Keras is one of the most popular Deep Learning libraries out there at the moment and made a big contribution to the commoditization of artificial intelligence. It is simple to use and it enables you to build powerful Neural Networks in just a few lines of code. In this post, you will discover how you can build a Neural Network with Keras that predicts the sentiment of user reviews by categorizing them into two categories: positive or negative.
The tech sector continues to prioritize cybersecurity innovation and investments. Digital diplomacy will represent an important topic of discussion in tech in 2019. Artificial intelligence and machine learning represent one of the most exciting trends in technology: virtual assistants, autonomous cars, self-learning algorithms. These are challenges many tech companies and startups at looking at to push innovation forward. Blockchain is going to be a revolution in IT security because every transaction against your infrastructure is a strongly and cryptographically authenticated and granularly authorized.
When it comes to bringing innovation to the world of banking and finance, what sort of apps might we see in years to come, and what areas are potentially ripe for development? What is driving the development of new types of apps, and how will gadgets and voice-activated assistants such as Facebook Portal, Alexa and Google Home play their part? It’s only a matter of time before home assistants are well ingrained in the Fintech development landscape.
A common fallacy exists for people building data science teams that: smart hires translate to successful data science teams. What are the number one reasons you think smart data science teams fail to offer business value? The number one reason smart data science teams fail to win and provide value at the rate that they should is money. Sure you pay them well, but they just don't get the business drivers. They can't speak the language your board members, managers, and customers need to hear. Despite their data genius, they are idiots in the business world.
In startup lingo, a “vanity metric” is a number that companies keep track of in order to convince the world — and sometimes themselves — that they’re doing better than they actually are. Vanity metrics are everywhere, and they can really hold us back when we optimize for them, rather that optimizing for something that matters. They cause us to spin our wheels, and not understand why our hard work isn’t leading to results.
DevOps is a new buzzword in computing circles. It encompasses many common sense ideas about the integration between business and technology and provides the narrative to bring development, delivery and operations together. DevOps is the practice of operations and development engineers participating together through the entire service lifecycle, from the design and development process all the way to production support. It replaces the traditional silo setup where you have a team that writes the code, another team to test it, yet another team to deploy it, and even another team yet to operate it.
Problems are only challenges if met with the right mindset and the tools with which to overcome them. AI and Big Data have become a powerful combination that effectively changes the way industries view daily operations. Whether it relates to enhancing the customer experience or developing completely new products to market, the basic value-adding proposition remains the same. AI is here, and it’s here to stay. How it is used to add value is yet to be fully discovered; hopefully, these provide a few ideas with which to build on.
For humans, forgetting is more than just a failure to remember; it’s an active process that helps the brain take in new information and make decisions more effectively. It’s possible that our brains and distinctly human processes, like forgetting, hold the map to creating strong artificial intelligence, but scientists are collectively still figuring out how to read the directions. Now, data scientists are applying neuroscience principles to improve machine learning, convinced that human brains may hold the key to unlocking Turing complete artificial intelligence.
Designing a machine learning model is a tricky task. A model may not work in practice although it has high performance on the training data. This article discusses the misuse of a machine learning model that causes the predictions not to work in the real world situation. The other reasons could be overfitting, duplicated samples, and unbiased data. It is always good to use your domain knowledge or talk to some experts and see if your prediction/recommendation results make sense or not.
Technology is now the cornerstone of our society. The retail industry is certainly no exception, with many innovative solutions and platforms taking center stage. The question is not whether or not technologies are reinventing retail — spoiler alert, they are — but instead what they will lead to in the future. The technologies listed here — and many more — are set to transform not just the retail industry but also the world as we know it. Beyond improved customer experiences, more efficient operations and a swath of cost savings, every business stands to benefit from its adoption.
What is clear is that data science is solving problems. Data is everywhere, and the uses we are making out of it (science) are increasing and impacting society more and more. Let’s focus on Data Science. Let’s start by taking a look at what has happened in 2018 and then focus on hot topics for 2019. In 2018, the main developments included automation of workflows, explainability, fairness, commoditization of data science and improvements in feature engineering/cleaning tools. And they will continue to be some of the main focus of data scientists in 2019, and the following years.
Traditional cloud computing architectures need to evolve to a more decentralized approach that processes data at or near the source. Not only could edge computing provide this capability, but it has the potential to increase data computing efficiencies. While traditional data centers will remain the core computing power for enterprises, we’ll begin to see edge computing technology become integral into data center strategies in 2019. Whether this means integrating a solution into current operations or small data centers built for edge analytics, the next year will be the year enterprises live on the “edge.”
