Although distinct from prior trends like cloud computing and machine learning, the trajectory of AI technology appears to parallel in terms of both hype and adoption:
- AI necessitates significant perseverance to process and restore unstructured data effectively.
- The organizational structure and job landscape of certain businesses are being reshaped by AI.
- Concerns arise regarding the potential misuse of AI-generated content resembling authentic photos or artwork to influence voting processes.
Here are five AI trends projected for 2024 that frequently revolve around conceptual models.
AI Integration: A Shift Towards Collaboration with Existing Software
Emerging AI applications are being introduced to the market, focusing on integrating with established software rather than developing entirely new platforms. A notable example is the introduction of copilots, conceptual AI assistants, into Microsoft’s 365 suite and offerings from organizations like SoftServe. Google also provides various copilots catering to tasks ranging from video production to safety measures.
These copilots are designed to sift through existing data or generate content that closely mimics human-produced work.
Notice: How effective are Google Gemini and ChatGPT in professional settings? (TechRepublic)
IBM raised concerns over current technology, emphasizing that while tools like Google’s 2018 Smart Compose are essentially “generative,” their primary function is not to revolutionize work processes. One key distinction between Smart Compose and modern conceptual AI is the bidirectional nature of contemporary models, enabling them to interpret and create images, videos, and charts.
Arun Chandrasekaran, a distinguished VP and analyst at Gartner, anticipates significant advancements in multimodal capabilities in 2024.
At the NVIDIA GTC 2024 event, some companies showcased bots running on Mistral AI’s large language models, utilizing open designs to develop custom-trained AI models with access to proprietary information. By leveraging specialized training data, these AI models can respond to inquiries regarding specific products, industrial procedures, or customer services without compromising sensitive company data. Various open models for text and videos, such as Meta’s Llama 2 and Stability AI’s suite of models including Secure LM and Stable Diffusion, are gaining traction.
Chandrasekaran noted, “There is a growing interest in integrating business data into large language models to enhance contextual understanding.”
Customizing open models can be achieved through rapid engineering, retrieval-mixed generation, and fine-tuning techniques.
AI Deputies: Streamlining AI Operations
In 2024, a notable trend is the emergence of AI deputies, described by Chandrasekaran as a pivotal development in AI advancement.
AI deputies automate the functions of other AI agents, eliminating the need for users to interact with individual models separately. Instead, users can issue natural language instructions to the deputy, which then coordinates various commands required to execute the task effectively.
Sachin Katti, Intel’s Senior Vice President and General Manager of the Network and Edge Group, also highlighted the potential of AI deputies during a prebriefing before the Intel Vision conference on April 9–11. Delegating tasks among AI agents could potentially perform the duties of entire departments.
Evolution of Business AI: Embracing Mixed Generation and Retrieval-Based Approaches
Retrieval-augmented technology empowers large language models to cross-reference their responses with external sources before providing answers. For instance, AI systems can compare their outputs with technical manuals and offer customers supplementary information with direct references to the source material. This approach, known as RAG, enhances accuracy and mitigates misconceptions.
RAG presents businesses with a cost-effective method to enhance the precision of AI models without incurring exorbitant expenses. In 2024, RAG is a prominent theme and is expected to remain significant throughout the year.
Sustainability Concerns in AI Development
AI applications, particularly those involving conceptual models, consume substantial resources and energy compared to traditional computing methods. While advancements in energy-efficient components and optimized usage practices could benefit businesses positively, the energy-intensive nature of conceptual AI workloads raises concerns about sustainability and resource consumption. As discussions around grid resilience and energy utilization gain prominence, the environmental impact of AI technologies may become a focal point for regulatory considerations in the future.
Tech companies are exploring eco-friendly initiatives, such as Google’s procurement of solar and wind energy for specific operations. NVIDIA, for instance, advocates for reducing server racks and enhancing GPU efficiency in data centers to promote energy conservation while supporting AI computations.
Environmental Impact of AI Infrastructure
NVIDIA’s plan to deploy 100,000 AI servers in 2024 is projected to consume 5.7 to 8.9 Terawatts of electricity annually, representing a fraction of current data center energy consumption. However, scaling up to 1.5 million servers by 2027 could significantly impact energy consumption, potentially consuming 85.4 to 134.0 TWh per year. These figures underscore the substantial energy requirements of AI infrastructure expansion.
Moreover, research indicates that the carbon footprint of AI operations can be significant, with the energy consumption of AI servers comparable to emissions from driving a gas-powered car over a considerable distance.
