Simplifying AI can optimize your entire business

Artificial Intelligence is less and less a futuristic technology and a more integrated aspect of today's business landscape.

The use of AI in business is revolutionizing every industry, and Gartner reports that at least 75% of organizations today use deep neural networks.

In financial services, AI automates menial tasks and reduces errors in traditional manual workflows.

The unfounded fears of AI

There is no doubt that companies using the right AI for the right reasons reap exponential benefits. Unfortunately, not all business units are as excited about the available AI solutions that financial services are equipped with. Change management is an important component of failure when implementing any transformative technology.

Many humans still have unfounded fears of it gaining sentience or replacing them, and workers fear becoming obsolete once their daily tasks are automated.

But that has never been the goal of AI, machine learning, and automation, because they augment human intelligence.

Humans are still very necessary

Let's take OpenAI's GPT-3 and Dall-E 2 text and image generation models as an example. While they can generate a 1,000 word blog post with images in seconds, there could be plenty of legal liability issues if you were to post raw content generated by one of these templates directly to your site. Web.

Content is never 100% accurate; human interaction remains key to training, implementing, and using AI across the enterprise.

Simplifying AI for the average worker

Datasets and AI results must remain accessible, and making them accessible means tapping into the wider organization to apply their professional judgment to datasets. This provides the speed, variety, and truthfulness of the machine as it learns.

AI in financial services

The use of AI in financial services is so successful because payroll, compliance, accounting, taxes, etc., are complicated, especially when you are a multinational or use the hand - remote global artwork unlocked by the pandemic.

Extended datasets

But you can import large datasets into the AI ​​to make it more useful. Streamlining it all and optimizing processes not only reduces errors, but also frees up human workers to perform more advanced analyzes that are closer to why they entered the industry in the first place. /p> How simplifying AI can open up possibilities for use

Simplifying AI for the average worker means they can focus on less menial, more innovative tasks and accomplish much more in less time.

GPT-3 and Dall-E 2 may not offer perfect, production-ready outputs, but they use neural networks on large datasets of around 175 billion parameters over 45TB textual data. Therefore, they are great for ideation and conceptual work to get a firm visual picture of the final product to work from.

pass it

Although their outputs look very different (text vs. images), both OpenAI creations work the same way. Although it seems that AI is leading to faster progress, what is really happening is that we are discovering an important concept that opens the door to new possibilities.

That's why it's important to put technology in as many hands as possible to see how others find use in the results it creates.

How AI brings more value to a business

While the quality of the content-generating AI debate rages in online media and forums, the uses of the technology for internal business functions are even more notable.

New ways of looking at things — skilled data scientists

AI across the enterprise continues to open up new ways of looking at things and enables skilled data scientists to develop complex models to predict everything you need to know, from the state of machine to possible market conditions and forecasts.