Analysis: Google drives the case for cloud adoption: Page 2 of 2
By Edwin Yapp April 7, 2017
AI, machine learning the differentiator?
Arguably one of the potential areas that Google can bank on in making its cloud offering a true differentiator and distance itself from the rest of the field is in the area of artificial intelligence (AI) and machine learning.
Google has been dabbling in AI and machine learning for a long time and has been rolling out some of its byproducts in its consumer software as standard features for some time now.
Machine learning is a subset of artificial intelligence, where software is taught to recognise patterns through the observation of massive data sets automatically, often without being explicitly programmed by humans.
Its goal is to automatically allow computers to take a certain action or draw certain conclusions from a large set of data, and where the decisions made are automatically refined and evolved given more data analysed.
Examples of these can be found in how Google Photos allows users to upscale their low resolution pictures to become better images. Or how Google’s antispam filters powered by machine learning are able to successfully capture rogue emails.
But as much as it has done well in integrating machine learning features into its consumer products, dealing with enterprise problems is a lot more complex than that. This is why Google snared renowned AI researcher Fei-Fei Li (pic, above), the director of Stanford University’s Artificial Intelligence Lab to drive this initiative last year.
In her keynote address on day one at Next 2017, Li said AI had been a long, winding and lofty academic pursuit for her personally and that it had taken a while to see change happening at different scales.
But she stressed that AI has somewhat arrived at an inflection point and that it was Google’s mission to democratise AI – make it available for everyone to use – and in a way that can help overcome a myriad of challenges facing enterprises.
“The next step for AI must be about democratising [the technology] and the lowering of barriers of entry, making it easy for users, developers and enterprises [to utilise],” she argued.
“Google Cloud already delivers applications to billions of users. If you can combine this massive reach with the power of AI, a lot can be achieved.”
Li argued that though AI is a sophisticated field, it’s a lot closer than most people think and can be used effectively in so many verticals today.
Examples she gave included:
- Supply chain optimisation: Where changing demand-supply trends could be predicted over time, routes and inventory can be optimised, and drone and autonomous vehicle deliveries can be made;
- Media consumption: Where personalised news can be suggested and where possibly fake news can be filtered. AI is also being used to weed out offensive advertisements.
- Retail: Where optimisation of shelf space and inventory can be made automatically without human intervention;
- Financial services: Where credit risks and personalised financial plans can be made, nefarious activities can be analysed, and where call centres can be replaced with robo-advisors and chatbots;
- Healthcare: Where automated visual diagnostics can be done, including automated scribing of electronic medical records, augmented surgeries, and the extension of healthcare services to rural and underserved communities.
Li also argued that many enterprises and companies today do not have the resources, budgets, and expertise to make such a leap into the application of AI and that’s where Google comes in.
“Machine learning can deliver, but it’s still a barrier for most,” she declared. “It requires rare expertise and resources that few companies can afford.”
It’s then that she stopped to showcase some of Google’s recent products such as the Google Cloud Machine Learning Engine, a platform designed for companies with data scientists and machine learning experts who are able to build their own unique machine learning models.
She also introduced its new Video Intelligence API, which showcased how it could easily comb through hours of video footages and pick out, say a dog (dachshund), in the video, marking when and where it happened, by seeking to understand the context of the search.
Machine learning also comes into play where security is concerned. In another demonstration on stage, Greg DeMichillie, Google Cloud product chief showcased how images could be automatically redacted in cases where too much information is shared accidently.
Dubbed Google Data Loss Protection (DLP) API (application programming interface), the software helps users minimise what data they collect, expose or copy.
For example, it can be used to automatically classify or redact sensitive data from a text stream before you write it to disk, generate logs or perform analysis. The programme can be used to alert users before they save sensitive data in an application or triage content to the right storage system or user based on the presence of sensitive content, according to Google.
Analysts weigh in
Overall, analysts Digital News Asia (DNA) spoke to noted that Google’s second cloud conference had turned out better than expected, and that it had made genuine progress under Greene’s leadership.
Ovum’s principal analyst 
Clement Teo (pic, right) told DNA on the sidelines of Next 2017 that Greene’s stewardship seemed to have stabilised the company’s cloud computing arm and made it more enterprise centric.
“Greene’s pedigree as an enterprise executive has helped Google hone itself better as an enterprise provider,” he opined. Teo said she has streamlined the parts of the companies that need to interface with large enterprises and created the CTO office, which helps to put everything enterprise under one roof.
That said, Teo concluded that Google still lacks the big names signing up for the entire Google Cloud stack, comprising networking, storage and compute.
“They can argue that there have customers on G Suite but where is the real meat? Customers should run their apps on their entire stack [compute, network and storage],” Teo told DNA earlier at the conference. “Right now, they’re not really there yet. I’m not saying they can’t get there but it’s still very early in the journey for them.”
Gartner’s research director Michael Warrilow added that Google's experience with education, media and marketing is now evident.
“So they need to continue to build-out their capabilities for other business and government organisations, and they also need to make good on spreading their data centre footprint in Asia, where they are still not as strong as their rivals,” he said, adding that Google's business presence in Asia is still very much perceived to be weak but customers big and small.
Asked if AI and machine learning can be a differentiator for Google against its competitors, Warrilow said both technologies are a key battleground for the big cloud providers, including AWS, Microsoft, Google and IBM.
“Google brings its pedigree in computer science and computer engineering to the market on a global scale, and that could be a differentiator,” he said.
Forrester Research senior analyst Naveen Chhabra believes that AI and machine learning will have profound impact on all industries, the space is going to be a “four-horse race.”
“AWS, Microsoft, Google and IBM are establishing themselves in this space and there isn’t much between them for now,” he said. “For Google, it’s certainly going to establish them as the tech leaders in the new age digital businesses.”
Analysts did however note that Google will have to contend with customers running a multi-cloud strategy, and that they would not just go all-in with one cloud provider given that different providers will have different strengths and weaknesses.
Chhabra explained, “Multi-cloud is a reality, and in fact in our research finds that many large enterprises are already doing that. What I would advise customers is to leverage Google’s expertise to build highly resilient systems and applications, as that will help the enterprises differentiate and win market share.”
An Asean customer in the healthcare space who spoke to DNA on condition of anonymity said, “Google probably has the best technology and pedigree insofar as innovation is concerned. But as a customer who needs to make every part of the cloud work for us, there are still gaps in its feature sets and integrating them can be a challenge.
“For now, AWS has the most complete building blocks for us to use but we’re confident Google will catch up, given time.”
Edwin Yapp reports from Google Next 2017 in San Francisco, at the invitation of Google Inc. All editorials are independent. He is contributing editor to Digital News Asia and Asean analyst at Tech Research Asia, an advisory firm that translates technology into business outcomes for executives in Asia Pacific.
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