It was the 1950s' when Alan Turing conceptualized Artificial Intelligence. Nobody would have imagined the vast array of applications that this technology could offer. He formed the mathematical & logical reasoning behind the concept of machines and computers replicating human intelligence, which today has outgrown to even replace humans for certain tasks.
Artificial Intelligence (AI) has profound use cases in almost every industry. From medical diagnostics systems to consumer electronics, from personal assistants to route detection, from financial analytics to fraud detection, AI technology is omnipresent.
In this article, we will discuss some of the trends that are ruling the AI ecosystem. Let’s find out how this technology is utilized to its best potential and what it has in store for us in the future.
Hyper-automation is expanding automation by combining Robotic Process Automation (RPA) with advanced technologies such as Artificial Intelligence, Intelligent Business Management Software. At an enterprise level, hyper-automation is not just an option but a condition of survival.
In effect to the COVID-19 pandemic, there is accelerated adoption of hyper-automation, which is also known as intelligent process automation or digital process automation.
Apart from RPA, Artificial Intelligence and Machine Learning are the key drivers of hyper-automation initiatives. These technologies ensure that automated processes are able to adapt to ever-changing circumstances and are capable of responding to unexpected scenarios.
Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (a subset of ML) are utilized to build ML models that make the systems learn with data. This data is either fed to the system or is automatically generated by the system depending upon situations or queries that the system comes across.
Maintenance, governance, and scalability are some of the key challenges that enterprises confront while deploying an AI solution. Gartner predicts that only about 53% of AI projects are successfully deployed.
With a massive computing capacity and huge data sets, scientists and engineers are able to create AI models and algorithms that are capable of making rapid and impactful decisions. However, these capabilities work only in a controlled environment, and making an AI system work beyond this is a challenge.
That is why businesses need to combine the principles of software engineering, system engineering, and human-centered design to create AI systems that are able to work according to human needs.
For this, AI engineering is needed. AI engineering is a discipline that focuses on developing tools and processes that could anticipate the change in operational environments and ascertain that human needs are translated into an understandable and virtuous AI solution.
By adopting AI engineering principles, it can be ensured that the AI system is human-centered, scalable, robust, and secure. AI engineering keeps a check on:
a) How AI systems are designed to align with humans, their behavior, and values.
b) How AI infrastructure, data, and models may be reused across problem domains and deployments.
c)How an AI system will work outside a closely controlled development and testing environment.
IoT devices collect and exchange huge amounts of data. They generate over 1 billion GB of data, every day.
By 2025, the number of IoT devices will grow up to 42 billion, globally. With this, there will be swaths of data and that’s where AI steps in. Artificial Intelligence (AI) will lend its learning capabilities to the IoT data, resulting in smart, self-learning solutions.
Just consider this. There is an industrial setting where IoT networks collect the operational and performance data of a manufacturing plant. This data, when analyzed by an AI system, will improve the performance of the production system, boost efficiency, and create an automatic prediction cycle that informs when a machine requires maintenance.
The COVID-19 pandemic impacted almost every sector. However, the industry that needed AI to support the most was healthcare. In many different ways, AI is helping to fight the COVID 19 pandemic. AI is helping in the prediction and tracking of virus spread, contact tracking, early diagnosis, etc.
Apart from the various innovative applications of AI in healthcare, the technology was a great support to researchers and scientists in the development of therapeutics. AI, ML, deep learning continues to be the core technologies being used to mitigate the impact of the pandemic in the healthcare sector.
There are numerous ways AI has been contributing to improve cloud computing services. For example, AI tools are improving cloud data management. Cloud platforms generate and collect a huge amount of data. The vast data repositories need to be cataloged and managed over time. That is why cloud solutions are integrated with AI tools that help in the specification of data and its management. For example, even a small financial organization would process hundreds of transactions, every day.
Moreover, AI tools are a significant part of SaaS-based platforms. SaaS providers embed AI tools into software suits to augment their functionality and deliver value to end-users. For example, Salesforce added Einstein, a tool that helps to capture customer data to track and personalize customer relationships.
ALSO READ: AI vs ML vs Deep Learning: What’s the Difference?
Conclusion:
Artificial Intelligence (AI) will continue to evolve and bring more possibilities to improve businesses. These AI trends shared above showcase the areas where AI has got prominent applications and is just the tip of the iceberg. AI technology is not just helping to overcome business challenges but enabling them to get the best out of their potential.
Want to know how AI can help to boost the productivity of your business? Schedule a free consultation with our experts from AI development company who would analyze your business, find out AI use cases, and recommends them.