Experts caution that multi-purpose generative architectures are notably more resource-intensive compared to task-specific systems, emphasizing the need for energy-efficient AI development practices.
Evolution of AI Roles and Responsibilities
In the tech industry, prompt engineering emerged as a sought-after skill set in 2023, with professionals commanding high salaries for expertise in training models like ChatGPT. As the field progresses, prompt engineering is evolving from a niche specialization to an essential competency for software engineers. Understanding prompt engineering at scale and utilizing advanced tools for model customization are becoming integral to navigating the evolving landscape of AI technologies.
The Role of Prompt Engineering in AI Development
Prompt engineering is identified as one of the fastest-growing applications of AI in software engineering, requiring individuals to guide and optimize AI models effectively. As AI technologies become more pervasive, proficiency in prompt engineering is poised to become a fundamental skill across various organizational functions. However, mastering prompt engineering at scale and leveraging sophisticated tools for model customization are critical for software engineers to adapt to the evolving demands of AI development.
The allocation of AI-related roles within organizations varies, with the prominence of titles like “prompt engineering” contingent on individual organizational structures. The trend towards consolidating data and tech leadership roles under overarching tech executives reflects a shift towards creating value from data assets and aligning tech strategies with business objectives.
Executive Leadership in the Age of AI
Data and technology executives are reevaluating the necessity of hyper-specialized roles like Chief AI Officers, opting for more comprehensive tech leadership positions that drive value creation from data assets. The evolving landscape of AI governance within organizations may lead to the integration of AI and data functions under the purview of Chief Information Officers (CIOs) or Chief Technology Officers (CTOs). The emergence of roles like AI officers and data and analytics officers underscores the growing importance of data-driven decision-making and AI governance in modern enterprises.
The U.S. Office of Management and Budget’s directive for federal agencies to appoint Chief AI Officers underscores the strategic significance of AI leadership in government entities.
The Rise of AI Art and Strategies for Content Protection
As AI-generated content proliferates across platforms, artists and regulatory bodies are exploring methods to authenticate and safeguard AI-generated works to combat misinformation and intellectual property violations.
Advancements in AI Artistry
Adobe Stock and other platforms are introducing tools for creating AI-generated art, expanding the accessibility of AI-generated content in creative industries. Notably, OpenAI’s Sora AI has been utilized in filmmaking to produce photorealistic visuals, sparking discussions on ethical AI practices and artist attribution.
However, concerns have been raised regarding the ethical implications of AI-generated content, with critics cautioning against unauthorized use of copyrighted material for training AI models.
Safeguarding AI Artwork: Watermarking and Data Poisoning
To mitigate the risk of misinformation and plagiarism in AI-generated content, watermarking and data poisoning techniques are being employed to authenticate original works and deter unauthorized usage.
Leading initiatives like the Coalition for Content Provenance and Authenticity collaborate with tech giants like OpenAI and Meta to implement watermarking solutions that track the origin of AI-generated content. While watermarking enhances content traceability, challenges remain in preventing the removal of watermarks and ensuring content authenticity.
Data poisoning tools like Glaze and Nightshade offer artists a means to protect their original creations from unauthorized replication by rendering AI models incapable of interpreting the tampered data. As the debate on AI art ethics continues, the development of innovative tools to safeguard artists’ intellectual property is expected to gain traction in 2024.
Addressing AI Hype and Real-World Applications
The surge in AI prominence has led to concerns of overhyping the technology in 2024. While generative AI has garnered significant attention, there is a cautious optimism regarding its practical applications and long-term impact on various industries. Gartner’s assessment of generative AI reaching the “peak of inflated expectations” signals a critical phase in the technology’s evolution, where realistic use cases and sustainable practices are essential for transitioning from hype to productivity.
The cyclical nature of technology adoption, characterized by phases like the “trough of disillusionment” and the “slope of enlightenment,” underscores the need for a balanced approach to AI deployment. While some products may experience fluctuations in public perception, sustained innovation and responsible AI governance are key to realizing the full potential of AI technologies.
In conclusion, the evolution of AI technologies in 2024 is marked by a shift towards collaborative integration, sustainability considerations, role redefinitions, and ethical implications in AI artistry. By navigating the complexities of AI adoption with a strategic focus on real-world applications and responsible practices, organizations can harness the transformative power of AI while mitigating potential risks and maximizing societal benefits.
