I write columns on news related to bots, specially in the categories of Artificial Intelligence, bot startup, bot funding.I am also interested in recent developments in the fields of data science, machine learning and natural language processing ...
I write columns on news related to bots, specially in the categories of Artificial Intelligence, bot startup, bot funding.I am also interested in recent developments in the fields of data science, machine learning and natural language processing
Few things have propelled the IoT's dizzying growth in recent years as much as machine learning and the innovators who are pushing it. Independent, intelligent machines that can comb through data to make their own decisions are, to some, the only reason such phenomenon as the IoT can exist in the first place. So what are the top three ways in which machine learning has and will shape the IoT?
Whether it's inspiring human creativity, surpassing human efficiency, or paving the way for even newer technologies to themselves break through and reshape the IoT, machine learning is the fuel that's driving the IoT forward into the 21st century. Here's how:
1. Making data useful
The gargantuan mountains of data generated by the IoT is perhaps it's defining characteristic. Nonetheless, all of the data in the world is completely useless if companies and individuals can't make sense or use of it. So how exactly has the market exploited this valuable data? Through machine learning.
Today's machine learning algorithms comb through data sets that no human could feasibly get through in a year or even a lifetime's worth of work. As the IoT continues to grow, with some estimating it could reach the dizzying heights of $1.6 b in value by 2021, more algorithms will be needed to keep up with the rising sums of data that accompany said growth.
Machine learning doesn't just sort through preexisting data to the benefits of companies, either. As ABI Research points out, recent advancements in machine learning have enabled it to do predictive analysis, meaning companies which employ these algorithms can better predict future market trends and more successfully target future customers.
Companies who want to succeed in today's marketplace understand the valuable potential hidden in machine learning, and are starting to justifiably treat their algorithms as valued parts of their workforce. But is machine learning only useful for those trying to make it in the commercial marketplace?
2. Making the IoT more secure
Machine learning isn't just used by companies or innovators hoping to make a quick buck off trading and using data. It's also used for security purposes; already, machine learning algorithms are scouring the Darknet for cyberthreats. IT officials can't patch their software or hardware which makes the IoT run if they're unaware of the challenges facing them, and often only a machine learning algorithm is efficient enough to find and bring to light those challenges.
Like data analytics, cybersecurity analytics can be greatly aided by the use of machine learning algorithms. Whether it's helping solve the labor problem in the industry, which is struggling to attract the top-dollar human capital needed to meet the demands of its wealthy clients, or by finding and closing IoT vulnerabilities, machine learning is a huge boon to the security industry.
The scope of operations these algorithms can handle is truly impressive, too. Machine learning can be used to more effectively monitor data exchanges, such as in Bitcoin mining, but can also analyze historical data to predict threats and crimes before they even happen.
Decades of refining machine learning capabilities have made its algorithms truly useful tools, which can be used not only to secure and make money off the IoT, but also expand it into other realms of life, too.
3. Expanding the scope of the IoT
One of the greatest boons that machine learning and its algorithms have delivered to the IoT is how easily it integrates into the IoT's platforms. The rapid proliferation of mobile devices around the globe, for instance, is one of the key drivers of the IoT, and machine learning often fits neatly into the world of mobile device development, programming, and maintenance.
Plenty of examples already exist to demonstrate how machine learning is tying into the specific gadgets which draw the most attention for the IoT; not only mobile devices, but autonomous vehicles and smart cities and factories, too, can benefit from machine learning. As IoT products and services are rendered cheaper to produce and easier to market and consume by adopting machine learning strategies, even more consumers will flock to it and help spread its reach even further around the globe.
The era of thinking-machines hasn't quite lived up to Hollywood's doomsday predictions, but it's certainly changed the world from the top down. As billions of more devices spread across the world in the next one or two decades alone, these algorithms and the cost-cutting advances they bring to businesses and consumers alike will only grow more indispensable. As more people sign up on social media platforms, buy smart devices and commute with autonomous vehicles, the vice grip of the IoT on society will only grow stronger, powered to a large extend by the wondrous world of machine learning.