Augmented AnalyticsEmerging Trends in Enterprise AI

Emerging Trends in Enterprise AI

Artificial Intelligence has been one of the prime technological themes of this century. Many businesses are increasingly becoming aware of its impact in today’s world. Thus, they are seriously considering AI adoption now more than ever.

While the COVID-19 pandemic may have affected many aspects of our lives and the way we do business, the research and development in the AI space remain unperturbed. Over the last five years, AI companies attracted nearly $40 billion in investment globally. The United States and China are currently leading the race with massive investments in AI research.

In this blog post, we will look at some of the top emerging trends in enterprise AI.

1. No code AI Automation Platforms.

Leveraging AI capabilities to expand and grow our businesses may sound exciting and help us stay relevant in the competitive landscape but could just as easily become tedious and time-consuming. This is where “No Code” AI platforms come in. It not only helps non-technical individuals experiment building AI solutions, but it also boosts the productivity of the technical team to create reliable and scalable systems at a faster pace. According to a report by Gartner, in a couple of years, over two-thirds of application development will be done by no code or low code platforms.

2. Predictive Analytics for Small Data

In general, the performance of a machine learning model improves with the quantity of data available. In today’s era of big data, not all organizations enjoy the luxury of having large data sets. This is not just a problem concerning only some organizations, but also the inherent nature of some issues that makes the data collection and preparation arduous and expensive. This may be one of the significant roadblocks which hinder taking full advantage of AI.

Machine learning models trained on small datasets are notorious for their tendency to overfit, meaning that they perform very well on the data used for training while yielding poor results when deployed for use cases in the real world. Thus, continuous research becomes critical for exploring and discovering methodologies that produce high accuracy despite limited data.

3. Explainable AI

The introduction of deep neural networks was a game-changer and truly revolutionary. They drastically improved the accuracy of predictive analytics and model performance in general. But one of the challenges was the black-box nature of such models. It was not possible to explain the reason behind the models’ predictions. For more organizations to employ AI for their businesses, it becomes extremely important to trust its predictions.

The models may inadvertently assimilate some biases in the dataset, and therefore an explanation with its prediction will prove useful. Let us take the instance of a model that decides if a credit card should be approved based on the customer’s information. While it is reasonable to deny approval from the model that bases its prediction on age and salary, it is not acceptable if the credit card is denied based on an individual’s gender, race, or country of origin.

Along with the predictions, Explainable AI would give us additional information such as:

What were the key features or variables considered while making the prediction?

What specific values or range of values resulted in the prediction?

How could the prediction change with different feature values?

Such explanations help us be more responsible and accountable while reducing the model bias and the cost associated with erroneous predictions. Explainable AI is still in its juvenile stage, but research in this area is rapidly gaining traction.

4. Quantum AI

Two important reasons why AI has gained popularity over the last couple of decades are the increase in the availability of data and computational power. While it is true that current computing capabilities have improved a thousand times over the last three decades, it is still not sufficient to process big data by executing some heavy algorithms. Employing the classical supercomputers available would take a lot of time to solve the problems at hand. This is where Quantum computing comes to the rescue. The use of quantum computing for executing machine learning algorithms speeds up the process drastically, enabling us to tackle more problems in a short time and paves the way to build Artificial General Intelligence (AGI) systems. Many tech giants have already started investing in this technology and carrying out research to achieve quantum supremacy.

5. AIOps

AIOps is an abbreviation for Artificial Intelligence for IT Operations. The amount of IT operations data getting generated every year is humungous. Managing such large volumes of data by the IT staff to understand the problems and analyze its root cause becomes difficult. To address this issue, AIOps was born.

AIOps uses machine learning capabilities to handle and process enormous amounts of data that arise from many IT components and applications. It would then intelligently detect notable events and anticipate potential problems related to system performance and availability. This will alert the IT team and enable them to address and provide a swift response reducing the mean time to resolution (MTTR).

6. Graph Neural Networks

Many real-world datasets are challenging for ordinary neural networks to handle. Some datasets include social network data, geographical map network data, chemical, and biomolecular structures, etc. But these datasets can be easily expressed as a graph, a mathematical way to represent and model relational information in the data. It led to the development of Graph Neural Networks (GNNs). They are specially designed to handle graph data to produce insights on the relational information present in the data. GNNs are new but have promising applications in various domains like social media, recommender systems, pharmacy, and pure sciences, etc.

7. Ethical AI

Although AI is changing the world for the better, we are faced with ethical and moral dilemmas in many scenarios. Consider the use of AI in the art industry. Let us say an AI model is trained on all the music composed by Beethoven. Now, when this model composes new music like Beethoven, who should be recognized as the original author? Should it be the company that organized the project, the engineers who created the algorithm, or Beethoven himself? AI can also be misused in many ways, potentially threatening human dignity, privacy and disrupting the way we operate in society. Thus, maintaining transparency, accountability, and developing a legal document with a global scope regarding the ethics of AI is of paramount importance. Indeed, AI is a double-edged sword, and it is up to us how responsibly we use it.

As many organizations are reaping the benefits of AI, companies have started ramping up their AI investments. Considering these trends, it is evident that AI is becoming a critical function for all businesses.

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The author

Sanjay is a Data Scientist at Subex AI Labs. His work focuses on building AI solutions for applications in Contract Lifecycle Management and Fraud Management systems. He is also a backend developer for "Hypersense AI Studio" - a no-code AI automation platform offered by Subex. He holds a Master's degree from the Indian Institute of Space Science and Technology with a major focus on the subjects of Machine Learning and Computer Vision. In his leisure time, he enjoys reading books on Science, Psychology and Philosophy.


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