With the increasing use of machine learning and AI, retailers can boost efficiency and productivity while actively engaging with consumers via digital and mobile platforms. This is the step forward that retailers should be taking, while keeping in mind the need to reduce waste and significantly improve efficiency. There is a real need for automation and deeper data analysis to identify patterns that support cost-effective and accurate decisions. Retailers have to figure out now how to meaningfully connect with consumers who expect intuitive and convenient shopping experiences.
We interviewed our expert Rebecca D. Wooten. Here's her insights on emerging technologies, and how apps and gaming softwares are changing.
Considerable business value in data science comes with the right application of exploratory analytics or statistical techniques. The bottom line is the need for data science capability in organisations to identify pertinent solutions to business problems, by leveraging the data at hand. How one can go about incubating a data science practice, or for that matter a startup with analytics offerings? Here are the 3 distinctive phases of growth, focus areas and skills needed in each, and share intelligence on how to acquire the right talent.
If your product or service isn’t working, you deliver the necessary assistance to your customers. To this end, you need an army of tireless, helpful employees and representatives to interface and interact with your influx of customers. In other words, your customer support channels need to be open and active nearly 24/7 to accommodate customer and audience demands. Chatbots are transforming how this process works, especially in regard to reliability and efficiency:. You might not believe it, but automated chatbots and messaging tools can supplement live support reps.
Machine Learning has made great advances in pharma and biotech efficiency. AI is also helping us more efficiently diagnose diseases, develop drugs, personalize treatments, and even edit genes. But this is just the beginning. The more we digitize and unify our medical data, the more we can use AI to help us find valuable patterns — patterns we can use to make accurate, cost-effective decisions in complex analytical processes. Machine Learning is particularly helpful in areas where the diagnostic information a doctor examines is already digitized. This post summarizes the top 4 applications of AI in medicine today.
This post is not really about how to lie with Data Science. Instead, it’s about how we may be fooled by not giving enough attention to details in different parts of the pipeline. There are different pitfalls that might occur when we try to publish some algorithm results or interpret others. The main idea to take from this is “When it looks too good to be true, it probably is”. When our model (or others) looks surprisingly good, we have to make sure that all of the steps in our pipeline are correct.
Having a portfolio of public evidence of your data science skills can do wonders for your job prospects. Even if you have a referral, the ability to show potential employers what you can do instead of just telling them you can do something is important. A portfolio of public evidence is a way to get opportunities that you normally wouldn’t get. It is important to emphasize that a portfolio is an iterative process. As your knowledge grows, your portfolio should be updated over time.
I recently discovered data science practice problems on Analytics Vidhya. These allow you access to simple datasets on which to practice your machine learning skills and benchmark yourself against others. I think they offer a great introduction to approaching these problems before perhaps moving onto something a bit more challenging such as Kaggle competitions.
General Data Protection Regulation (GDPR) is an attempt to regulate the source of information and legalize the flow of information based on the prospect or customer’s permission. GDPR is a regulation that defines how organizations need to protect the personal data of EU citizens. If your idea of prospect reach-out was mass emailers, cold calls or anything that intrudes a prospect’s experience – then GDPR is going to affect you badly. However, there is immense opportunity for marketers and sales people to build quality relationships and engagement, and more importantly an opportunity to improve the quality of data you work on.
Foundation of trust is slowly fading as a new generation of AI-doctored videos find their way into the mainstream. Famously known as “deepfakes,” the synthetic videos are created using an application called FakeApp, which uses artificial intelligence to swap the faces in a video with those of another person. Educating people on the capabilities of AI algorithms will be a good measure to prevent the bad uses of applications like FakeApp having widespread impact—at least in the short term. We also need technological measures to back up our ethical and legal safeguards against deepfakes and other forms of AI-based forgery.
Getting the most out of your upcoming trade show will require a lot of work before, during and after the show takes place. There are leads to follow, people to mingle with and a brand to build. Doing a trade show is like networking among the experts. While a trade show can be a fun experience, you're there to get the best deals possible. Unfortunately, the leads you generate from trade shows may not turn into customers if you leave them alone. Leads take work, and you must follow the leads to work.
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.
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: