Latest Trends on Generative AI
For research scholars and stakeholders, it has been a tough year to find the latest trends in generative AI other than the same old recycled AI stuff. To be honest and practical, once the novelty of a new thing wears off, it becomes usual. Take smartphones, for example; they no longer excite us as they are not a novelty anymore.
For researchers, these saturation factors obstruct them from keeping their critical thinking alive. But luckily, there are plenty of research gaps in generative AI that have started to expand the domain further.
We have curated the most recent and innovative trends in generative AI that offer fresh insights and new topics for research. We will be looking at those topics and their future applications in the following sections. They will surely pique your interest!
Generating data is far more preferable than collecting data any day. But there is no guarantee that it will be accurate and reliable enough as much as real data. That’s why synthetic data is ideal for data enthusiasts. Synthetic data generation became a new sensation in generative AI that replicates the statistical properties of real-world data.
As they are artificially generated, it completely eliminates the risk of data privacy issues. They can be used to simulate hypothetical scenarios that may not be possible in the real world. For example, the Riemann hypothesis, collective consciousness theory, and many more; the opportunities are endless.
Synthesising drugs for medical needs and targeted therapies cannot be done overnight, even for urgent health crises. It requires extensive time and efforts. It is an iterative process; the 2020 Pandemic is a good example of that.
Automating and simulating such a complex task of drug discovery with AI will improve the overall process multiple times faster. Identifying targets, modifying chemical compounds, and even clinical trials can be simulated using generative AI and ML models. This significantly reduces the hurdles associated with drug discovery.
AI code generation is the process of automating the coding process, replacing the lengthy process of manual programming. It has the ability to generate complex and nuanced codes based on our instructions. Now, it has created a situation where only theoretical knowledge and planning are enough to program.
Using AI to code will improve the infrastructure and workflow of the organisation. At the same time, it terminates the so-called professionalism around programming as it allows anyone to generate a desired script for their specific needs.
As a collaborative effort of numerous scientists and researchers, we now have a system that nearly resembles a human mind- Multimodal AI systems. Just like humans, these multimodal AI systems can hear, see, and think like us. They process images, video, audio, and contextual information in a single system.
Multimodal AI improves computer vision, robotics, and natural language processing by allowing co-relation among varied data types. These integrated data structures will further improve the understanding of AI models.
Generating 3D objects solely from a text prompt based on textual descriptions. With advanced machine learning techniques and deep learning models, they interpret input text and generate 3D representations that match geometry, textures and materials.
The following industries stand to immensely benefit from this technology, and it will bring about a massive transformation.
- Gaming
- Film and Animation
- Virtual Reality
- E-Commerce
Generative AI is transforming mental health care by providing supportive tools like AI chatbots, virtual assistants, therapeutic conversations, etc. It has also been proven that interacting with AI chatbots will positively improve mental health states and increase a sense of peace and support.
Now, generative AI has further progressed by integrating advanced natural language processing techniques with ML algorithms that understand human emotions and nuances better than before. It’s more like understanding the tone behind words.
Even though it is still largely theoretical, the idea of trans-humanism exceeds the limitations of human conditions. It shows what AI could really do for humanity that nature cannot. It envisions improving physical, and cognitive abilities, and extending the lifespan of humans by integrating biotechnology with AI.
It often deals with ethical usage implications and the possibility of inequality in this. To address these kind of issues and to explore the possibilities, a dedicated research methodology is very much essential.
Generative AI models have started to evolve from being in their infancy stage to revolutionise industries with their groundbreaking innovations. Above are the outcomes of such evolutions. But they won’t stop here; they will continue to evolve as they start to think independently. Therefore, it is mandatory to stay updated and constantly look out for advancements as a researcher in this domain.
Researching these things uniquely is not as easy. It requires a strong workflow with a critical approach. It may even exhaust you to the core of giving it up, but still, overcoming them is quite easy and effortless with an affordable research service. PhD Assistance offers a seamless solution for streamlining your research and simplifying your hurdles. Our field experts will guide you to conquer research challenges and empower you to accomplish your academic goals.