Artificial Intelligence (AI) and Machine Learning (ML) are buzzwords that come up whenever topics like Big Data Analytics and technology in general are discussed.
What’s the difference?
Machine Learning is an idea to learn from examples and experience, without being explicitly programmed. Instead of writing code, you feed data to the generic algorithm, and it builds logic based on the data given.
Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that can be considered “smart”.
How are AI and Machine Learning applied today?
Machine Learning algorithms have the ability to spot patterns and process a vast amount of information much faster than we humans can. Google has developed a ML algorithm that detects breast cancer and this approach has achieved 89% accuracy compared to 73% for doctors. Stanford researchers have trained an algorithm to diagnose skin cancer by making a database of nearly 130,000 skin disease images and the algorithm is used to visually detect potential cancer. AI and Machine Learning doesn’t stop there with most major healthcare players already investing and recognizing the major role this technology will play in this industry.
Kaspersky Lab said that, in 2017, it detected 360,000 new malicious files daily. Machine Learning algorithms have the ability to look for patters in how data in the cloud is accessed and reports anomalies that could predict security breaches. Currently, a majority of ML approaches in cyber security are used as a “warning system” that often requires a human to make the final decision. However, Machine Learning is becoming more and more accurate than humans are -mainly due to improvements as well as the difficulty in growing the cyber security human talent pool.
Machine Learning is behind some of the biggest online retailers in the world. ML provides a highly personalized service to its customers. Online recommendations are generated through ML based on your previous purchases and/or activity. The algorithms also track the patterns of price changes and sets prices accordingly. The goal of ML in retail is mainly to deliver better customer service by deciphering customer behavior and needs.
Predicting the outcome of a stock is what we’ve tried to do since the creation of stock exchanges over 150 years ago. People have come close but Machine Learning algorithms are getting closer all the time. An example of how an algorithm works is by relying on probabilities and even trading with a relatively low probability at a high enough volume resulting in profits for trading firms and their clients. It’s obvious that humans can’t possibly compete with ML algorithms due to the vast quantities of data or speed. That’s why most prestigious trading firms use the help of ML to predict and execute trades at higher speeds and at higher volumes.
Both Artificial Intelligence and Machine Learning have a lot to offer today. Both help in making routine tasks automated and offering insights that can be used in all industries from healthcare to manufacturing and more. Increased speed has always been the goal and with the human-like AI that will eventually develop, these technologies are changing our lives both at home and at work. There is no need to worry about job losses – instead, we envision a trend toward integration with technology for more effective use of human talent.
Considering exploring options for your organization to gain insights and agility, build a competitive edge and reduce costs with AI and Machine Learning? Learn more on how to modernize your business processes today.