Applying AI to Wi-Fi is the first logical step to automating IT and indeed is the only way to realize the concept of optimization of Quality of Experience for every device connected to every access point in real time. IT staff can then be freed up to focus on the differentiated business issues their organization faces rather than running networks. In broader terms this becomes another step in Network as a Service (NaaS) resulting in networks being delivered as a utility in the not too distant future.
Data evangelists have written countless articles and given numerous keynote speeches about the ways that big data has changed our lives. They have mostly focused on the implications for consumers and administrators. There hasn’t been as much discussion about the changes the field has created for people seeking a career in big data. However, that is an important trend that warrants discussion. So many factors are causing big data jobs to increase over the next decade. This is a promising development for anybody working in the field.
With all the data breaches coming to light, cybersecurity is becoming one of the most in-demand fields in the tech industry. Because the field is growing and the demand for people with a cybersecurity degree is going up, many are considering breaking into the cybersecurity field. Since many jobs don’t require a degree, it’s fair to ask if a cybersecurity degree is worth it. Do you need to spend the time and is a cybersecurity degree worth it?
Random Forest is a flexible, easy to use machine learning algorithm that produces, even without hyper-parameter tuning, a great result most of the time. It is also one of the most used algorithms, because it’s simplicity and the fact that it can be used for both classification and regression tasks. In this post, you are going to learn, how the random forest algorithm works and several other important things about it.
As data analytics becomes an increasingly critical component of pharma innovation, the problem of data silos stands out as a barrier urgently in need of attention. Here we discuss how data silos in pharma prevent the latest advancements in machine learning and data analytics from operating at their full potential, how AI and blockchain are transforming incentives in data sharing practices to promote greater transparency, and how companies can leverage open data to drive innovation and lower drug prices.
In the last 10 years, there’s no field where AI has been more consistently applied than in digital marketing. That’s because, compared to other industries, internet companies: collected bigger, more structured, datasets, employed more data engineers and have a more tech-focused culture. But even though the big tech giants are using machine learning a lot to optimise marketing, many organisations are still just getting started. If you are wondering how best to use machine learning / AI in marketing, here’s an overview of the top applications today.
A.I. is a rapidly growing industry. A ton of jobs formerly done by people have been outsourced to robots and computers. But there’s no reason to worry, because these losses have been offset by the growth of jobs in A.I. However, to get a job in A.I. you need a fairly impressive skill set, and a resume that properly demonstrates that skill set to a potential employer. Here are six essentials for your artificial intelligence resume.
Regardless of whether they are prepared annually, or initiated by an incident or event, compliance and other HR reports require analysts and administrators to manually find and collect data from a variety of disparate sources, and then reconcile, extract and input it into spreadsheets for analysis. And with so much of HR’s time focused on these mundane tasks, other business-critical duties can quickly fall to the wayside. The good news for HR professionals is that self-service data preparation tools are designed to simplify and automate these operational tasks – making reconciliation, analysis, reporting and compliance completely painless.
AI and IoT are complimentary. IoT data sensors provide the raw material for AI-based applications and create enormous data volumes, often streaming. Today, much of that is unused or lost. Data without analytics is value not yet realized. An analytics-based AI application sifts that data stream for insights and automates actions. With new IoT applications creating a wealth of data, an AI approach might be the most efficient and fastest way to pull insights from the real-time data stream.
Today, we’re going to write our own Python image recognition program. To do that, we’ll explore a powerful deep learning architecture called a deep convolutional neural network (DCNN). Convnets are the workhorses of computer vision. They power everything from self-driving cars to Google’s image search. So why are neural networks so powerful? One key reason: They do automatic pattern recognition. So what’s pattern recognition and why do we care if it’s automatic? Patterns come in many forms but let’s take two critical examples: The features that define a physical form.
Blockchain was created to be a trustless self-regulating system to facilitate the transparent exchange of information between parties while protecting the privacy of all involved. However, the groundbreaking technology isn’t without vulnerability, one that hackers are already busy exploiting. “Blockchain, it’s an immutable distributed ledger,” is something we’ve all heard from bitcoin enthusiasts time and time again, but what does that really mean? How safe is it? How can we secure tech’s most secure technology?
When we talk about bias we mean the same thing whatever our discipline. Whether we are talking about cognitive bias, social bias, statistical bias or any other sort of bias, bias is an inaccuracy which is systematically incorrect in the same direction. This article will provide enough of a technical intuition about the causes of biases in algorithms, while offering an accessible take on how we are inadvertently amplifying existing social and cognitive biases through machine learning — and what we can do to stop it.
