Open source computing is hugely important to software development. It is the model that everyone benefits from. The open source foundations that support this development play a crucial role. Open source foundations have emerged to help sustain and manage open source projects. These foundations provide space for companies and people with a stake in open source software (OSS) project to come together. Their status as independent, non-profit entities provides neutral ground for competing companies to work together. Let’s see who’s behind many of the tools software developers and data scientists use every day.
What steps are you taking to navigate your organization through one of the biggest business shifts ever? What are the company’s Digital Transformation goals and how will the transformation is successful? Are you considering the “humanscape” of the organization as changes are being integrated into the business? Digital transformation is enabling organizations to open up new sales channels, develop new markets and grow opportunities. Those that have made this transformation have proven increased revenues, and improved efficiencies. But to fully embrace digital transformation, enterprises must start with capturing, integrating, and utilize quality data.
A data set is called imbalanced if it contains many more samples from one class than from the rest of the classes. Data sets are unbalanced when at least one class is represented by only a small number of training examples (called the minority class) while other classes make up the majority. There are many reasons why a dataset might be imbalanced: the category one is targeting might be very rare in the population, or the data might simply be difficult to collect.
Bringing new technologies and devices aboard is non-negotiable for businesses these days. Whether it’s cloud computing for data access or a new productivity app that keeps every member of a team safe, technology is helping us do more with less — and remain profitable as competition heats up. But the data powering today’s business technology introduces potential risk too. Here’s a look at how to remain security-minded as you figure out how to make your business data more mobile and accessible.
While Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) are becoming more important for businesses due to their applications in Computer Vision (CV) and Natural Language Processing (NLP), Reinforcement Learning (RL) as a framework for computational neuroscience to model decision making process seems to be undervalued. Besides, there seems to be very little resources detailing how RL is applied in different industries. Despite the criticisms about RL’s weaknesses, RL should never be neglected in the space of corporate research given its huge potentials in assisting decision making.
Repeatable finance process such as routine transactions recording and reconciliations are ripe for automation. Finance functions must, therefore, embrace the disruptors of today to transform their own operating models and unlock an environment of extreme automation. Technologies are “extremely automating” finance operations as we know them and slowly but surely developing intelligent finance functions that are viewed as strategic advisors to the business. There are at least seven technologies that will deliver extreme finance automation.
AI has begun to impact nearly everything we do. The same technology that has made consumer internet search more personalised, connected, and ubiquitous is also starting to reveal itself in employee-facing search solutions, supporting enterprise search. Workers who depend on corporate search solutions often struggle to find relevant information in an ever-expanding pool of largely unstructured proprietary data. Companies can expect to see an increase in employee engagement, efficiency, and cost savings thanks to smarter search mechanisms, an embrace of open-source applications, and AI elevating virtually every aspect of data discovery.
A vastly disproportionate number of hires are the result of referrals from employees who already work at a company. So your best way in, nine times out of ten, will be through a relationship with someone who works at your target company, rather than a generic channel, like a jobs board. Relationships are great because they give you a signal boost, but they also make it much more likely that you’ll get feedback on your application. But how do you build meaningful relationships with established data scientists?
Agile data science research is hard, how can you give time estimation when you are not sure that your problem is solvable? How can you plan your sprint before looking at the data? You probably can’t. Agile data science requires many adjustments. In this post, I am going to share some of the best practices that work best for agile data science research. Every machine learning project should start by defining the goals of the project.
User Interface design (UI) or user interface engineering is deployed for machines and software, such as computers, home appliances, mobile devices, and other electronic devices, with the focus on maximizing and simplifying usability and the user experience. User Experience design (UX) dictates UI design. Today UX design has evolved not only because of the omnipresence of smart technology, but also because developed economies are increasingly focused on the service industry, where customer experience is crucial. In the future UI/UX will become the USP for most of the products sold across the globe.
In recent years, deep learning has revolutionized computer vision. And thanks to transfer learning and amazing learning resources, anyone can start getting state of the art results within days and even hours, by using a pre-trained model and adapting it to your domain. As deep learning is becoming commoditized, what is needed is its creative application to different domains. Today, deep learning in computer vision has largely solved visual object classification, object detection, and recognition. In these areas, deep neural networks outperform human performance.
The rise of HaaS and IaaS in 2019 will shed light on a central insecurity in PaaS cloud strategy, as the staff controlling cloud environments have access to the information and materials stored and used in the cloud. Overall, while we ask ourselves “What is dangerous about PaaS?” we need to understand that attention needs to be given to protecting the system snapshots from a HaaS and an IaaS perspective. Most importantly, we should continue asking, “Who really has access to the virtual machines and snapshots?” Until this happens, PaaS remains a real threat.
Companies are leveraging chatbots for marketing activities, to generate leads, to entertain their target audience, and for customer service. Consumers are also aware of the benefits of interacting with smart bots. So if you’re interested in leveraging chatbots to better engage with your customers, what’s the best way to approach it? There are a few ways to incorporate chatbots into your enterprise offering. If you’re serious about using chatbots to drive enterprise value, you just can’t take the uncertain route of a DIY bot platform. There’s too much risk and not enough return to justify such an investment.
Analytics is inextricably linked to digital transformation efforts. It’s reasonable to say that without analytics, digital transformation is unlikely to be successful. With IoT-generated data rapidly increasing, businesses must have a clear picture of their desired outcomes in order to ensure that the analytics technology used to gain insight from that data is aligned with business needs. Any complete implementation of IoT analytics will require hundreds of decisions, but there are three vital ones that profoundly shape the optimal architecture for a business.
Every year, there are exciting new developments in medicine and technology in healthcare. 2019 promises to be no different. Technologies that were in development in 2018 are set to deploy this year to improve patient outcomes. Laws and regulations are also changing, driving a shift in how care providers think about and deliver treatment. Patients’ role in their own treatment is also evolving alongside these technologies, allowing them to be more involved in their own treatment plans. So what tech trends can we expect to see in hospitals this year? These 8 may have the biggest impact.
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.