In this article, we will answer more questions: will our efforts to grasp the essence of generative AI compel us to revise our definition of intelligence and understanding? And, from a commercial perspective, will generative AI fulfill its potential for delivering enduring value to users and vendors?

As generative AI technology gains momentum in everyday workflows, it will influence and guide the future of AI tools. A recent IBM survey of over 1,000 employees at enterprise-scale companies shows the top three factors driving AI adoption to be:

1) advances in generative AI tools that make them more accessible; 2) cost reduction factors; and 3) the ability to automate key processes. In that case, we can expect an increased rate of AI integration into standard off-the-shelf business applications.

Advancements in AI Technologies

Since 1956, when the term “artificial intelligence” was first used by John McCarthy, a Dartmouth College professor, we have witnessed numerous advancements in computer vision and innovations in data science. The following paragraphs will highlight the trending AI topics from the past few years. It is crucial to ensure transparency, fairness, ethics, and compliance when implementing AI, carefully vetting training data and algorithms for bias, and considering controls and AI regulations alongside experimentation.

AI Trends on a Timeline

2023 was a year of almost Beatlemania-level enthusiasm about how to get started with generative AI and the steady forward march of LLMs. As 2024 spins down, businesses are increasingly enthusiastic about integrating it into internal workflows and customer-facing applications.

LLMs on the AI Trend Line

Given this vortex of excitement and anticipation, developers are seeking techniques to expand LLM performance in domain-specific tasks. These initiatives are loosely termed “fine-tuning”—but that sometimes encompasses techniques that don’t truly update LLM weights. Fine-tuning LLMs, however, time- and resource-intensive, pushes developers to tailor LLMs for domain-specific tasks through prompt engineering (crafting high-quality inputs).

RAG on the AI Trend Line

Retrieval-augmented generation (RAG) is a prompt-driven engineering tactic—an intermediate step between a user’s submission of a prompt and the LLM’s subsequent response with output. For example, an LLM-based application seeks and offers additional information of greater relevance and higher quality that was not part of its training data. A RAG-enabled application, without retraining, may offer more relevant outputs, and—importantly—fewer hallucinations.

It’s not difficult to foresee that RAG adoption will grow throughout and beyond 2024. However, when evaluating the suitability of an RAG-enabled application for a given use case, businesses should consider the sources from which the application retrieves data.

When comparing RAG vs fine-tuning LLM, RAG provides relevant outputs without retraining, while fine-tuning updates model weights with domain-specific data.

Small(er) language models and open-source advancements

When analyzing domain-specific LLMs, it assumes a point of diminishing returns from larger parameter counts. Although massive models launched an AI phenomenon, they have their drawbacks. Only mammoth enterprises possess the funds and server space to train and maintain energy-ravenous models with hundreds of billions of parameters. One estimate from the University of Washington speculates that a standard day of ChatGPT queries approaches the daily energy consumption of 33,000 U.S. households.

Small language models (SLMs), on the other hand, are far less resource-hungry. Training smaller models on more data yields superior performance than training larger models on fewer data. Much LLM innovation therefore focuses on obtaining greater output from fewer parameters. Recent belief is that models can be downsized without much performance sacrifice.

Three Important Benefits of Small Language Models

1- Smaller models help democratize AI: they can run at lower cost on more attainable hardware, empowering more amateurs and institutions to study, train, and improve existing models.

2- They can be run locally on smaller devices, This allows more sophisticated AI in scenarios like edge computing and the Internet of Things (IoT). Furthermore, running models locally—like on a user’s smartphone—helps to sidestep privacy and cybersecurity concerns that arise from interaction with sensitive personal or proprietary data.

3- They make AI more explainable: the larger the model, the more difficult it is to pinpoint how and where it makes important decisions. Explainable AI is essential to understanding, improving, and trusting the output of AI systems

Trends in Hardware and AI Advancements

The rapid evolution of AI has driven significant advancements in hardware. Enhanced CPUs, GPUs, and specialized AI chips like TPUs are meeting growing computational demands. Edge computing is also gaining traction, enabling faster processing and enhanced privacy. Read on to explore how these innovations are shaping the future of AI applications.

