Most IT security pros say that protecting an IT environment starts with safeguarding privileged accounts. The automation that is part and parcel of the cloud and DevOps mean privileged accounts, credentials, and secrets are being created at breakneck speed. If breached, these provide attackers with an ideal platform from which they can gain access to sensitive data across networks, data and applications, or cloud infrastructure they can use for illicit cryptomining activities. More organizations are acknowledging this security risk but nevertheless adopt a lax approach to cloud security.
Machine learning in finance may work magic, even though there is no magic behind it. Still, the success of machine learning project depends more on building efficient infrastructure, collecting suitable datasets, and applying the right algorithms. Machine learning is making significant inroads in the financial services industry. It helps reduce operational costs thanks to process automation, increase revenues thanks to better productivity and enhanced user experiences, and better compliance and reinforced security. Let’s see why financial companies should care, what solutions they can implement with AI and machine learning, and how exactly they can apply this technology.
Machine learning can help streamline the delivery of healthcare services, and one of the biggest issues that need to be addressed is the future role of machine learning in healthcare litigation. The real benefit of machine learning is that it can process massive data sets to help healthcare professionals make better decisions to improve patient treatments, yield more accurate diagnoses and minimize costs without compromising the quality of care. Healthcare litigators have realized that big data is changing their profession in countless ways. They are exploring new ways to use machine learning algorithms to find the most lucrative cases and develop winning strategies.
The business landscape changes daily and with that comes new “buzzwords.” You know those ones that really bug you – those where you kind of know what they mean, but they can mean lots of things and different things to different people. One such word is “servitisation.” This term captures so many of the other current industry terms and buzzwords around Industry 4.0, digitisation, IoT, mobility and much more. What is needed is a focus on the data, the foundation of servitisation, and how to extract value from that data quickly through an “analytical life cycle.”
While neural networks are responsible for recent breakthroughs in problems like computer vision, machine translation and time series prediction — they can also combine with reinforcement learning algorithms to create something astounding like AlphaGo. Deep reinforcement learning (DRL) is a machine learning method that extends reinforcement learning approach using deep learning techniques. Recent advances in Deep learning area has also fueled in Reinforcement learning as it doesn’t need hand-engineered features any more because of this ability. After appropriate many backpropagations, deep neural network knows which information is important to do the task.
Women encompass half of the world’s population. However, their numbers are not reflected in technological fields and corporate boardrooms. Data science is one of the highest-ranking careers for employee satisfaction. Big data leadership opportunities can offer women a successful career path. Hopefully, positive career prospects and salaries will encourage more women to pursue prosperous technology careers. By following the example of great women leaders in technology, aspiring female executives might one day take on the role of empowering their coworkers and organizations.
Natural Language Processing (NLP) is a field at the intersection of computer science, artificial intelligence, and linguistics. The goal is for computers to process or “understand” natural language in order to perform tasks that are useful, such as Performing Tasks, Language Translation, and Question Answering. It is certainly one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. This 2-part series shares the 7 major NLP techniques as well as major deep learning models and applications using each of them.
Here are some useful advice and questions and answers for machine learning/data science ‘starters’. We cover key books, foundation knowledge, mathematics, and programming tools needed to kickstart the journey. A curiosity to learn new things and a passion to work hard for it is necessary. You have to acquire knowledge, practice, and internalize concepts as you go. Do your own reading, understand what it is and what it is not, where it might go, and what possibilities it can open up. Then sit back and think about how you can apply machine learning or imbue data science principles into your daily work.
Analytic Modules are pre-built engines that can be assembled to create specific business and operational applications. They produce pre-defined analytic results or outcomes, while providing a layer of abstract that enables the orchestration and optimization of the underlying machine learning and deep learning frameworks. One example of an IoT analytic modules would be Anomaly Detection. Anomaly detection is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. A number of different machine learning techniques can be used to help flag and assess the severity of detected anomalies.
“Multiple Persona Disorder” occurs when you force all user personas through a single user experience even if they have different requirements. There’s a good chance it’s always been done that way for your product. Enterprise software users are typically provisioned based on role, meaning that each role logs in, accesses and interacts with the application in the same way. Role-based access defines the data each sees but the experience is the same. Whatever you call it, MPD is a major issue in enterprise applications. Forcing too many different user personas through a single user experience inevitably adds complexity and hinders adoption.
The mobile app development domain is exciting and challenging at the same time. It is interesting to see how IoT impacts the mobile application development. The mobile domain always provides scope to access the IoT-enabled devices. Mobile apps are useful to access IoT ecosystems. Both IoT devices and mobile apps are two sides of a coin. They complement each other to create the third product, which is highly useful for the enterprises and enables entrepreneurs to stay ahead of the curve. Find a few ways in which IoT can affect the mobile app development.
The level of complexity, speed and detail in modern manufacturing processes has become almost impossible to manage via manual or human effort alone. Assistance from technology and engineering have been prevalent since the introduction of the steam engine, but as we navigate through the Fourth Industrial Revolution (Industry 4.0), artificial intelligence (AI) is becoming a common theme. When we apply artificial intelligence or machine learning to the manufacturing process, what do we mean? This is about understanding data, extracting insight and learning from the outputs.
