You may have noticed that the talks of AI or Artificial Intelligence have become more and more prevalent on the internet. The impression that people have with regards to it, is mixed with awe to fear.
However, can you trust AIs with your business? What can they offer to help? Does your website need to embrace them now? If not, then when?
The Entrance of AI in Businesses
The internet produces and stores billions and billions of data every day. The human brain can only take too much before it gives up with information overload.
The data that are produced every day is essential for business owners and marketers alike. Data collected from the internet will tell them how their business is faring. Are they making a profit or losing money? What do customers expect from their companies?
With AI, they can easily track and measure their progress in business based on the analysis that the AI has made from relevant internet data.
So Why Integrate Them Now?
Due to stereotypical movies and the fear of the unknown, many talks of AIs replacing people’s job and starting World War III circulate the internet.
It is understandable that most people have reservations about utilizing AI on their website. However, it is essential that you integrate them into your business.
And here are a few of the many reasons why you should embrace AI for your business sooner than later.
Improve User Engagement
Users matter. They are the people that can become potential customers in the future. By integrating AI into your website, you will be able to improve your user engagement and foster a relationship thus converting them into loyal customers.
AI can help you evaluate the data that was collected based on your website visitor browsing behavior and present the appropriate content for them. They can also provide you useful information to help you improve your methods such as what content your users are more interested in, UX design, and more.
Just like how JP Morgan Chase is utilizing AI for their business.
Cater to Customer Needs Anytime, Anywhere
The internet never sleeps. There will always be people online and your website is automatically a business that’s open 24/7 365 days a year. People are impatient and they wanted a response as soon as possible.
Thankfully, AI can provide a solution for that. Incorporating chatbots in your website enables customers to submit their queries and would be able to temporarily cater to customer needs such as inquiring about a product as well as checkouts.
Major companies such as Google and Amazon are investing a lot in AI to improve their services.
Your Competitors are Doing It
No ones to get bested by their competitors. With all the benefits that AI can provide, competitors who are utilizing one for their website will surely gain the upper hand.
If the competitor makes user and customer engagement much more comfortable for your customers, chances are more people will go to the competitor. Get that edge first and implement the use of AI to improve your services as well as your SEO to get ahead of the competition.
Embrace Change: AI is Part of That Change
Although there are still many skeptics and doubts that revolve around the use of AIs, no one can deny the fact that they make work and life more manageable. The internet is not the only one that keeps on changing and evolving.
People, and more importantly, the world are functioning dynamically. There is no such thing as no change.
We are already at this stage in life and generation due to change. So does your website need to embrace now? Yes. If not, then as soon as your able. If you want your business to grow and succeed, you have to embrace change. And AI is part of that change.
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If you’re looking for a new skill to learn. Or boost up your resume. Then learning to code would probably be the best way to go. It’s a useful skill to have, whether or not you’re looking for careers in professional fields of web design, gaming, big data, artificial intelligence, etc. By learning how to code in at least one programming language, you not only broaden the scope of your own opportunities. But you also improve certain functional aspects of yourself, which you didn’t think was likely. Studies have reported that learning how to code helps you hone your problem-solving techniques, improve cognitive thinking skills and teach you to think out of the box.
And because of the magic of the internet, it’s possible to learn to code from the comfort of your home. There is a number of websites which offer both paid and free online courses in coding. Top online learning websites like Khan Academy, Udemy, Udacity and Coursera offer some of the best content if you are a beginner. They teach you right from the ABCs, to more advanced techniques.
There are also a number of mobile apps who offer quick bite-sized lessons for coding. While some of the websites mentioned earlier do have mobile app versions, their courses do not focus solely on the art of coding. So here are a couple of cool apps dedicated to teaching beginners how to code. As well as help them practice and test out their own code.
1. Code Monk by Hacker Earth is a curated list of topics to help users improve their programming skills. They offer a series of weekly tutorials which cover concepts like the basics of programming, algorithms, implementation of the code, data structure, maths and more. You can choose to learn various programming languages like C, C++ and Java. And to test your understanding of what you have learned, there are regular coding contests and challenges for you to take part in. The app is not only cool for beginners but is also a great way for experienced coders to practice and brush up on their skills.
2. CodeHub is a minimalistic, modern and comprehensive app. It compromises of several courses, of 50 small lessons each, which takes you through the entire process of coding. An interesting feature of CodeHub is that it categorizes its lessons into 4 difficulty levels. You can select whichever level suits your preference and divide the lessons accordingly. If you already know the basics, you could easily skip the beginners’ levels and move on to higher levels. Currently, the app offers courses for Web, HTML 5 and CSS3, but plan on including more languages soon.
