With growing amounts of computational power, machine learning and deep learning are increasingly making their way into numerous sectors. They are widely used for recognizing objects, translating speech in real-time, determining potential outcomes, understanding consumer habits, making personalized recommendations, and a lot more. Still, some questions remain uncertain. What do these two entail? And should companies invest in machine learning development to benefit their businesses? We provide the answers to these questions in our article.
GAN (Generative Adversarial Network) addresses the lack of imagination haunting deep neural networks, the popular AI structure that roughly mimics how the human brain works. GAN technique proposes that you use two neural networks to create and refine new data. There are many practical applications for GAN. GANs might prove to be an important step toward inventing a form of general AI, artificial intelligence that can mimic human behavior and make decisions and perform functions without having a lot of data. GANs can’t invent totally new things. You can only expect them to combine what they already know in new ways.
What do artificial intelligence (AI), invention, and social good have in common? While on the surface they serve very different purposes, at their core, they all require you to do one thing in order to be successful at them: think differently. When it comes to AI, invention, and social good, the possibilities are endless. Technology will only continue to become more advanced, creating new opportunities to fix societal problems related to health, sustainability, conservation, accessibility, and much more. If you’re thinking of jumping into AI for good, just remember the most important rule: think differently.
IoT can be used not only to improve existing business operations but also to create new offerings and new business models. Business models require thinking through the consumption side of your offer — how it is bought and used, and the production side of the offer — how it is created and delivered. We need to think of the offer from the consumer’s lens, i.e. buyer, user, and operator. It is time to go beyond predictive maintenance and reimagining our offerings with IoT. Time to flip some tiles!
As companies scale transaction volumes and integrate with more and more third party software, they get a growing inflow of data and services. On the downside, this subsequently increases the risk of data breaches and cyber-attacks. fintechs currently suffer from the mismatch between innovations and regulations as the latter don’t keep up with the technological advancement in the financial industry. What’s more, early-stage startups usually don’t have adequate compliance teams. Such unstable regulatory environment creates additional security and compliance challenges for the financial market players. Then how to scale up without compromising security?
Artificial Intelligence has been buzzing around more frequently with higher and ever-increasing intensity every passing day. Reason is as clear as a crystal: Its power and possibilities it can create. It doesn’t really matter who thinks of AI highly or lowly but one thing is certain: Its time has come. Before you buy time in realizing and delay in deciding of its adoption, it may come and stare straight in your face! You may not even have the bargaining power that you still enjoy today.
VPN evolution has transitioned the technology from point-to-point connectors that facilitate remote access to one that's based on sophisticated security multipoint connectivity. VPN evolution has taken place over the years, adapting to the networks that have been shaped by broadband connectivity, the cloud, and mobility, as well as the endpoint devices themselves. Reflecting back on the early days of VPNs and how far we have come, the evolution and the history of VPN technology can be broken down into four phases. Secure communication is one of the most important foundations for our future, and it is imperative to protect data in motion with VPN evolution.
Data has become important for everyone like never before, because it makes us to take informed decisions, improve operations. We can only improve things & activities which we can measure, and when we measure anything, it is described in a form of data. If you want to leverage and operationalize data proactively, you need to invest in your underlying data architecture and compile the information map for your organization. Solid information architecture will also set up your foundation for a data governance program. You have to know what the data is and assign business meaning to it, with the proper terminology.
we continue to explore how technologies can help fintechs solve scalability challenges. We’ll try to answer the following question: How fintechs can find new revenue streams and extend their market reach? When fintechs find the technological capacity to build a scalable and reliable solution and manage to keep their operational costs low, they want to grow bigger, raise profits, and scale their business reach. However, that may be a daunting task due to strong cards of other financial services companies operating in the market.
Helping to fuel interest in data lakes are the digital transformation efforts underway at many enterprises, spurred by the emergence of the Internet of Things (IoT). The connected objects in the IoT will generate huge volumes of data. As more products, assets, vehicles and other “things” are instrumented and data ingested, it’s important that IoT data sets be aggregated in a single place, where they can be easily analyzed and correlated with other relevant data sets using big data processing capabilities. Doing so is critical to generating the most leverage and insight from IoT data.
Bitcoin is the leading edge of a movement I think of as Open Source money, and here are a few ways of thinking about this that might help. Here are 100 things to know for those new to bitcoin. The goal isn’t for you to understand all of these assertions, but if they contain unfamiliar words or concepts, you can then google those concepts and become informed. Understanding what is behind these 100 assertions will help you become knowledgeable about Bitcoin. If you want you can see how many of these things you already knew and give yourself a score out of 100 at the end.
What problems do fintechs need to solve to scale up and grow profits? They need building an easily scalable software product, partnering with other companies and engaging new customer segments, and complying with regulation and security standards while scaling up. In our series of articles we will dwell on how technologies can help you solve these 3 key challenges. We've collected and analysed findings from PWC, CBInsights, Forbes, etc., and fintech software development cases to elaborate a strategy on how to build a successful fintech business that generates profits, attracts investments and can achieve economies of scale.
