Experfy Insights

Big data helps firms unearth information that consumers and clients may not reveal via common market research tools such as surveys and questionnaires. In some cases, big data provides answers to questions that the research tools of the past could never hope to reveal. Big data analysis can help business leaders figure out whether their online presence is helping or hurting their brand. While big data reveals hidden patterns, they do not always result in actionable insights. This makes it important for marketers to understand the meaning behind the results. 

AI creates its own children that outperform humans, but this inaccuracy is all too often because AI researchers and AI practitioners do not participate in these debates. Using the terms such as “child” and describing the output of this network as smarter than what humans would design is sure to make for some good headlines. However, describing what happened as “algorithm uses a set of predefined rules to achieve a slightly better performance” is nowhere near as scary but it is probably more accurate.

Welcome to this series of tutorials around Microservices hosted on AWS, we will start out with the basics and follow up with more advanced tutorials, if you have any special requests for a specific topic, just let me know and if enough people shout out about it, I will cover that topic.

 In this article, we show how the simple sorting algorithm is at the heart of solving an important problem in computational geometry and how that relates to a widely used machine learning technique. Although there are many discrete optimization based algorithms to solve the SVM problem, this approach demonstrates the importance of using fundamentally efficient algorithms at the core to build complex learning model for AI. A dizzying array of clever algorithms are being developed continuously for solving ML problems to learn patterns from streams of data and build AI infrastructure.

CNNs (convolutional neural networks) are one of the reasons deep learning is so popular today, they can do amazing things that people used to think computers would not be capable of doing for a long, long time. But they have their limits and some fundamental drawbacks and that is why Capsules neural networks are picking up pace, which introduce a new building block that can be used to overcome these limits & drawbacks of CNNs. Capsule networks (CapsNets) are a hot new neural net architecture that may well have a profound impact on deep learning, in particular for computer vision.

Building an in-house AI development team is not always feasible. AI engineering resources are scarce and expensive. Moreover, setting up and managing R&D is going to take a lot of time a company could spend on developing its core expertise. And although finding a qualified AI solution provider is not easy, outsourcing offers tangible benefits for businesses.So when choosing an AI development vendor, consider its ability to allocate the necessary resources, its experience in delivering AI solutions, knowledge of your domain, technological expertise as well as its capabilities to satisfy your business requirements.

With the advent of AI, marketers are moving towards mass personalization and mass hyper-personalization. But the key question is – is it relevant or creepy? Are you using AI effectively and sensibly, so as to not freak out your customers but delight them instead? Unless you discover the right signals, you will invariably be looking at the wrong insights and interpretations, leading to bad intelligence. A significant difference, between coming across as creepy and relevant, depends on how to build a conversation using the insights you have gathered. 

Big Data has been the talk of the technological and business world for a while now. While we already see many businesses leveraging from it and its impact on processes, we decided to bring forward an expert opinion on Big Data. Here's our conversation with Dr. Rich Huebner on the same. 

Before launching an IoT initiative, organizations need to have a comprehensive strategy in place. Otherwise, there’s a risk of overspending, exposing data to security and privacy threats, limiting the payback from IoT technologies, as well as other negative outcomes. Without three key elements — strong leadership, a sensible business plan, and a commitment to culture change — all the sensor, networking and data analytics technology in the world is not going to deliver optimum results. Your IoT initiative will likely face many challenges before you can proclaim it a success. Here are some important considerations to keep in mind.

The insurance industry collects and generates a large volume of data on a daily basis, including a customer’s health records, sensor data from vehicles, confidential legal papers, to name a few. The data, if analyzed thoroughly, gives actionable insights that the insurance industry can use to improve its services. Deep learning comes with neural networks that are capable of analyzing swarms of data and learning from it. Deep learning in insurance not only enhances customer experience but also helps the industry detect fraudulent activities.

With access to data analytics and big data, companies know more about their customers than ever before. For corporations, this has been a huge boon, allowing them to target local markets online. On the other hand, small businesses have had trouble integrating effective customer analysis into their marketing strategies without alienating their local customer base. However, showing love to the local community while harnessing data analytics to expand outreach can help a small business compete in the strange new landscape of the digital age.

Data scientists are often tasked with analyzing data to help the business, and this requires a level of business acumen. It’ll be extremely valuable for any aspiring data scientists to learn data mining — the process where one structures the raw data and formulates or recognizes the various patterns in the data through the mathematical and computational algorithms. This helps to generate new information and unlock various insights. Here are 10 mining techniques that I any data scientists should learn to be more effective while handling big datasets.