Advanced Analytics is comprised of numerous sophisticated analytical techniques, designed to parse, explore and analyze data and produce results to support business decisions. Fortunately, today’s new self-serve business intelligence solutions allow for ease-of-use, bringing together these varied techniques in a simple interface with tools that allow business users to utilize advanced analytics without the skill or knowledge of a Data Scientist, analyst or IT team member. Advanced Analytics provides a 360-degree view of data from Data Marts, Data Warehouses, best-of-breed, legacy systems, and other data sources.
You want to know how Blockchains work—the fundamental technology behind them. Remember that a blockchain is an immutable, sequential chain of records called Blocks. They can contain transactions, files or any data you like, really. But understanding Blockchains isn’t easy. Learn by doing. It forces you to deal with the subject matter at a code level, which gets it sticking at the end of this guide, you’ll have a functioning Blockchain with a solid grasp of how they work.
Predictive analytics, as an evolving domain based on Big Data and AI, requires a considerable amount of information for training the model. Sometimes, locally, this is scarce or unscalable, but imagine putting together data from thousands of similar users. Suddenly, the lack of data is no longer a problem and patterns emerge more easily. This gives a real chance to smaller companies, like start-ups, to take advantage of the blockchain model for their operations and use the data generated in the process as a by-product to feed various prediction models.
A proper Information Management system – including healthy Enterprise Content Management (ECM) and modern archiving strategies – will definitely help to manage and integrate existing data and content across the enterprise, as well as limit or overcome the pollution of such information, by granting chain of custody and compliant governance along the data’s entire lifecycle. Modern ECM and archiving systems provide all the compliance, enterprise-grade functionalities to solve these requirements and enables organisations to start their journey to the new world of big data with the right approach and clean data streams to fill their data lakes with.
The global healthcare Data Analytics market will grow, as healthcare companies increasingly use data for financial applications, and to improve operational and administrative processes. The industry’s reliance on Data Analytics is also being driven by the increased use of electronic health records (EHRs) as well as the digitization of financial records and insurance claims processing. While the use cases for data in healthcare are endless, in this post, we’ll take a look at how analytics outcomes can specifically impact administrative and financial offices in healthcare organizations of all types and sizes.
DevOps has evolved big time since many of us thought it was just a buzzword. Now we know that is a myth. DevOps has become a main focus and has been shaping the world of software for the last few years. Experts say that DevOps is going to be the mainstream and its popularity is going to reach its peak in 2019. Here is the Google trend shown for the term “DevOps” and a hypothesis of its projected growth in 2019.
The problem is most guides talk about tensors as if you already understand all the terms they’re using to describe the math. So what is a tensor and why does it flow? At its core it’s a data container. Mostly it contains numbers. Sometimes it even contains strings, but that’s rare. There are multiple sizes of tensors. Let’s go through the most basic ones that you’ll run across in deep learning
Bias in the training data surely plays a role, but I don’t think that it is the primary explanation for the bias. The usual explanation is that the systems are trained on the “wrong” data and merely perpetuate the biases of the past. If they were trained on unbiased data, the explanation goes, they would achieve less biased results. It appears that the bias comes substantially from how we approach the notion of fairness itself. We assess fairness as if it were some property that should emerge automatically, rather than a process that must be designed in.
Looking ahead to 2019, businesses should use what they’ve learned from the past year to understand and take action towards improvement, starting with their central command – the database. With data being produced, analyzed and stored at exponential rates (thanks to the growing technological ecosystem), the database is perhaps the most crucial element in overcoming – and preventing – similar outages and data breaches in the coming year. There are several things likely to occur in 2019. Here’s what database managers and developers can expect to see happen in the coming months.
Despite having more data, it’s difficult to extract value from it in a timely fashion. If you want to be fast and agile with your data, you need a strategy built on enabling you to do that. Your data strategy needs to include more than just raw processing power. This is where data warehousing comes in – offering unified, governed, large-scale support for analytics. When it comes to your data warehouse, you need a way to get it moving quickly – and automation can help.
Whether they’re ready or not, companies around the world have a new data challenge – one that they must succeed in meeting, if they don’t want to lose huge amounts of money in fines and penalties. Among its many rules, the GDPR, Europe’s new data privacy and security regime, requires that companies delete personal information on European residents within 48 hours of being asked to do so – providing Europeans with the “right to be forgotten,” and failure to do so could cost a company a lot of money.
AI is already being deployed in a range of arenas, from digital assistants and self-driving cars to predictive analytics software providing early detection of diseases or recommending consumer goods based on shopping habits. More executives view adoption of AI and other emerging technologies as a top operational challenge rather than an opportunity. This suggests a consensus among global executives that these technologies are important, but that it is not yet clear whether this will help or hurt their business in the short term.