Today we understand our customers better than ever. The data we gather and analyze determines the success of our business. Business Intelligence, big data, data science, and data analytics provide companies with illuminating insights. Those who adopt these technologies early, gain huge competitive advantages. Therefore, the demand for professional data analysts is far exceeding the supply. To deal with the scarcity of specialists and soaring prices for their services, many business leaders consider data analysis outsourcing. But how to make it right? Our article will highlight the most important aspects of data analysis outsourcing and tips on choosing the best data analysts.
Many startups invest too heavily in sales before really understanding their customers. Whether it is based on the belief that good salespeople can sell anything, spending too much money on sales personnel too early is a good way to fail fast in a bad way. Bringing on your sales talent before you are ready, can costs start-ups millions of dollars. Hiring your sales talent should instead be a natural outcome of understanding your customers, developing your segment playbooks and your learnings (wins and losses) from customer interactions.
When digital disruptors squeezed their way into financial services industry, one could think banks were doomed. Fintechs are luring customers away with better online service, lower fees, and cheaper transfers. Consider a huge cost of digital transformation for banking corporations and you see legacy players can have a hard time catching up with the newcomers. Apparently, the banks were not going to simply give up. Over the last five years, they spent tens of billions of dollars setting up innovation hubs, buying competitors, and changing operations.
Solving business problems with blockchain ensures efficient functioning of an enterprise. When an organization suffers a loss or a specific problem that manages to disrupt its rhythmic flow , authorities frantically search for options that can help them solve the problem. Blockchain is a technology that focuses on increasing transparency and introducing decentralization that will allow the technology to enable everyone on the network to view information stored on ledgers. Blockchain has many applications that contribute to its popularity amongst people. Here are four common business problems that can be solved using blockchain.
There are only two cardinal rules of cryptocurrency that arise directly from public key cryptography. As you probably know, modern cryptography is based on paired keys, a public key and a private key. Because of the blockchain, public keys and incidentally wallet addresses should be verified before sending. Due to the principle of immutability and the lack of a trusted intermediary, there’s no way to contest or reverse a transaction once it’s been made.The second law is simply that the owner of the private key is the owner of the wallet and all the funds therein.
Data clustering is the classification of data objects into different groups (clusters) such that data objects in one group are similar together and dissimilar from another group. Many of the real world data clustering problems arising in data mining applications are pair-wise heterogeneous in nature. Clustering problems of these kinds have two data types that need to be clustered together. In an industrial setting, despite collecting data from tens of thousands of sensors, less than 1% is actually utilized. We can move rapidly into Industry 4.0 by combining subject matter expertise, data collection methods and next-generation data science tools, beyond many of the "me too" products.
Business units need to work together, and companies need to know their consumers. By integrating data across teams, enterprises make it easier to analyze consumer information and complete marketing tasks. Big data and data-driven marketing is the new status quo. Marketers need the reports generated by big data to find new business, optimize campaigns and help companies make a profit, and each business has its own needs and its own way of making the most of big data technology. Big data technology, cross-functional integration and social media adoption are powerful and effective ways to reduce costs, discover new opportunities and launch new services and products.
Banking runs on a set of regulatory guidelines and deals with numbers, it was only about time that it would board the AI bus. Secondly, there is this deviation angle. As we fully realize the fact that anything handled by a human is prone to deviation or personal discretion; so to be vigilant, all inputs are to be taken with a generous pinch of salt. In other words, everything must undergo a reviewing pair of eyes. How about implementing a system that can auto-understand and auto-function and auto-verify without or with minimal human intervention barring some very delicate cases?
Few industries will see as big an impact from the internet of things as the insurance sector. Indeed, IoT has the potential to touch nearly every facet of insurance, with the promises of both benefits and risks for carriers as well as their customers. IoT will impact how insurance underwriting and pricing are done for markets including transportation, home, life, healthcare, workers’ compensation and commercial. And it will transform the way insurers gather information about customers and their environments to process claims, determine risks and calculate costs.
Are you taking your social media strategy as seriously as you should? Your customers are driven to your social media pages—but once they get there, they don’t find 24×7 empathy-building communication or proud, positive brand representation, and a fast, solution-focused approach. Instead they are faced with thread after thread of negative customer feedback and lacklustre product support. Simply put, you’re doing it wrong. Think smart about your social media strategy, and start with understanding the three ways today’s fashionable feedback tool can make (or break) your CX
In the near future, digital assistants will help with all kinds of mundane work tasks -- from setting up conference calls to replenishing office supplies. The majority of these digital assistants use voice recognition technology as their primary interface, which means they are always listening, even when they are not in use. With hacker activity and state-sponsored surveillance also on the rise, will digital assistants become the proverbial Trojan horse that allows attackers to sneak past our defenses unnoticed? While digital assistants are all very convenient, will using them be at the expense of our privacy and security?
While some things are easy to measure, intelligence is not one of them. Intelligence is a very abstract and complex thing to measure. How do we, people, perceive artificial intelligence and what are our expectations from it? It seems when people judge AI’s ability, we are harsh. We want AI to be perfect. We do not show the flexibility we provide to human mistakes proportionally to AIs. We want AI to be “like a human”. Whatever the reason is, it is a common pattern. We expect artificial intelligence to be comparable to human intelligence.