3. Encode is a free-for-download learning app powered by JavaScript. It was made by Upskew Pty. Ltd. and is available only to Android users. It’s the perfect place for beginners to get started with the basics of coding. And due to the app’s interactive coding challenges and short lessons, beginners can gradually learn how to write more complex codes, along with running and testing out their own. Currently, their teaching pathway supports lessons from programming languages like Java Script, Python, HTML and CSS.
4. Programming Hub offers lessons across the biggest range of programming languages. Put together with the help of experts from Google, the app has course material which covers more than 20 languages, and over 5000 programs and code snippets. The creators of the app emphasize on making the process of learning coding fun, interactive and personalized for the users.
5. SoloLearn is actually a series of apps, each of which aims to teach you a specific coding language. The collection of apps has been described to be dynamic, interactive, simple and easy to use. Another great app for beginners, SoloLearn helps you learn the concepts of programming and coding by making you follow a lesson plan. The lessons compromise of short texts and quizzes, and learners are individually scored. The app is completely free, and compatible with both iOS and Android, making it a super accessible app.
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Cryptocurrencies like Bitcoin have proved to be the most secure virtual currency, but it has not been spared of its share of cyber-attacks. In January 2018, panic spread across Bitcoin investors when were unable to withdraw their cash following a cyber-attack which left their money stuck in the system. In July 2017, around $30 million worth Ethereum Currency was stolen by hackers through a cyber-attack launched on the three of the largest wallets. Bitstamp was recently in the news when 19,000 BTC were pilfered after the exchange’s virtual wallets were compromised.
Why is it happening repeatedly? Experts say that it is mostly because all bitcoin transactions are irreversible. Official authorities can undo the transactions only in the case of an error. It is interesting to note that both buying and selling parties are unknown to each other and despite numerous techniques, it is difficult to track stolen bitcoins.
Now, what are the best ways to protect one’s Cypto wallet? There is plenty of security measures available right now to add a significant layer of protection to one’s cryptocurrency funds, but According to Nikolos Faslow, a Security Engineer from Bestvpnrating, one of the wisest ways to protect electronic cash so far is hiding it with a private network, popularly known as VPN. Virtual private network possesses a lot of features that guarantee user’s virtual freedom.
Although it is a primitive basis of online security, some people still ignore the tips like “create complicated passwords”, “create different passwords for different wallets”, and “never keep your passwords written and stored in a place anyone can reach”, like a sticker on your PC. A VPN will not save your money from your own negligence. A VPN is a secure solution that allows users on the Internet to transfer data while maintaining the secrecy of a private network. VPNs conceal your IP address by taking on one provided by the VPN service provider, so your activities online are essentially untraceable.
Second, while using a VPN, you have access to restricted websites, because this is what a VPN does, blocking access either due to geographic policy, or because such websites are considered improper/unreliable by the government of the state. So, while VPN, on the one hand, protects you from being monitored, it may expose you to websites and users that may pose danger to your finance. So, even using a VPN, you should be a bit paranoid about clicking each and every link you might want to follow.
The main function of any VPN is making user invisible for anyone else in the network. Optionally, our computers, when online, are sending signals directly to chosen web address. When there is a VPN installed, the signal is first transmitted to a certain virtual server, which grants it with new dynamic IP (the one that actually belong to this server), and only after that it reaches that site we wanted to visit. Tracking all this way is impossible, so nobody can reach our private data, including online wallets.
Click here to get more information on how to set up a VPN.
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With the evolution of technology, data is present everywhere. Thanks to the internet, which has enabled inter-connectivity of millions of devices across the globe. There has been an unprecedented growth of data usage in the recent years which is likely to expand exponentially even further. Big Data is one such term which has taken the world by storm. Its growth has been incredible. This has aroused curiosity in the minds of many.
Big Data has superimposed traditional data processing applications with newer and refined datasets. Two of Big Data’s most trending technologies which are creating a furore among end users in the analytics world — Apache Spark and Hadoop. These two crucial frameworks which form a significant part of the Big Data family. Some people view these two technologies as major competitors in the Big Data space. Although it ain’t that easy to compare both since they are similar to each other in many aspects. Yet there are some areas in which Hadoop and Apache Spark don’t overlap. In this blog, we shall discover which framework has an edge over the other.
Apache Spark
Apache Spark is a Big Data framework which operates on distributed data collections. It furnishes in-memory computations for improved and quicker data processing over MapReduce. It is a cluster-computing framework which is designed for faster data computations. It includes a huge variety of workloads which may be used for iterative, interactive and batch data computing. Apache Spark uses a hybrid processing framework by combining the various workloads for data processing and interactive queries together.
