Tag: AI

Feed: Featured Blog Posts - Data Science Central. Author: ajit jaokar. In the previous post, ten strategies to implement ai on the cloud and edge, I discussed strategies for end to end deployment for machine learning modules. How this relates to Agile? Deployment of AI comes within the scope the normal SDLC (software development lifecycle) So, normal Agile techniques like scrum, sprints, backlog planning, release planning, test driven development and acceptance criteria also apply to deployment of machine learning models in production. However, combining DevOps with development adds an extra level of complexity to machine learning. We need end to ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: William Vorhies. Summary:  Just how much should you trust your AI systems?  Best practice points to constant review, strong governance, and the willingness to override results that seem illogical. Just how much do you trust your AI?  This is not intended to be the skeptical consumer view about bias or black box outcomes.  It’s intended to see if leaders in actual implementation see AI through proverbial rose colored glasses or whether and how much skepticism they think is warranted. As a source, I love these very professionally done surveys of AI ... Read More

Feed: Microsoft Azure Blog. Author: John 'JG' Chirapurath. Enterprises today are transforming their businesses using Machine Learning (ML) to develop a lasting competitive advantage. From healthcare to transportation, supply chain to risk management, machine learning is becoming pervasive across industries, disrupting markets and reshaping business models. Organizations need the technology and tools required to build and deploy successful Machine Learning models and operate in an agile way. MLOps is the key to making machine learning projects successful at scale. What is MLOps ? It is the practice of collaboration between data science and IT teams designed to accelerate the entire ... Read More

Feed: R-bloggers. Author: Learning Machines. We already covered the so-called Accuracy-Interpretability Trade-Off which states that oftentimes the more accurate the results of an AI are the harder it is to interpret how it arrived at its conclusions (see also: Learning Data Science: Predicting Income Brackets). This is especially true for Neural Networks: while often delivering outstanding results, they are basically black boxes and notoriously hard to interpret (see also: Understanding the Magic of Neural Networks). There is a new hot area of research to make black-box models interpretable, called Explainable Artificial Intelligence (XAI), if you want to gain some intuition ... Read More

Feed: SAS Blogs. Author: Claudio Broggio. AI innovations range from unique modelling techniques and computer vision efforts to medical diagnostic tools and self-driving cars. Within that wide range of technology, what should you consider patenting? Which of your discoveries are true intellectual property that necessitate protection? A little thought upfront could help you to know what action to take to protect your most valuable innovations. Intellectual property, machine learning and generative adversarial networks Intellectual property and AI Do not doubt that plenty of people are already protecting their artificial intelligence inventions. Since the 1960s, over 300,000 applications for AI-related patents ... Read More

Feed: Domo Blog. Author: Scott Resch. A digitally transformed organization is one that is “alive and awake to everything that is going on around it” and knows how to leverage connections to take advantage of its place within a complex ecosystem.  That is one of the key points made in the inaugural episode of Domo Connects, a podcast that explores matters related to digital transformation and organizational optimization through compelling conversations with some of the brightest minds in business. In the episode, I speak with Peter Sole, Helen Mumford-Sole and Julie Rufo of digital transformation insight provider Mumford Sole Partners ... Read More

Feed: The Connector by Workato. Author: Katharine Marais; Novel fintech like cryptocurrency and blockchain have disrupted and fundamentally reimagined such previously eminent and staid realities such as the government monopoly on currency-creation. As fintech has risen to prominence, an unlikely mascot has emerged to represent the convenience enabled by novel financial technologies; mobile banking ads have portrayed ballerinas paying for goods while performing- logging into Visa or Chase apps from the wings, or hilariously, paying for goods while doing a “grand jete between two grand pianos,” in one tongue-in-cheek commercial by Chase. They portray a shared sentiment: that we are ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Bill Schmarzo. Imagine this scenario: You enter your dentist’s office as a follow-up visit to the 6-month checkup you had last week. You’re nervous because the checkup revealed bleeding in your gums and cracks in your fillings, all things that require your dentist’s repair on this occasion. So, you’re there nervously fidgeting in the lobby waiting for your name to be called, envisioning all sorts of dentistry implements of torture that will soon be inside your mouth. Finally, when your name is called, you’re shown to the room of horrors and ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Roman Chuprina. Machine Learning is a term heard around the world these days. Industries like Retail, Healthcare, and Manufacturing are taking the best out of it. So does the leaders in Banking and Finance. There is enough time and room before the technology will truly explode. However, we can still talk about some real-world use cases and ways your business can benefit. AI and ML in financial services Machine learning in finance is rapidly developing – there are already dozens of options for its use in the financial sector. So why ... Read More

Feed: R-bloggers. Author: Douglas Pestana - @DougVegas. Data Scientists and Analysts are starting to overflow their cups with mostly non-value added work at the request of stakeholders. And many don’t have the courage or confidence to say “No” to that. This also applies to many working and business professionals in other verticals and professions. Put simply, you need to build confidence, stop being a wimp, and start saying “No” to pointless work.Data Scientists can become the new decision-makers at companies and should be picking their own value-added projects When a company hires a data science team, the customers of that ... Read More