In 2022, the world saw a new AI model take over the front line. This AI could not only exceed human capacity in analyzing data but it is also able to create things itself! It did reach a significant amount of viewers. Moreover, businesspeople realized it and came in 2023 to adopt it as a way of making their work faster and more efficient. Now, the question is: how do we want humans to control this smart AI to enrich lives even more?
Imagine how computers have developed throughout history. The very first were the big computers that belonged to those companies which could afford them only. This was followed by computers getting smaller and cheaper to manufacture, and now business enterprises and educational institutes can invest in these. And at last, the computers were so small, so mini, and so simple that everyone could have one at home. Generative AI is similarly conducting its development. At the moment, it’s more like those early computers- which are great on the inside, but the outside part is not very friendly to use.
The hopeful note is the speed with which it is changing. Therefore, 2023 was the year of the AI wave, and many AI systems were complicated and highly effective. There are also enormous possibilities for the existence of these tools that come with it. It is not only for the big companies, but anyone can use it.
Here’s the point: the very beautiful thing probably is not the most significant one. Well, the new AI Trends systems will attract a lot of attention, with people excitedly discussing how good they are. It is the unseen work that would make the real breakthrough. This not only involves issues such as ensuring AI safety and reliability, as well as green energy and user-friendliness, but also the social and economic dimensions that may be further affected in the future. The most appealing part of this is the fact that by doing this, we will be able to experience things differently daily.
Key Trends in Artificial Intelligence for 2024
Here are some important current AI trends to look out for in the coming year.
- Prioritizing Realistic Objectives
- Multimodal AI
- Small Language Models and the Rise of Open Source
- Addressing GPU Scarcity and Cloud Usage Costs
- Mass Production of Optimized Models
- Localized Models & Data Pipelines
- Empowering Virtual Agents with Increased Capabilities
Prioritizing Realistic Objectives
At first, when this new kind of AI came out, business leaders mostly just saw ads and news stories about it. They didn’t know how it worked, and their only experience was playing around with some online tools. But now, things have calmed down, and businesses have a better idea of what this AI can do for them.
The experts have different opinions on the particular problem of when exactly AI will change things. Pessimists even claim it will be a huge disappointment ahead. While some suggest that it will make big changes very fast, others say that it will do the opposite, allowing sustainability only. The truth most likely lies somewhere in between. Generative AI quickly opens up brand-new opportunities unthinkable beforehand, but it won’t be suddenly good for us all.
Hype can create an impression that new tech will do everything, but most of them remain nothing more than just a marginal tool. Reflecting on the time, Gmail was great with smart new features like “Smart Compose” that some people didn’t see the significance but now these text-generating services are everywhere.
It became as evident with industrialization which widened the gap between machines and human beings. Rather than the great leap, they are making additions to existing programs such as, for instance, writing in Microsoft Office or editing photos in Photoshop. They are not trying to change everything; they are only making things a little less stressful and useful.
The future of AI isn’t only about how powerful an AI tool will be and how fast it can be implemented in different work situations. According to a recent study conducted by Webcom Systems, organizations will be more inclined towards AI applications that are simple to implement, help them save on tasks, and are well-integrated into the software that they use daily. Thus, the real influence of AI is not that it wows us with considerable advanced technology, but rather it is the convenience it adds to our lives.
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Multimodal AI (and Video)
Research in generating AI that can handle different data models has become a priority for researchers due to the rapidly developing levels of artificial intelligence. However, models like CLIP and Wave2Vec have been most effective within specific fields. Thus, the transformative era in language models is enforced by the introduction of multidisciplinary models like OpenAI’s GPT-4V, Google’s Gemini, and open-sourced models such as LLaVa, Adept, and Qwen-VL. Nowadays, models do not simply operate on natural language processing (NLP) or computer vision tasks, and they are now able to incorporate video data. As an example, Google’s Lumiere, which is a text-to-video diffusion model, is a breakthrough in the world of generative AI as it extends the capabilities of generative AI by merging video processing tasks and image-to-video conversions.
