in job market dynamics, necessitating new strategies for workforce adaptation and skill development.

Government and Policy

In October 2023, an Executive Order was issued to establish a coordinated approach to safely developing and utilizing artificial intelligence within government operations. This order comprises over 100 requirements, anchored by eight guiding principles such as advancing equity and safeguarding privacy. Federal agencies have been working to meet 13 specific requirements from this order, with full implementation expected by March 2024. These efforts are crucial for laying the groundwork for cohesive government-wide AI initiatives.

On the legislative front, bipartisan efforts continue in the U.S. Congress to advance AI-related legislation, despite challenges in passing comprehensive laws. The Senate Commerce, Science, and Transportation Committee has moved forward with multiple AI-focused bills. These include the Validation and Evaluation for Trustworthy (VET) Artificial Intelligence Act and the Promoting United States Leadership in Standards Act of 2024, both of which emphasize developing guidelines and standards for AI systems. The Future of Artificial Intelligence Innovation Act of 2024 also aims to promote AI innovation through public-private partnerships.

Additionally, the Senate Homeland Security and Governmental Affairs Committee advanced the PREPARED for AI Act, which proposes a risk-based framework for AI procurement and usage by federal agencies. It also seeks to establish a Chief AI Officers Council and an agency AI Governance Board to harness AI advancements effectively.

Regulatory developments also feature prominently, with the White House launching a new Task Force on AI Datacenter Infrastructure. This initiative aims to streamline policy coordination across government entities to align with economic, national security, and environmental objectives. Furthermore, the Federal Communications Commission (FCC) and the Federal Trade Commission (FTC) have initiated measures to regulate AI applications, such as fraudulent robocalls and surveillance pricing products. The U.S. Patent and Trademark Office has updated its guidance on patent subject matter eligibility concerning AI, while the U.S. Copyright Office has released a report on digital replicas and copyright issues related to AI.

These efforts reflect the federal government's proactive stance on integrating AI safely and effectively into both government operations and the broader legislative landscape.

Ethical Considerations

In March 2024, discussions around the ethical considerations of artificial intelligence (AI) have intensified, particularly concerning the application of AI technologies in sensitive areas and their impact on vulnerable populations such as children. Researchers from the Oxford Martin Programme on Ethical Web and Data Architectures have highlighted the pressing need to integrate ethical principles specifically tailored to the developmental needs and rights of children within AI systems. Source

Despite a growing consensus on high-level ethical AI principles, there remains a gap in effectively applying these principles to benefit children, with challenges identified in accounting for developmental stages, the role of guardians, and the need for child-centred evaluations.

Moreover, as AI adoption accelerates across various sectors, ethical dilemmas concerning privacy, bias, and accountability have become critical issues. Algorithmic bias poses significant risks by potentially exacerbating existing societal inequalities, necessitating a systematic approach to mitigate such biases and promote fairness. This includes careful data selection, algorithm design, and continuous monitoring and evaluation of AI systems to ensure equitable outcomes.

Additionally, the rise of AI technologies has contributed to an increase in electronic waste, posing environmental and financial challenges for businesses. Sustainable practices, such as utilizing cloud platforms for computational needs, have been suggested to address this issue. Data privacy and security also remain paramount, with AI's dependency on large datasets heightening the risk of data breaches and cyber threats. Businesses are encouraged to adopt advanced security measures to protect sensitive data.

Ultimately, the ethical implementation of AI systems depends on building trust through transparency, reliability, and accountability. This involves explaining AI decision-making processes, ensuring consistent performance, and taking responsibility for outcomes, including correcting errors and biases. Engaging with stakeholders and prioritizing ethical considerations are essential steps in fostering trust and enabling the positive integration of AI technologies in society.

Public Perception

The public perception of artificial intelligence (AI) has been shaped significantly by recent developments and discussions surrounding its ethical implications and potential societal impact. According to Deloitte's third annual report on the "State of Ethics and Trust in Technology," there is an increasing awareness of the benefits and risks associated with AI, particularly generative AI (GenAI). Source

The report highlights that while almost half of the professionals surveyed believe cognitive technologies like AI can create social good when used responsibly, over half also recognize these technologies pose significant ethical risks. This dual perception underscores the necessity for ethical guidelines to govern AI use, with only about 27% of organizations currently having distinct ethical standards for GenAI.

Moreover, safety and data privacy are paramount concerns among the public, as indicated by the 78% of survey respondents prioritizing "safe and secure" as key ethical technology principles. There is a notable decline in trust towards organizations' ethical messaging about AI, especially among younger generations, reflecting a need for improved transparency and communication. The potential for reputational and financial damage if ethical standards are not adhered to further amplifies public scrutiny on AI development and deployment.

In addition to ethical concerns, there is a growing demand for AI skills in the workforce. Despite a reduction in tech job numbers, the need for employees with AI expertise has surged, representing 10% of all job postings, up from 6% two years ago. This shift reflects an industry trend towards prioritizing AI competencies alongside soft skills, as companies reshape their workforces to meet emerging technological needs. The public's perception of AI, therefore, is a complex blend of cautious optimism about its potential and apprehension about its ethical and societal implications.

