Machine Learning Is Solving These 7 Big Problems

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Artificial intelligence’s (AI) growth has ushered in a new era in which machine learning (ML) developers can apply principles of AI and ML to solve a variety of problems. Every industry across the globe has challenges that can be resolved practically by applying ML. Here are a few of our favorite ways ML is changing business and life all over the world.

Moving Transportation Tech Forward

Traffic congestion is a daily problem for hundreds of millions of people. By adding AI and ML to vehicles, they can talk to each other as well as traffic infrastructure to help drivers avoid high-congestion areas and on-road hazards.

For example, Pittsburgh is using AI with camera and sensor data to track traffic flow. The analysis then adjusts traffic lights accordingly. As a result, traffic flows more seamlessly, with the added benefit of fewer emissions since cars spend less time idling.

In manufacturing, AI and ML are helping factories and enterprises take charge of cheaper, more frequent predictive maintenance over corrective and preventative maintenance. ML can pinpoint which machines often have a high unexpected failure rate by using historical machine data, analyzing the data against a dynamic environment, visualizing workflows, and creating better operational feedback loops.

Healing Healthcare

AI and ML are revolutionizing healthcare, one disease at a time. With AI, Atomwise developed drugs used to treat Ebola’s spread in Africa; the company utilized an algorithm to sort through existing medication and deduce which ones could be repurposed to fight Ebola.

According to CB Insights, 86% of healthcare providers are already using AI; by 2020, these providers will have invested $54 million in various AI initiatives.

For the majority of healthcare, AI and ML are set to improve work-life balance, the patient experience, and results of invasive surgeries.

Diagnosing will also look different in the next decade; costs should decrease, while ML improves accuracy rates. Right now, ML algorithms are making correct diagnoses at excellent rates, recommending medications, predicting patient re-admissions, and alerting doctors to high-risk patients.

And the algorithms don’t need any identifying information to make these predictions — all data is anonymized.

More Robust Renewable Energy

Wind energy generation companies are using AI and ML algorithms to optimize energy generation times and turbine propellers. Using inputs like custom operational data alongside real-time weather numbers, algorithms determine the wind speed and direction for each propeller.

The propellors then adjust their position accordingly. This optimizes energy generation to bring in more power than ever before.

On the maintenance side, Stanford University’s National Accelerator Laboratory uses AI and ML to predict power grid vulnerabilities. The lab then uses that analysis to strengthen the power grid appropriately to avoid failure during risk-heavy periods. The algorithms take inputs from satellite images, power sources, and battery storage data to predict failure risks.

Challenging Climate Change

Natural disasters are destroying our environment; their occurrences have almost tripled since 1980. More than 1/5 of all animal species face extinction. And climate change is also causing other threats through rising ocean levels, increased oceanic acidity, and other imbalances.

Microsoft is funding a study at Columbia University about the effects of Hurricane Maria. The team is using AI and ML to analyze photos and match data to each one about plant species. The researchers hope to predict how hurricanes and tropical storms can affect tree distribution in an ecosystem.

This analysis offers huge potential to improve environmental conservation efforts.

Smarter Financial Analysis

The finance industry has no shortage of data; it’s probably the industry responsible for creating the most data since the beginning of time. All of this multi-dimensional data is a treasure trove for AI and ML applications. Algorithms are already being used in algorithmic trading, fraud detection, and portfolio management.

And according to London-headquartered Ernst and Young, ML will improve the loan underwriting industry with better detection of anomalies and nuances in an application.

Eventually, and unfortunately, the firm predicts most underwriting positions will be replaced with ML algorithms.

Algorithms will also fuel the adoption of chatbots and chat interfaces for customer service, sentiment analysis, and security in financial institutions like banks and advisory firms.

Stopping Spam

Flagging messages as spam was probably one of ML’s first broad applications. Email providers used a basic set of ML algorithms four years ago to detect and remove spam. In today’s world, these algorithms are now self-learning; they optimize themselves with new rules from new insights.

Google even uses neural networks in its spam filters to create a highly efficient spam rate of 0.1%. The neural network uses information from many inboxes to recognize spam mail and phishing emails.

Social networks like Facebook are using ML find and filter abuse, improving platform safety and scrubbing unsafe or violent behavior before too many people see it in their feeds.

Expanding Perspectives With Image Recognition

Image recognition can be creepy, like when Facebook identifies non-humans as people, but it can also be incredibly useful for a multitude of industries: healthcare, transportation, user experience, and much more.

Chinese tech company Baidu developed a prototype of DuLight, a device for visually-impaired people that integrates computer vision technology with the user’s surroundings. The device then narrates an interpretation of the user’s environment, drastically improving quality of life and situational awareness for users.

Machine Learning Applications Are Infinite

The examples we covered may not apply directly to your business. But AI and ML are so versatile that almost any gripe you have, whether it is an operational workflow or customer retention strategy, can be improved through their use. AI and ML enable us to work on more important tasks while letting machines take care of the legwork.

What are your favorite ML applications? What industries can really use some AI innovation? Let us know in the comments below!