What was once used for the occasional game of checkers, today is our virtual personal assistant trusted to manage our everyday activities (Alexa) and all that thanks to the same technology. Machine learning can recognize patterns by using examples, it constantly learns without relying on rules-based programming, makes decisions based on data and changes its behaviour. As an exciting subset of artificial intelligence, machine learning is changing the way we execute innovation, making it smarter and far more efficient.
But can we describe the distinctive nature of it as a futuristic concept or has it become our everyday companion? Although its foundation is reaching way back, the impact it is making today is huge and now more measurable than ever. Applications of this technology are numerous in existing business models, and the opportunities it opened in a short time span is something we simply can’t ignore. For instance, we’re not to be drowned in unmanageable volume and complexity of the big data, anymore. On the other side, it has shown to be very useful in gaining fresh business insights, putting the big data to good use.
Here are the top 3 most innovative use cases of machine learning and also areas where machine learning is thriving in recent years.
Using advanced analytics through machine learning changed the way physicians are informed about the patient’s medical condition. Processing data that shows patient’s race, gender, socioeconomic status, family history, blood pressure readings, lab analysis test results, and latest clinical trials can result in much more useful and comprehensive information about patient’s risk for stroke, kidney failure, and coronary artery disease. Also, getting this kind of results in a fraction of time with a high level of accuracy leads to increased patient satisfaction, lower cost of care and ultimately leads to a better outcome. Of course, the processes that are standardized or repetitive are more suitable for the use of machine learning than others. Radiology, pathology and cardiology are disciplines with large image datasets, which makes them pretty strong candidates. This way, it’s possible to bring value from the application of this technology in healthcare. Introducing machine learning to daily clinical practice should be incremental so the medical workers can adjust to a new landscape along with improving their own efficiency.
Companies have been racing to develop fully autonomous vehicles, but we can already see the benefits of using semi-autonomous cars developed by Tesla. Tesla Autopilot has an advanced driver-assistance feature that includes lane centering, adaptive cruise control, self-parking, automatic lane change and the ability to summon the car from a parking spot or garage. An excellent example of the impact AI is making on our everyday life. But what is behind all of these incredible features? There are multiple AI subfields that have to be put together to make a vehicle that navigates itself such as deep learning, voice search, motion detection, image recognition, processing etc. They also gather a combination of high-tech sensors and innovative algorithms in order to detect and consequently respond to their surroundings. That includes radar, laser light, GPS, drive-by-wire control systems and computer vision. All these networked components provide data for self-driving cars and the intellect for making autonomous decisions.
Machine learning plays an important role in cybersecurity since most attacks come from software, not individuals; these are few and sporadic. It is why cybersecurity is looking for assistance from this innovative technology. There is too much volume of malware attacks for humans to handle and machine learning has the ability to sort through millions of files and identify potentially dangerous ones. It is increasingly being used to reveal threats and eliminate them before doing damage. For instance, security is being a critical concern for self-driving cars. The good news is that machine learning can be deployed to protect them from cyberattacks and malware. Even regular automobiles have millions of lines of code and electrical components communicating via an internal network. The first step to deploying machine learning to avoid security risks is collecting and storing the correct data. If a car’s internal network uses a platform for monitoring, capable of storing and analyzing logs, the vehicle is able to detect malicious activity and prevent it. Since autonomous vehicles require a lot of processing power to make decisions based on sensory input, the second-best solution is to alert the driver, at least. Once the self-driving car is set to collect and store user logs, machine learning is called to detect anomalies in communication behaviour or unusual commands like activating parking mode while the car is on a highway. Machine learning algorithms can run 24/7, waste-free, which makes them a perfect tool for maintaining a high level of protection.
As the quantity of data we generate continues to grow and expand, it becomes essential for our computers to have the ability to process and analyze and, most importantly, to learn from past data. Implementation of machine learning models across industries is one way to improve efficiency, reliability, and accuracy of processes. In some industries, it may take time for machine learning to enter daily practice. Until then, it’s our job to innovate relentlessly and never stop exploring new possibilities to make the present a better place.