IoT is rapidly emerging as the next giant technological leap towards global integration and interconnectivity. Combining the expansive geographic reach of Internet with the pervasiveness of everyday objects makes the Internet of Things a truly global network, where everything can communicate with everything else. The applications of this technology, or rather a phenomenon, though not fully realized, are already emerging everywhere around us.
A part of our customer understanding comes from what we think is valuable to our customers, average deal size, repeat purchases, etc. But usually, these insights are narrow and not in real time. While it is one thing to capture data, ingest and get insights – the accuracy of data becomes more important. To create a long-term customer isn’t only about catering to their needs but also being able to pick up their buying signals, understand their behavioral patterns and more importantly, being able to use the insights to personalize their customer experience (CX).
One of the domains where the General Data Protection Regulation (GDPR)will leave its mark prominently is the artificial intelligence industry. Data is the bread and butter of contemporary AI, and under previously lax regulations, tech companies had been helping themselves to users’ data without fearing the consequences. That will change on May 25, when GDPR comes into effect. GDPR requires all companies that collect and handle user data in the European Union to be more transparent about their practices and more responsible for the security and privacy of their users.
Realizing the growing security risks in the legally complex and increasingly regulated global economy, software development outsourcing companies put a lot more emphasis on complying with industry regulations, policies, methodologies, and technologies used to protect data. They conclude well-thought-out service-level agreements (SLAs) with their clients and look for more efficient solutions for responding to potential vulnerabilities in the development process and tackling the security challenges. We understand the importance of information security when working with international clients. Therefore, we’d like to share our knowledge and experience in the most effective information security procedures when outsourcing software development.
The digital revolution can be brutal, and companies that fail to keep pace with it will quickly find themselves replaced by one of their more technology-savvy competitors. That means companies need to step up their IT activities to avoid a competitive disadvantage — and they need to find the right technologies and ensure they remain secure along the way. Organizations must be prepared for worst-case scenarios while pursuing their digital transformation objectives. Cloud technologies offer a golden opportunity for any business that aims to fuel its digital transformation process.
Specific knowledge about fintech operations is a must for efficient fintech app development that includes compliance aspects, understanding how different types of fintechs operate, background in finance and banking, and more. Also to ensure growth, fintechs need a reliable and easily scalable platform built in compliance with the best industry practices. Here are 15+ rules for fintech app developers and grouped them according to 4 underlying principles. In Part 1, we focused on security and compliance, and API-led connectivity. In part 2, we dwell on the rules related to software infrastructure scalability, and specific domain expertise.
Edge computing is a means of processing data physically close to where the data is being produced, i.e. where the things and humans are — in the field area, homes, and remote offices. Since they don’t live in the cloud, we need to complement cloud computing with many forms of computing at the edge to architect IoT solutions. Since we’re referring to computing close to the source of things, data, and action, a more generic term for this type of data processing is: proximity computing. In the next year, we will see reference architectures evolve to support new application patterns for IoT that incorporate proximity computing.
One of the most exciting things about cryptocurrency is that the future hasn’t been written yet. And one of the things that strongly distinguish people in the cryptocurrency movement is how they deal with consent. If you care about consent, the safest thing to do is to ask every time. We are all creating this movement together. Let’s all do our best to build the culture of consent. The future of cryptocurrency, the future of open source money, and therefore the future of our financial infrastructure depends on it.
Fintechs currently face the challenges of growing and scaling. Yet most will likely fail because: they could not find the right product-market fit, the high cost of scaling up, inability to find the right partner, and the struggle to create, launch, and quickly gain market share for a differentiated product that cannot be replicated. And to overcome those hurdles, they, first of all, need a reliable and robust platform built in compliance with the best industry practices. We outline15+ time-tested rules of fintech app development.
As enterprises delve more deeply into IoT, there will be a growing need for an operational intelligence-oriented data analyst as many IoT use-cases demand near real-time operational insight. So you can expect to see a huge uptick in demand for people who have technology and business skills related to the Internet of Things (IoT), as organizations continue to ramp up their IoT projects in a big way. As enterprises delve more deeply into IoT, there will be a growing need for an operational intelligence-oriented data analyst who is also an “AI/ML data engineer.”
Deep learning, the spearhead of artificial intelligence, is perhaps one of the most exciting technologies of the decade. There’s no doubt that machine learning and deep learning are super-efficient for many tasks. Deep learning is often compared to the mechanisms that underlie the human mind. However, they’re not a silver bullet that will solve all problems and override all previous technologies. Beyond the hype surrounding deep learning, in many cases, its distinct limits and challenges prevent it from competing with the mind of a human child.
Today’s marketers and salespeople are harnessing technology advancements to unearth insights in real-time, which was unimaginable few years ago. However, the key question is – are we looking at the right data? If our intent is to collect and understand customer feedback, we are looking at only partial information. A lot of information is usually in hidden in what is usually referred to as “dark data” or in simple terms unstructured data. You need to go beyond structured data and demystify the dark data that can give you relevant information.