Artificial intelligence (AI) is not a new term. It has been around for years and first used in the mid-1950s. Since its inception, AI has been successful in enabling computers to perform some tasks that are normally done by humans.

Since AI became more prevalent in the last few years, there are myths around this technology. The idea of machines learning and making decisions like the human brain is itself seen as the biggest threat. Scientists around the world have been warning about the dangers of AI for ages.

The first such claim was put forth in 1958 by Herbert Simon and Allen Newell. They wrote, “There are now machines in the world that think, learn, and create. Furthermore, their ability to do these things will rapidly increase until – in the visible future – the range of problems they can handle will be coextensive with the range to which the human mind has been applied.”

Myth #1: AI is smarter than people.
There is no intelligence without the human brain. The people who create the algorithms and provide information to it make up the AI. To build and teach it, you need to feed information. AI is as smart as you program it.

Myth #2: AI will make medical diagnoses
Medical professionals use technology for efficiency. A radiologist who is an expert in the evaluation of X-rays, CT scans and other medical imagery will use AI for easy primary level of diagnosis. However, a human doctor will be the one determining a diagnosis and making medical decisions.

Myth #3: Modeling determines the outcome
AI initiatives begin as test projects. You may get excellent results during the testing phase but the final results come after you deploy it to production. Training your AI model is never complete. As you feed more data to the model, it evolved and accuracy goes up.

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As the use of machine learning has grown, so has its reputation — including expectations that can be overblown or even inaccurate. These are the “myths” of machine learning.

Let’s start with a definition.
Machine learning is the use of computerized algorithms to analyze large amounts of data; for the machine to learn from this data; and, to make predictions and continually apply learning to new data — all accomplished faster and more efficiently than humanly possible.

In a marketing context, machine learning identifies what outcomes deliver better performance, measured in clicks, leads, sales and revenue.

While the results are real, some hyperbole is not. Here are seven common misconceptions on working with machine learning – plus strategies to optimize your efforts.

Myth One: You do not need a clear objective.
The most basic: start with a business objective, a reason for leveraging machine learning. What do you want to achieve or solve? The objective is the way to tell the machine what it needs to learn.

Myth Two: You do not need to form a hypothesis.  
Let’s clear this up early: simply loading bundles of data into your marketing platform is not an effective strategy. A more logical starting point is to form a hypothesis. More granular than the objective, the hypothesis is an assumption that you want to test against alternatives. Of course, one beauty of machine learning is the ability to test multiple assumptions and alternatives simultaneously.

Myth Three: You do not need to calculate sample size and test duration.
Like any type of marketing analytics, the sample size must be large enough to have confidence in the statistical significance and the performance results. How long does this take? The answer depends on the amount of data, number of variables, and the degree of consumer response – and, ultimately, the “learning curve” from the machine itself.

Myth Four: Eventually machine learning will determine a one-size-fits-all winner.
A tough one for marketers with backgrounds in disciplines like direct mail, where, in simplistic terms, you are working to determine a new control. Think differently regarding machine learning, with emphasis on targeting, personalization and experience. With the machine’s ability to ingest consumer attributes and test multiple experiences, the goal is to determine the best outcomes for each customer type, not a one-size-fits-all experience.

Myth Five: Machines can learn to target immediately.
Building from your hypothesis, think of the first phase of machine learning akin to a random test. By serving different experiences, the machine learns what consumer attributes and factors correlate – and what is effectively engaging customers. This experimentation takes time, while the machine learns and targeting capabilities improve.

Myth Six: Machine learning takes the place of random A/B split testing.
In the world of machine learning, there is room for both A/B and multi-variant testing. A/B testing may be all that is required when decisions are simple, data is not available in real time or you simply want initial insights before starting more complex testing.

Myth Seven: Machine learning can always outperform.
Machine learning often delivers amazing insights and outcome – but it doesn’t always achieve more. The quality of inputs is critical to achieving performance outcomes. Four areas where machine performance may go amiss: input attributes are not relevant; too many attributes prevents statistical significance; target audience is too homogeneous; and, creative execution is not relevant to targets.

One last myth: machine learning will eventually replace the need for marketing and analytics experts. Quite the contrary: machine learning simply enables us to be more strategic and empowered as we make very human decisions on products, media, positioning and customer experience.

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Very few subjects in science and technology are causing as much excitement right now as artificial intelligence (AI). In a lot of cases this is good reason, as some of the world’s brightest minds have said that it’s potential to revolutionize all aspects of our lives is unprecedented.

On the other hand, as with anything new, there are certainly snake-oil salesmen looking to make a quick buck on the basis of promises which can’t (yet) be truly met. And there are others, often with vested interests, with plenty of motive for spreading fear and distrust.

So here is a run-through of some basic misconceptions, and frequently peddled mistruths, which often come up when the subject is discussed, as well as reasons why you shouldn’t necessarily buy into them.

