Introduction
If you are even the slightest bit of a tech follower, you must have come across the term AI-ML. In Fact you might even know about it. But in this article we are going to be discussing the rapid emergence of Artificial Intelligence and Machine learning capabilities. PwC’s global AI study says that the global economy will see a boost of 14% in GDP by 2030, amounting to a potential increase of $15.7 trillion. The emerging trends in AI looks promising with machine learning redefining the key components of the software industry.
So far we have mostly seen centralised AI based solutions. But with the growing web3 industry, things can all together take a completely new turn. Along with being decentralised with the help of blockchain, Artificial Intelligence can help to make the experience more personalised for the user.
What is AI-ML
The role of Artificial Intelligence is to mimic the human brain. This is achieved through an algorithm which tries to simulate human intelligence including problem solving. This algorithm can learn beyond what is told to it. The aim is to make the algorithm make decisions like humans. Siri and Google translate recommendations are some of the examples which use AI.
Machine Learning is an area of Artificial Intelligence which uses data and algorithms to learn and adapt. It allows computer systems to predict and make decisions using the information or data provided. The focus is to learn by creating its own algorithm. Human expertise is required to initially train the model.
Emerging Trends
Generative AI
This is a form of Artificial Intelligence which uses huge data sets, neural networks and deep learning to generate something completely new. Models like Chat GPT also make use of natural language processing to generate content. Generative AI is gaining popularity now because of its accessibility and due to more advanced and accurate models.
The emergence of generative AI can be traced back to 2017 when Google first proposed its neural network architecture called the Transformer. It was costlier back then and required heavy computing resources which most people were not willing to invest in. But from 2022, things started to become more accessible when the cost was reduced. The growth of this technology is fuelled by big advances in machine learning models and GPU speeds.
Augmented intelligence
This subsection of AI operates to enhance human intelligence rather than operating independently. It does so by improving human decision making and suggesting the actions to back the improved decision. While Artificial intelligence tries to automate the processes by digging into data and answering independently, Augmented intelligence uses AI technology to assist humans to help in key decision making.
This is being used in Tesla’s self-driving cars. It can also help retailers identify the bottleneck by analysing data and helping to make good decisions. Another important use case is in the healthcare industry where it can be used to analyse blood tests to detect cancer. It is expected that the global augmented intelligence market will account for more than $100 Million by 2028.
No-Code Tools
No code tools allows the company for quick and efficient development of applications without requiring extensive coding knowledge. This is thought of as a replacement for developers as companies can now build the required application quickly and easily. But a drawback to this right now is the code optimisation which can’t be done without the help of developers because the tools available are not yet advanced.
This can lead to more open source alternatives in the market and more scalability. The integration of no code/low code tools with developers can allow for better code without errors meanwhile providing good optimisation too.
Cyber Security
AI has the potential to significantly improve cybersecurity by automating tasks, identifying threats and responding to incidents more quickly and efficiently. This can allow cyber security professionals to focus on more strategic tasks and also help in reducing the workflow in the organisation.
AI can scan and analyse any suspicious activity in the network. It can track the user behaviour and identify the anomalies in the system before the breach. Updating the security patches in the system can be automated using AI. This will lead to a growing market because more and more people are becoming aware and privacy and cyber security will be the priority.
Natural Language Processing (NLP)
This is a subset of AI which finds its importance in analysing the unstructured data. It uses advanced machine learning models to understand unstructured voice and data. This field is growing with the aim to improve human interaction with machines. We already have voice assistants like Siri, Alexa and Google Assistant which provide personalised solutions. But in the coming future, the virtual assistants are going to be more intuitive and responsive.
NLP has a huge variety of use cases like for example the chatbots which reduces the efforts of the customer support executive in the same time providing personalised solutions. There are many multilingual models underway which will help in removing the language barrier among people.
Challenges
Carbon emissions
Training an AI model emits a huge amount of carbon emissions. The large number of parameters in the model to make it more personalised require more power usage. As can be seen in the graph, the carbon emitted by GPT-3 was the amount that would be enough to power the average American home for about 41 years.
Efficient code and effective power usage must be implemented for future models. One other way is to use green energy sources for training the models and becoming more sustainable.
AI controversies
The growth of AI has got its bad side too. With recent advancement, we have seen that AI can be used for evil good too. The fake news/videos can be made to sound more genuine using AI tools and softwares. It also sometimes tends to provide biassed solutions depending upon the situation. There have been reports of racist and unfair answers provided by AI tools. Many a time a developer is unable to figure out how the model arrived at a particular solution which may be completely off and incorrect.
Right now the focus should be made in the right direction to implement best practices in training the model, making it unbiased and understanding the model efficiently. Regulation should be in place to avoid the misuse of the technology.
Conclusion
The artificial intelligence sector today is nearly a $100 billion dollar market with Open AI leading from the front with a valuation of about $30 billion dollars. The emerging trends in AI are here to stay and make the improvement. The generative AI sector is itself pulling in the major funding in the industry.
While the global AI funding took a plunge down. But this was partly due to a slowdown in $100M+ mega-rounds to AI companies (8 in Q1 ‘23). The global AI funding fell to $5.4B in 2023 with a 43% quarter on quarter drop.
Artificial Intelligence is superior to human intelligence in some of the areas, providing better and accurate analysis. As we continue to understand the applications and implications of AI, we can expect notable advancement and innovations. AI services can automate various tasks and provide improved security over personal data. Personalized experiences for users will increase and provide the users with better experience. The Future looks promising, and it will be interesting to see how the technology evolves.