How the Top AI Companies are Changing the Game with Machine Learning   

top ai companies

The Top AI Companies and many startups have embraced the disruption that artificial intelligence has been causing in the past few years. According to The New York Times, there are more than 40 AI firms who are just working on chips. That leaves so many others furthering the fields of deep learning, machine learning, and AI projects.

All you need do is study the AI Index that Stanford University’s Human-Centered AI Institute have put out. You’ll begin to notice how AI draws significant investment from all kinds of key players. Those include venture capitalist firms, academic research institutes, and even giant firms like Microsoft and Google.

But what is Machine Learning (ML) and How is it Different from AI?

An expert from the Top AI Companies would tell you that while the usage of these terms has become synonymous, they don’t mean the same thing! If you want to define AI, you may think of it as an umbrella term. That makes Machine Learning one of its subsets. The latter consists of advanced techniques and models that computers use to figure things out from the data without being explicitly programmed for such tasks. The outcome in the case of ML are numerous AI applications.

Now, we will look at some industries and how they are evolving due to ML:


Is your business a part of the healthcare industry? Then you may be working in a field holding the most promise for machine knowledge processing. Forbes has singled out academics at the Courant Institute of Mathematical Sciences in New York as making great progress in the field of healthcare.

From improving how doctors can diagnose a disease to treat it, we are looking at endless possibilities. When experts in ML coordinate with medical teams, even more, rich opportunities arise. Going through past records that signal the onset of a disease may help doctors catch diseases like cancer in the early stages.


Many of the Top AI Companies are using ML as a game-changer in their marketing techniques too. For instance, since recognition of patterns and connections that we, humans, can’t see is one of the benefits of ML, companies can use it to get ahead of the competition. They may predict user behavior and purchase trends by looking at their purchase history. Then brands may alter or tweak their products to suit their customers better. Or, they could target the right customer demographic with their existing line.

Similarly, the ability to predict customer churn has now improved. Retailers can identify the customers most likely to churn and prevent it – without human involvement – with necessary actions, such as by sending them coupon codes, discount offers, etc.


Top AI Companies have also found a use for ML in the field of renewable energy. While fossil fuels are incredibly harmful to the environment, they are also amongst the cheapest ways of generating power. Switching to other methods of producing electricity will require a major overhaul of the existing systems. In the meanwhile, what can be done, though, is the integration of these power sources onto a single grid. Since the grid will be designed based on human demands, such a step can minimize the number of fossil fuels that we will burn.

The two resources vary seasonally, and ML algorithms can sort through huge amounts of data to make the grid a reality. Whether it is studying real-time weather conditions or deciding how to alternate power plants, ML is invaluable in sustainable power generation.


Any industry that functions based on digital workflows and databases is a perfect candidate for some ML-based improvements. The finance sector is one such example. It will benefit greatly with the incorporation of AI and machine learning in its daily operations. While ML has various uses in finance, we will just focus on one for the sake of this article: fraud detection.

If there is finance, then it won’t be wrong to assume that some kind of fraud is also taking place. Even if it isn’t happening yet, there is a huge potential of it happening in the future. Fraud is one of the major reasons why the finance sector loses large amounts of money every year. Where do the costs eventually go? The companies pass them on to their customers. Increasing costs may increase customer churn rates for businesses.

Therefore, ML can analyze the signs of fraudulent behavior in past data. By comparing them against recent or real-time data, similar patterns can be looked for. Preventing or apprehending fraud quickly are both possible now due to ML.

Would you like to follow the Top AI Companies? Work with us to discover new, novel uses of machine learning to get your competitive edge back. Call 4sights Data Analytics today!

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