Welcome to the AI innovations in 2024, where amazing advancement is taking shape the future of AI technology. From healthcare to finance and almost every industry now-a-days are directly or indirectly using AI technology.
With the unprecedented advancement in AI, it has given a lot of thrills to the people and more importantly to the AI enthusiasts. As this technology is taking to rise there can be seen a huge demand for the AI professionals.
Previous year chat gpt has created a lot of waves in the AI technology which seamlessly provides amazing results. But after that a lot of companies are now heavily investing on AI to solve various burning problems. Hence, there can be a race between companies.
As technology is on the rise, this year will also see a lot of top trends in AI and it is going to transform the world we live in right now.
So let’s discuss some of the best AI trends
Reality check
As the technology becomes smarter and smarter day by day it is utmost important to use it responsibly. Heavy implementation of generative AI potentially leads to duplicated content which looks like original content.
So implementation of generative AI in organizations pose a high degree of concern for most of the CEOs.
So to avoid this, reality check of AI could be one of the trends which entrepreneurs are looking for. Perhaps many AI professionals are looking into these solutions.
In reality check , the AI will assess the biases, limitations, ethical implications of AI systems in response to real world scenarios. In short it will check whether the AI systems are achieving the goals ethically or not.
Multimodal AI
Now come to the second trend which is multimodal AI which refers to generating the information from different modes such as text, speech, image and video.
It is like mimicking human ability just like how we sense everything. By cross pollinating all these modalities we can produce the most desired results.
Take an example , suppose you have very few vegetables left in your home and you are unable to figure out what dish you will cook. For that simply, you can take the image of those left vegetables and based upon this the AI will give the best result in speech or video format depending upon your choice.
The multimodal capabilities have potential to strengthen models and also allow the model to learn better. In this way multimodal AI can enhance their perception by understanding the real world just like we do.
Agentic AI
Agentic AI has the ability to act independently without human intervention. The agentic AI has to be trained to understand the environment and can set goals and act on it to achieve the goals too.
Agentic AI will be very beneficial in monitoring the environment where the agent can collect the data, analyze it and can give better predictions of weather reports or other environmental threats.
Agentic AI along with multimodal AI has the capabilities to provide amazing possibilities. Certainly combination of both these techniques can enhance decision making in real time, improve human interaction and can assure robustness and reliability.
Open source AI
Building an open source model for developers enables them to interact with other developers for better AI research which can significantly reduce the cost.
The old data Github allow the developers to share their work with other developers to learn better. This year Github shows a huge discussion on Generative AI.
Though building and open source AI is great for collaboration and it enhances the better and ethical development of AI still AI experts show concern over it.
Retrieval augmented generation
Though generative AI is really incredible in creating amazing responses still it is possible that it can give incorrect responses.
That’s why many enterprises are hesitant to adopt the AI instantly but to avoid this kind of incorrect responses and biases the RAG could possibly reduce the incorrect responses.
RAG has the ability to give better accuracy of AI generated content. RAG can help LLM to produce appropriate responses and hence improves the quality.
Generally RAG works in two steps i.e. 1)Retrieval and b) Generation. In retrieval it gets all the relevant information from a large pool of text like Wikipedia.And in the generation part, it generates the accurate response in the proper context.
Custom enterprise model
Building large language models takes a massive amount of money. For example Chatgpt which trained on huge sets of data and in its making a huge amount of money invested by its makers. But enterprises are hesitant to incorporate this chat gpt like model to their organizations because of their security concerns.
So to avoid this problem there can be a rise of custom enterprise models which can serve narrow purposes and help the niche markets.
These custom enterprise models are organization specific and are trained on a company’s datasets which includes customer data, financial data and other business related datas.
The custom enterprise models can help the organization in various ways such as improving decision making, enhancing efficiency, personalizing customer experiences, risk management, cost reduction and also helps in innovation and competitive advantage.
Shadow AI
Shadow AI refers to the use of AI in the background without human awareness. These shadow AIs can silently analyze data and make decisions without human intervention.
There is a chance of increased use of shadow AI by the company employees which is a great concern for the company owners.
As employees might use shadow AI, the company or organization must take serious steps to avoid this and always prefer human related work. However, this completely depends upon the ethical concerns related to AI.
While using shadow AI the employee might share sensitive information to the public LLM. So this year the organization has to take necessary steps to manage the risk associated with AI. The company must come up with acceptable AI policies to what extent their employees use AI in their workplaces.
Need for AI talents
As AI is in a rising stage there is a continuous need for AI talents which will be the biggest AI trends in this year. Machine learning models require much more training and designing. To do that highly skilled professionals are needed.
This year AI will be a hot career option for most of the technological aspirants in particular MLOPs etc. The knowledge of data science, statistics and programming will be crucial for the aspirants who want to take their career to the next level.
As there is a huge shortage of talent, companies are facing difficulties in finding the perfect talent for AI innovations. The public LLM is sometimes more biased and comes up with incorrect responses. So to solve this problem, companies must hire a diverse set of experts.
Rise of AI regulations
In this year AI regulations would be a pivotal aspect of the AI technology which governed by changing of various policies and frameworks globally.
EU’s AI act allow the proper goverance in developing and use of the AI technologies within the European union. This act ensures better transparency and more towards the ethical use of AI.
Apart from that globally and US’s federal agencies takes various steps for AI safety. So as the technology is in progressive stage it is associated with change in policies too.
Bottomline
As we are moving towards the latest AI trends, an unprecedented growth more importantly to the AI research and innovation will be forefront in coming years.
Though it sound very exciting and very cool still it is associated with ethical, societal and regulatory implications. As AI continues to empower humanity in greater way with there must be safeguarding against potential risks too.
FAQs
Q. What are 5 current AI trends in AI?
A: Reality check, AI regulations, multimodal AI, agentic AI, AI in monitoring enviornment etc are the major AI trends.
Q. What is the next big trend in AI?
A: Quantum AI is the next big trend in AI.
Q: What is the trend in machine learning in 2024?
A: Data augmentation is the machine learning.