Artificial intelligence and machine learning have made exponential progress over the past couple of years. While science fiction often portrays AI as robots with humanlike characteristics, AI can encompass anything from Google’s search algorithms to IBM’s Watson to autonomous weapons.
Leaders of the world’s most influential technology firms such as Amazon, Facebook, Google, and Microsoft are showing their interest in AI and machine learning (ML). Here’s something Google CEO Sundar Pichai said at the Google Pixel phone launch event in October 2016:
The last 10 years have been about building a world that is mobile-first. In the next 10 years, we will shift to a world that is AI-first.
A simple example of AI is the virtual assistant that you use, like Siri, Google Now, and Alexa, which works by recording your voice and uploading the recording data to the cloud, then processing the words and sending back the answer. Everything you say to your virtual assistant is funneled into these data-crunching AI engines and retained for future analysis and improvement.
AI needs big data
All this said, below the surface of all this rapid advancements and business applications lies the most important truth about AI engineering: Nothing matters if you don’t have the data. In order for AI to work its miracles, it’s going to need data. Massive amounts of data. In fact, the bigger the data sets, the smarter the AI.
The big data infrastructure, the deep learning models, and everything else exist to serve the data, not the other way around. AI provides the large-scale analytics needed to extract meaning and benefit from big data, while big data provides the knowledge needed for AI to continue to learn and evolve. To make the most of AI’s potential to generate insights from the data, companies need to first find a way to consolidate and streamline the data. This is why acquiring enough data is one of the most important factors in AI innovation.
Today, it has become easier to develop AI for your business. You have many options for infrastructure, algorithms, and platforms that are readily available that help you to achieve your goal. However, accurate and relevant training data is what sets one apart from the rest. That’s why so many large AI platforms like TensorFlow, Rekognition, Polly, and IBM Watson are more than happy to let you use their services for free, as long as they get to use your data to improve their AI algorithms.
Companies that leverage users effectively
Using data from multiple sources, AI can build a knowledge store that will enable accurate predictions about your user that are based not just on what they purchase, but also on how much time they spend in a particular section of your website — what they buy compared with what they don’t — ultimately getting to know them and what they really want. Here are a few companies that are already leveraging the power of data and AI to achieve next-level growth.
Netflix uses its massive amounts of information to identify user patterns to train their software engine to provide recommendations. Their goal is to stop recommending movies based on what you’ve seen, and instead make suggestions based on what you actually like about your favorite shows and movies.
Uber charges different riders different fares. The new fare system is called “route-based pricing,” and it charges customers based on what it predicts they’re willing to pay. Daniel Graf, Uber’s head of product, said the company applies machine-learning techniques to estimate how much groups of customers are willing to shell out for a ride.
The photos, connections, updates, likes, etc. we all provide Facebook gives them valuable data about who you are, who you know, and what you like. Its investments in AI and big data infrastructure are essentially a means to get you to share more data, and then analyze it to sell ads against it. Facebook’s photo search system wants to do more than just show a user all of your cat photos. It wants to show your friends’ cat photos as well, plus ads and whatever related content it can serve up.
Pinterest started using image recognition with user data to show ads for products that are visually similar to other things a user has searched for or pinned. Deep learning makes it possible to take what you like and show you more stuff like that. That means that instead of using just keywords and other kinds of traditional tagging, advertisers will get the benefit of their ad showing up next to a pinned item that is related to the ad based on its core visual search technology.
The road ahead
These are just few examples of how the top internet companies are making use of big data combined with AI to make powerful decisions that completely change the way their products work. While organizations are focusing on building top-class AI infrastructure for their products, they should also try to capture the relevant and impactful data that fuels the evolution of AI and ML. Data is essential for companies, and it has brought an era of innovation as companies attempt to achieve targeting with hyperprecision.
Many AI techniques are based on having a lot of data the algorithm is trained on to form models that allow it to operate on new data. For these techniques, data is absolutely vital. Performance often has more to do with the quantity and quality of the data than the specific algorithm used for learning. After all, a machine is only as smart as the humans who train it.
Raj Bhatt is the assistant manager for marketing at PromptCloud, a data-as-a-service provider.