Small AI Is Beautiful – Lots of Data Complements Buckets of Money

A reporter at a major business magazine recently asked CSA Research, “Which of the mega-tech companies won the AI war? Which of them will likely prevail in the battle over the next 10 years?” Our answer was that their users were the real victors – and those users typically run apps from Amazon, Google, and Microsoft. 
Our first reaction was that this question will seem hopelessly quaint very soon, much like asking in 1983 “who won the PC wars?” or in the early 1990s declaring Sears the winner of American retail sales. Stuff continues to happen.
It’s the same in artificial intelligence and those enormous platform companies. In the multi-supplier world of high tech and social media, users of their products regularly and necessarily touch a different part of the AI innovation provided by competing suppliers. And the companies themselves are the first runners-up in the AI wars – a competitive market compels them to keep investing and innovating not only in the same app categories, but everywhere.
The world of artificial intelligence is bigger than this platform battle royale. Much of today’s AI innovation is centered on making computing and communications faster, cheaper, and more accessible – for example, by automating processes that a machine could do more efficiently. That often requires evaluating language in its original form, transforming it for use in other languages or channels, and analyzing what it means. These requirements led to deep analytics and widespread development of machine translation. In fact, the role of natural language processing (NLP) was at the core of the earliest research into machine translation as far back as the 1930s, continuing through the decades with the Turing Test, Chomsky’s syntactic structures, Woods’ augmented transition networks, and dozens of other influential experiments and innovations. 

It Takes Money and Data to Build AI

Of course there’s more to artificial intelligence than these enormous technology providers. Success relies on an entire ecosystem of developers, practitioners, users, and sometimes victims as they create value by consuming and exploiting resources. AI is no different – success comes from leveraging two assets, free cash flow and enormous amounts of data:

• The first asset, free cash flow (FCF), is the money that a company generates after paying to support operations and maintain assets. FCF provides capital for investment in areas of near-term or future opportunity (or simply to disburse in executive compensation or to shareholders as dividends or stock buybacks). Of course, this is cash that in larger enterprises has to be spread across multiple projects, initiatives, spending packages, and other business needs. The amount of free cash flow can be staggering – in 2018, the last full year for which data is available, FCF at Microsoft pushed past US$32 billion while Google parent Alphabet yielded nearly US$23 billion. By way of contrast with publicly traded language technology (langtech) developers and LSPs, SDL returned nearly £32 million in FCF that year, while RWS (not a langtech company) had access to almost £35 million. It can be misleading to compare these smaller companies with the mega-tech firms, but the disparity of the amounts says a lot about how and what the larger companies can do with all that money. 
   
• The second asset is more democratic in a way − enterprises that process enormous amounts of data benefit from what CSA Research labels a “rich and reliable data flow.” This rich data flow is the legacy of big-data initiatives that began in the 1990s, along with the growing and relatively cheap availability of CPUs-on-demand starting in 2006 with AWS and Azure. While the mega-tech companies have leveraged this data into MT and speech platforms, AI bots, and other innovations, the langtech vendors have focused on using the data that passes through their systems to create many optimizations on the source and target content they process. They want to use this data in aggregate along with machine learning to lessen the cognitive load on linguists and project managers, letting them instead concentrate on higher value and hopefully more meaningful tasks.

What AI Means to the Language Crowd