GenAI Trends
Artificial Intelligence, tracing its origins to the 1950s with pioneers like Alan Turing and John McCarthy, took a significant leap with the introduction of Generative Adversarial Networks (GANs) by Ian Goodfellow and his team in 2014.
The concept gained massive popularity with the emergence of ChatGPT in 2023, quickly becoming a hot topic. Powered by Large Language Models (LLMs), Natural Language Processing (NLP), and Machine Learning (ML), GenAI can analyze and create extensive text and images based on user prompts. This article explores key Generative AI trends and their impact on various industries.
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The Generative AI market is forecasted to reach a market size of $66.62 billion by 2024 and is projected to grow at an annual rate of 20.80% from 2024 to 2030, reaching US$207.00 billion by 2030.
In 2023, Generative AI became a big deal. It’s like having competent robot helpers. For example, ChatGPT from OpenAI can help with writing, finding information, and understanding what you ask nicely.
GPT-4 is an updated version of ChatGPT, which allows users to write better and understand more by reading even images. Then there’s DALL·E, also by OpenAI, which can turn your words into pictures.
From generating text content to creating graphics, videos, and even business presentations, that’s how Generative AI has evolved in the last year.
McKinsey called 2023 the breakout year for Gen AI.
2023 was more like an exploration year for Generative AI, where people ran harmless experiments like summarizing work documents, building better prompts, and implementing the model into new applications such as Jasper, copy.ai, etc.
GenAI is already turning from a potential technology to a value-adding technology in 2024.. Let’s see how it was done for a large telecom company in the Pacific.
A telecommunications company in the Pacific region appointed a Chief Data and AI Officer to harness data and AI for business advantage. Collaborating with the organization, the officer developed a strategic roadmap, identifying home-servicing/maintenance as a priority domain. They aimed to create a gen AI tool to enhance dispatchers’ and service operators’ abilities to predict service requirements. Cross-functional teams were assembled to develop this tool, alongside an academy to train employees in data and AI skills. The officer selected a large language model and cloud provider to support these endeavors, while also implementing a robust data architecture for reliable data delivery, resulting in enhanced business benefits.
Similarly, we predict that the following Generative AI trends will significantly help enterprises bring value from data.
The AI tool Dall-e introduced numerous unexpected capabilities, marking the first instance of an AI-generating artwork from minimal inputs. While its initial version struggled to produce high-quality art, its current iteration has significantly improved, closely aligning with user requests.
Beyond visual art, the capabilities of such AI tools extend to generating real-time animations, music, and audio for a wide range of applications. This area is poised for ongoing expansion, empowering musicians, songwriters, artists, sound effects specialists, and everyday users to fully leverage generative AI technologies for creative expression.
Coca-Cola has recently joined forces with Dall-E and OpenAI to introduce “Create Real Magic,” a platform to elevate their marketing efforts with advanced AI technology. This collaboration is an interesting example of creative advertising strategies designed to captivate consumers’ imaginations while embracing the latest trends in Generative AI to enrich customer interactions with innovative content.
Hyper-personalization is one of the significant Generative AI trends in several industries. Let’s take an example of the Pharmaceutical & Life Sciences industry, where hyper-personalization is the key to winning drug launch campaigns.
Commercial pharma teams connect with health care professionals (HCPs) on a personal level to promote new drugs, which requires extensive research on the HCP’s domain and mapping the drug with their specialization.
Generative AI can empower commercial pharma teams to create hyper-personalized content for healthcare professionals by analyzing vast amounts of data to tailor messages and materials to individual preferences and needs. This enables more targeted and effective communication strategies, ultimately improving engagement and outcomes in the healthcare industry.
This personalization extends across various sectors, from e-commerce to entertainment, where AI algorithms analyze vast amounts of data to predict and adapt to user preferences.
For example, Gramener helps create personalized content for commercial pharma teams, where creating promotional content is challenging. This is due to extensive databases of medical journals, clinical trial records, and healthcare professional (HCP) profiles. They efficiently pinpoint crucial information, create engaging content, and guarantee adherence to regulatory requirements.
Conversational AI is changing drastically with GenAI, one of the most discussed Generative AI trends in 2024.
Generative AI is enabling conversations with human-like interactions. Through advanced natural language processing and machine learning techniques, generative AI models like GPT (Generative Pre-trained Transformer) can understand context, generate coherent and relevant responses, and personalize conversations based on user history and preferences.
GenAI is making Conversational AI more intuitive, dynamic, and capable of handling complex interactions seamlessly.
For example, recently, Gramener implemented a Conversational AI Bot for a public policy think tank. This brilliant bot operates its knowledge portal, interprets policies, schemes, and reply to queries related to public policy. The civil and admin team can leverage this Conversational AI bot to build impactful strategies for a brighter future.
Learn more about our other Generative AI Projects.
Generative AI is changing how research papers are summarized, especially in the medical and pharmaceutical fields, offering a more efficient approach to digesting complex information.
This technology leverages the power of LLMs to efficiently condense lengthy documents into concise, comprehensible summaries. It enables researchers, practitioners, and industry professionals to quickly grasp key findings, methodologies, and implications without delving into the full text.
Gen AI-driven summarization tools streamline the literature review process, significantly reducing the time and effort of extracting vital data. This enhances research productivity, facilitates more informed decision-making, and accelerates the development of new treatments and drugs, ultimately contributing to advancements in healthcare and patient outcomes.
Gramener’s DocGenie is a Gen AI-enabled document processing tool with many features, from document summarization to document processing and digitization to data anonymization in documents.
