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Artificial Intelligence (AI) and Machine Learning, are buzzwords backed by extremely deep concepts with practical use cases. These concepts are being implemented into products so rapidly that Merrill Lynch predicted the global value for AI and robots will near $153 billion by 2020.
Integrating AI and Machine Learning within workflows has been shown to increase overall productivity and effectiveness. It is for this reason that enterprise leaders are eager to adopt AI and Machine Learning backed innovations to streamline their processes and innovate by adopting smarter solutions than their current ones.
Recent technological innovations have made the prevalence of AI and Machine Learning commonplace, leaving many to wonder – Is 2017 the year Machine Learning will finally become mainstream?
What are AI and Machine Learning
Before talking about how these innovations are going to change the world we live in, it’s important to first discuss the difference between AI and Machine Learning.
The idea of AI came about in 1956 as a term given to describe the desire to make machines exhibit human behavior. As technology advanced, the idea of machines performing human actions simply wasn’t enough. Scientists and innovators wanted machines to use algorithms and to intelligently comprehend data in order to gain meaning; specifically over a period of time. In other words, they wanted machines to learn – and so Machine Learning as an industry was born.
With Machine Learning, the technology learns the more it is used and the more data it collects, without the need for additional programming – a significant advantage for industries looking to retain a competitive edge and improve efficiency over an extended period of time.
Machine Learning Changing the Face of Finance
As machines have the ability to process information quicker than their human counterparts and with more accuracy, Machine Learning has the potential to impact the finance world more than almost any other industry.
The existence of robo-advisors and digital financial planning platforms will likely increase in popularity and replace humans in the upcoming year as a result of the rise in impact Machine Learning is having on the finance industry. Instead of talking to a banker about your finances and financing options, a smart robot with machine learning capabilities will be providing assistance. Robo-advisors are able to provide automated, algorithm-driven financial predictions with little to no human supervision. Their accurate assessments take only moments to predict, thanks to machine learning that is being continually improved over time, and they have the ability to factor in thousands of scenarios when making financial predictions.
Robo-advisors are impacting individuals’ and companies’ investment decisions with platforms such as Vanguard and Betterment, that bring AI and Machine Learning together into a simple platform. Asset management by robo-advisors is also on the rise, and its value is estimated to increase 68% a year, reaching $2.2 trillion by 2020.
The immersion of AI and Machine Learning in the financial industry is expected to eliminate roughly 25 million jobs within the industry – while this is bad news for the workforce, it presents a huge opportunity for entrepreneurs and startups with machine learning innovations.
Machine Learning Taking a Front Seat in Autonomous Cars
Many dream of the day they’ll be able to sit back in their car and be taken where they need to go. That dream is one innovators are working hard to make a reality, however, they need cutting edge machine learning technology in order to do so.
In order for cars to operate without human interaction, it is imperative that the cars are able to independently make smart decisions based on the information they collect in real time. Aggregating information recorded on cameras, sensors, GPS and driver assistance systems won’t get a car from point A to point B without the ability of the car to have a certain level of predictive capabilities and the ability to learn as more information is collected. How are cars “learning”? You got it – they’re using Machine Learning.
Machine Learning Transforming Healthcare
In a past blog post, we discussed the need for the healthcare industry to introduce innovations in order to meet the needs of patients. What we didn’t discuss is the impact that Machine Learning has on those innovations.
Machine Learning technology can quickly compare a patient’s symptoms against aggregated data in order to detect and provide an accurate diagnosis. Zebra Medical Vision, for example, is doing just that by combining Machine Learning and medical imaging and using that information to transform patient care by delivering accurate image readings and diagnosis. Additionally, Machine Learning is innovating healthcare by being implemented within hospitals to help them operate more efficiently, reducing patient wait times and improving resource allocation.
Unlike other industries, advances in Machine Learning capabilities in healthcare will likely be offered at little to no charge to the consumer due to the impact of the innovation on insurance companies. By assessing the potential health concerns a patient may have, insurance companies are able to adjust rates accordingly, making innovations in Machine Learning in the healthcare industry desirable by both patients and doctors alongside investors who are eager to enter the developing space.
To date, 106 healthcare + AI startups have received funding, and experts anticipate that number will only rise as the search for the smartest Machine Learning algorithm continues.
The Long-Reaching Arms of Machine Learning
The ability for Machine Learning innovations to transform our lives doesn’t start with finance or end with healthcare – it is long reaching and we are just at the beginning of the revolution.
Though already disruptive, the #MachineLearning and #AI revolution has only just begun Click To TweetMachine Learning can be used to determine online shopping habits and improve retail capabilities, search engine marketing can be hyper-targeted based on past and potential digital behavior, factory lines can better utilize raw material – and the list goes on!
As the race for innovation continues, more and more enterprises across all industries are looking to startups to run proof-of-concepts (PoCs) in order to try and find the solution that best works for their industry. We’ll be sure to tell you about the prooV superstar collaborations that bring the next big change in Machine Learning!