Hadoop
Hadoop is an open-source Big Data framework with a distributed data infrastructure. The distributed data is stored across multiple nodes within a cluster of commodity servers. It is an inexpensive software which is a fundamental need to most Big Data projects as it allows to store vast datasets across various cluster platforms. Initially, it was used for searching web pages and data collection purposes, but gradually it got recognized as a means to store distributed datasets across multiple servers. Over time, Hadoop has become a de facto model in the Big Data space.
Now the question is— Apache Spark or Hadoop : What’s the difference? Who wins?
Data Processing and Storage
Apache Spark is a hybrid data processing tool which upscales batch processing through in-memory computation and data process optimization. It can process huge workloads by utilizing both streaming and batch methods which is popularly denoted as Lambda Architecture. It offers programmers with a programming interface for storing data items in Resilient Distributed Dataset (RDD). On the contrary, Hadoop creates new algorithms to expedite access for enormous batch data processing. Hadoop MapReduce, an indigenous batch processing appliance can store large datasets in its own persistent disk.
Easy of Operation
Spark is comparatively easier to operate than Hadoop. It uses various foolproof APIs like Python, Java, Scala etc. for simplifying data processing and streaming. Use of such interactive methods like REPL (Read-Eval-Print Loop) allows end users of Spark to obtain immediate feedback from programming commands. Whereas, Hadoop is pretty difficult to program as it uses Java for data absorption. Hadoop doesn’t have any interactive mode like Apache Spark. Although there are other frameworks like Hive and Pig which makes it convenient for the users to operate programs.
Real-Time Functionality
Apache Spark allows data processing on a real-time basis. For this reason, social media network like Facebook and Twitter rely on Spark’s ability to process live data streaming effectively. On the other hand, Hadoop MapReduce fails miserably in the real-time function. Reason being, we have always known Hadoop as a batch data processing tool which primarily focuses on storing voluminous data on-disk.
Cost Factor
From the cost perspective, Spark is a pricey deal as it consumes a majority of RAM space for in-memory data computation. Buying a RAM may prove pretty extravagant for an end user. Whereas Hadoop is disk-bound and distributes datasets over multiple systems and does not use RAM for storing datasets. It saves the cost of investing more money in buying expensive RAM and is far more reasonable and cost-effective than Spark.
Takeaway
Both Apache Spark and Hadoop are open-source projects of the Big Data ecosystem. To conclude, Spark has an upper hand over Hadoop in terms of certain interactive, batch, or streaming requirements. However, choosing between the two frameworks completely depends upon the needs and obligations of a business organization as both are compatible with each other.
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Blockchain technology is the single most polarizing tech innovation in the market right now. Many industries are still undecided on whether blockchain technology will be an asset that will unlock new levels of value OR a threat that could potentially send them into oblivion. The many applications of blockchain technology includes cryptocurrency, smart contracts, decentralized ledgers, and consensus protocols. Yet, beyond these, blockchain has the potential to disrupt many industries ranging from real estate, to shipping, and all the way to prescription drugs. This piece examines how blockchain technology could directly influence the turn of events in the big-data industry in the medium to long terms.
Big data is simply a large data set that is too voluminous to be managed by traditional data-processing software. When analysed computationally however, they can reveal interesting patterns, trends, and associations betraying underlying human behaviours.
Big data is big across many industries and the world is starting to pay special attention to the wisdom of the crowd by being patient enough to hear what the data is saying. Big data is one the industries that is set to experience first-hand, the disruptive power of blockchain technology. Below are three ways blockchain could influence the big data industry
1. More efficient data storage
The world is generating an unprecedented amount of data as the Internet of Things enables all kinds of devices record activity across different parameters. In 2017, IBM reported that about 2.5 quintillion bytes of data are generated every day as enterprise continue to strive towards data-driven decisions. The problem however is that enterprise clients, governments, and sometimes individuals are at loss on how to securely store the huge treasure troves of data that they are generating or capturing.
Traditional data storage methods are expensive because of the huge resources needed to manage a centralized data centre. Secondly, storing data in a central location increases the odds of a data breach since a security breach at any of the locations makes all the data vulnerable. More so, storing data in centralized locations also increases the odds of data loss in the event of a mishap.
With the decentralized nature of blockchain technology, data is stored on a decentralized network; hence, all the potential failpoint for a data breach or data loss become less off a worry. The fact that no single individual or company oversees the storing or keeping blockchain data also ensure that accuracy, integrity, and incorruptible nature of data sets.