Multimodal AI as a core influence has kicked off the creation of complex and natural AI models instantly, like AI applications for Google Assistant. These AI systems write in a human-friendly way, and their responses involve media types such as text and images, as well as videos. Virtual reality platforms create a lot of interaction, and the possibilities are endless. Users can talk to the image, or they can receive verbal communication or visual aids while being guided step by step by paragraphs of text.
Small Language Models & the Rise of Open Source
In recent years, the domain of natural language processing (NLP) has seen the transition from the adoption of small language models to bigger language models. This phenomenon is directly linked with the ascending role and importance of open-source projects in the AI field. Small language models, which possess efficiency and versatility, are changing the course of conventional NLP task-solving processes and making them easier than before. Apart from this, the open-source is making the state-of-the-art NLP technologies available to all through the movement which promotes teamwork, innovation, and quick improvements. With small language models and open source leading the way, the era of NLP research and applications is profoundly changing. This is reshaping the future of artificial intelligence language processing through AI.
Looking Ahead, this Momentum in NLP will Fuel the Emergence of Several Exciting Trends:
The Rise of Autonomous Agents
Autonomous agents, autonomous software programs created to fulfil pre-determined goals without human assistance, will cover an essential part of generative AI. Such agents rely on highly sophisticated algorithms and a machine learning system which enables them to learn new knowledge on the go and react to different situations all on their own. Human-looking robots are expected not only to improve customer experience in various industries such as travel, tourism, retail, and education but also to reduce the employment and costs of human beings.
Open Models Comparable to Proprietary Models
It is predicted that open-source generative AI models will soon start to evolve significantly, reaching the level of competitive models. Prominent cases such as Meta Llama 2 70B and Mixtral AI’s Mixtral-8x7B have made the public confidence in these models equivalent to proprietary ones like GPT 3.5.
Reality Check and Realistic Expectations
There will be more attention on the creation of the right AI technology that will have the capability to work according to the set expectations in the AI industry.
Throughout the process, these trends emphasize that AI is constantly improving and affecting our businesses, which should motivate us to have real expectations, move forward with AI-focused initiatives that embrace multimodal capabilities, and optimize the language models for the efficiency and accessibility of our tasks.
Addressing GPU Scarcity & Cloud Usage Costs
Special computers are needed to run powerful AI programs, but these computers are getting harder to find and more expensive to use. Because of this, people are making smaller AI programs that are just as good but don’t need as much power.
The artificial intelligence (AI) programs of large companies are all like that. The big problem is the shortage of particular parts of the computer required to do them all. This is like everybody liking/wanting an uncommon toy, and eventually, this toy becomes pricey and rare. Since then, experts have attempted to develop methods that would make these parts cheaper and easier to make or even discover different ways of running AI programs without them.
A late 2023 O’Reilly study found that most of the computing load for AI applications is taken by cloud providers. Only a few of them are running their infrastructure. The currently in-place equipment shortages are believed to further increase set-up costs and raise the barriers to running on-premise mature servers. Such a scenario will thus increase cloud costs because providers need to build up their infrastructure to take into account the growing demand generated by generative AI applications. In such a dynamic environment, enterprises have to be agile in accepting the flexibility of their AI models and deployment methods.
In such a dynamic environment, enterprises have to be agile in accepting the flexibility of their AI models and deployment methods. Models from the open-source community are constantly getting better because of the continuous improvements made in optimizing them. As 2023 rolled out, certain techniques like Low-Rank Adaptation (LoRA), Quantization, and Direct Preference Optimization (DPO) have emerged and become prominent ones. They have significantly improved the function of AI models.
Mass Production of Optimized Models
The latest work of the open-source community is serving a trend that fosters the development of models with maximum efficiency for a given amount of space.
Both the discovery of new foundation models and new techniques and resources for training, adjusting, refining, and reconciling small models continue to drive the latest developments (and will continue to drive future ones). Notable model-agnostic techniques that took hold in 2023 include
Low Rank Adaptation (LoRA)
Normally, fine-tuning a large language model involves changing billions of tiny settings. LoRA is a new method that keeps most of these settings the same. Instead, it adds just a few small changes to each part of the model. This makes fine-tuning much faster and even allows you to fine-tune multiple tasks at once on less powerful computers.
