The lack of knowledge on what’s actually in the archive will also prevent the company from taking full advantage of the often huge and unexplored goldmine that lies in the archived information and using it as fuel to accelerate and spur on new services and innovations. Businesses ability to‘re-envision’ their data can have a strong impact on the success of any enterprise in the digital transformation race. But how do they get there?
To drive innovation to a whole new level we’re seeing a rise in the use of Natural Language Processing (NLP) and Artificial Intelligence (AI) to make finding and using trusted, quality data that much easier. In fact, leading industry analyst firms have noted that Machine Learning, AI and NLP are quickly becoming table stakes for analytics. That requires significant heavy lifting at the infrastructure level and it’s not an easy thing to do.
Big data is disrupting every industry it integrates with, altering the corporate landscape by determining market trends, behavioral analysis, and other insights that only improve business decisions. Detecting and understanding large, verified data sets of hidden patterns, as well as unknown correlations, it’s helping startups make better decisions as they seek financial growth and recognition, build effective business strategies, and maintain customer satisfaction. Big data is changing people’s lives for the better, offering a more convenient, yet technical, means to entrepreneurs and investors securing a financial infrastructure within growing companies, while attempting to jump-start brand awareness.
Whether you're brand-new to the workforce or you're a veteran, it's clear modern technology has always played a role in reshaping how we work — and the places where we do that work. With that in mind, let's talk about the effects modern technologies have had on our workspaces and what they look like, what they're like to work in and what they might look like someday soon.
In order to start practising data science, it is better if you challenge a real-life problem. This blog post will guide you through the main steps of building a data science project from scratch. It is based on a real-life problem — what are the main drivers of rental prices in Berlin? It will provide an analysis of this situation. It will also highlight the common mistake beginners tend to make when it comes to machine learning.
When working in healthcare, a lot of the relevant information for making accurate predictions and recommendations is only available in free-text clinical notes. Much of this data is trapped in free-text documents in unstructured form. This data is needed in order to make healthcare decisions. Hence, it is important to be able to extract data in the best possible way such that the information obtained can be analyzed and used. State-of-the-art NLP algorithms can extract clinical data from text using deep learning techniques such as healthcare-specific word embeddings, named entity recognition models, and entity resolution models.
This article guides you through getting a powerful deep learning machine setup and installed with all the latest and greatest frameworks. We’re going to build our own Deep Learning Dream Machine. We’ll source the best parts and put them together into a number smashing monster. We’ll also walk through installing all the latest deep learning frameworks step by step on Ubuntu Linux 16.04. This machine will slice through neural networks like a hot laser through butter.
Blockchains and cryptocurrencies are a new technology, while Bitcoin has existed for 10 years. If you have followed the discussion that has emerged around this technology, you have probably encountered debates about scaling. Scaling is the process of engineering blockchains to be able to process a greater number of transactions, computations, and applications. Essentially, it is the question of how do we make this technology more efficient and applicable. Blockchains are notoriously slow, inefficient, and more difficult to scale than traditional technology due to the constraints of working with a decentralized network.
General intelligence is not simple, or well understood. Whatever the challenges of artificial general intelligence, the chances of us actually achieving it will be greatly improved if we have a better idea of just what we are trying to create. So far, that means better understanding human intelligence. We may not need human-like intelligence for solving specific problems, but it looks like it could be critical for developing artificial general intelligence.
If you've had even a brief encounter with the real estate market, you know how much information is exchanged in the average interaction and how many considerations go into a purchasing decision. If anything, you'll have to stay sharper than ever to use the new data and analysis tools available to you to end up with the most compelling results. It's not exactly a surprise, then, that big data is bringing significant change to how the real estate market conducts itself and even how residential and commercial properties are maintained. Here are four of the most interesting.
The data we use is usually split into training data and test data. The training set contains a known output and the model learns on this data in order to be generalized to other data later on. We have the test dataset (or subset) in order to test our model’s prediction on this subset. Let’s see how to do this in Python. Following a short overview on the topic, an example on implementing it in Python is given.
In an era of consumerization of IT, users demand a new way of interaction and organizations may finally have to face the reality that user created profiles are unreliable and they must embrace new methods of authentication that balance security requirements and user experience, opening the path to the use of trusted digital identities. The problem is — how can an organization verify that they are interacting with the proper end user?
