Big data has become part of almost every part of our lives, certainly the one we live online. Internet companies have searched for and built upon what is akin to the next gold rush: data. With enough data and intelligent ways in which to parse through it, companies have become more profitable advertisers, social media giants, and workplace necessities.
And big data has sprung off its own set of niche needs as well. Machine Learning, defined by some as the ability to have computers learn and act as humans do, has become a multi-billion dollar industry. Companies can be secretive about just how much they’re investing and using algorithms which teach their technology to act like humans, but we can sure it’s vast.
The Gold Rush Response
The rise of machine learning has been an urgent call to arms for most private and public enterprise. According to Forbes, “data is growing faster than ever before and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet.” The data represents profitability, yes, but it also is a way for businesses and projects to keep up with the changes in the world. What are the biggest buying trends? What’s happening to consumers, searchers, influencers? It’s imperative that these answers have solutions and the solutions are in the data.
So far, the response has been swift and capital-intensive. Companies like Facebook and Google have increased their market capitalizations by tens of billions by utilizing data intelligence and machine learning insights. Companies specializing in simple data collection, aggregation, and charting have become billion dollar companies themselves. The world is responding to the gold rush.
Here’s more proof:
- By 2020, 57% of business buyers will depend on companies to know what they need before they ask for anything. This means having solid prediction capabilities with your AI will be the key to keeping your customers. (Salesforce)
- Netflix saved $1 billion this year as a result of its machine learning algorithm which recommends personalized TV shows and movies to subscribers.” (StatWolf)
- AI software will grow from $1.4 billion in 2016 to $59.8 billion by 2025. (Tractica)
The Cautionary Tale
But here’s where things get cautionary. We’re starting to see the downside of data—both in an intrusive sense and one of data security.
Surely some will point to Facebook and its troubles this year, and they’d be right to do so. Facebook, with its expansive amount of personal data, can be used for advertising targeting that has made some feel intruded on, or susceptible to propaganda that has a resounding impact. Others point to the intrusion of commerce brands like Target knowing when the right time to sell personal items might be.
Perhaps the bigger worry is security. If personal data falls into the wrong hands it could be catastrophic; particularly if that data is already realized and packaged into insights, or the algorithms which respond to that data because mechanized for malintent. We’ve seen this in credit agency hacks and other invading forces; looking for ways of utilizing personal information for some gains.
The Blockchain Solution
As it has been trumpeted across so many industries, the blockchain—an innovative technology that self-records uneditable data—can be a hero in this story. Why? Well, there are a few reasons but the security aspect above is a good starting place. Blockchain can keep data secured through decentralized channels, but also it can be a record of those who access data, so users wouldn’t be left in the dark.
That’s just the start though. Blockchain also helps in the storing of this data; de-centralizing the bits of data instead of loading them onto a central server (itself capable of failing and surrendering that data to a blackout or, worse, a hack). Blockchain also opens avenues for sharing information as well; since it can be accessed from outside participants, a blockchain can split data its aggregated for different purposes.
This is where projects like Daslink (Das stands for ‘Decentralized Analysis System’) come in. It can and will, as machine learning systems do now, aggregate troves of data for highly realized insights. But it can go a step further. The blockchain addition to this superpower the machine learning capabilities; now it can run these insights securely and continuously. It need not rely on a centralized organizer to run insights or maintain its database, instead, it can run through user bases, case studies and more.
From the Daslink whitepaper:
Based on blockchain technology, DAS integrates big data and artificial intelligence to build a decentralized event-driven service architecture that can trig calculation per events on blockchain (structured or unstructured data like news, price, emergencies, etc.), and integrates market entity relationships by constructing knowledge maps, as inputs of specific business scenarios and computational models, to obtain real-time analysis results
The Daslink ecosystem will provide data aggregation, data quantization, and social aspects within its work. It will use proprietary algorithms for its own data analysis, but pledges to also provide social services—which one example provided would be cryptocurrency investment analysis.
The Power Of Blockchain Machine Learning
Through projects like DAS, the blockchain will become a vital enabler of machine learning going into the next decade. As data projects continue to assemble, aggregate, and organize terabytes of data every day, technologies like blockchain—and decentralization efforts—will prove to be necessary for upkeep, security, accuracy, and access channels.
Algorithmic processing will be part of the next decade and beyond for the world’s business functions, and it will power a new frontier of possibility. The blockchain will help ensure that the frontier lives up to its promise.
