How Influential Businesses Like Google Use AI and What We Can Learn
How Influential Businesses Like Google Use AI and What We Can Learn
It’s not an exaggeration to see the democratization of AI as the huge game-changer that technologists are suggesting it is. We are limited only by our imaginations.
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AI is a hot buzzword right now, and businesses of all shapes and sizes are trying to find a way to leverage its power for their own particular needs. Exciting, futuristic, and vaguely science-fictiony, AI is currently seen as a must-have tool for turbocharging one’s business and improving the quality of customer experience. It’s been a game changer for enterprise companies like Google, Microsoft, Amazon, and IBM who have the computing power and resources to access field experts. Small companies often aren’t in the same ball game, yet there remains a need to get into the action if they are going to keep up with the times. Fortunately, cloud computing and open-source machine learning APIs are bridging the technical gap for smaller companies, making it possible for them to take advantage of the endless possibilities offered by AI.
More than ever, this is the time for small businesses to jump in and start experimenting. The real challenge, however, is knowing how to utilize these new resources well. Here again, small businesses can look to larger enterprise companies such as Google to gain insight and build on their ideas. Here’s what we can learn!
Democratizing AI: An Open World
This move to democratize AI is a huge game-changer for businesses. Two of the major efforts have come from Google with TensorFlow and IBM with its Watson developer tools. Paul Chong, director of Watson Group, EMEA, explains:
“We want to get to this stage where you simplify the use of the technology to the point where you actually put it in the hands of the business owners. So we're creating the cognitive platform to be the API economy of choice for you to build cognitive systems. We’re making this open.”
The Verge describes it best when they wrote that it’s “like the difference between a command line interface and a modern desktop OS” — just as the move from DOS to graphical user interface computing was a sea change that massively opened up the platform, drag-and-drop AI implementation will be similarly revolutionary.
So this is great news for all of us who are smaller than Google, right? "But why the charity?" you may ask. On one hand, these companies do see themselves as having some philanthropic responsibility to make the world a better place. But on the other hand, they also benefit from the access they will gain to a universe of even more data. Both factors were addressed at this year’s Google Cloud Next keynote. Dr. Fei-Fei Li, the chief scientist of artificial intelligence and machine learning at Google Cloud, explained:
“We can witness a greater improvement in quality of life than at any other time in history. This is why delivering machine learning and AI through Google Cloud excites me.”
Eric Schmidt, executive chairman of Google’s parent company Alphabet, added:
“We are seeing people are using big data to do customer analysis, pattern matching analysis, and customer targeting that really does produce extra insights. And that extra insight is worth billions.”
Furthermore, historically, dominating a platform has always led to dramatic success. As Tom Simonite explains:
“Microsoft became the giant it is today because Windows was the place developers went to reach PC users; Apple is the world’s most valuable tech company because the iOS app store made the iPhone into a smash hit.”
Straight-up charity would be a little suspicious in this customer acquisition race to become the biggest AI provider. But as we can see, the democratization of AI is proving to be a win-win for all who get involved.
Image Recognition: Seeing Further
Image recognition is a key skill available on AI APIs right now. Google Cloud Vision is able to determine the content of images for a range of uses:
Identification and flagging of inappropriate content.
Identification and reading text in images.
Detecting, but not identifying, faces.
Isolating dominant colors.
Suggesting cropping for artistic applications.
Searching and finding similar pictures on the web.
Many companies are coming up with creative ways to put these skills to use. For example, Ocado grocery delivery service in the UK has begun using AI vision to scan barcodes and recognize grocery items in order to speed packing processes. Image recognition has been valuable for French multinational energy firm Engie as well as Minnesota’s Department of Transportation, which uses a combination of drone and AI image processing technologies to inspect infrastructure and aid preventative maintenance.
The ideas are endless for web apps, as well. Say, for example, that you run an app or website where you host user photos as feedback from their experience such as showing off their purchases, experimenting with beauty or fashion, or pictures from travel. With AI vision, an enormous number of photos can be processed for display for other users — they can be sorted into meaningful groups, filtered for image quality or unsuitable content, plus other invaluable features like blurring out trademarked logos. Another business model is one where users communicate their needs to an app via photos, say a photo of an outfit one would like to buy, the kind of food one feels like eating, or the kind of place one would like to vacation at.
