The Future of Online Marketing
Artificial intelligence (AI), enhanced through computers, has been around since the 1950s. It’s only recently, however, that new hardware innovations have revitalized the field and provided a much-needed boost to its activity. As a result, our machines have more accurate sight, hear sounds with greater clarity, and understand location and spatiality. Powerful processors allow computers to make very complex decisions, sort through endless possibilities and plan outcomes. They can even learn from mistakes. The potential associated with future applications of this technology is mind-boggling and its implications vast. This article discusses what AI is in general, what Machine Learning is in some detail, and how its applications are helping to usher in The [online marketing] Paradigm Shift that is ONPASSIVE.
Here is a list of categories (fundamental terms) for Artificial Intelligence:
1. Strong AI (or General AI) is what you see in Sci-Fi movies. We usually see this type of tech in an artificial being that behaves like a human it will exhibit all the emotions and quirks that we have. Strong AI really has not been created yet so mankind is safe… for now.
2. Weak AI (or Narrow AI) This is much more common. This is used in systems like Siri and Google Home. This technology is very good at executing narrow tasks, such as sorting pictures, looking up the weather, setting your alarm, or scheduling appointments.
3. Machine Learning (ML) This is a little different form the first two. Machine Language is a program that has a distinct advantage; it can learn. It can also identify patterns by analyzing large amounts of data. Though it is self-learning, much like AI, the data it analyzes is usually specific. This makes it an excellent tool in sectors like.
- The Financial Services Industry. …
- The Retail Industry
- The Automotive Industry
- Government Agencies
- Transportation Industries
- Oil & Gas Industries
No doubt we are already witnessing the early applications of Machine Learning whether we recognize them as such or not. ML is the driving force behind the recommendations for global customers concerning online services such as Netflix or Amazon. It’s also powering virtual assistants like Siri, Alexa and Cortana. Machine learning is also largely responsible for the improvements for healthcare imaging and scanning systems used to detect cancer, as well as self-driving (autonomous) vehicles.
Machine learning has surfaced to become as one of the biggest and most promising technologies and, as such, its poised to greatly transform our lives, especially in applications that determine how companies are conducting business.
ONPASSIVE is one such company. Fully immersed in its endeavor to change the world, its public launch is being touted as the single, largest catalyst of change of the 21st century and is expected to usher in the next evolution of online marketing.
As branch of artificial intelligence (AI), at a basic level machine learning is software able to make decisions based on its experience and does not require traditional rules-based programming to do it. It learns [and gets “smarter” over time] by retraining itself through its “experiences” by using statistical algorithms.
There are three other technology trends coming together that are taking machine learning from a concept to commercially viable applications that are separately improving business.
1. Raw computing power: This allows us to the accomplish the volume of transactions or “experiences” required for machine learning.
2. Massive amounts of information: Have you heard of “Big Data?” That’s what this is. It includes external data shared from Internet of Things (IoT) applications and provides the broad context machine learning needs.
3. Advances in complex neural networks: This is “Deep Learning,” By analyzing enormous quantities of data points, these models are creating efficiencies beyond what linear models can provide.
These things give us the ability to go beyond simply detecting patterns to being able to proactively adapt and optimize specific solution. This ability is what makes machine learning valuable. Machine learning is especially effective for applications that process vast amounts of data, particularly where traditional linear models can be limiting. With the ability to process unstructured data, machine learning can discover patterns and correlations that were previously undetectable. Beyond fraud prevention and other obvious uses, machine learning is also automating tasks that can be tedious, allowing IT staff to focus on more strategic projects.
Successes achieved early on have been in the consumer space for the most part. Now, however, we see the rise of new business applications for machine learning technology. To more fully appreciate the great potential [to early adopters like ONPASSIVE] that exists across multiple business functions, consider the following:
- For the sales team, machine learning delivers more relevant insight from Customer Relationship Management (CRM) systems. It provides a more meaningful understanding of customer “churn” and buying trends which improve customer service and shorten sales cycles. This will be a huge competitive advantage to those who adopt early and get it right… like ONPASSIVE!
- Machine learning can help bridge the gap between sales and marketing. It does this by determining the correlation between marketing programs and unit sales. Additionally, it can help to more effectively divide customer categories to support marketing campaigns, promotions and efforts to close on sales.
- HR departments will implement machine learning for recruiting and retention of top talent.
- As a result of machine learning, Operations will get smarter at planning, resource deployment, scheduling, and purchasing.
- Machine learning will also be used in the finance arena to help manage cash flow, speed account reconciliations, and improve overall financial planning.
ONPASSIVE is an incredibly forward-looking company firmly grounded in the Information Technology Space. As such, we utilize the tremendous benefits of machine learning. But, while great strides in machine learning are being made, it’s still very early in largely uncharted territory. But, take heart, Data Scientists are developing best practices that could define the future of machine learning and other forms of AI.