2. Speedy data processing for real-time analytics
Big data is not much valuable in itself unless the data can be analysed to mine out the underlying behavioural patterns. Real-time data analysis makes its easier to make on-demand decisions – hence, organizations will find it much easier to run effectively and with significantly lower overheads.
Blockchain technology enhances the possibilities of real-time data analytics – all that will be required of developers are smart contracts that acts based on the of “if this, then that” logic of your smart contract. Blockchain technology can also be instrumental in unveiling new methods for monetizing the data.
3. Improved fraud detection and prevention
Blockchain technology can be a powerful tool for preventing data fraud by ensuring the immutability of data. Different kinds of data are valuable in different ecosystems – for instance, sales volume, inventory, and overhead expenses are some important data for a retail business.
The decentralized nature of blockchain technology ensures that you can’t edit, tweak, or manipulate data once stored on the blockchain. Where applicable and allowed, changes to a data set stored on the blockchain will be visible throughout all the nodes of the platform. Hence, it will be practically impossible to pull off data fraud on data stored on the blockchain.
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For today’s business, leveraging the power of big data isn’t a nice option — it’s a clear necessity. For nearly every industry, from communications to energy, architecture to real estate, the power of big data to provide intelligent insight can’t be overstated. The fine-grained detail and big picture are both visible at this level, both captured by careful analysis of huge datasets.
But make no mistake: huge isn’t an exaggeration. When a dataset reaches into billions of points of information, it simply exceeds human capacities. Even an expansive team of analysts can’t deal with data that large. That’s where cutting-edge artificial intelligence (AI) and machine learning come in. Driven by processing power that can handle datasets of that size, the most advanced AI can deliver the goods for 21st-century business.
For example, ride-sharing services like Uber need some way to manage demand and make sure that their customers don’t wait too long for a ride they’ve hailed. And by using machine learning to predict the details of demand, they’ve made massive gains in efficiency. For instance, Uber Eats, Uber’s food-delivery service, adopted AI to improve its customer satisfaction. As Danny Lang, the head of machine learning at Uber, explained, they moved from “a finite approach — where you compute the time using the distance between you and the restaurant, the average speed and the time to prepare the meal — to taking the delivery times for thousands and thousands of meals and basing the prediction on that. Overnight, that improved our estimates by 26 percent.”
The popularity of AI and big data stall as they expand
That kind of wizardry has been an easy sell to companies already occupying a high-tech niche. But as AI begins to mature, really coming into its own, its developers are finding it increasingly difficult to impress less tech-oriented companies and more conservative industries. As Joe McKendrick writes for Forbes, “To be sure, there is no shortage of excitement around the possibilities AI and machine learning bring to enterprises. But most organizations are still tepid about embracing these approaches in a big way.” In fact, in industries like real estate, the major players are holding off on AI until it can demonstrate clearer return on investment. “The real estate industry is conservative, technology-averse, and not prepared to take technology on board as a product,” cautions Paulo Scarpelini Neto, a real estate tech entrepreneur.
Indeed, a new Imprev Thought Leader Survey revealed that a solid majority of decision-makers in real estate were giving AI a pass, despite expressing interest in the power of big data. As they report, “Don’t hold your breath for widespread adoption of Artificial Intelligence (AI), Augmented Reality (AR) and Virtual Reality (VR) 3D tours in the next five years. Real estate execs expressed their doubts by giving these emerging technology [sic] lukewarm ratings. In fact, AI was ranked highest among the emerging technology that executives were ‘least likely’ to invest in, followed by AR and VR.”
That may put the breaks on the market penetration of AI, and with it, greater adoption of big data strategies in business. But we think that’s simply wrong.
But this is because AI is poorly understood
The power of big data and AI is too good to miss, and especially for conservative industries like real estate, architecture, and retail, big data offers revolutionary gains. Want to know more about your customers and their preferences, allowing you to personalise offers to them in real time? Big data is the answer. Need to design a building that can accomodate a long list of competing demands? Big data is the answer. Having trouble matching prospective buyers with properties or figuring out who’s really looking from the merely curious? Big data is the answer.
This isn’t hype — big data really can deliver.
But the problem McKendrick and Neto point to isn’t an issue with the tech or the data. Instead, because AI is so new and so high-tech, it’s poorly understood by most companies and their leaders. It’s understanding that’s at issue, not the utility of big data and AI.
