Are Google Fast Approaching in the Artificial Intelligence Race?
With the race on to produce the best forms of artificial intelligence, the names that are always in the forefront of people’s names are Amazon, Microsoft, and IBM. But, what about Google?
With the race on to produce the best forms of artificial intelligence, the names that are always in the forefront of people’s names are Amazon, Microsoft, and IBM. But, what about Google? Although Google is well known for their artificial intelligence expertise, we as consumers do not always get to see it. Let’s take the Google Cloud platform, for example. It is far behind in both the capabilities and earnings of both Amazon and Microsoft’s cloud platforms.
However, now it looks like finally, Google are ready to start revealing some of that artificial intelligence expertise they have gained over the years. In March this year, Google announced that they would bring machine learning, big data, and analytics to the Google Cloud Platform which will allow developers to build a whole new class of intelligent applications.
A recent new feature that has been added to Google’s cloud platform is the Natural Language API which allows users to reveal the structure and meaning of a piece of text or even speech in a variety of languages including English, Spanish, and Japanese. However, if Google wants to keep up in this race, then they will have to keep moving as both Microsoft and IBM have been working on ideas to strengthen machine learning and analytics.
Google certainly have the resources and expertise to get to the front of this race, but whether they will do it remains to be seen. But, one thing is for sure is that if they do not at least keep pushing forward in the area of artificial intelligence, then they will find themselves dropping out of the race altogether.
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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