Ways Machine Learning Impacts our Everyday Life

Machine interactions were a problem in the past. But decades later, Machines have evolved into ultra-smart, self-learning, automated versions that are sweeping the human landscape, thanks to subsequent computing progress.

Machine Learning (ML), a subset of Artificial Intelligence and a popular buzzword in today's digital-first world, is the underlying technology that's reinventing machines to tailor human experiences. In essence, it's about using Big Data to programme robots/bots with the ability to learn on their own. For actionable intelligence, data from numerous touchpoints is examined and used to forecast intentions.

When voice-controlled personal assistants like Amazon's Echo and Alexa were released, humans got their first experience of Machine Learning. With the rise of smart homes, these devices have become the new normal. Driverless automobiles, once thought to be a sci-fi dream, are no longer a distant possibility. These cutting-edge vehicles, which are designed to reduce human work, are being tested all over the world for their utility benefits.

The concept of intelligent machines sounded strange at first. It wasn't common for machines to act on behalf of humans. However, as Machine Learning becomes more accessible and evolves in our daily lives, the human environment is fundamentally changing and in what ways.

We've listed seven ways that Machine Learning is impacting our everyday lives below. Let's get right into it.

1. Commute Estimation: Traffic Prediction by Google Maps

When we go out and need directions or traffic data, we use Google Maps. How many of you are aware that Google Maps is powered by a powerful machine learning algorithm and big data?

Let's have a look at how Google Map uses Machine Learning and Big Data to predict traffic.

Colored lines on Google Maps show traffic conditions on key routes. These colourful lines indicate the maximum speed that may be achieved on that road. Clear, slow-moving, and extremely congested traffic are indicated by green, yellow, and red coloured lines, respectively. If the line is grey, it means there is no traffic information available at that location.

2. Personal Smart Assistants

From Personal Smart Assistants like Siri and Cortana to Google Assistant, there are plenty of functions when it comes to the personal assistant along with Amazon Alexa and Google Home.

If AI is fully implemented, these home devices and personal assistants will follow one's orders, such as setting a reminder, searching online for information, adjusting lighting, and so on.

Ml algorithms are used by these devices and personal assistants, such as ML chatbots, to collect data, analyse one's preferences, and enhance the experience based on previous interactions with individuals.

3. Enhanced Health Care and Medical Diagnosis

Hospitals may soon take your health in the hands of artificial intelligence, which is good news. Hospitals that use machine learning to help with patient treatment see fewer accidents and hospital-related illnesses like infections. AI is also tackling some of medicine's most difficult problems, such as using predictive models to help researchers better understand hereditary diseases.

Machine learning can also be used to predict disease breakthroughs, drive medical data for outcomes research, plan and support therapy, and manage patients. Along with Machine Learning, AI in healthcare is used to improve monitoring efficiency.

4. Social Media Networking

Facebook: When you upload a photo to Facebook, it automatically recognises faces and recommends tags for your friends. Face recognition on Facebook is done with the help of AI and machine learning.

Pinterest: It uses computer vision to distinguish things in photographs or "pins" and then recommends other pins that are similar.

Snapchat: It has facial filters (called Lenses) that filter and track facial movement, as well as the ability to tag animated images or digital masks that shift as users move their faces.

Instagram: Emoji feelings may be recognised using machine learning techniques. Emojis and emoji hashtags can be created and recommended automatically by Instagram. Emoji are widely utilised across all demographics and are used by Instagram to describe and explore on a large scale through emoji-to-text conversion.

5. Personal Finance and Banking

Fraud Prevention: In most circumstances, daily transaction data is so large that it becomes difficult for humans to manually analyze each transaction. How can they tell if a transaction is fraudulent?

To address this issue, AI-based systems are being developed that learn whether transactions are fraudulent. This is how banks use artificial intelligence.

Companies are using neural networks to identify fraudulent transactions based on factors such as recent transaction frequency, transaction size, and merchant type.

Credit Decisions: When applying for credit cards or loans, financial firms must make a quick decision on whether or not to approve the application. And, if the idea is accepted, what are the particular conditions to be offered in terms of interest rate, credit line amount, and so on?

Financial firms use machine learning algorithms to make credit decisions and assess each user's risk individually.

Conclusion:

As you can see, machine learning has a daily impact on all of our lives. Whether we're using navigation, making an online purchase, surfing social media, or watching a favourite show on a streaming service, machine learning is influencing our decisions in some manner.

If you’re curious how AI and machine learning can impact your business too, speak to an expert at Theta Technolabs.

Theta Technolabs is one of the finest machine learning companies in Ahmedabad that provides significant solutions of AI and ML. Write us at hello@thetatechnolabs.com