Top MNC's using AI & Machine Learning

Google

Google is regarded by experts to be the most advanced company in the field of AI, machine learning and deep learning.

The main reason for this is probably the amount of money the company has spent acquiring startups Google has spent more than any other, according to CB Insights.

Whether it’s something it acquired or developed in-house, Google last week launched an AI chatbot which answers your messages for you like a more sophisticated auto-response email in a range of contexts, including Skype, Slack and Twitter direct messages.

But Google’s strongest point in this area is probably the range of cloud-based services it offers developers, including the Google Cloud AI machine learning tools.

It’s worth noting that Facebook, Microsoft and others are encouraging the development of chat-bots for use in their messenger apps.

And although chat-bots have had some bad publicity for using profane language and displaying signs of burgeoning bigotry, chat-bots can be useful in filtering out spam and, er, bigotry, ironically enough.

There are, of course, other companies offering similar services, and we will mention some of them below. But, without evaluating them on a technical level, we can’t really say if one is better than the other it’s just our perceptions about a range of factors.

Apple

There might be a perception that Apple is late to the machine learning party, but that’s probably not true, especially since it was the first to launch a voice assistant on a smartphone.

Millions of people talk to Siri, even if we don’t, and Apple is looking to extend the application of the talking assistant through its new smart home device or speaker, the Home Pod.

Amazon’s Alexa-powered Echo devices and Google Home Assistant for competition, Apple has its work cut out.

But if these companies do take the competition seriously, the inevitable result will be the technology improves and the end user benefits.

Apple has also been active in acquisitions — second only to Google. One of the more notable purchases has been Lattice Data, which has a machine learning system for converting unstructured data — like random text and pictures — into structured data.

Apple paid $200 for Lattice Data, according to 9to5Mac.com.

The company has also allocated significant resources for the development of in-house machine learning systems, many of which are available through its developer program.

IBM

A long time ago way back in the 1990s IBM challenged Russia’s greatest chess player, Garry Kasparov, to a match against its Deep Blue computer.

Kasparov trounced Deep Blue in the first match, but then the Russian grand master raged against the machine after losing to it in subsequent contests.

He probably still hasn’t got over it, claiming there was something amiss in the way IBM played. Others, however, have urged Kasparov to play along and admit defeat, but he still refuses to do what he’s told.

The successor to Deep Blue is the famous Watson AI computer, which beat the best contestants on a US television quiz show called Jeopardy!.

Watson Machine Learning is available much like Google Cloud AI machine learning is, but being elitist, IBM probably charges more.

And IBM’s chess challenge has been usurped in terms of media hype by the recent human-vs-machine contest over an ancient board game called “Go”, which was won by the machine, of course.

In this instance, the machine or algorithm was developed by Deep Mind, which Google bought a few years ago for about $525 million.

Microsoft

Microsoft has actually been the third-biggest spender on acquisitions over the past five years, according to CB Insights.

The company is well and truly into the internet market, especially after its $26 billion purchase of LinkedIn a couple of years ago.

LinkedIn may provide the best platform for Microsoft to showcase the enterprise applications it develops based on machine learning.

But probably the most significant acquisition Microsoft made in the machine learning space was Maluuba, which the tech giant says has “one of the world’s most impressive deep learning research labs for natural language understanding”.

Voice computing does seem to be the key area of competition between tech giants because success in this field would open up more than just voice search. It would make typing and many other tasks unnecessary.

Intel

Data processing for artificial intelligence or machine learning applications is said to be slightly different from other types.

Whatever the details, experts say that a new generation of chips being built will be more capable of running AI and machine learning apps.

Still the largest chip maker in the world, Intel clearly does not want to miss out on this nascent market, and has been active in acquisitions.

One of the largest purchases Intel made in the past few years is the $400 million acquisition of Nervana Systems, which build chips for data centre servers.

Nervana chips are said to be able to transfer data in and out at 2.4 terabytes per second at very low latency. That is supposed to be five to 10 times faster than the fastest input-out interfaces for traditional chips, according to a report on Forbes.

Similarly fast chips for AI have been released by Google and NVIDIA, among others, so this could be a critical area of competition for Intel. Amazon is also said to be developing a custom chip for its Alexa AI system, according to TheVerge.com.