Quantization
Quantization, akin to lowering the bitrate of audio or video to decrease latency during inference and save memory space, reduces the number of data elements representing model data points. For instance, consider a large image file that loads slowly. To make it load faster, we can compress it. This is like what quantization does for machine learning models. It shrinks the size of the model by using less data to represent each piece of information.
QLoRA combines this data-shrinking technique with another trick called LoRA. Together, they make it possible to train large models on smaller computers without sacrificing performance.
Direct Preference Optimization (DPO)
Chat models usually apply RLHF (reinforcement learning from human feedback) to determine the outputs that resemble human communication. Therefore, although RLHF proves highly effective, it also presents significant complexity and instability. DPO offers a range of the same advantages but is considerably faster and less demanding.
In addition to the ongoing progress of open source models in the 3 to 70 billion parameter space, such development trends might be the shift in the dynamics of the AI landscape by making it available to small players, like startups and hobbyists. These complex AI capabilities were previously out of reach.
Localized Models & Data Pipelines
In 2024, businesses will distinguish themselves by developing customized models rather than by wrapping repackaged AI services from “Big AI” around open-source AI models and tools. They can align existing AI models and tools with each particular industry, including customer support uses, supply chain management, and complex document analysis.
The open source approach enables organizations to develop powerful custom AI models, refined using their proprietary data and fine-tuned for their specific needs, readily and with zero or near-zero infrastructure investments as a necessity. Domains such as law, where pre-trained models do not cover legal terms and concepts to learn specialized vocabulary, can be an applicable example of it.
The most applicable industries that utilize models with small sizes to run on devices with moderate resources include legal, finance, and healthcare. Localizing AI training, inference, and RAG (retrieval augmented generation) can eliminate the risk of external use of proprietary data or sensitive personal information for training closed-source/third-party models or passing them on. Storing relevant information on RAG (Referential Artificial Generator) rather than reflecting all knowledge in the learning machine itself helps reduce model size and increase speed. In addition, this improves cost.
Empowering Virtual Agents with Increased Capabilities
More advanced, intelligent tools and a year of market feedback help businesses develop more complicated use cases for virtual agents than just talking to customers about simple problems.
Speeding up AI systems and making them more intelligent also means increased responsibilities for communicating and task automating tasks. “In 2023, the entire AI arena centred on dialogues.” Companies worldwide were trying to develop something enabling humans to type their questions and receive written answers. However, in 2024, AI systems will enable you to complete various tasks, such as booking, planning your trip, and connecting to other services, just like others do.
Multimodal AI is a perfect example that presents customers with a great possibility of conversing with virtual agents in a natural dialogue. Example: Instead of just putting an inquiry about recipes on a bot, the user with a camera on a fridge open can request recipes providing the ingredients available. Be My Eyes, a mobile application that pairs sight-impaired and low-vision individuals with volunteers to be able to accomplish small tasks is testing AI tools that directly use multimodal AI for the users to get access to their surroundings rather than waiting for the volunteers.
The Bottom Line
The AI revolution, along with its impact on many aspects of people’s lives, characterizes the current age as a crucial stage in the sector of AI. The era demands learning and developing the skills and knowledge to adjust to the trends, make better use of generative AI solutions to avoid potential risks, and responsibly adopt AI technologies. The setting for artificial intelligence is moving at a fast rate. Also, there has been a significant increase in open-source models that are helping smaller organizations to have access to the best AI tools that were not available previously. In 2024, the expected democratization of AI models will shape AI management dynamics in a manner that nurtures transparency, enhances ethics, and drives innovation.
In addition to the reality check-marked by quality, security, and ethical concerns, and integration complexities with existing systems that companies may experience in the transition from the experimental phase to the implementation of the generation AI technology at a full-scale level. Webcom Systems helps organizations navigate the complications associated with adopting generative AI solutions through integration support, dealing with security and ethical issues, and enabling companies to benefit from AI technologies seamlessly in a dynamic environment.
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