The field of AI is broad and has been around for a long time. Deep learning is a subset of the field of machine learning, which is a subfield of AI. The facets that differentiate deep learning networks in general from “canonical” feed-forward multilayer networks. Deep learning has been a challenge to define for many because it has changed forms slowly over the past decade. Here are the 10 powerful deep learning methods AI engineers can apply to their machine learning problems.
For sales managers, the process of drawing up realistic and attainable goals amid a backdrop of intensifying market and competitive pressure can be anything but straightforward. The long and short of AI for sales is that it can improve your relationships with your customers by adding value to your organization. Whether it does this by helping your teams sell more effectively, connecting you with an audience you didn't know you had or just staying better organized behind the scenes, AI and sales is an obvious pairing with compelling use cases — and true staying power.
Unlike software development positions which have more standardized interview processes, data science interviews can have huge variations. This is partly because as an industry, there still isn’t an agreed upon definition of a data scientist. So before starting to search for a role, it’s important to determine what flavor of data science appeals to you. Despite the differences in the types, generally speaking, they’ll follow a similar interview loop although the particular questions asked may vary. In this article, we’ll explore what to expect at each step of the interview process, along with some tips and ways to prepare.
Machine learning can help brands develop more personalised conversations with their customers. It is more important than ever for brands to keep up a steady conversation with their customers. Those who become complacent with client communication could soon find foot-loose customers wandering in the direction of their competitors. As they say, out of sight, out of mind. That is why personalised conversations with their customers is vital. Machine learning can help make it happen.
Organizations across industries are adopting graph analytics to reinforce their anti-fraud programs. Most anti-fraud applications are able to connect simple data points together to detect suspicious behavior. But these applications fall short on more complex analysis. The graph databases we’ve seen emerge in the recent years are designed for this purpose. In this post, we examine three types of fraud graph analytics that can help investigators combat insurance fraud, credit card fraud, and VAT fraud.
Right from filtering our mailboxes to AI-powered chat bots or AI enabled recommendations; we were always surrounded by the Artificial Intelligence. AI has also impacted various fields, like aiding in synthesizing new chemicals, medical diagnoses, identifying the faces of criminals in a huge crowd, self- driving cars, and even creating new works of art. Even though there is a lot of debate and discussion going on about the future of AI but for now Artificial Intelligence is here to stay.
Today we have AI metrics we applaud, like deaths-per-100-million-miles-driven. The metric sounds dark, but it is going down thanks to self-driving cars. The darkest metric will be discussed in private, away from the public eye. As AI war vendors compete on billion dollar contracts they will use kill-death-ratio (KDR) as a selling point. The Manhattan project of AI could be the physical modeling of the human body in these war simulations. The medical research efforts might accidentally enable a darker use case for the military. A big breakthrough with robots and AI is the ability to train them in a virtual environment.
Today, predictive analytics is, and must be, accessible to business users, if your enterprise is to grow and respond to the need for data democratization and increased productivity within the enterprise and to the rapid changes in the market, competition, resource and supplier needs and customer buying behavior. Every business user must have the tools to analyze data and make accurate, timely predictions and decisions. Your organization can truly benefit from predictive analytics and from the ease-of-use and sophistication of these self-serve tools.
As is true of many other industries, technology is having a major influence on the current growth and development of the insurance market. In fact, it’s outright disruptive in some cases with new platforms and hardware significantly altering how conventional operations are handled. For example, it’s entirely possible to create a mobile-only insurance company that offers all features and support via a mobile app or browser. But it’s much bigger than that, of course. Many different technologies and opportunities are available, some of which showcase what it will be like in the future. Here are four of them.
Data inventory, data integration and infrastructure are the three key areas to build the right foundation to help brands build effective AI programs to enhance data-driven marketing strategies. Because data is so intricately tied to how companies function and how people do their jobs, an AI program can require long-term organizational and cultural shifts. Ultimately, this will result in a more highly functioning organization. A comprehensive data set can provide the informational Holy Grail that companies want in an era of heightened personalization — a 360-degree view of their customers.
For many people, the mention of bots in the workplace immediately conjures up fears of job losses and a changing status quo, but the reality of AI is quite different. While these new technologies will indeed be able to perform many of the tasks that human workers complete now, we are more likely to see a marked change in employee roles and skill sets, rather than mass job losses. The reality is that no matter how advanced it appears; artificial intelligence will always need to be managed to some degree by line managers.
Robotic Process Automation has become one of the most intensive technologies in the digital world. With the adoption by various industries, RPA has experienced robust growth. 2019 is coming up with the latest RPA trends which we’re going to benefit businesses, enterprises, market, and individuals as well. The major RPA trends in 2018 include Smart Process Automation, Contribution to the big data and IoT, Integration with other tools, Increment in the adoption of RPA tools etc. Eager to know new RPA trends in 2019?