How Machine Learning Affects Everyday Life

How Machine Learning Affects Everyday Life

Enterprises today are finding it exceedingly meaningful and resourceful in the massive amounts of data they generate and save every day. The required algorithms, applications and frameworks to bring greater predictive accuracy and value to enterprises’ data sets are available; therefore, businesses need to make sure they have data sets of sufficient size and quality. It is due to the excessive need to do a better job in capturing and utilizing data.

The rise of deep learning and neural networks has spread in everyday lives. It took about six years for neural nets to show impressive results, first in speech recognition, then computer vision, images, image detection and diagnostics, and more recently, in natural language processing. After all, Siri and Google have taken voice recognition mainstream.

Smart devices use Internet technologies like Wi-Fi to communicate with each other, your laptop and sometimes, directly with the cloud. Some smart devices also talk to a central hub that serves as a control point for many different devices, like the Revolv.

In financial markets, a huge amount of trades is done by algorithms and learning machines. Above all-in-all probabilities, your pension and retirement savings are changing hands and are currently in the hands of micro-trading machines conducting millions of trades per day.

All of this is having a profound impact on us as we struggle to keep up with the changes they bring.

Producing valuable data models

Data is immensely valuable, high performing, challenging and huge. A wide variety and huge volumes of data are readily available to be explored, such as computational processing, which is cheaper and more powerful, and with affordable data storage.

Data emerges from the infrastructure of cities, as well – from sensor-equipped buildings, trains, buses, planes, bridges and factories. All these imply that it is automatically possible to produce valuable data models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. The end result is high-value predictions that can empower valid decisions and smart actions in real time without human intervention.

One of the most important factors in machine learning is producing smart actions in real time, and that’s done through automated model building. Analytics thought leader Thomas H. Davenport wrote in The Wall Street Journal that with rapidly changing, growing volumes of data, "... you need fast-moving modelling streams to keep up." And you can do that with machine learning. He says, "Humans can typically create one or two good models a week; machine learning can create thousands of models a week."

Advantages of machine learning

Machine learning in everyday life

Machine learning is not just an IT industry phenomenon. PayPal, Netflix, Skype and Uber were also once dismissed as small players, and in recent years, the number of technologies in the small players phase has risen sharply: 3D printers, digital cash, self-driving cars, smartwatches, Internet TV, 3D goggles, robots, smart clothing, massively open online courses (MOOCs), drones, expert systems, do-it-yourself medical tests, the quantified self, artificial intelligence and more.

There are many machine learning applications and opportunities – to mention a few:

  • Amazon and Netflix online recommendation systems
  • Google Adwords (which generates more than $60 billion dollars per year)
  • Bing search engine (while not as big as Google or Yahoo, it still uses machine learning).
  • Facebook automatic face recognition and friend recommendations
  • IBM's Watson and Kindle Store speech recognition systems (speech-to-text or customer service stuff)
  • Google News (news clustering, related stories, handwriting recognition, questionable content identification, automatic closed captioning, and machine translation)
  • Spotify and Pandora personalised playlists
  • Google personalised searches and ads
  • Uber prediction of customer demand for cars and their pre-location status

These above examples all give you recommendations which are based solely upon the information provided by you. Machine learning technology even tracks how long you watch a movie and the various kinds of products you purchase – many of these sorts of actions are being monitored and recorded. Machine learning is quite ubiquitous and useful with some information that you may never have thought about it in the first place.

Key factors enabling machine learning growth today

  1. Internet of Things. IoT is here in the devices, sensors, cloud infrastructure, data and business intelligence tools you are already using today. Experts believe the next big wave of technology lies within the IoT, which refers to things that connect to the Internet like TVs, refrigerators, garage doors and even cars that collect data about the way we live. It’s deeply tied in with data, analytics and cloud to both enable it and to improve solutions. The key goal is to make sure there is value to both customers and businesses.IoT will have a huge impact in terms of cloud and big data applications. Cloud systems will have powerful machine learning and artificial intelligence engines that ingest feeds of data being produced by these IoT devices and produce business logic that drives operational decisions.
  2. Big data. Applications are beginning to be used to enhance user experience by predicting what actions a user would prefer next, a concept derived from the need for increasingly dynamic web-based applications. From governments to airlines to rental car companies, insurance firms and banks, big data is transforming how we understand the world, do business and implement public policy. As more and more companies realize the worth of implementing big data strategies, more services will emerge to support them.
  3. Unstructured data: Big data analytics also brings unstructured data into the fold – managing the exponentially-growing information gleaned from social media feeds, email, blogs, videos, call logs, customer service records and other sources. Big data will play a big role on the network.
  4. Cloud computing: As more and more organizations are creating their new applications in the cloud it is becoming an accepted norm that in 10 years’ time the cloud is predicted to host their own applications. It is also a fact that the today’s concerns about security of the cloud will be reversed in a decade. Cloud companies will be storing more than 100 petabytes on Cloud Databases. They will also have their choice in terms of offering cloud services that have a retail cloud, a health care cloud, a finance cloud, and the likes.

Machine learning all around us

Machine learning algorithms are very good at arriving at predicting outcomes and optimizations because calculations are based in milliseconds. Whenever a miscalculation happens, the error is rectified, and the machine learning algorithms correct the error and begins another iteration of the data analysis.

All the necessary algorithms, applications and frameworks bring greater predictive accuracy and value to enterprises’ data, leading to machine learning’s ability to scale across IoT, big data and cloud computing.

The adoption of machine learning is made possible through simulation, as well. Right from inception, growing and optimizing a supply chain network with the help of generating terabytes of data – mainly through advanced simulation techniques – it is possible to forecast accurately by deploying several optimization techniques.

It is important to remember that machine learning algorithms are iterative in nature and always looking forward to positive, optimized outcome. The future potential is virtually endless.

Article written by Raj Kosaraju
Image credit by Getty Images, DigitalVision Vectors, exdez
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