Talk of artificial intelligence (AI) and the cloud is not simply the work of technologies that are desperate to combine their two favourite topics into one conversation.
Let’s pretend for a second that the cloud computing and AI industries are mutually exclusive. The global AI market is expected to be worth almost $60 billion by 2025. Standing at around $2.5 billion at the end of 2017, it is one of the fastest-growing markets in the world today. On the other hand, the cloud industry has already trodden the path from hype to broad adoption and is a different beast altogether.
The public cloud industry alone already stands at over $200 billion and is forecast to be worth over $1,250 billion by 2025. So, with the cloud industry propelling its growth, could the forecasts about AI adoption ultimately prove to be conservative?
It’s 25 years since Richard Stallman wrote the GNU General Public License that spawned a generation of open source software projects. Open source and free software enabled the likes of Google and Amazon to create vast server farms, at a cost that wasn’t simply possible if they had to pay licensing fees. AI is finally taking off, and this is in no small part due to the cloud.
The cloud is fundamental to the AI model in two ways: First, given that the data sets these companies are using would not be accessible if it was not for the cloud, and secondly, because only the cloud can enable businesses to cope with the phenomenal scale required by providing such data-intensive services to multiple clients at an affordable cost.
Of course, one of the biggest factors holding AI back from reaching critical mass is the shortage of people within enterprises with the skills to programme it. This means that while businesses may know how they want to use AI, they don’t have the means of building an application or algorithm to produce the results they crave.
>See also: Big data (and AI) in the enterprise
The cloud changes this as it means that years of research and tools are available to developers tasked with creating AI solutions. This can completely change the way businesses scale as those startups were founded by incredibly smart people that are building new and exciting AI functionalities and have infinite resources waiting to be drawn upon in the cloud.
There are some success stories out there in the startup world already using AI to find new solutions to existing problems. Veritone has developed an operating system for AI using a cloud-based cognitive computing platform which analyses a vast number of datasets from different sources. Veritone believes that the full potential of its “cognitive cloud” platform will only be unlocked when it is open to all businesses, institutions, and individuals (as right now 80% of the world’s data is unreadable by machines).
Another example of a company that has scaled rapidly thanks to the combination of AI and the cloud is Quantifi, a company using analytics software based on AI and machine learning to optimise digital advert placements for brands. As well as the ability to analyse datasets at a rate of knots, this model unleashes the ‘test and learn’ capabilities of AI and the cloud.
Quantifi clients can harness the power of data which has been collected from thousands of other digital ad experiments, which means they can deliver results quickly and grow at scale. This would not be possible without the cloud, enabling Quantifi to continually add new information to its existing pool of data.
These examples show how AI can be used to make sense of the huge amount of data out there and companies with the skills to do this can scale incredibly quickly as they are generating results which other businesses want but can’t make themselves.
Alexa, take me to the cloud
As well as startups using the data resources of the world to create new revenue drivers through AI and machine learning, the big four cloud hyperscalers have all declared an interest in AI during the last couple of years.
AI requires a huge amount of compute power, so the public cloud, with its near-infinite computer and data processing power, is the ideal place for such applications to be built. The aim of companies such as Amazon, Microsoft, Google and IBM is to create innovative AI applications that businesses can use; therefore, driving increased traffic through their public cloud ecosystems.
The explosion in investment that there has been for the hyperscalers in AI is almost definitive proof that the technology is inextricably linked to the cloud. IBM Watson’s natural-language searches have been used to develop cognitive retail as well as DNA analysis in cancer patients.
Furthermore, popular voice-recognition solution Amazon Echo has made the leap from the kitchen table to the enterprise R&D lab. Partnerships with the likes of Hive and Nest mean that you can use Alexa to turn your heating up or down, and later this year Toyota drivers will be able to ask Alexa for new updates, build shopping lists and control connected smart home devices from their vehicle.
The number of companies innovating with these AI-based platforms speaks dividends to the desire to invest in the capabilities of cognitive technologies. This is an example of the cloud going some way to solving the big problem for AI, which is that not enough people have the skills to use it. The second major challenge, alluded to earlier in this piece, is that of unstructured data.
When dealing with machines, the quality of the analysis and the outcomes that fall out of it depend on the quality of the data you feed into the algorithm. Solutions such as Talend’s cloud and big data software allows businesses to maintain the quality of their data from input through to insight. Given the needy nature of AI – businesses using it must have a robust cloud strategy as well as the software required to optimise it correctly.
Sourced by Ciaran Dynes, senior vice president, Product, Talend
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