+91 95109 87700
+44 20 3290 7090
Since the past few decades, the world has witnessed innumerable innovative and advancing ideas in the technological realm. From the introduction of autonomous driving mode in vehicles to the establishment of a decentralized blockchain ecosystem, the list is endless. But what truly paved the way for a much-awaited transformation is artificial intelligence. The term is no longer anonymous to the audience because now and then you will encounter a software or feature providing AI-based functionalities, whether it’s generating computerized images or creating content through bots. In fact, AI is not just a bubble that can burst anytime and come to a standstill. Instead, it is an ever-emerging and transforming technology whose limit is beyond human comprehension.
So, if you are planning to begin an artificial intelligence development project, it’s important you know about the latest trends that will shape the future in the coming years. Without this, you won’t be able to harness the hidden potential of AI and utilize its offerings at a full scale. That being said, we have created a comprehensive guide below, explaining the major ten trends that will revolutionize the AI development realm in 2024 and the following years.
Multimodal AI
Most AI models operated nowadays in the industry are unidirectional, meaning they take a single type of data as the input and generate a specific datatype at the output nodes. For example, CLIP models accept texts as the only input datatype and generate images at the output. If you provide any other data, like a speech or a video, the model will fail to function and evaluate. This has greatly restricted the capabilities of artificial intelligence because the human brain can interpret multiple data types without any constraint.
So, in the coming years, it is expected that AI multimodal applications will create a buzz in the industry. These will be compatible with different input datatypes, including videos, and will have great contributions in interpreting big data in any format. For example, users can use speech as instruction for querying about an image and the AI multimodal software can generate answers in the form of NLP or speech.
Advancements in NLP’s Capabilities
The amalgamation of machine learning algorithms with NLP will also influence the realm of artificial intelligence development to a great extent. Considered a subset of AI, it enables the models to interpret human languages and generate accurate and desired results without overlapping any information or providing haphazard answers.
Although NLP-enabled models are quite prevalent across multiple industrial domains, the coming years are likely to witness an expansion like none. It will play a significant role in sentiment analysis, allowing AI models to evaluate the sentiments and emotions of users and generate human-like answers that can resonate with their state of mind. Also, NER or Named Entity Recognition will help data analysts extract big data and work on unstructured datasets with ease. Semantic search can be integrated with AI models to analyze the users’ intent of the search and accordingly generate results that can meet their expectations.
Autonomous Driving Systems
One of the most emerging trends in AI development that will impact multiple industries, including the automobile sector is autonomous driving vehicles. Although they will leverage multiple technologies, like motion sensors, IoT devices, 5G antennas, and many more, artificial intelligence will certainly take the top seat. Obstacle detection algorithms fed to the vehicles will be based on machine learning and artificial intelligence models. Similarly, AI will enhance the ability to automatically determine the traffic flux on a certain route, optimize delivery channels, and even find the fastest route through real-time GPS integration. Autopilot driving mode will also depend on AI and ML models along with the interaction between IoT sensors.
Introduction of Smaller Language Models
The use of larger language models or LLMs is quite prevalent in the market. However, they have several restrictions owing to their larger size, architectural complexities, and high-end infrastructure requirements. As a result, professionals are willing to take a detoured approach and focus on developing smaller language models that can utilize generative and predictive AI techniques for giving accurate and precise outputs. These will have significant benefits over the conventional larger language models.
For instance, startups and smaller institutions can use these to integrate AI models into their day-to-day operations. Moreover, they do not need an extensive infrastructure or complicated hardware network to operate. The cost will also reduce significantly in their development and maintenance. Multiple smaller language models can be deployed onto the server, each made for a dedicated purpose.
Model Optimization for Enhanced Performance and CPU Usage
Another trend that will be witnessed in artificial intelligence development is model optimization through a series of practices. Organizations can leverage optimized models to overcome the hurdles they usually face with today’s fine-tuned larger language models. For example, Low Rank Adaptation or LoRA will enable professionals to freeze the pre-trained layers while focusing on the intermediate trainable layers. In other words, the trainable models will have scalable parameters that can be fine-tuned on the go, thereby lowering the need to store pre-tuned models in the servers. Apart from this, quantization will also prove to be an ace playing practice in AI model optimization. Lowering the precision of data inputs will significantly cut down CPU consumption and enhance operational efficiency.
Customizable Locally Run AI Models
In industries like healthcare, legal, and finance, data sensitivity and confidentiality are the major criteria that have to be met without fail. However, the prevalent closed source AI models have to go through third-party authorities for data verification and other tasks. This exposes the highly sensitive data to different types of cybercrimes, thereby contradicting the privacy and security norms of the AI models. So, AI developers can focus on creating and deploying smaller language models on the local server, ensuring they utilize the private network channels of the organization only. It will not only offer a higher security level to the sensitive transaction information but also optimize the performance due to reduced dependency on an extensive infrastructure.
Introduction of Virtual Agents with Enhanced Capabilities
With the focus now shifted to multimodal AI algorithms, it will be easier for developers to create a bot that can accept computerized vision or input and generate advanced results. It will primarily focus on improving the communication between the human user and the virtual agent. For example, you can use an AI bot to capture an image of a city and inquire about the best places to visit there. Similarly, you can also provide an ingredient list to the AI bot and ask about the best dinner recipes to make. Such an advanced level of virtual assistance will revolutionize how AI bots are currently utilized.
Ethical AI
One of the major concerns regarding artificial intelligence development is the implementation of ethical and legal practices. Due to this, the focus on ethical AI will be more in the coming years. It is a set of practices that will help professionals train the AI models to adhere to a pre-defined set of legal guidelines and governance protocols. The underlying algorithms will be more transparent, allowing end-to-end visibility and traceability. It will play a major role in the DeFi ecosystem also since AI-based applications can easily meet the security and privacy needs of the transparent and immutable blockchain system.
Prevention of Shadow AI
Nowadays, employees often use generative AI and other models for personal use within an organization. For example, a coder can use ChatGPT to generate a piece of code needed for the project. However, it will risk the exposure of confidential project information on an open-source platform. This is why AI development will see a significant focus shifted on preventing Shadow AI or the practice of using an AI model deployed on the corporate SaaS infrastructure for personal use. Strict corporate policies and role-based access can help organizations tackle the risk of exposure to the outside world. Furthermore, the development of AI models that can detect any shadow practice will also be on the rise in the upcoming years.
Hyper-Automation in Manufacturing Processes
Although AI models have paved the way for process automation in the manufacturing industry, several untraversed areas need improvements and technological transformation. This is where hyper-automation through AI models will come into the picture. It will help in factory automation at a much granular level, thereby increasing the overall efficiency of the manufacturing units and reducing production leaks. For example, detecting the demand cycle through AI-based modules in the supply chain application or optimizing delivery routes through RPAs will witness significant growth in the coming years.
Conclusion
Now that we have discussed the major trends in the artificial intelligence development process, you won’t have any problem streamlining your next AI project for the benefit of your business and the community. Adhering to the principles and focusing on customer-centric approaches will help you create AI models that can generate accurate and human-like responses.
