Whether at work or involved in other normal life activities, machine learning is playing a role in more people’s daily lives. Machine learning is a subset of artificial intelligence (AI) but the two terms are often used interchangeably. Nathan Sinnott (@SinnottNathan), founder and Chief Executive Officer of Newpath WEB, explains why some business people favor the term “machine learning” over “artificial intelligence.” He writes, “The term ‘machine learning’ might not mean much to you. You might imagine a computer playing chess, calculating the multitude of moves and the possible countermoves. But, when you hear the term ‘artificial intelligence’ or ‘AI,’ however, it’s more likely you have visions of Skynet and the rise of our inevitable robot overlords. But, the truth of artificial intelligence — and particularly machine learning — is far less sinister, and it’s actually not something of the far-off future. It’s here today, and it’s shaping and simplifying the way we live, work, travel and communicate.”
Artificial Intelligence versus Machine Learning
You might have noticed Sinnott used the terms AI and machine learning interchangeably. Technically, such usage is incorrect. Robert Triggs explains, “The two terms are often conflated and, incorrectly, used interchangeably, particularly by marketing departments that want to make their technology sound sophisticated. In fact, artificial intelligence and machine learning are very different things, with very different implications for what computers can do and how they interact with us.” Nevertheless, common practice today is to use AI as an umbrella term covering a number of techniques like machine learning, cognitive computing, and neural networks. In the following video, Gary Sims (@garysims) explains the differences between AI and machine learning.
If you watched the video, you might have noticed Sims talked about Weak AI and Strong AI, the latter term being equated with Artificial General Intelligence (AGI). These definitions were probably sufficient in the early days of AI since weak AI systems were being trained to do one thing, like play chess. At the very least, current discussions should distinguish between weak, strong, and general AI:
- Weak AI: The Wikipedia definition of weak AI remains relevant: “Weak artificial intelligence (weak AI), also known as narrow AI, is artificial intelligence that is focused on one narrow task.” In other words, weak AI developed to handle/manage a small and specific data set to answer a single question. Its perspective is singular, resulting in tunnel vision.
- Strong AI: As noted above, strong AI originally referred to Artificial General Intelligence (i.e., a machine with consciousness, sentience and mind), “with the ability to apply intelligence to any problem, rather than just one specific problem.” Today, however, there are cognitive systems that fall short of AGI but far surpass weak AI. These systems were developed to handle/manage large and varied data sets to answer a multitude of questions in a variety of categories. Cognitive AI can deal with ambiguities whereas weak AI cannot.
- General AI: The AGI Society notes the ultimate goal of AGI is to develop “thinking machines” (i.e., “general-purpose systems with intelligence comparable to that of the human mind”).
I think it’s fair to say most people’s lives are touched more often by machine learning than other forms of computer/human interactions.
How Machine Learning Affects the Business World and Beyond
“The implications of machine learning on industries, professions and the workforce are considered miraculous by some and catastrophic by others,” Sinnott states. “Your opinion will largely depend on your profession and the work you do. Machine learning has the potential to automate a large portion of skilled labor, but the degree to which this affects a workforce depends on the level of difficulty involved in the job. Machine learning at present allows the automation of singular tasks, whereas many jobs involve multiple tasks and even multitasking at a level machine learning isn’t capable of yet.” As Sinnott notes, workers made redundant by machine learning aren’t going to be happy; but, we need to look at the bigger picture. Below are six areas in which many analysts predict machine learning will benefit society as a whole.
