Using technology is a fundamental right that all people have. That’s not a pronouncement of law, a fundamental truth, or even a declaration of de facto general public understanding. The consensus is that this is the case. In the last three articles of the series dedicated to AutoML, we highlighted the technical aspects of the …
We hope that the first two articles of our AutoML series would have given you a good idea about AutoML, its workflow, pros and cons and the way it add value to a business’s deployment of AI technology and business model. This article will take you through the same in terms of practical use cases …
In the first article of our series dedicated to AutoML, we explored what AutoML is all about and how it works; however, the way AutoML lets businesses upscale the AI is another important aspect to discover for AutoML learners, deployers, experts and professionals. In this article, we will highlight what is meant by democratizing AI …
According to Forrester’s recent research, 61 percent of data and analytics business decision makers who deployed AI, stated that they had either implemented or are in process of implementing AutoML software. This is because AutoML has the potential to up-scale the AI at any business. When machine learning algorithms are automated, the results are more …
AI systems are evolving at neck-breaking speeds. They aren’t just handling human tasks more efficiently, they also take on responsibilities that we can’t do. Such roles include providing relevant and instant recommendations to consumers or reviewing and approving hundreds of bank transactions at once. Moreover, there’s a growing need for automated systems to help industries …
Strategies for Trustworthy AI Selection and Building Team Trust Read More »
Traditional AI solutions are built on models that learn how to handle data in a certain way for real-world applications. Models generally consist of a training channel for collecting and grouping data and a prediction channel that establishes patterns as it analyzes these data. However, these models are fixed or static, so they either become …
Artificial intelligence is steering the fintech industry towards a future of efficient financial management and real-time data analysis. AI is constantly evolving as we speak and come 2030, AI technology will cut down banks’ operational costs by 22%. As society breaks through with technological advancement and computing solutions, the financial sector is set to witness …
How AI Is Reinventing the Finance and Banking Experience Read More »
The global lockdown in 2020 ushered in a new era for banking. Contactless payments, mobile banking, and virtual banks are driven by the innovations of artificial intelligence (AI) for fintech. AI in banking welcomes next-level customer support and process automation while boosting overall growth. 80% of financial institutions report being aware of the potential of …
The Need for AI in Cybersecurity Thirty-seven billion. That is the number of records comprised in 3,932 data breaches in 2020 according to the Year-End Data Breach Report of Risk-Based Security. While breach events were just about half of the previous year, the number of comprised records grew significantly and marks the highest number of …
Over a year after the outbreak, the world continues to struggle to fight the COVID-19 pandemic. While most people have adapted to the new norm, fresh cases and new coronavirus variants are still being reported in many areas across the globe. World economies are also still reeling from the impact of lockdowns and business disruptions. …
The Role of Big Data Analytics in the Age of COVID-19 Read More »
