Technology | Smart Tech & IoT | artificial intelligence
One of the biggest buzzwords in the tech industry over the past decade has been “artificial intelligence” — also referred to as “AI.” In fact, when looking at innovation initiatives, it’s almost difficult to find a press release that doesn’t mention AI. Case in point: startup companies on the forefront of innovation that have been using AI received more than $7.4 billion in funding during Q2 2019.
Today, we’re going to explain why AI has felt like a force of nature the past few years and, more importantly, define what AI is — all while clearing up some common misconceptions along the way.
What We’ll Cover:
- Artificial intelligence definition and the definition of machine learning.
- The differences between AI and machine learning.
- Examples of AI across industries.
- How you can utilize AI in your business.
Artificial Intelligence vs. Machine Learning: Which is Which?
Unfortunately, it’s become commonplace for businesses around the world to either misrepresent or misunderstand what their technology is capable of and in turn classify it incorrectly. The terms “artificial intelligence” and “machine learning,” while related, are commonly used improperly and sometimes interchangeably. It’s important to define them individually to understand what makes them different.
Machine learning is a subset of artificial intelligence and can be simplified as the process or algorithm where machines learn from data in order to make predictions or perform other functions more accurately as they take in more data. This can be seen as the learning part of AI (though it’s still an important part overall).
Roberto Iriondo from Medium provides a great example of machine learning:
“...if you provide a machine learning model with many songs that you enjoy, along with their corresponding audio statistics (dance-ability, instrumentality, tempo, or genre)… It ought to be able to automate (depending on the supervised machine learning model used) and generate a recommender system [that suggests] music in the future that (with a high percentage of probability rate) you’ll enjoy.”
To expand on this example, if you added new songs to the model, it should be able to cross-reference them with the songs already in the data set to determine if any of its past conclusions apply to the new songs. Basically, you’re letting a machine have access to data and learn for itself.
Artificial intelligence, on the other hand, is a much broader concept. Merriam-Webster defines it as, “the capability of a machine to imitate intelligent human behavior.” In a sense, AI is machines displaying behavior that could be hard to differentiate from human. This goes beyond simply “learning” and could teeter on things like “understanding” or even “choosing.”
As technology continues to advance, and things once thought impossible become easier to understand, the goal post of what could be defined as “AI” continues to move. Zack Lipton, Assistant Professor at Carnegie Mellon University says that, “...the term is aspirational, a moving target based on those capabilities that humans possess but which machines do not.”
So, is it wrong to ever describe existing technology as “artificial intelligence?” Not necessarily. AI is a great umbrella term for the ongoing research into the bleeding edge of computer capability and interpretation.
What’s Going on in the World of Artificial Intelligence
AI and machine learning have been dominating across almost every industry. This includes industries like banking, insurance, healthcare, and manufacturing (just to name a few.) Let’s take a look into how both have shaken up their respective workplaces, and could potentially redefine how they operate in the years to come.
Banking
According to Emerj, “The seven leading US commercial banks have prioritized technological advancement with investments in AI applications to better service their customers, improve performance and increase revenue.”
Their research into AI has resulted in a large improvement in chatbots — bots that can handle questions from clients and assist with directing them to proper communication channels if necessary. These chatbots are expected to see success rates in their interactions with customers reach over 90%, drastically reducing the time needed for real-life associates to deal with smaller issues and saving banks $8 billion annually by 2022.
Insurance
Artificial intelligence and machine learning are also making major moves in the insurance industry. Similar to banks investing in chatbots, insurance companies are turning to AI to simplify their processes. Juniper Research reports that, “...despite still being in [the] early deployment stage, the introduction of AI in the claims process will generate significant cost savings. Juniper forecasts that across property, health, life and motor insurance, the annual cost savings will exceed $1.2 billion by 2023, a five-fold increase over 2018.”
While insurance companies will see gains over the coming years as they continue to invest in the development of AI and how they handle their customers’ data, the auto insurance industry may be in for a significant shake-up. One application of AI and machine learning that is improving year over year is the development of driverless vehicles. As vehicles go driverless and continue to get safer for consumers, it’s expected that the auto insurance industry could shrink up to a stunning 60% over the next 25 years. Driverless cars could also reduce accidents up to 80% by 2040, paving the way for a much safer future for drivers.
Healthcare
One of the most promising and rapidly developing industries for AI, healthcare providers are already utilizing complex, machine learning algorithms to help them make decisions with their patients. According to HIMSS Media, 63% of surveyed research participants say that “AI and machine learning are already delivering value in specialty care including radiology, pathology and pharma.” For complex conditions, machine learning can prove invaluable to healthcare professionals as they track the development and recovery of diseases.
This technology has also played a lead role in the birth of personalized healthcare — the process of taking into account of patient’s unique characteristics such as clinical history and risk factors to provide them personalized treatments. From wearables to data analysis, AI has enabled personalized care to start to tackle the advanced complexities of each individual's genetic code. Everyone is different, and as a doctor that can make diagnosis difficult — that’s where machine learning comes in.
Manufacturing
AI and machine learning are being embedded in the technology used to manufacture products (alongside IoT and blockchain.) By the end of 2021, around 20% of leading manufacturers will rely on embedded intelligence to automate processes and increase execution times by up to 25 percent. These technologies are primarily being used to perform maintenance and ensure product quality; however, they have many more applications as manufacturers are finding that repetitive processes are easy for these algorithms to interpret. As cost savings with AI and machine learning become more apparent, adoption rates are continuing to increase. Capgemini reports that more than half of European manufacturers (51%) are implementing AI solutions, with Japan (30%) and the US (28%) following not far behind.
AI, You, and How We Can Help
The industries we just covered only make up for a fraction of those that are finding benefits with machine learning and AI. Whatever your industry is, it can make a serious impact on your business. Even by breaking it down by business function, you can find the benefits in each department. Companies who used AI for sales purposes were able to increase their leads by more than 50%, reduce call time by 60-70%, and realize cost reductions of 40-60%. In B2B marketing departments, 64% consider AI valuable for their sales and marketing strategy. And even in human resources, 96% of recruiters believe AI can greatly enhance talent acquisition and retention.
Whatever your business need is, it may be time to consider how AI can set you up for your moonshot.
If you’re ready to take the next step with your technology and discover what’s possible, let’s start talking strategy. We’ll get to know your organization, your goals, and your existing tech infrastructure before mapping the journey to your moonshot so you can reach your greatest potential.
