One thing about AI which you can't disagree is that it will reshape how all of us live and even have a deep impact on the work we do. Humanity will benefit to a great extent as people will waste less, live longer, work smarter, have a better understanding and will be able to predict anything which can be measured. However, one needs to be cautious with the benefits and promises AI and other computing technologies bring as there might be a certain risk. These risk can include threats to a job, privacy, security of a nation as well as public safety. Still, it doesn't hamper the extensive growth that artificial intelligence has and still making in today's era. It might be surprising to most people, but reports have shown that countries have taken the advancement and rise of artificial intelligence in the right step.

Which of these levels of personal detail do you feel comfortable sharing with your smartphone? And should every app on that device have the same level of knowledge about your personal details? Welcome to the concept of siloed sharing. If you want to keep relying on your favorite device to store and automatically sort through your data, it's time to start considering whether you want to trust device-, app-, and cloud-level AI services to share access to all of your information, or whether there should be highly differential access levels with silo-class safeguards in place. Your phone already contains far more information about you than you realize. Depending on who makes the phone's operating system and chips, that information might be spread across storage silos -- separate folders and/or "secure enclaves" -- that aren't easily accessible to the network, the operating system, or other apps.

We have progressed from the development stage of artificial intelligence (AI) and machine learning (ML) technology into widespread implementation across industries. This advanced technology is everywhere, but most people don't know they interact with it daily. From virtual personal assistants like Siri and Alexa to the recommended shows that Netflix serves up based on viewing habits, ML powers these tools we've become so accustomed to using -- even if we don't stop to think about how it happens. What's the difference between AI and ML? AI is computer science that is focused on the capabilities of machines to imitate intelligent human behavior. AI allows computers to process large amounts of information and data and provide a computer-generated conclusion.

We continue our series on artificial intelligence (AI) in public relations with Kerry Sheehan, Artificial Intelligence in PR Vice-Chair with the CIPR. You can read the full articles on our Patreon page. While you're here, take a look at our new podcast! Patreon support keeps this podcast alive and free for all listeners! The pace of the development of software and machine learning is increasing and by 2023 artificial intelligence will impact areas of PR such as stakeholder mapping, risk analysis, auditing, and behavioural analysis.

Have you ever matched with someone on a dating app that seems'too good to be true'? They may well be, according to an online dating consultant. Scammers have been letting'malicious bots' loose on dating apps in an attempt to convince people to part with their money, says online dating expert Steve Dean. He warned against'people' who open chats with'here's my phone number, you can call me here' with a link as soon as you swipe them on Tinder. Often these links take you to a scamming or live webcam site.

What we've learned from the early days of CX technology is that forcing customers to conform to rigid mechanical processes (language choices, service versus support options) doesn't provide a customer-centric approach. It didn't take long for people to figure out pressing "O" to get a live operator--or shouting "agent" in frustration--was almost always the best choice. Thankfully, AI, powered by Machine Learning and evaluated and fine-tuned by humans, has come a long way in the last decade. AI-powered virtual assistants are now able to ask-- and respond to--open-ended questions like, "What can I do for you today?" Marketers fulfill a dual function within their organizations. But because of the unique insights that they gain into how their customers think, feel, act, behave, and buy, they're also customer advocates.

Chris Hemedinger and Jared Dean describe how they used SAS Viya to build the topic recommendation engine for the SAS Support Communities. They discuss all phases of building a recommendation engine: data preparation, machine learning to build the analytical scoring model, the REST API to score topics for visitors, Docker and Kubernetes to host and scale the engine, and a DevOps approach to rebuild and redeploy the model each day with new data. Chris and Jared demonstrate the eclectic mix of SAS products and open source tools that they used, including SAS Visual Data Mining and Machine Learning, SAS Enterprise Guide, VS Code, Gitlab, Python, SAS Model Manager, Google Analytics, Apigee, Microsoft Teams, Docker, and Kubernetes. See the "Recommended by SAS" widget for yourself: visit https://communities.sas.com and sign in for personalized topic recommendations. Content Outline 01:09 – Demonstration of the live recommendation engine 03:05 – Data collection and preparation 04:37 – Python and SAS code (PROC FACTMAC) in Jupyter Notebook 08:04 – REST API built using Python Flask and SAS Micro Analytics Service 09:11 – DevOps infrastructure to deploy to production 11:30 – Calling the API from SAS Communities 13:01 – Monitoring APIs with Apigee and alerting with Microsoft Teams 15:19 – Google Analytics to track events, SAS Model Manager to predict success Additional Resources Building a recommendation engine with SAS - https://blogs.sas.com/content/sasdumm... Learn more about SAS machine learning APIs and developer tools SAS For Developers – https://developer.sas.com

Conversational artificial intelligence (AI) is on the rise, and both Gartner and Accenture believe the integration of conversational AI will emerge as a top priority for the c-suite by 2020. Conversational AI is a voice assistant that can engage in human-like dialogue, capture context and provide intelligent responses. Examples include Apple Siri, Amazon Alexa and Google Virtual Assistant. Developments in AI in the past few years, including machine learning, natural language processing and image and speech recognition have promoted its use, according to Kenneth Research. A recent Gartner report finds that by 2022, 70% of white-collar workers will interact with conversational platforms on a daily basis.

Overhead BR30 (or bulging reflector) floodlights in recessed lighting setups keep plenty of homes lit. If you're looking to upgrade wide beam light bulbs like those, you'll almost certainly want to go with an LED over a fluorescent model or incandescent bulbs. You'll find plenty of picks in your local lighting aisle that are bright, dimmable, efficient, durable and as affordable as ever. And with the most promising lifespan that lasts years or even decades, it'll be a long while before you have to break out the ladder again. So which of these new options is the right one for you?