Human-in-the-Loop (HITL) is an interesting Generative AI trend in 2024, highlighting the harmonious relationship between AI advancements and human oversight. As Generative AI systems become increasingly complex, integrating human feedback into the AI training loop ensures these models remain aligned with ethical standards, cultural sensitivities, and real-world applicability.
This approach improves the accuracy and reliability of AI-generated outputs and builds a collaborative environment where human expertise guides AI evolution.
By leveraging HITL, organizations can harness the creativity and efficiency of generative AI while maintaining control over the output, ensuring that it meets the nuanced demands of diverse applications.
In one of our researched articles, we identified how LLMs are prone to hallucinations and can generate made-up results if a human in the loop is not addressing the complexities.
We’ve streamlined customer feedback classification by pinpointing key customer interactions. Our advanced Deep Learning Multiclass Classifier, coupled with human-in-the-loop for inputs, efficiently categorizes comments into relevant themes. With continuous learning, accuracy improves over time. Our Active Learning framework, leveraging BERT, has slashed manual classification efforts by 90%, achieving over 90% accuracy in theme mapping.
The ambition of Generative AI is rapidly expanding beyond single-domain performance to embrace multimodal models that can process and interpret multiple types of data.
Though text-to-image and speech-to-text models like CLIP and Wave2Vec have laid the groundwork, the latest advancements aim to develop more versatile models capable of seamlessly transitioning between tasks like natural language processing (NLP) and computer vision and even incorporating video processing capabilities, as seen with Google’s Lumiere.
This new wave of AI, including proprietary models like OpenAI’s GPT-4V and open-source options like LLaVa, is set to create more intuitive and flexible applications, enabling users to interact with AI in more complex ways, such as receiving visual aids with verbal instructions.
Moreover, these multimodal models can improve their understanding and generate more accurate outputs by handling a broader range of data inputs, significantly enhancing AI’s utility across various fields.
Generative AI (GenAI) offers a wide range of possibilities, spanning the creation of complex art, music composition, pharmaceutical design, and the emulation of human speech. It has emerged as a center of both enthusiasm and critical examination.
The role of open-source projects is crucial in the progression of GenAI, as they democratize access, invite contributions from varied backgrounds, drive innovation, and help identify and address biases within the development process.
This collective approach fosters a more inclusive environment for innovation, encourages sharing of knowledge and resources, and facilitates the rapid identification and correction of biases and errors.
Moreover, open-source initiatives in GenAI are crucial for ensuring transparency, fostering trust, and enabling ethical considerations to be at the forefront of AI development.
As a result, open source is not just a trend but a foundational element in the sustainable growth and ethical advancement of generative AI technologies. Some of the examples of GenAI in Open Sources are TensorFlow and TensorFlow Models, PyTorch and Hugging Face’s Transformers, GPT-Neo and GPT-J, Stable Diffusion, etc.
Adhering GenAI to regulatory guidelines is a significant Generative AI trend to look out for. The advancement of multimodal AI and its accessibility have raised concerns over privacy and biases.
The ambiguity surrounding regulations might impede the adoption of AI technology, as businesses may hesitate to invest amid concerns that future laws could render current investments outdated or unlawful. The Artificial Intelligence Act proposed by the EU aims to regulate AI and promote transparency, particularly for high-risk systems. In contrast, in the U.S., the primary hub for AI innovation, regulatory efforts are still in flux, despite endeavors to set standards for AI usage in government and developers’ pledges to uphold ethical practices.
GenAI, a leading application in the pharmaceutical industry, is improving regulatory compliance processes by producing compliant-ready materials for drug launches, promotions, and outreach to healthcare professionals (HCPs). It supports regulatory affairs by automating document creation in line with rigorous industry standards, facilitating rapid and error-free preparation for market entry and ongoing compliance. This significantly boosts efficiency and minimizes the likelihood of regulatory violations.
Bring Your Own AI (BYOAI) refers to the practice of individuals or organizations integrating their custom or preferred artificial intelligence models into existing platforms, systems, or services. This approach allows for greater customization, efficiency, and alignment with specific needs or goals. There are not many published real-world examples of BYOAI. However, healthcare providers are implementing AI algorithms they’ve developed or tailored to analyze patient data, predict disease outcomes, or customize treatment plans.
For example, Gramener has developed a custom GPT chatbot called Pharma Insight bot. This GPT bot is rigorously trained on proprietary pharmaceutical data for internal and restricted use and provides more accurate pharmaceutical-related information.
Banks like JPMorgan Chase have invested in developing their own AI systems called Index GPT to enhance risk management and customer service, reflecting a BYOAI approach even if not labeled as such.
In 2024, AI-augmented applications and services stand at the forefront of Generative AI trends, marking a pivotal shift in how technology enhances human capabilities across various domains.
This trend involves integrating sophisticated AI algorithms into various software and platforms, enriching user experiences with personalized, intelligent functionalities.
From dynamic content creation tools that adapt to individual writing styles to smart healthcare apps providing customized treatment recommendations, AI-augmented solutions are redefining efficiency and personalization.
We have been adapting Gen AI to our existing solutions to enhance the customer experience. We are enabling Gen AI to be included in our products and services for sectors including pharmaceuticals, manufacturing, supply chain and logistics, retail, BFSI, etc. We have already successfully delivered more than 20 Gen AI projects, with recorded business impacts.
If our GenAI solutions pique your interest, book a free demo and learn more about how they can solve your challenges.
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