And just imagine the health and medical industry potential here! Artificial intelligence and machine learning will undoubtedly bring about tremendous developments and technological aids for individuals with vision impairments.
Given that AI is open-source, there’s really no reason not to be benefitting from this rich visual context and applying these developments to their businesses.
Conversational Spaces: Engaging Environments
Natural language understanding (NLU), natural language processing (NLP), and automated speech recognition (ASR) have also seen impressive improvements and progress in the last year. Frameworks are being intelligently designed to analyze both written text and audio files with human-like precision and understanding. Although it’s not perfect yet, companies like Google are making great strides. Both Google Cloud Natural Language API and IBM Watson Natural Language Understanding offer the ability to deliver actionable insights on product reception or user experience by extracting meta-data and determining sentiment and emotion from content, all of which they can do in multiple languages.
This wealth of information can be scraped from social media conversations, messaging apps, or conversations at call centers. For example, our friends at UNDER/TONE in Quebec City are tackling the linguistic aspect of brand voice and tone and developing new frameworks that address contextual personalization nuances in an AI context. The opportunities are endless and smaller businesses are racing to implement these conversational agents into their models.
For example, firms in the UK are charging ahead by putting NLP to work for them. The National Health Service is trialling chatbots to take over the 111 helplines servicing the 1.2 million people in North London. The chatbot will help them determine the urgency of their symptoms, which will have an enormous impact on the health industry. Also, the Royal Bank of Scotland has begun using a chatbot “Luvo” to answer RBS, Natwest, and Ulster customer questions and perform simple banking tasks like money transfers. It only hands customers over to staff if it’s unable to answer. Grocery delivery service Ocado has had success improving customer service by processing their large number of feedback emails with AI.
And the Las Vegas Sands Corporation, which owns hotels such as the Venetian and Palazzo, uses a Facebook bot as a concierge, giving guests more immediate access to in-hotel requests, questions, and service. Jonathan Catling, former Director of Global Data Architecture with Las Vegas Sands, offers his valuable insight, saying that customers don’t want to communicate via text or email for these kinds of needs:
“The conversation is the key to the customer, they’re not interested in the technology but the chat.”
The way that these businesses implement conversational AI is broadly applicable, as customer service, customer experience, and client relations are major components of every business. Small businesses should make the most of these conversational technologies and use them to gain better user insight, increase their value added, and foster a community around their brand in a way that’s relatable to ultimately solidify their brand loyalty.
Locational Detection: Wherever and Whenever
Location and context APIs use data provided by mobile device sensors to make it possible to determine a user's current actions and environment. Google’s Awareness API has made it a priority to provide these services while respecting user privacy and reducing battery drain. Within the field of location and context detection, the data points collected include:
Location (both geographical, i.e. GPS, and contextual, like “Starbucks” or “park”).
Activity (like running or driving).
Beacons (nearby registered businesses/entities).
Headphones (in or out?).
Location and context information provides a number of advantages both to the user and businesses. Users can access info about anything in their vicinity, record their own data (such as with fitness apps), find and communicate with nearby devices, and receive contextually relevant notifications. Businesses now have new opportunities to connect and re-engage customers and potentials in their vicinity. Plus, they can make their product or service offerings more contextually relevant to the user, thereby increasing the likelihood of customer satisfaction.
Imagine that you’re a tourist in a new city and you’re suddenly caught out in the rain when you get a notification on your phone telling you that the cute little shop around the corner is offering a sale on umbrellas. Or perhaps you run a recreational app, and your location and context AI gives you the ability to suggest games or send notifications to the user when you detect a combination of contextual factors (like the user is sitting, at home, and it's the weekend). In addition to these great new contextual personalization opportunities, businesses will also gain invaluable insight from the raw user data collected by device sensors.
You see, there’s a wealth of opportunities made available to us today, and I encourage everybody to start thinking about how artificial intelligence can add value to their businesses. I hope this post has left you with the same feelings of excitement and inspiration as it has for the Arcbees team. I think it’s not an exaggeration to see the democratization of AI as the huge game-changer that technologists are suggesting it is. Truly, we are limited only by our imaginations when putting these skills to use, and as the abilities become better refined and more skillfully implemented, the range of possibilities for start-ups expands right along with it.
So, how could your business benefit from the artificial intelligence advancements made by large corporations such as Google? Let me know in the comments below or on Twitter.
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