Artificial intelligence – key technologies for the financial industry

13.01.2022

Artificial intelligence financial industry

From robots on the factory floor to decision-making in investment banks, technology has always driven the financial service sectors. John McCarthy first coined the term artificial intelligence in 1956, but for many this concept from the world of science fiction is only becoming a reality today.

The potential of this technology has driven billions of dollars into research and development around the world; however, there are no clear examples or benchmarks that show us exactly where we may end up regarding making machines think like humans.

Artificial intelligence (AI) is a crucial tool in the financial sector. AI covers everything from chatbot assistants to new systems and tools designed to quickly detect fraud. In addition, AI tools can be used to improve task automation in the financial industry, helping to increase efficiency. While AI may provide a lot of obvious advantages, it’s important to recognize that even now, a significant amount of a bank’s manual procedures are still being done manually.

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Human in the Loop: the human in the machine

17.12.2021

Human in the Loop ML

Man in the machine – a buzzword familiar from science fiction novels of the early 20th century. What this term is about in the 21st century is clear: it is about Artificial Intelligence. The development and training of AI requires the intervention of natural intelligence at many points: human in the loop. In this loop, the human acts in a similar way to a teacher.

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The new Google search: MUM

10.12.2021

Google MUM

MUM — these three letters stand for Multitask Unified Model. MUM is the new Google algorithm for capturing search queries. What is it all about? What is changing in Google search? And what impact will MUM have on search engine optimization and online marketing?

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How to Accelerate ML Development with Pre-Trained Data Models

02.12.2021

Pre-Trained Data Models

Recent advancements in artificial intelligence (AI) like autonomous systems, computer vision, natural language processing (NLP), and predictive analytics are all powered by machine learning (ML). In those scenarios, ML helps to move data in the value chain from the informational level to the knowledge level.

Most smart systems you’ve interacted with today were probably developed leveraging supervised learning. Supervised learning is all about building ML models from scratch. However, this approach isn’t always the best. Many AI and ML projects fail because of a lack of resources and, of course, a lack of useful AI training datasets.

Supervised learning demands time, money, and significant human effort to make it work. That’s why it’s vital for enterprises to find viable alternatives to supervised learning. While for many years there has been no way around this problem, ML engineers have recently identified new ways to optimize ML models.

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Top 5 Common Training Data Errors and How to Avoid Them

19.11.2021

Avoid training data errors

In traditional software development, the code is the most critical part. In contrast, what’s crucial in artificial intelligence (AI) and machine learning (ML) development is the data. This is because AI training data models include multi-stage activities that smart algorithms must learn in order to successfully perform tasks .

In this scenario, a small mistake you make during training today can cause your data model to malfunction. This can also have disastrous consequences—for example, poor decisions in the healthcare sector, finance, and of course, self-driving cars.

So, what training data errors should we look out for, and what steps can you take to avoid them? Let’s look at the top five data errors and how we can prevent them.

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