Central Processing Unit (CPU)

CPUs employ a sequential computing method, issuing one instruction at a time. Subsequent instructions must await the completion of their predecessors. In contrast, AI chips harness parallel computing to execute numerous calculations simultaneously. Such parallel computing is a dimension-swifter and more efficient.

At its Ignite developers’ conference, Microsoft debuted chips designed specifically to execute AI computing tasks. Qualcomm and MediaTek also offer on-device generative AI capabilities through coming chipsets for flagship and mid-range smartphones. So conventional CPUs are eclipsed by specialized processing units optimized for executing AI models. Next-generation chipsets are even evolving to integrate on-device generative AI capabilities

GPU shortages and cloud costs

The trend toward smaller models is driven by both necessity and business ambition, as cloud computing costs increase while hardware availability decreases. There is huge pressure not only for increased GPU production but also for innovative hardware solutions that are cheaper and easier to make and use. Cloud providers currently bear much of the computing burden: relatively few AI adopters maintain their own infrastructure, and hardware shortages will raise hurdles and costs of setting up on-premise servers. This may put upward pressure on cloud costs as providers update and optimize their own infrastructure to meet generative AI demands.

Model optimization is growing more accessible

Maximizing the performance of more compact models is a recent trend, stimulated by the open-source community. Many advances are driven not just by new foundation models, but by new techniques and resources—such as open-source datasets—for training, tweaking, fine-tuning, or aligning pre-trained models. Recently notable model-agnostic techniques include Low-Rank Adaptation (LoRA), Quantization, and Direct Preference Optimization (DPO). These advances shift the AI landscape by providing startups and amateurs with sophisticated AI capabilities they could previously only dream of.

Customizations and Localized Solutions

Enterprises in 2024 can differentiate themselves through bespoke model development, rather than building wrappers around repackaged services from “Big AI.” Now, with the right data and development framework, existing open-source AI models and tools can fit into almost any real-world scenario—customer support, supply chain, project management, and even complex document analysis

Open-source models enable organizations to develop powerful custom AI models, trained on their proprietary data and fine-tuned for their specific needs. They do this quickly, and without requiring prohibitively expensive infrastructure investments. This benefits industries like leveraging generative AI in banking, insurance, and using large language models in healthcare, where highly specialized vocabulary and concepts may not have been learned by foundation models in pre-training. As 2024 continues to level the model playing field, competitive advantage will increasingly be driven by proprietary data pipelines that enable superb fine-tuning.

Increasing Power and Reach of Virtual Agents

With sophisticated, efficient tools and bounteous market feedback, businesses and big tech companies are primed to expand virtual agent use cases beyond mere customer experience chatbots. As AI systems speed up and incorporate new streams and formats of information, they expand the possibilities for not just communication and instruction-following, but also task automation. Today, we are seeing agents actually address and complete customer needs—make reservations, plan a trip, and connect to other services.

AI Trends and Service Timeline

AI in Commerce Essential Use Cases for B2B and B2C

Integration of AI in commerce depends on trust from customers in data, security, brand, and those behind the AI. Four use cases are already transforming the customer journey of AI in commerce. Now brands can create seamless, personalized buying experiences that increase customer loyalty, customer engagement, retention, and wallet share across B2B and B2C channels.

Use case 1: AI for Modernization and Business Model Expansion

AI-powered tools help optimize and modernize business operations throughout the customer journey. Using machine learning algorithms and big data analytics, AI can uncover patterns, correlations, and trends that might dodge human analysts. This information helps businesses make informed decisions, improve operational efficiencies, and identify growth opportunities, offering customers seamless, personalized shopping experiences.

Use case 2: AI for Dynamic Product Experience Management (PXM)

The power of AI enables brands to transform their product experience management and user experience by delivering personalized, engaging, and seamless experiences at every commerce touchpoint. These tools can manage content, standardize product information, and drive personalization. With AI, brands can inform, validate, and build the confidence needed for conversion.