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Finally, after many long winters, spring has come in the field of Artificial Intelligence (AI). Experts believe it’s the “new electricity”. AI influencer and Guru, Andrew Ng has said – “this time the hype around artificial intelligence is real.” Its ubiquity was evident in this year’s Consumer Electronic Show (CES 2017) where “its impact was all-pervasive and its presence could be felt throughout the show”.

Artificial Intelligence is the driving force behind remarkable advancements in speech recognition, computer vision, language translation, search engines, recommendations systems and many other applications. In certain specialized tasks and complex games, AI has beaten experts. A widely cited example in this category is that of AlphaGo, a deep learning based system from Google’s DeepMind, that has defeated the world champion Lee Seedol in a 2,500 year old Chinese board game, Go. AI based autonomous driving technology is making rapid progress and hopefully, within a decade, we may find large scale deployment of fully autonomous vehicles. AI enthusiasts have now valid reasons to believe that this time, spring will be eternal.

The resurrection of AI in recent years can be attributed to significant developments in machine learning systems, especially in one of its sub-field called – deep learning. Machine learning imparts “computers the ability to learn without being explicitly programmed”. Deep learning is a class of machine learning algorithms that use deep artificial neural networks with multiple hidden layers.

While evolution in machine learning drives the current AI boom, the hype has caused certain misconceptions around the capabilities of these systems. Some of these misconceptions have risen to the level of myths. In this article, we discuss three such common myths around machine learning.

Myth#1: Machines can learn autonomously

Reality: Machine learning is orchestrated by programmers who design the machine’s learning architecture and feed it with the necessary training data.

Most of the machine learning algorithms require large amounts of structured data. Programmers decide the learning approach (e.g., supervised learning, unsupervised learning, reinforcement learning etc.), the learning architecture (e.g., the number of layers of artificial neural network and the number neurons per layer), the learning parameters and the appropriate training data as per the system’s design. In many applications of machine learning, the human effort is enormous.

For example, consider the case of autonomous cars. An article published in Financial Times highlights how self-driving cars are proving to be “labor-intensive for humans”. It talks about how humans are putting significant efforts behind the scene to painstakingly label and tag different objects in the captured images for the training purpose. Sameep Tandon, the CEO of Drive.ai has been quoted in the article, saying – “The annotation process is typically a very hidden cost that people don’t really talk about. It is super painful and cumbersome.”

Myth#2: Machines can learn like humans

Reality: Machines are not even close to the way chimpanzees learn.

However, hype is taking precedence over reality – that’s why we find tall claims in some of the articles creating an impression that AI algorithms “can learn like a human”!

If we compare the learning process of a machine with that of a child, it becomes evident that machine learning is still in its infancy. For example, a baby doesn’t need to watch millions of other humans before it learns how to walk. She sets her own goal of walking, observes other humans around, intuitively creates her own learning strategy and refines that through trial and error until she succeeds. Without any outside intervention or guidance, a baby displays curiosity to learn and successfully walks, talks and understands others. Machines on the other hand requires guidance and support at each step of learning.

Moreover, a child easily combines inputs received through multiple sense organs to make the process of learning holistic and efficient. In one article, Dave Gershgorn indicates that “AI research has typically treated the ability to recognize images, identify noises, and understand text as three different problems, and built algorithms suited to each individual task.” Researchers from MIT and Google have published papers explaining the first steps on how a machine can be guided to synthesize and integrate inputs from multiple channels (sound, sight and text) to understand the world better.

Myth#3: Machine learning can be applied to any task

Reality: Currently, machine learning can only be applied to tasks where large number of input data sets exist or can potentially be captured.

Andrew Ng, in one of his HBR articles, points out that “despite AI’s breadth of impact, the types of it being deployed are still extremely limited. Almost all of AI’s recent progress is through one type, in which some input data (A) is used to quickly generate some simple response (B).” Also, most of the successes in AI have come in the applications where companies like Google and Facebook have access to enormous data sets (texts, voices or images) coming from a variety of sources.

Hence machine learning cannot be easily applied to tasks that are not of the type mentioned above or where sufficient data sets are not available. A write-up published in The Verge reiterates this point and observes that “the problem is even bigger when you look at areas where data is difficult to get your hands on. Take health care, for example, where AI is being used for machine vision tasks like recognizing tumors in X-ray scans, but where digitized data can be sparse.”

Some startups are trying to overcome the bottleneck of large data dependency for machine learning algorithms. For example, Geometric Intelligence, which was acquired by Uber in last December, is attempting to develop systems that can learn tasks with little data.

Bottom-line

Advances in machine learning and deep learning have brought AI out of its long hibernation. While remarkable innovations have taken place, many more key breakthroughs are awaited in these fields.

The hype around artificial intelligence and machine learning has also led to exaggerated expectations about the current capabilities of these systems. If not corrected, these misconceptions or myths may lead to collective blind spots about the state of progress in these fields.  In this article, we have discussed about three common myths on machine learning and contrasted those with corresponding realities.

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