Mini-series: Artificial intelligence and the power and potential of machine learning – Part 4

If you missed the earlier posts, this is the final instalment in Mediative’s educational mini-series on ‘Artificial Intelligence and the Power and Potential of Machine Learning’. Previous posts looked at:

    1. What are the differences between artificial intelligence, machine learning, and deep learning?
    2. How can artificial intelligence, machine learning, and deep learning be leveraged to benefit businesses?
    3. What should marketers be doing today to harness the power of AI, ML and DL?

Here we look at examples of companies that are doing AI and ML right.

Part 4: Examples of businesses making use of AI and ML

Walmart

Did you know, Walmart.com has 60 million items for sale? To manage tasks like organizing inventory data, pricing items, and fixing problems, the company uses machine learning algorithms. If sales for a particular item decline, an algorithm can spot the anomaly and identify what caused it. For example, a clerical error may have led to a change in the price causing the decline in online sales.

Walmart’s size means it has a massive amount of high-quality data, an advantage if you’re trying to leverage machine learning technology.(Source)

Tesla

Tesla leverages IoT and AI for “ecosystem intelligence”. Their entire fleet of self-driving vehicles learn from each other. The experience of one Tesla is transmitted to and “learned” by all other cars in the network. (Source)

Auto Trader

Auto Trader uses machine learning to provide better vehicle valuations. Mohsin Patel, principal database administrator at Auto Trader says One way we are doing this is by making algorithm-based data decisions around the value of a car, and also introducing machine learning to help understand differences between features [like a satellite navigation system], and also recognize data which may not necessarily be a tangible feature. We have to teach our systems depending on what features a car might have, or what derivate of a car the customer is viewing as the price could differ, and that’s where the [machine] learning comes in.” (Source)

Google

Google is also using deep learning in many areas of their business, including more accurately measuring store visits, in an announcement for a major update to its store visits conversion tool in May 2017. The tool determines the number of users who clicked on a search ad and then visited a physical store by using machine learning models that process hundreds of first-party location signals and aggregated, anonymized data from users who have opted in to activate their location history. In fact just anonymizing all that data to protect Google users is in itself a huge AI feat. You go Google!

According to TechCrunch, “Google has gotten fairly good at using wifi signal, location, mapping and calibration data to estimate store visits, but the company still struggles to deliver insights to customers that operate in dense cities and multi-story malls. Long tail use cases like these elude traditional estimation techniques. To address the unreliability, Google is turning to deep learning. Its hope is that it can restore accuracy by funnelling a greater amount of diverse training data into a deep learning model to account for more use cases.” (Source)

Shell Oil

In 2015, Shell Oil became the first company in its industry to develop a virtual assistant, using natural language interaction to let customers and distributors type product-related questions into an online messaging window and get answers from avatars named Emma and Ethan.

The service responds to queries 
about where to buy lubricant products, what sizes are available
and information related to safety and technical specifications. Shell reported the service can also make product recommendations and coordinate with other customer-focused services. (Source)

Facebook

In November 2017, Facebook announced that it was using AI technology to detect suicidal posts in an attempt to help save lives. The technology scans posts for patterns of suicidal thoughts, and, if necessary, can send mental health resources or friends to the user. According to TechCrunch, “Facebook trained the AI by finding patterns in the words and imagery used in posts that have been manually reported for suicide risk in the past. It also looks for comments like “are you OK?” and “Do you need help?”

Conclusion

Artificial intelligence is already part of our daily lives, at home and at work, but its full potential is only just beginning to be realized. While AI can introduce a level of risk and uncertainty to organizations, it also represents a huge opportunity to drive organizational efficiencies at scale, reducing the need for human intervention, and the natural errors that occur with human involvement. With the staggering amounts of data available, and the potential that it represents, it seems absurd that any company wouldn’t want to harness that in some form.

Are you harnessing your data for AI?

Download the eBook: “AI and the Power and Potential of Machine Learning

Source:

      1. Artificial Intelligence for Marketers 2018: Finding the Value Beyond the Hype, eMarketer, October 2017
      2. Artificial Intelligence for Marketers: The Future is Already Here, October 2016
Rebecca Maynes
Rebecca Maynes is Mediative’s Manager, Content Marketing and Research. Her expertise lies in the creation of engaging thought leadership for Mediative. From compiling eBooks and case studies, to conducting research, analyzing data and writing white papers and reports, Rebecca is an integral part of Mediative’s Marketing and Research team. Rebecca began her career with Yell.com in England, and, after emigrating to Canada in 2005, she has gone full circle, joining Mediative, a Yellow Pages Group Company, in 2009. Prior positions include Marketing for a B2B Software company. Rebecca graduated from Cardiff University in Wales, UK, with a First Class Honours BSc in Business Administration.