The divide between consumer health and fitness wearables, and medical-grade devices is slowly being bridged as technology evolves to offer advanced sensors and form factors that combine the best of two worlds. The result is vast amounts of higher quality data to feed the complex algorithms which not only deliver the personalized results the growing market of medical-grade devices, designed for consumer/patient access without the need of a prescription, is enabling a new category of remote patient monitoring.
It’s time to take our identity into our own hands. As social creatures, we are the sum total of our interactions with others. Blockchain brings new opportunities to the field of identity management. It does so via the qualities of immutability and distributed access (anyone, anywhere in the world, can verify that information exists). Together, these enable a new paradigm of trustlessness: I don’t need to trust you, a stranger, because I trust the immutability of the blockchain. Blockchain-based peer-to-peer marketplaces are the future.
The convergence of IT and OT dramatically alters investing activities in corporate development. We’ve been seeing a new type of acquisition by large, vertical-specific OT players acquiring ventures focused on vertical industries. The new digital trajectory of OT affects the strategic investment considerations of a corporate development leader in OT or IT and the strategy of an entrepreneur. How do you align the new target’s investments with internal business unit’s goals? Is the new technology enabling multiple internal businesses? How should it be structured and measured internally if acquired?
Data science is the discipline of making data useful. Data science is a ‘concept to unify statistics, data analysis, machine learning and their related methods’ in order to ‘understand and analyze actual phenomena’ with data. When all the facts you need are visible to you, you can use descriptive analytics for making as many decisions as you please. It’s through our actions — our decisions — that we affect the world around us. So it is making data useful.
Many enterprises that scrambled to put a minimally GDPR-compliant set of privacy policies in place are now lulling themselves into complacency. A closer look at the steps taken by many of these companies reveals a GDPR strategy that it is only skin deep and fails to identify, monitor or delete all of the Personally Identifiable Information (PII) data they have stored. To address these risks, companies need a holistic strategy to manage their data—one that automates the process of profiling, indexing, discovering, monitoring, moving and deleting all of their data as necessary.
Algorithms have the potential to help us overcome rampant human bias. They also have the potential of magnifying and propagating that bias. I firmly believe this is an issue and it is the duty of data scientists to audit their algorithms to avoid bias. However, even for the most careful practitioner, there is no clear-cut definition of what makes an algorithm “fair.” In fact, there are many competing notions of fairness among which there are trade-offs when it comes to dealing with real world data.
Business intelligence hasn’t lived up to its promise to give users unprecedented access to business insights. Vendors have spent millions trying to improve their user experiences and deliver self-service. But nearly every BI tool forces users to leave their current workflows and open standalone applications to analyze data. Increasingly, application teams are looking for new ways to deliver analytics that encourage user adoption and meet customer demand. Demand for standalone BI is waning.
The term Lean startup was coined about ten years ago. Since that time it has grown to become one of the most influential methodologies for building startups, especially those that fall in the category of web-based software companies. Lean came of age during the internet revolution. We now sit on the cusp of a different revolution — one ushered in by machine learning algorithms. It is safe to assume that most or all software in the near future will contain some element of machine learning. But how compatible is Lean with machine learning, in principle, and in practice?
Analyzing past phenomena can provide extremely valuable information about what to expect in the future from the same, or closely related, phenomena. In this sense, these algorithms can learn from the past and use this learning to make valuable predictions about the future. While learning from data is not in itself a new concept, Machine Learning differentiates itself from other methods of learning by a capacity to deal with a much greater quantity of data, and a capacity to handle data that has limited structure. This allows Machine Learning to be successfully utilized on a wide array of topics that had previously been considered too complex for other learning methods.
There's much more to implementing Robotic Process Automation than plugging in a software package. It's about the business as much as technology. it is only in the last couple of years the market has seen meaningful growth, driven primarily by the financial services and healthcare industries. However, as with many emerging technologies, a large dose of hype and myth accompany their growth and adoption. Understanding some of the common hype and myths about RPA may help you better appreciate the limitations – and opportunities – of this technology.
While many of the industries professing to make revolutionary use of distributed ledgers hardly offer a relevant use case, social media is one of the fields that can benefit immensely from blockchain and tokenized economies. Social media data is where blockchain can help. In a nutshell, blockchain is a distributed ledger or database. When you port social media to the blockchain, the immediate benefit users will gain is exclusive ownership of their data.
Data science still carries the aura of a new field. Most of its components — statistics, software development, evidence-based problem solving, and so on — descend directly from well-established, even old fields, but data science seems to be a fresh assemblage of these pieces into something that is new. The core of data science doesn’t concern itself with specific database implementations or programming languages, even if these are indispensable to practitioners. The core is the interplay between data content, the goals of a given project, and the data-analytic methods used to achieve those goals.
