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IBM leads in machine learning research with 5400 patents in the sector

IBM leads in machine learning research with 5400 patents in the sector

Last updated 15th Dec 2022
Disclosure
  • IBM's 5400 patents have placed it at the forefront of global research in the machine learning sector.
  • The company beat other giants, Microsoft and Google, by far to claim that spot.

It's 2022, and artificial intelligence (AI) is everywhere. Your car parks itself, your phone can tell you where the nearest grocery store is, and you don't even need to type in your credit card number anymore! Behind all of this is some serious tech.

There's a lot of development that goes into making machine learning (ML) work well and become more intuitive. One indicator of the developments that companies are pursuing in this space is patent applications. These point us to what they're working on, where they're trying to innovate, and the tech industry's future.

And guess who has the most patents? IBM! According to a BanklessTimes data presentation, they registered 5400 different ML patents between 2017 and 2021. They beat Microsoft and Google for the top spot by a long shot, with Microsoft coming in second place with 2108 applications and Google in third with 1342.

IBM is ramping up investments in AI

The popularity of machine learning tools has skyrocketed in recent years. This is due to both a growing trust in their accuracy and a reduction in costs. Many businesses now use machine learning to provide accurate predictions and quickly analyze large data sets.

It's against that backdrop that IBM is ramping up investments in AI. The firm says that its inventors are developing new AI tech to spur businesses in scaling their AI usage.

It’s focussing on initiating change through natural language processing (NLP), automation, and developing trust in AI. Additionally, it’s continuing to inject new abilities from its R&D arm into its products.

IBM says the next step in AI is what it calls fluid intelligence. According to the firm, current machine learning technology is narrow.

Consequently, using trained models for emerging needs requires a significant amount of time and fresh data training. So we need AI that mixes a wide range of information, explores causal linkages, and discovers new experiences by itself.

Artificial intelligence systems have become common not only in everyday life but also in business. They're vital tools in aiding decision-making. That's because their complexity and efficiency provide possibilities of revealing meaningful insights across various uses.

Nevertheless, the wide adoption of such systems requires human faith in their output.

Towards future centric AI systems

IBM holds that people are inclined to trust technology that they understand. That's because they've assessed it and believe in its safety. IBM further insists that users need to know that it's fair, reliable, and safe for users to trust an algorithm.

As such, its R&D department is pursuing different approaches that'll help it build future-centric AI systems. These align with societal values because they're solid, explainable, and accountable. That'll ensure that their ML applications are efficient and fair throughout.

The company is also big on NLP. It sees the information that's central to transforming enterprises as being inaccessible. To that end, it's building cutting-edge AI systems that can break up vast amounts of text to have a wide application in any language.

Moreover, IBM is developing new architectures and devices with vast processing abilities. That hardware is robust and fast enough to handle the massive reams of data we produce daily. These will enable us to reap the full potential of AI.

Elizabeth Kerr

Elizabeth Kerr

Elizabeth is a financial content specialist from Manchester. Her specialities include cryptocurrency, data analysis and financial regulation.