Healthcare. Machine learning probably has the greatest potential for good in the healthcare sector. Christina Mercer (@christinamerc91) explains, “We already know that through algorithms, small and hard to spot abnormalities can be discovered in all kinds of things, from legal documents to financial papers. So it’s fitting that the same can be said for healthcare. Spotting irregularities in test results is just the start.” Some people fear the rise of machine learning will dehumanize healthcare. Danny Lange, VP of Machine Learning at Unity disagrees. He told Mercer, “In the grand scheme of things, our focus as humans will be much more on the human aspect. For example, if you’re a patient, you’re not going to interact with a machine.” Letting machine learning handle some tasks previously requiring a physician’s attention should give them more time to spend with patients — which studies have shown to have a positive impact on care. Sinnott adds, “Machine learning is taking a bigger part in our health and well-being on a daily basis, and it is already being used for faster patient diagnosis. Even the prevention of illness in the first place have been aided by predicting the potential health problems one may be susceptible to, based on age, socio-economic status, genetic history, etc.”
Education. Common sense tells you students learn at different rates and have different needs. Too often teachers can’t find enough time to deal with individual student challenges. Sinnott believes machine learning can help. He explains, “Teachers are required to wear many hats: educator, diplomat, analyst, counselor, mentor, ally, referee and plenty more. There’s no computer or robot that can fulfill those functions yet, but through machine learning, some of those tasks can be automated. … Computers can be programmed to determine individual study plans, specific to each student’s needs. Algorithms can analyze test results, drastically reducing the time teachers spend in their leisure time on grading.” Mercer agrees. In addition to things mentioned by Sinnott, she believes virtual and augmented reality technologies will play important roles. “The use of VR and AR in education has already been proven,” she writes, “with student surgeons able to practice life-like medical procedures and children able to explore countries without leaving the classroom.”
Entertainment. VR and AR are also likely to play a larger role in the entertainment sector; but, machine learning will advance entertainment in other ways as well. Mercer notes, “Within the entertainment industry new technology can emerge quite quickly, from new lighting and sound techniques to all out CGI advancements. If machine learning is introduced the experience as a consumer will totally change.”
Retail. Mercer writes, “Retail will benefit hugely from data and the meaningful insights and actions machine learning can throw up. It’s a sector crying out to be disrupted and machine learning could do just that.” Cognitive technologies, which use machine learning, can help marketers find the right channel, the latest trends, the best message, and the right product to reach consumers. For example, the Enterra Shopper Marketing and Consumer Insights Intelligence System™ can leverage all types of consumer data to provide high-dimensional consumer, retailer and marketing insights.
Transport. Mercer notes that sensors are playing an increasingly significant role in the transport sector. The data provided by those sensors can be leveraged by machine learning to improve transport efficiency. “With machine learning, you’re able to expose vehicles to millions of potential scenarios and make sure the computer in the car, bus or truck acts in a certain way.” Lange told Mercer, “Transportation will be made much more efficient, not just by the creation of self-driving cars however. We’re already feeling it by better routing and better sharing of resources.” Sinnott predicts, “Within the next decade, the majority of our shipping and rail networks will be controlled autonomously. China is currently testing driverless public buses.”
Home Life. Sinnott notes the Internet of Things (IoT) has connected many devices we use throughout our homes and the trend will continue in the years ahead. “The automation of our domestic lives is already occurring,” he writes. “Amazon’s Echo and Alexa allow for the voice-activated control of your smart-home (the dimming of lights, closing of blinds, locking of doors, etc., all at your command). Even the humble fridge has been given the 21st-century makeover and is now connected to the internet. You can be at work and still see inside your fridge to know exactly what food you’re running low on. You don’t even necessarily need to go to the shop to restock. Your groceries can be ordered on the road and delivered to your door at your convenience. In the very close future, we can expect the automation of practically every aspect of your home.” He didn’t even mention the security systems many people have installed in their homes.
Those are some of the ways machine learning is changing our lives.
 Nathan Sinnott, “How Machine Learning Is Changing the World — and Your Everyday Life,” Entrepreneur, 25 April 2018.
 Robert Triggs, “Artificial Intelligence vs Machine Learning: what’s the difference?” Android Authority, 30 January 2018.
 Christina Mercer, “How machine learning will change society,” Techworld, 9 November 2018.