Like any other career path, there are benefits and drawbacks to working as a data scientist. Data scientists must acquire a large amount of training to grow proficient in their field. Upon entering the workforce, some specialists are tasked with rebuilding a company’s information structure from scratch. At other firms, institutionalized executives may want the benefits of the latest information technology, but aren’t willing to provide the requisite funding to launch a full data initiative. To prepare the next generation of data science professionals, forward-thinking academics are working to promote a learning environment where students train in near real-world environments and conduct interviews with field experts. 

In today’s world, you have a lot of Business Intelligence (BI) tools to pick from. Some are free and are open source others can cost you thousands of dollars per month. Some have automatically created dashboards while others require hours of set-up and know how to get started. If you are looking to implement a BI tool in your company or department here are some questions to ask/consider when making your decision: 

Spurred by the capabilities of deep learning, and how it has so far defied the norms set by traditional software, many organizations and visionaries are thinking once again that strong AI is on the horizon and want to catch it before others do.  But while all this talent focuses on finding a way to create strong AI that can compete with the human brain, we’re missing out on plenty of the opportunities and failing to address the threats that current weak AI technology presents.

If cybercrime were a country, it would have the 13th highest GDP in the world.  The global crime economy has become a self-perpetuating organism — an interlinked web of profit where the boundary between the legitimate and illegitimate is often unclear. Today, engaging in cybercrime is as simple as legitimate e-commerce. The dependency on the availability and performance of IT infrastructure among legitimate enterprises is increasing heavily, which makes them more vulnerable to breaches that can wreak havoc on business. Cybercriminals are clearly adept at leveraging existing platforms for commercial gain.

A great many traditional IT engineers are enthusiastic about learning/contributing to the exciting field of data science and machine learning/artificial intelligence. However it will be incomplete in your preparation for having solid grasp over machine learning or data science techniques without having a refresher in some essential mathematics. Then the question is: What are the essential topics/sub topics of mathematics that an average IT engineer must study/refresh if (s) he wants to enter into the field of business analytics/data science/data mining? How much mathematics does an IT engineer need to learn to get into machine learning?

Professional big data developers are mostly valued when they have a strong technical background and great problem solving skills. Furthermore, the knowledge of data analysis and business requirements analysis are essential for developing a clear understanding of the business needs. Specialists with such skill sets may handle diverse sources and huge amounts of raw data seamlessly and provide valuable insights from it. This enables big data engineers to use technical solutions that leverage innovative technologies to drive real benefits for your business. So which criteria to use when choosing big data developers?

Customer data comes from varied sources such as website forms, social media, email lists, and more. We all come across fake lead information, every now and then. Therefore, as a marketer, this data is not enough. You neither know if the prospect falls under your target industry, organization size, job title, revenue, etc. nor do you know where they are in their purchase cycle. This is where data enrichment as a practice comes into play. Data enrichment apps fill up the gaps of inadequate data or inaccurate information. 

You’ve arrived here because your goal is to get your first job as a data scientist. Currently, there are more data science jobs than there are people to fill them, so these types of jobs are in big demand today. Now becoming a data scientist is not going to happen overnight, but there are some core skills and education that you will need to land that first data scientist job. Here are my thoughts on what you can do land the first role as a data scientist or data analyst.

Industrial enterprises typically look to systems integrators to bridge the gaps with custom software development.  A few IoT vendors are now beginning to build more fully-integrated IoT service creation and enrichment platforms (SCEPs), designed to support an AFML IIoT architecture. SCEPs allow complex IoT architectures, applications and orchestrations to be efficiently created and evolved with minimal programming and administrative effort. These next-generation IoT platforms will help companies eliminate IoT data exhaust and harness IIoT data for use as a strategic business asset. 

Data is providing feedback to every corporation, which can then use the data to better themselves and get more business. Architects are no different and are coming to use technology in the same ways. With VR improving daily, they can even show clients exactly what they're paying for before any construction begins. In short, now is a special time to be an architect. Technology working for you and becoming a tool for business is exactly what the world has been waiting for.

In part 1, we introduced the field of Natural Language Processing (NLP) and the deep learning movement that’s powered it was introduced. We also walked you through 3 critical concepts in NLP: text embeddings (vector representations of strings), machine translation (using neural networks to translate languages), and dialogue & conversation (tech that can hold conversations with humans in real time). In part 2, we’ll cover 4 other important NLP techniques that you should pay attention to in order to keep up with the fast growing pace of this research field.

Internet of Things is partly about value creation, using the ability to communicate and control things over connections and automating how we get work done. Products with embedded intelligence talking to the cloud bring the power of remote control to everyday things, much like the iPhone and iCloud has done. All this requires information technology (IT) to be embedded in our business systems that let us operate our businesses. In other words, it is operational technologies (OT) with IT inside. Ergo, IT + OT = IoT in a technological sense.