Artificial Intelligence in its purest benchmark is referred to as a technology that strives to mimic human intelligence. So, at the dawn of a new era (will that be the 5th industrial revolution/evolution?) let’s stay pragmatic and try to learn more about what is going on, what Pragmatic AI can actually do for us today, what are the basic algorithms that allow machines to learn, where we stand with BMIs technologies and much more…
Most IT departments use an average of nine different tools to monitor their environment. Very rarely do these apps even talk to each other. Managing multiple monitoring tools is not only cumbersome, it’s incredibly time-consuming. The IT team is too busy reacting to beeps and alerts to focus on its core mission of supporting the business. For IT to play a more strategic role and provide measurable value, a better approach is needed, one that’s based on the business impact of the events being monitored.
RPA works best when application interfaces are static, processes don’t change, and data formats also remain stable – a combination that is increasingly rare in today’s dynamic, digital environments. The problems with RPA, however, aren’t that the tools aren’t ‘smart’ enough. Instead, the challenge is more about resilience – dealing with largely unexpected changes in the IT environment. Adding cognitive capabilities to RPA doesn’t solve these resilience issues – you simply end up with smarter technology that is still just as brittle as before.
In what will probably feel obvious in hindsight, artificial intelligence and other technologies may be about to compete with or even displace management consultants and some of the services consultancy firms offer. The reality of the situation will take a moment to unpack, but here's a spoiler — the result should be better insights for the organizations retaining consultants, along with greater capabilities and less busywork for the consultants themselves. Another way to look at this is that AI is adding powerful new tools and vast new opportunities to the modern consultant's toolkit.
If you’re a developer or sys-admin you probably already use a lot of libraries and frameworks that you know little about. You don’t have to understand the inner workings of web-scraping to use curl. The same is true with AI. There are a number of frameworks and projects that make it easy to get going fast without needing a data science Ph.D. The math helps you feel confident about what’s going on behind the scenes. If you want to start using AI, you can do that today. Let’s get started with some practical projects.
What can our minds trust? What's real today? The world is filled with technology, media, data and a massive amount of content that provides conflicting viewpoints. Consumers are growing increasingly cautious as marketers leverage big data technology to refine their innovative techniques for separating people from their hard-earned money. The information overload conundrum will grow more relevant as the digital universe expands. One field that's heavily affected by the proliferation of big data is healthcare. Today's healthcare leaders must adapt to a new operating environment that's a moving target with no hope of satisfying the clear majority of consumers – for now.
AIOps is an umbrella term for using complex infrastructure management and cloud solution monitoring tools to automate the data analysis and routine DevOps operations. Processing all the incoming machine-generated data on time is not humanly possible, of course. However, this is exactly the sort of tasks Artificial Intelligence (AI) algorithms like deep learning models excel at. The only remaining question is the following: how to put these Machine Learning (ML) tools to good work in the daily life of DevOps engineers? Here is how AIOps can help your IT department.
Modern business depends on flexibility perhaps above all — and that might go double when it comes to procurement. In a nutshell, technology represents your best chance at "perceiving value" — that is, revealing hidden opportunities within the reams of data your company already produces. For a start, data-driven technologies can help businesses all the way up and down the modern supply chain with tasks. Finding new ways to let technology make our processes leaner and less wasteful — and our use of resources less reckless — isn't just a business imperative — it's a civil and social one too.
Personalization remains a tough spot in the rush for better customer experiences. However, there’s little doubt about the benefits it can bring. It’s just a question of how. For many businesses, getting a significant number of existing customers to make another purchase in the next month is little more than a pipe dream. For the many different organizations looking for a slice of the pie, it’s a highly lucrative offer – and the services offering the best personalization will likely be the ones that come out on top.
In addition to protecting their organizations from external threats, IT leaders mustn't neglect the internal breaches — intentional or accidental — that still pose a major threat. Continuous trainings and clear instructions help build awareness among staff, and policy enforcement and monitoring can ensure that employees will pay attention to them. Instead of treating security as a bothersome cost, the smartest enterprises will make online security a regular part of doing business and use it to differentiate themselves from their competitors who are still behind the curve.
There’s much confusion surrounding artificial intelligence and machine learning. Some people refer to AI and machine learning as synonyms and use them interchangeably, while other use them as separate, parallel technologies. In many cases, the people speaking and writing about the technology don’t know the difference between AI and ML. In others, they intentionally ignore those differences to create hype and excitement for marketing and sales purposes. This post disambiguates the differences between artificial intelligence and machine learning to help you distinguish fact from fiction where AI is concerned.
Many machine learning practitioners may have come to a crossroads where they have to choose the number of features they want to include in their model. This decision is actually dependent on many factors. Machine Learning practitioners often work with high dimensional vectors to create a model. Here is an illustration of the Curse of Dimensionality, a powerful concept where the volume of the vector space grows exponentially with the dimension, causing our data to be highly sparse and distant from one another.