The advent of technology has truly defined the beginning of the millennium. Social media sites, the internet, the increase of start-up ventures and the streamlining of every and any kind of process has made access to our heart's desires as easy as a push or a click. Have fresh, hand-cooked food delivered to your door in a matter of 30 minutes. Now there is no need -- Instagram, Twitter, Facebook, Snapchat will give you everything you want to know (and perhaps do not want to know) about their lives at this present moment. If express trains or other forms of optimised public transport are not enough, you have the likes of Uber and Lyft to take you directly to your destination if you choose.
Facebook has become the latest company to admit that human contractors listened to recordings of users without their knowledge, a practice the company now says has been "paused". Citing contractors who worked on the project, Bloomberg News reported on Tuesday that the company hired people to listen to audio conversations carried out on Facebook Messenger. The practice involved users who had opted in Messenger to have their voice chats transcribed, the company said. The contractors were tasked with re-transcribing the conversations in order to gauge the accuracy of the automatic transcription tool. "Much like Apple and Google, we paused human review of audio more than a week ago," a Facebook spokesperson told the Guardian.
AI is everywhere from Google Search to Waze, from Chatbots to intelligent automatic email responses. As a B2B marketer, this is just another technology that we need to consider as part of the martech stack. So how do we evaluate how AI can help or optimize our marketing? Many marketers feel intimidated by AI because they don't know what it is and how to take advantage of it in the context of marketing. In my previous blog post, I explained AI, Machine Learning and Deep Learning in relationship to Marketing.
Machine Learning is a buzzword in the technology world right now and for good reason, it represents a major step forward in how computers can learn. The need for Machine Learning Engineers are high in demand and this surge is due to evolving technology and generation of huge amounts of data aka Big Data. On an Average, an ML Engineer can expect a salary of ₹719,646 (IND) or $111,490 (US). So, let's discuss some of the Applications of Machine Learning. I'll be discussing the following Applications of Machine Learning one by one: Now, Google Maps is probably THE app we use whenever we go out and require assistance in directions and traffic.
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A few years ago, I missed out on a huge promotion because I didn't comply with an action from a CEO of my then establishment. He instructed me to hold back a delinquent customer's payment, so their mortgage would be transferred to the non-accrual status, so that his colleague could supposedly submit an offer to purchase this prime property. Reality check: doing the right things may not bring on the welcoming committee rather you may be beaten for it. I faced the brunt of his wrath thereafter. I knew then I didn't have a future in that organization.
Man or machine: who will shape the future of work? Randstad CEO Jacques van den Broek shared his vision with us at The Next Web 2019. As far as HR is concerned, AI and the'human touch' are becoming increasingly intertwined. For now it's a predominantly human business, but there's no denying that this will change in the future. Intelligent machines, software and algorithms are rapidly reshaping the industry as we know it.
In a recent blog post, Baidu, the Chinese search engine and e-commerce giant, announced their latest open-source, natural language understanding framework called ERNIE 2.0. They also shared recent test results, including achieving state-of-the art (SOTA) results and outperforming existing frameworks, including Google's BERT and XLNet in 16 NLP tasks in both Chinese and English. ERNIE 2.0, more formally known as Enhanced Representation through kNowledge IntEgration, is a continual pre-training framework for language understanding. We proposed a continual pre-training framework for language understanding in which pre-training tasks can be incrementally built and learned through constant multi-task learning. In this framework, different customized tasks can be incrementally introduced at any time and are trained through multi-task learning that permits the encoding of lexical, syntactic and semantic information across tasks.
This video is part of an online course, End-to-End Machine Learning with Tensorflow from Google Cloud. About this course: In the first course of this specialization, we will recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization. One of the best ways to review something is to work with the concepts and technologies that you have learned.