Workforce and Job Automation

The integration of artificial intelligence (AI) and automation into the workforce is increasingly shaping the future of work, presenting both opportunities and challenges that need to be carefully managed. The deployment of AI and automation technologies is significantly transforming industries by enhancing efficiency, fostering innovation, and creating new job categories, thus heralding a new era of human-machine collaboration. This transformation allows human creativity to merge with machine precision, potentially leading to unparalleled productivity gains. Source

Benefits of AI and Automation

Innovation and Job Creation

Despite apprehensions regarding job displacement, AI and automation can lead to the creation of new job categories and expand job opportunities. For instance, roles such as AI system trainers and AI maintenance specialists are emerging, focusing on teaching algorithms to recognize patterns and ensuring system efficiency, respectively. The demand for professionals skilled in AI ethics, policy, and governance is also growing, driven by concerns around bias, privacy, and accountability in AI systems. This trend highlights the potential for AI to not only improve workplace efficiency but also create entirely new career paths focused on responsible technology use.

Enhanced Accuracy and Safety

Automation plays a crucial role in improving the accuracy and safety of workplace operations by reducing the margin of error in routine tasks and taking on dangerous jobs in hazardous environments. This aspect of automation is particularly beneficial in industries like deep mining and chemical manufacturing, where robots and automated systems can minimize workplace accidents and enhance worker safety.

Drawbacks of AI and Automation

Displacement of Workers

One of the most pressing concerns with the rise of AI and automation is the displacement of workers, especially in sectors heavily reliant on routine tasks. Technologies like self-checkout machines and automated assembly lines have reduced the need for roles such as cashiers and assembly line workers, respectively. Addressing this challenge requires strategic responses, including retraining programs and policy interventions, to help affected workers transition to new roles and prepare for future workforce shifts.

Skills Mismatch and Inequality

The rapid transformation of the workplace due to AI increases the demand for skills that complement new technologies, potentially leading to labor market gaps and disadvantaging workers without the necessary education or experience. This situation can exacerbate economic inequalities and increase unemployment among those unable to acquire high-demand skills. Implementing a robust career navigation system that provides resources and opportunities for workers is essential for fostering a more inclusive labor market.

By balancing the innovative potential of AI with its challenges, it is possible to harness the transformative power of AI and automation to create a brighter, more inclusive future. Through targeted interventions, continuous learning opportunities, and collaborative efforts among stakeholders, the workforce can navigate this evolving landscape effectively, ensuring that technological advancements benefit society as a whole.

Notable Events

In March 2024, the Computing Research Association (CRA) hosted the 2024 CRA Conference at Snowbird, which featured a prominent panel discussion on the opportunities and risks associated with Generative AI (GenAI) technologies. The panel was organized and hosted by Tracy Camp and Janine Myszka, with Divesh Srivastava extending the invitation to the participants. The session explored the revolutionary impact of GenAI techniques such as large language models and diffusion models, which have demonstrated capabilities in areas like in-context learning, code-completion, and text-to-image generation. Source

These capabilities, however, also present a dual-use dilemma, as they can be exploited by malicious actors for harmful purposes. The discussion brought together three leading computer scientists to deliberate on several critical questions: the most exciting directions for GenAI, the associated risks, and the role of the computing research community in this rapidly evolving landscape. The panelists emphasized the need for foundational theories to evaluate GenAI models and systems and called for significant research to understand and mitigate the potential threats posed by adversaries leveraging GenAI.

The session also included an interactive segment driven by audience questions, which covered topics such as AI in warfare, AI-generated content pollution, and the unequal distribution of GenAI benefits and risks. Notably, the panelists highlighted DARPA's ongoing efforts to build robust AI systems and address challenges such as content attribution and accountability in the face of misinformation. This event underscored the crucial role of both industry and academia in shaping the future of GenAI, focusing on open-ended research for broad societal impact.

Future Prospects

As companies recalibrate their approach to generative AI (gen AI), the year 2024 is poised to be pivotal in proving the technology's value and overcoming the challenges faced in its initial adoption phase. Businesses are recognizing that while launching pilots is relatively straightforward, scaling these initiatives to create meaningful value requires a broad set of organizational changes. Source

This realization is prompting a reevaluation of strategies, with an emphasis on building both organizational and technological capabilities necessary for widespread digital and AI innovation. One of the key lessons from previous digital and AI transformations is that competitive advantage arises not merely from implementing technology but from effectively rewiring business operations to leverage these tools at scale. Companies are advised to act swiftly to achieve early successes with gen AI but must avoid assuming that the technology will bypass the need for substantial organizational restructuring.

A telecommunications company in the Pacific region exemplifies this approach by prioritizing domains like home-servicing for gen AI tool deployment, illustrating a methodical way of integrating AI into business processes. The future of gen AI also hinges on differentiating between being a "taker," "shaper," and "maker" of AI technologies. Currently, the most feasible path for many businesses is to adopt a "taker" model for improving productivity, while developing "shaper" applications for gaining a competitive edge.

This approach allows organizations to harness gen AI's ability to enhance existing job functions by acting as copilots—tools that assist employees in tasks ranging from coding to maintenance operations. Companies must strategically determine where these copilots can provide substantial impact, such as in critical business domains like industrial maintenance, to maximize the technology's potential benefits.

Upskilling existing talent remains a priority as companies seek to build gen AI-specific competencies such as model fine-tuning, prompt engineering, and context engineering. While technical skills can be developed through training, the most effective capability building occurs through apprenticeship and the cultivation of practitioner communities. As the market for skilled gen AI practitioners rapidly expands, businesses are encouraged to bring in experienced engineers to accelerate their gen AI initiatives.

In conclusion, the evolving landscape of AI and CAVs presents both opportunities and challenges, necessitating strategic legislative, regulatory, and industry responses to harness their full potential.