Mini-series: Artificial intelligence and the power and potential of machine learning – Part 1

This is the first of four posts in Mediative’s educational mini-series on ‘Artificial Intelligence and the Power and Potential of Machine Learning’.

Stay tuned over the next couple of weeks to get a full overview of Artificial Intelligence and Machine Learning, what they are, how businesses can benefit, what marketers need to be doing now, and companies that are doing it right.

Part 1: What are the differences between artificial intelligence, machine learning, and deep learning?

What’s the one daily task you wish would do itself? Do you wish your clothes would iron themselves? Or that your car could drive itself? Perhaps you wish your floors would mop themselves? Imagine how much time you could save in a day if even just one of your mundane daily tasks could be automated using Artificial Intelligence (AI). It may sound like a thing of the future, but the future is here. AI is already a reality, and probably without even realizing it, you’re already interacting daily with AI, just not to the self-driving car extremes…yet.

      • We see AI when we use Spotify and get recommendations on playlists and new artists suggestions based on previous interests.
      • AI is in play whenever we use a Facebook, Google or Apple product, or every time we receive a relevant ad when reading an email. When we conduct a Google search it watches how you respond to the results and learns what type of results to deliver in the future. For example, if you search for “dolphins” and click on a result about the Miami Dolphins, Google is more likely to provide results related to the sports team rather than the mammal in future searches regarding “dolphins”.
      • A common AI solution most people are familiar with is Netflix’s recommender tool that suggests new movies based on a viewer’s past movie selections. If you access someone else’s Netflix account, chances are they see a whole different set of movie titles.
      • AI is behind smart thermostats and alerts to mobile phones that the house temperature has dramatically dropped. These notify home owners to a door being left open, a tripped power breaker, a blocked air vent etc.
      • When our email providers filter out Spam, that’s also AI.

Karim Sanjabi, executive director of cognitive solutions at independent media agency Crossmedia believes we probably interact with AI 30 or 40 times a day and may not know it (1).

The concept of artificial intelligence isn’t new, but people often conjure up images of robots from movies when they think of AI, and it has been surrounded by so much hype that some have written it off before its full potential has been realized. However, AI and machine learning (ML) have become really hot topics, seeing great breakthroughs in 2017. Companies that do not have AI on their radar are quickly going to slip behind those that are embracing the existing and emerging technologies, and the fact that the potential for these technologies, both for businesses and individuals, is endless!

“Marketers and business decision makers polled believe AI-powered marketing will shift the role of marketing toward more strategic work (79%) and make marketing teams more efficient (86%) and effective (86%), as well as enabling them to focus on value-generating tasks as AI automates workflows (82%) and reinventing the way that marketers work (82%).” (Source)

Emarsys commissioned Forrester Consulting to conduct a survey of 717 business leaders and decision makers. From the survey, the #1 reason for businesses to take on AI was to drive revenue growth, followed by wanting to serve customers better, meeting rising customer expectations, to remain competitive, and to strengthen their brand.

The differences between artificial intelligence, machine learning, and deep learning

Artificial Intelligence (AI), Machine learning (ML) and Deep Learning (DL) are often used interchangeably, but they are not the same thing. McKinsey & Company defined eight subcategories of AI: natural language processing (NLP), natural language generation (NLG), speech recognition, machine learning (including deep learning), decision management, virtual agents (including chat-bots and digital virtual assistants), robotics process automation and computer vision (used for face recognition, augmented reality, gesture analysis, etc).

So what’s the difference?

According to Dropbox Business:

    • Artificial intelligence is “a machine’s ability to carry out tasks in a way that we would consider “smart.” Today’s AI uses technologies to be able to perform specific tasks as well as, or better than, humans can. (i.e. face recognition on Facebook). These technologies exhibit some facets of human intelligence. But how? That’s where Machine Learning comes in.
    • Machine learning takes “smart” to “intelligent”. It’s the “ability to give machines access to data and let them learn for themselves”. That is, the ability for devices to learn from their interactions with users, service providers, and other devices, using algorithms to analyze data, learn from it, and then make a determination about something. This data, training and inference is what distinguishes ML from AI. Google, for example, arrived at a viable translator because it had billions of pages of other languages to learn from. The size of the data set is a very important part of Machine Learning.
    • Deep Learning is a subset of machine learning, and is based on neural networks in the brain. A comprehensive article on deep learning on Forbes.com states “Deep Learning focuses even more narrowly on a subset of ML tools and techniques, and applies them to solving just about any problem which requires “thought” – human or artificial.” The same article adds that “Deep Learning is used by Google in its voice and image recognition algorithms, and by Netflix and Amazon to decide what you want to watch or buy next.” If we ever get to the point in the future that we have self-driving cars, they would be powered by deep learning, using sensors and analytics to learn how to “drive”. Emarketer defines deep learning as “A branch of machine learning concerned with building and training neural networks with many layers. Each layer of a network can find patterns in the output of the layer above it. Like most other machine learning networks, deep networks shine at sorting and classifying large amounts of complex data and identifying anomalies in data patterns.”

According to a VentureBeat article, “the recent resurgence of AI is driven by the colossal amounts of data that billions of existing IoT [Internet of Things] devices have been generating for decades. Conversely, AI’s role in the IoT market is to accelerate and deepen analytics, parsing signal from noise to enable new services.”

Data is behind everything AI and ML. It’s the ability to harness the vast amount of data available (and by vast, I mean 163 zettabytes, or 163 trillion GBs estimated to exist by 2025, and this will grow ) that leads to AI and ML.

However, even though AI is based on a machine’s ability to learn, they cannot learn unless they are provided with the right inputs. Marketers will play a key role in ensuring those inputs, along with the right business objectives being successfully designed.

The second instalment in this educational mini-series on ‘Artificial Intelligence and the Power and Potential of Machine Learning’, we look at how artificial intelligence, machine learning, and deep learning can be leveraged to benefit businesses.

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

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