Generative AI can automate the creation, classification data analysis, and optimization of product content, creating new content tailored to individual customers—product descriptions, images, videos, and even interactive experiences.

Use case 3: AI for Order Intelligence

With generative AI and automation, businesses can make data-driven decisions to streamline processes across the supply chain, reducing inefficiency and waste. Inadvertent errors can cost as much as $95 billion in annual losses in the US. AI-powered order management systems provide real-time visibility into all aspects of the critical order management workflow, enabling companies to proactively identify potential disruptions and mitigate risks, ensuring orders will be delivered exactly as promised.

Use case 4: AI for Payments and Security

Intelligent payments are welcome in the payment and security process, improving efficiency, confidence, and accuracy. Such technologies process, manage, and secure digital transactions—and provide advance warning of potential risks and fraud. The tools they use include intelligent payments for B2C and B2B customers making purchases in online stores.

Traditional AI optimizes POS systems, automates new payment methods, and facilitates multiple payment solutions across channels. Generative AI creates dynamic payment models for B2B customers while addressing their complex transactions with customized invoicing and predictive behaviors. They offer risk management and fraud detection, plus compliance and data privacy.

Expect Multimodal AI Expansions

The next wave of advancements focuses not only on enhancing performance within a specific domain but on multimodal models that can take many types of data as input.

Multimodal AI increases opportunities for seamless interaction with virtual agents. Multimodal AI users can, for example, enquire about an image and receive a natural language answer, or ask aloud for instructions to repair something and receive visual aids alongside step-by-step text instructions! Multimodal AI allows a model to process more diverse data inputs, enriching and expanding information for training and inference. Video, in particular, offers great potential for holistic learning.

Future of Artificial Intelligence

As we venture into the evolving landscape of artificial intelligence, this section examines pivotal developments that could dramatically reshape the future of AI technologies and applications.

How will Quantum Computing Impact Generative AI?

Quantum computing is a new trend in computer science that employs quantum mechanics to perform complex calculations at unprecedented speed and scale. It is transforming AI and machine learning, with potential to reshape the landscape of AI-generated content and creativity.

Unlike classical computers, which process information in binary bits (0s and 1s), quantum computers utilize quantum bits, or qubits, which can exist in multiple states simultaneously through the phenomena of superposition and entanglement.

Quantum computing can accelerate the training and optimization of AI models almost beyond belief. While traditional deep learning algorithms require massive computational resources and time to train complex neural networks, quantum computing speeds this process by exploring huge solution spaces effortlessly.

Generative AI models can inhabit a much larger solution space and comprehend more complex patterns and relationships within data. AI algorithms can generate more sophisticated, creative outputs across various domains. At this time, though, quantum computing hardware is still in rather early phases, so advances will be needed for it to reach fuller potential.

As quantum-powered AI systems take their place at the lead, clear regulatory frameworks and ethical guidelines must be in place to control potential risks, threats, and biases. New standards of ethical stewardship will be essential.

AI Avatars are the Future of AI applications

AI avatars have become the new AI trend and will grow very fast in the near future. They are poised to revolutionize digital interactions by bridging the gap between traditional, text-based chatbots and human-like communication.

Unlike chatbots that rely solely on text and predefined scripts, AI avatars can engage in fluid, dynamic dialogues using facial expressions, gestures, and tone of voice. This capability allows for more personalized and empathetic interactions, making users feel understood and valued.

In business environments, AI avatars can greet visitors, provide detailed information, and streamline internal processes, enhancing both customer experience and operational efficiency. As these digital ambassadors continue to evolve, their ability to adapt to specific customer needs and preferences will further solidify their role as indispensable tools in the future of AI applications.

Conclusion

As AI reaches a pivotal year, adaptation is crucial for businesses. To harness Generative AI’s potential, responsible scaling, and ethical considerations are necessary. Advanced tools like enterprise AI Copilot and AiseraGPT, our fine-tuned large language models (LLMs), empower enterprises by considering ethical aspects like the TRAPS (trusted, responsible, auditable, private & secure) framework. Discover the future of AI today by booking a custom Gen AI demo to explore its capabilities!