Artificial intelligence is everywhere, and it is making data more valuable than ever. This is because AI platforms rely heavily on data to function effectively. Many platforms and services collect data from their users to fuel these algorithms. LinkedIn has recently been found to do this—by default—without properly informing its users or updating its terms of service.
Clear Sailing IT Solutions Blog
One of the many tasks undertaken by the United Nations is to protect human rights around the globe while also working to create more sustainable and climate-friendly development. As such, the UN has recently taken a healthy interest in the development of artificial intelligence, hoping to develop guidelines that allow us to get the most value out of AI without creating more significant problems.
Businesses are constantly trying to find a way to best use their data. Whether it is creating a business intelligence strategy, integrating artificial intelligence, or for simple analytics, without having accurate, reliable data, the insights you derive can be misleading and end up costing you. That’s why it is important to know how to scrub or clean your data. Having access to clean data is essential for anyone involved in business intelligence or AI. Today, we will discuss the issue and give you a simple guide to help you get started.
While AI is far from perfect, I always love discovering ways that it can help do something mundane and speed up a workflow here or there. I’m no expert in Photoshop either, so if I need to edit something, I usually depend on someone with a little more experience, but this was a really neat trick I was able to do in just a few minutes!
Over the past few years, artificial intelligence has become a bona fide buzzword amongst businesses of all sizes, with 97% of respondents to a Forbes survey seeing a potential benefit in some way, shape, or form. However, with it being integrated everywhere in our modern lives, it is important that we remember that AI is still a human invention, as such, it is vulnerable to our own implicit biases.