Similar to the Digital Revolution, where software significantly improved the efficiency of numerous business operations, AI is rapidly emerging as the evident optimal choice for various tasks. As we step into 2024, several exciting trends are emerging out of the 2023 AI onslaught, revolutionising the landscape. In this article, we’ll delve into five key AI trends that are expected to dominate the scene: Multi-Modal Models, Longer Context Models, Industry-Specific Models, User Experience (UX) and AI Security.
Multi-Modal Models mark a significant shift in AI, allowing systems to process and comprehend information from various sources, such as text, images, and audio, akin to human sensory interpretation. OpenAI’s DALL-E, an extension of the ChatGPT architecture, exemplifies this paradigm by generating images from textual prompts. Users can describe scenes, and DALL-E produces vivid and imaginative images, paving the way for innovative applications in creative content generation across diverse domains. Another noteworthy multi-modal model is CLIP, developed by OpenAI, enabling image understanding in the context of natural language. CLIP’s capabilities have far-reaching implications for image recognition, visual search, and content analysis, enhancing the versatility of AI systems.
A prime example is Meta’s SeamlessM4T model, seamlessly integrating text and speech functionalities. Tailored for translation across nearly 100 languages, it supports both speech and text input in multiple languages, offering text transcription in nearly 100 languages and speech output in 36 languages, according to Meta AI’s 2023 report. This trend underlines the growing importance of models capable of handling a spectrum of inputs and outputs for more comprehensive AI applications.
Longer Context Models:
Developers employing ChatGPT or its API equivalent frequently encounter a limitation concerning the specific context they can feed into the model. This limitation, known as the model’s attention span, plays a pivotal role in the utility of the model, especially for enterprises aiming to leverage their proprietary data for guided outputs. Expanding the attention span is a key objective in training new foundational models, and the current Standard GPT-4 has a context length of 32k (32,000) tokens. The Turbo version extends it to 128K tokens, which is approximately equivalent to a 300-page book. It also boasts an information cut-off date of April 2023 whereas the former version was September 2021.
GPT-4 Turbo is designed to handle longer contextual information, making it more adept at understanding nuanced conversations and complex text passages. This enhanced capability is particularly valuable in applications like content generation, chatbots, and language translation, where maintaining context over extended interactions is crucial.
AI is increasingly being tailored to meet the specific needs of various industries. Industry-specific models are customised to understand the unique challenges, terminologies, and requirements of a particular sector. One example is the healthcare-focused AI model, MOLLY (Medical Language Understanding and Generation System), developed to assist medical professionals in processing and generating medical reports.
MOLLY is trained on vast datasets of medical literature, enabling it to comprehend and generate contextually relevant medical information. This specialisation enhances the accuracy and efficiency of tasks such as medical transcription, diagnosis assistance, and information retrieval in the healthcare domain.
User Experience (UX) Becomes Augmented Intelligence Experience (AIX):
The resulting transformation could cause a paradigm shift in how business applications are designed and used in enterprise solutions. Enterprise software was originally designed to be UX driven (forms, controls and charts), and then context driven (CX) to reduce the need to have to hunt for information in forms – there could be a new approach based on augmented intelligence experience (AIX).
The concept is that AI will provide access, via multiple avenues, to recommendations or options for human operators to perform business actions. The focus will be on “push” of decisions or choices, versus the current traditional “pull,” where users have to spend time on screens in multiple systems to perform work.
There are already a few companies out there producing this type of user experience. Take Fusel lab Creative they have designed interactive dashboards for all types of industries but according to their website they believe the medical industry will benefit most from this experience.
With the rapid pace that AI is being adopted by businesses, it is also crucial to ensure you have policies in place to deal with this.. Not only within your workplace but also advocate for policies to be employed by governments to ensure AI is being used for the right purposes and ensuring safety and privacy for all who use it.
According to the ‘It Tech Trends 2024 Report’, 77% of AI adopters will be using AI for business analytics and intelligence, 63% of Transformers (organisations that rank themselves at the top of Info-Tech’s IT maturity scale), say they will adopt generative AI features from vendors either in beta or when they become generally available, and 68% of transformers say AI will define business strategy by the end of 2024.
The emergence of generative AI has had a profound effect on organisations and industries, presenting both opportunities and disruptive forces. To move beyond the initial excitement, it is vital to formulate and implement a carefully planned strategic roadmap for the adoption of generative AI, guaranteeing a competitive advantage.With the mass adoption of AI, there is one more thing that should be added to the list and that is the rise and need for new roles in the Industry. Universities or TAFEs might consider implementing a program offering a Bachelor of Arts with a specialisation in AI Engineering.
Employing AI to enhance product manufacturing or inventory management has the potential to spark a manufacturing job revolution 2.0. This could lead to significant investments in training the existing workforce to effectively incorporate AI into decision-making processes. The focus will shift towards augmented intelligence, utilising AI to bolster various business functions.
The 2024 AI landscape is marked by exciting developments in Multi-Modal Models, Longer Context Models, and Industry-Specific Models. These trends signify a move towards more versatile, context-aware, and industry-tailored AI applications. As these innovations continue to unfold, we can anticipate a future where AI seamlessly integrates into our daily lives, driving advancements across a myriad of domains.