Shahid Hanif is the Chief Technology Officer and Founder of Shufti Pro, a biometric identity verification (IDV) solution.
We live in a time of rapid technological advancement. With new technologies emerging at an unprecedented rate, there is competition among technology developers to create new and unique technologies. However, it is crucial to emphasize that this “competition” is symbolic and is about progress and innovation.
The combination of technologies of different origins and the production of a single product is called technological convergence.
To better understand the concept of technological convergence, let’s take the metaverse as an example. The metaverse is a convergence of six main technologies. These technologies include artificial intelligence, deep learning, big data, blockchain technology, computer vision and extended reality (XR).
The AI-ML revolution: redefining innovation
Recent advancements in the AI and ML industries have enabled developers to create intelligent, autonomous machines. Software and devices can now learn from examples, intelligently define patterns, and perform appropriate command execution.
It is essential to note here the importance of data. AI and ML models mainly depend on the data fed to them. A single Internet user produces approximately 146,880 megabytes (MB) of data daily. This is where technologies like big data analytics come into play. So all technologies are somehow correlated and complement each other on some level. Now let’s talk about some technologies that are products of the AI-ML alliance.
Automation technology
The ability to learn from models and then run them against predetermined data sets is a young and vital commodity. It allows machines to operate with minimal human intervention. This consumes fewer resources (time and money) and reduces the chance of human error, as humans are prone to making mistakes, especially when dealing with large and repetitive data sets.
This versatile technology can streamline tasks ranging from data processing to onboarding new users.
Predictive analytics
Using advanced machine learning mechanisms, software solutions can now build accurate models. Machines can now refer to these datasets and take them as examples to solve a similar problem. For example, cybercriminals and hackers can be identified by referring to data sets obtained by analyzing the behavior and activities of previous cybercriminals on a company’s radar.
The scope of this technology ranges from effectively eliminating threats before they cause damage to accurately predicting economic and financial situations.
Virtual assistants and large language models
Artificial intelligence and machine learning mechanisms have combined to create virtual assistants and large language models. Virtual assistants include Alexa, Google Assistant, etc., while large language models such as ChatPT and Google Bard are products of AI/ML mechanisms. These models have access to a wide range of databases on the Internet and their goal is to help users in all kinds of theoretical information collection. Natural language processing (NLP) is also crucial in these models.
These technologies can increase user productivity, conduct in-depth research and implement them practically.
Leading technology sectors shaped by advances in AI and ML
The technological convergence between AI and ML has resulted in the creation of several useful technologies, but now let’s talk about some industries that have been most affected by this merger of AI and ML. The AI market is expected to reach $407 billion by 2027, experiencing substantial growth from its estimated revenue of $86.9 billion in 2022.
Data analysis
Security and protection are major aspects of data analysis algorithms. These algorithms aim to identify illicit entities and use the collected data to avoid such entities in the future. Targeted messages and advertising are also commonly used by companies with large amounts of user data. Apart from this, virtual support bots and automation are also supported using user data. Thanks to modern data analysis practices, combatting misinformation and fake data is also relatively easy.
The fintech industry
Machine learning and artificial intelligence practices have had a significant impact on the fintech industry. Companies using these technologies are now leveraging automation and data analytics practices, effectively improving their processes. A major contribution of these technologies is fraud prevention for financial institutions. Identifying fraudulent activity and anomalies is now more accessible and efficient than ever. This ensures safe activities. Additionally, personalized services tailored to a user’s needs are the product of AI and ML algorithms.
The health sector
Machine learning and artificial intelligence are transforming the healthcare industry. Bioengineering, combined with these modern technologies, has created solutions to reduce costs and enable better treatment of individuals. Since then, healthcare professionals have been able to achieve better patient outcomes. Medical researchers can now develop better solutions by adapting AI and ML methods.
The cybersecurity sector
Another sector that AI and ML have completely revolutionized is cybersecurity. SaaS developers are now developing better IDV solutions for fraud prevention and protection. These solutions range from identifying cybercriminals to preventing AML/money laundering activities. They use advanced ML and AI algorithms to develop models against hackers and other suspicious entities. An automated system is set up, automatically eliminating all matching entities.
Integration of AI and ML technologies: what to expect?
The integration of artificial intelligence (AI) and machine learning (ML) is primarily based on APIs and allows users to integrate these technologies into their daily business processes. This process may seem simple, but it is quite complicated.
Users must first determine the extent of this integration and proceed accordingly. Companies need to train their employees as per the system requirements for smooth processes. To harness the true potential of these technologies, companies must approach them with feasible business plans; otherwise, the integration will run into bottlenecks and ultimately fail.
The best way is to adopt competitive and credible software solutions in the market that provide real-time support to businesses for smooth processes. There is still a long way to go for AI and ML technologies to be fully practical for users, so it’s best to proceed with caution until then.
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