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10 Common Uses for Machine Learning Applications in Business


Machine learning has advanced from the age of science fiction to a major component of modern enterprises, especially as businesses across almost all sectors use various machine learning technologies. As an example, the healthcare industry is utilizing machine learning business applications to achieve more accurate diagnoses and provide better treatment to their patients. Retailers also use machine learning to send the right goods and products to the right stores before it is out of stock. Medical researchers are also not left out when it comes to using machine learning as many introduce newer and more effective medicines with the help of this technology. Many use cases are emerging from all sectors as machine learning is being implemented in logistics, manufacturing, hospitality, travel and tourism, energy, and utilities.

How to implement python in Machine Learning


Machine Learning is one of the hottest futuristic technologies in the industry right now, and companies are rushing to incorporate it into their products, particularly apps. And it's no surprise, given that this branch of computer science helps one to do something we couldn't even imagine before. So what exactly does it do? To improve the user interface, Airbnb, for example, uses it to categorize room styles based on pictures. Carousel uses visual recognition to make the bid posting process easier for vendors; while a machine learning powered recommendation feature helps buyers locate better listings.

6 AI Myths Debunked


"Artificial intelligence (AI)I will automate everything and put people out of work." "AI is a science-fiction technology." "Robots will take over the world." The hype around AI has produced many myths, in mainstream media, in board meetings and across organizations. Some worry about an "almighty" AI that will take over the world, and some think that AI is nothing more than a buzzword.

Hybrid GA and SA dynamic set-up planning optimization


Set-up planning is used to determine the set-up of a workpiece with a certain orientation and fixturing on a worktable, as well as the number and sequence of set-ups and operations performed in each set-up. This paper presents a concurrent constraint planning methodology and a hybrid genetic algorithm (GA) and simulated annealing (SA) approach for set-up planning, and re-set-up planning in a dynamic workshop environment. The proposed approach and optimization methodology analyses the precedence relationships among features to generate a precedence relationship matrix (PRM). Based on the PRM and inquiry results from a dynamic workshop resource database, the hybrid GA and SA approach, which adopts the feature-based representation, optimizes the set-up plan using six cost indices. Case studies show that the hybrid GA and SA approach is able to generate optimal results as well as carry out re-set-up planning on the occurrence of workshop resource changes.

Cybersecurity in Healthcare: How to Prevent Cybercrime


Because COVID-19 made it difficult for consumers to venture out and run their usual errands, FIs needed to find other ways to provide their services. The only way for them to really keep up with the speedy digitization was through the implementation of AI systems. To further discuss all things AI, PaymentsJournal sat down with Sudhir Jha, Mastercard SVP and head of Brighterion, and Tim Sloane, VP of Payments Innovation at Mercator Advisory Group. Jha believes that there were two fundamentally big changes that occurred in banking during the pandemic: the environment began constantly shifting, and person-to-person interactions were abruptly limited. "Every week, every month, there were different ways that we were trying to react to the pandemic," explained Jha.

Hill Climbing and Simulated Annealing AI Algorithms


Redeem Get Udemy Coupon What you'll learn Udemy Coupon Best Description Search Algorithms and Optimization techniques are the engines of most Artificial Intelligence techniques and Data Science. There is no doubt that Hill Climbing and Simulated Annealing are the most well-regarded and widely used AI search techniques. A lot of scientists and practitioners use search and optimization algorithms without understanding their internal structure. However, understanding the internal structure and mechanism of such AI problem-solving techniques will allow them to solve problems more efficiently. This also allows them to tune, tweak, and even design new algorithms for different projects.

Octopai announces new layered data lineage platform


Israeli data discovery firm Octopai is today announcing a new platform, Data Lineage XD, to take on and automate the discovery and documentation of data transformations and flows within customer data estates. The "XD" stands for cross-dimensional, and that name is not mere marketing gimmickry. The Octopai system can map out what the company calls cross-system lineage, inner-system lineage, and end-to-end column lineage. Executives from Octopai briefed ZDNet on Data Lineage XD and demoed facets of the platform. They explained that cross-system lineage maps the flow of data across systems, from initial ingest and extract, transform and load (ETL) through to reporting and analytics.

Smart Speakers Go Beyond Waiting to Be Asked WSJD - Technology

The Amazon Echo Show 10 automatically moves its display to face the user, even if it is performing a task that doesn't need user input, like showing a recipe on the screen. Get weekly insights into the ways companies optimize data, technology and design to drive success with their customers and employees. Proactive or not, features in smart-home devices need to address a real user need, not stack the product with unnecessary and potentially confusing tools, said Ashton Udall, senior product manager at Google. The company developed sensor technology to monitor sleep, for example, because its research showed that consumers frequently forget to use or charge the wearables often employed for sleep tracking, or find the devices uncomfortable, he said. Amazon and Google hope the experiences will help them compete for users and more fully integrate their devices into people's lives.

Council Post: Nice Chatbot-Ing With You


Martin Taylor is the Deputy CEO and Co-Founder of Content Guru. Siri and Alexa -- the robots we couldn't live without. Throughout the pandemic, these voice assistants have proven invaluable to many, as users turned towards Alexa and Google Assistant for entertainment, education and emotional help. In fact, according to one survey, 3 in 5 users believe that their voice assistant has helped them get through isolation, and 40% will continue to use their digital assistants more as a result of the pandemic. These smart assistants are so effective because they're driven by artificial intelligence (AI).

Improving customer service with an intelligent virtual assistant using IBM Watson


Gartner predicts that "by 2022, 70 percent of white-collar workers will interact with conversational platforms on a daily basis." As a result, the research group found that more organizations are investing in chatbot development and deployment. IBM Business Partners like Sopra Steria are making chatbot and virtual assistant technology available to businesses. Sopra Steria, a European leader in digital transformation, has developed an intelligent virtual assistant for organizations across several industries who want to use an AI conversational interface to answer recurrent customer service questions. In developing our solution, we at Sopra Steria were looking for AI technology that was easy to configure and could support multiple languages and complex dialogs.