In fact, as Kriti Sharma reports for Business Insider, “43% in the United States and 46% of respondents in the United Kingdom admitted that they have ‘no idea what AI is all about’”. That’s understandable — just a few years ago, this tech wasn’t available; it takes time to adjust and adapt. Moreover, as Michael Chui, James Manyika, and Mehdi Miremadi explain for McKinsey, “It is hard to reach a leading edge that’s always advancing.”
In plain English, the tech has outpaced understanding. And to make sense of it, business leaders “need to understand not just where AI can boost innovation, insight, and decision making; lead to revenue growth; and capture of [sic] efficiencies—but also where AI can’t yet provide value”, they insist. “What’s more, they must appreciate the relationship and distinctions between technical constraints and organizational ones, such as cultural barriers; a dearth of personnel capable of building business-ready, AI-powered applications; and the ‘last mile’ challenge of embedding AI in products and processes.”
AI explained, very briefly
Most people don’t get AI and big data. They don’t understand what it is and what it can do, nor do they have a sense of its limitations. Let’s go over those quickly.
What makes artificial intelligence and machine learning unique is that it learns to do something without being specifically programmed to do so. Using sophisticated neural networks that mimic the way human beings think and learn, AI uses carefully labelled data to teach itself. Consider facial recognition. In this application, a neural network might be shown millions of pictures of faces, each expressing a carefully identified emotion. By learning to associate the identified feelings with ‘maps’ of each face — the positions of the corners of the eyebrows, the distance between the edges of the mouth and the nose, etc. — a machine learning system actually…well…learns. After a while, it can look at unlabelled faces and have a very, very good sense of what that person is feeling.
It takes an enormous amount of data to get this process started, and there are technical challenges. Bias in the algorithms or the data can cause problems later, and sometimes, the AI learns to do what it does without us knowing exactly how, a problem known as the ‘black box’. And once it’s learned to recognise emotion, for instance, it can’t then automatically apply what it has learned to do something else with faces, like recognise gender. Its learning is often poorly generalisable, or in other words, limited to a very specific task.
But as AI advances, so too does its promise. And we predict that as businesses recognise the added value of big data — and the AI that manages it — they won’t be able to say no. And the undeniable benefits of AI are simply amazing, even in the most conservative industries.
AI in retail
These are real obstacles, fundamental challenges to the application of AI. But none of these are unworkable issues. And especially for routine tasks like tech support, in which the vast majority of calls relate to lost passwords, or customer service, in which the vast majority of tasks don’t demand the skills of a living, breathing human being, intelligent chatbots are revolutionising how we do business. For instance, Mai-Hanh Nguyen reports for Business Insider that “60% of US consumers have not completed an intended purchase based on poor customer service experience”. That’s a number that should terrify anyone who sells anything, not just the high-tech giants. But with intelligent chatbots that have learned to recognise emotional states and respond appropriately, your business can field top-notch customer service, 24 hours a day, 7 days a week. Take a look at IBM’s “Tone Analyzer”, for instance.
AI in real estate
Don’t underestimate the power of intelligent chatbots in real estate, either. Whether it’s fielding calls from clients, answering simple email queries, or organising showings, bots can free up valuable time for agents. And with emerging AI in the guise of smart speakers, some revolutionary changes are on the horizon for realty.
But where big data and AI really shine in real estate is in matching buyers to properties they’ll love. By using massive datasets, artificial intelligence can do some pretty amazing things. Already in 2016, simple bots demonstrated that they were better than human agents at predicting buyer preferences. With the more advanced machine learning and better data available now, AI offers realtors a ground-breaking new approach to marketing homes.
AI in architecture
Though traditionally slow to embrace change, the architecture industry will soon give AI more than a passing glance. Generative design is an exciting new approach in architecture. And by tasking AI with drawing up plans, and giving it a sense of what you need a building to do, it can run thousands upon thousands of designs to see which permutations give the best results. For instance, when Autodesk wanted to build a new headquarters, they realised that weighing competing design goals one against another at this scale was Herculean, so they handed the project over to AI. As Danil Nagy, a designer and senior research scientist for the company, explains, “The starting point for our use of generative design was trying to determine which aspects of the architectural design process were the most complex and difficult for humans to think through, and figuring out how to get a computer to work them out for us … It’s all about isolating those very tricky practical issues, and then using a computer to automate the development of solutions for those problems.”
Don’t listen to the naysayers
If anything’s clear, it’s that big data and AI are here to stay. They’re not just flashy tech and empty hype, and as these examples suggest, even industries reluctant to adopt new tech will soon happily embrace the awesome power they offer.
It’s understanding — not utility — that’s lacking.
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