At the recent Code Conference, there was a lot of talk among industry insiders about how artificial intelligence (AI) will advance the adoption of new technologies and solutions. IBM CEO Ginni Rometty predicts that, “Within five years, cognitive AI will impact every decision made.”
Although AI has been around since the 1960’s, advances in graphic processing units and networking, along with the demand of big data, have put it back into the forefront of many companies’ minds. Given the explosion of data from applications and Internet of Things (IoT) sensors, and the need for real-time decision making, AI is quickly becoming a key requirement and differentiator for major cloud providers.
As a result,he adoption of machine learning in the enterprise may be closer than predicted as leading cloud providers are making AI more accessible “as-a-Service” via open source platforms. According to the Financial Times, AI in the cloud is “the next great disrupter” and opens up opportunities for businesses to create powerful new AI applications fast, without building the tools, infrastructure or expertise in house.
Here are some examples of AI services in the cloud that are now available:
- Amazon’s in-house AI expertise, such as for predictive analytics, is available on Amazon Web Services (AWS) via its Machine Learning Service. Amazon is also releasing as open source software the Deep Scalable Sparse Tensor Network Engine (DSSTNE), which drives Amazon’s customer recommendation capabilities, such suggesting the types of books you may like to read or movies that you may want to watch.
- Google Cloud Platform offers a number of home-grown AI capabilities, such as predictive analytics, speech recognition, translation and image content identification. Google also offers its Tenssor Flow recommendation software library, similar to Amazon’s DSSTNE, through an Open Source Apache license. Google recently announced Springboard, which helps enterprise customers leverage Google’s AI-based search interface to quickly surface information within the Google products suite. In addition to offering the platform, Google is able to leverage its other products to improve its AI. For example, the more pictures that Android users take of cats that are uploaded to Google, the better Google’s model is for identifying cats.
- Microsoft currently offers its Distributed Machine Learning Toolkit to allow users to run multiple and varied machine-learning applications simultaneously, such as analyzing images and using Microsoft Computer Vision and language comprehension.
- IBM’s Watson Developer Cloud enables developers to incorporate Watson intelligence in their apps and provides its Watson AI engine as an analytics cloud service.
Consider the following complex problems in the transportation industry. Shipping companies, such as FedEx and UPS, want to figure out the most efficient and cost-effective way to deliver the most packages. Public transportation organizations need to identify city traffic patterns to keep vehicles moving without creating gridlocks. From analyzing how to fit the maximum number of packages in a delivery van, to calculating and navigating the fastest routes to deliver those packages, multiple technologies such as the IoT and big data analytics require AI to solve these complex problems.
Also, the lessons we learn from the commercial use of AI can be applied into the day-to-day operations of the enterprise. For example, analyzing traffic patterns to determine the most efficient delivery routes can be leveraged to develop optimized enterprise networks. The principles behind learning how to efficiently pack a delivery van can be applied to IT optimization and how to best organize and distribute workloads to reduce the number of on-premises servers or more efficiently make use of cloud resources.
When people think of AI, they tend to think of “human-like” or “general” intelligence. And while that may be possible in the future, today’s platforms and models are fragmented and capable of solving only very domain-specific problems. So for enterprises with various complex problems to solve, it requires multiple services from disparate platforms working together, which is why making AI technology and applications available via open sources is so critical to the enterprise. By leveraging multiple AI cloud services, companies can innovate solutions to solve an infinite number of complex problems.
With more than 500 cloud service providers making Equinix their home, we see our role as an intersection point for these cloud-based AI platforms because we make them accessible to our customers through direct and secure interconnection.
Look for future posts that will explore the exciting opportunities for the enterprise to harness AI-as-a-Service.