The right go-to-market (GTM) strategy is needed to ensure you have product-pallet fit to reach your buyers. How your GTM adapts for a connected world is as important as reimagining your product strategy. We’ll look at how these IoT offerings are sold and bought. We will start by looking at channel partner structure in IT and OT worlds and then show them side-by-side to see the almost bewildering impact on GTM strategy when IT meets OT.
As technology advances, marketers are looking at providing unique and more relevant experiences for their prospects and customers. In the age of millennials, no one likes to be marketed to or sold to. A simple yardstick would be to check how many of us have ad blocks on our browsers. Millennials like to be engaged with. This is where we as marketers, can leverage data and machine learning. The right and intelligent augmentation of humans and technology is the future of millennial marketing. And that mix will vary with every enterprise.
Are you ready to delegate making your business and legal agreements to smart contracts? Is it safe, feasible, and effective? Smart contract development offers numerous benefits as it is secure, fast, automated, and irreversible. Ethereum is one of the most popular platforms for smart contract development as it enables to solve almost any computational task. Thus many businesses across a variety of industries hire Solidity developers to build their smart contracts.
Both big data and AI are path breaking technologies in their own right. However, when big data meets AI, the two complement each other, helping us analyze and implement large data sets in unique and unexplored ways. By applying machine learning algorithms, we can make ‘intelligent’ machines, which can employ cognitive reasoning to make decisions based on the data fed to them. Big Data, on the other hand, is a blanket term for computational strategies and techniques applied to large sets of data to mine information from them.
Fintech and blockchain technology merge quite well. The number of blockchain fintech startups is soaring, while distributed ledger technologies slowly taking ground in the financial services industry. It will be interesting to see if this technology and smart contracts can live up to their expectations and transform financial ecosystem for the better. Let’s see how smart contracts and blockchain can transform the global FinServ, explore the major blockchain platforms, and check out several fintech startups that use this technology.
Statistical learning is a framework for understanding data based on statistics, which can be classified as supervised or unsupervised. Supervised statistical learning involves building a statistical model for predicting, or estimating, an output based on one or more inputs, while in unsupervised statistical learning, there are inputs but no supervising output; but we can learn relationships and structure from such data. One of the simple way to understand statistical learning is to determine association between predictors) & response and developing a accurate model that can predict response variable on basis of predictor variables.
Although technology is quickly changing, your goals as a manufacturer likely haven’t. You still aim to please your customers by delivering quality products, while increasing productivity and profitability. Yet, new and unprecedented innovations will potentially impact all aspects of the execution of those goals at the operational level. Smarter connected devices that use open IoT protocols are rapidly penetrating factories. At the same time, the Industry 4.0 trend is showing how people, connected devices and artificial intelligence can work together to make factory automation more efficient and effective. To remain competitive, you must quickly adapt.
Deep Learning requires a lot of computations. It typically involves neural network(s) with many nodes, and every node has many connections — which must be updated constantly during the learning. As the deep learning and AI fields have been moving extremely fast in the last few years, we’ve also seen the introduction of many deep learning frameworks. Deep learning frameworks are created with the goal to run deep learning systems efficiently on GPUs.
Nowadays, there are so many tools out there that allow anyone to get started learning Machine Learning. No excuses! Machine learning can help us understand our world in ways we couldn’t otherwise. It can help us create and discover new things orders of magnitude more efficiently than ever before. You’ve got the power, use it wisely. The four foundation stones of Machine Learning are data, computations, algorithms, and education.
Interviews can be nerve wrecking, especially when it comes to Big Data. But as someone who has spent decades in this industry, I think it doesn’t have to be. If one is prepared, then it can just turn out to be a good dialogue and explain your value to prospective employers. But as they say, one who has prepared well has half won the battle! So to make things easier, I’m going to share a few questions that I’ve been asked over the years and some that I used while interviewing candidates when building a data science team. And by no means are they exhaustive.
Radiology remains a solid career path, and AI will only serve to dramatically improve radiologists’ workplace conditions. While AI may take over certain tasks currently performed by radiologists, jobs in the field will remain abundant—and growing reliance on AI technology will only augment the other tasks that occupy a radiologist’s day. Given mounting workloads and the severe shortage of radiologists in the face of rising demand, AI augmentation will be a tremendous boon to the profession—not an existential threat. Here are four reasons today’s budding radiologists need not fear AI displacement.
It is a function that measures the performance of a Machine Learning model for given data. Cost Function quantifies the error between predicted values and expected values and presents it in the form of a single real number. Depending on the problem Cost Function can be formed in many different ways. The purpose of Cost Function is to be either: Minimized - then returned value is usually called cost, loss or error. The goal is to find the values of model parameters for which Cost Function return as small number as possible.