Welcome to our HR Modernization Playbook: Tomorrow's people – Why HR matters more than ever in the age of artificial intelligence. Digital transformation is happening faster than ever. The adoption of artificial intelligence (AI) and automation will redefine jobs, enhance employee productivity and accelerate workforce development. In fact, skills and culture – not technology – are the biggest barriers to business growth in the AI era. This means CEOs are looking to their CHRO to lead culture change, manage talent and drive down costs.
Brands that communicate with their audiences with a 2-way communication that engages and excites the prospective clientele without shying of from creating appreciative content and embrace digital technologies, are the once who will survive in 2019! We live in an era where "Digital" and "Marketing" are terminologies, that are changing its essence & meaning, every single day. Users that brands wish to engage with have a different set of expectations and are paying attention only to brands who are communicating content that they want to hear. Gone are the days of one-way communication, where a brands Target Audiences' Consumer Journeys are based on "ADVERTISEMENTS" Since we've entered digital era and have been communicating with our audiences in 2-way conversations, audiences have been interested in a brand, only if the brand talks to them'things' (content) that they want to hear. But as we move towards the future, in 2019 what also matters is how you're keeping them engaged.
Coach is a python reinforcement learning framework containing implementation of many state-of-the-art algorithms. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms, and allows simple integration of new environments to solve. Basic RL components (algorithms, environments, neural network architectures, exploration policies, ...) are well decoupled, so that extending and reusing existing components is fairly painless. Contacting the Coach development team is also possible through the email firstname.lastname@example.org One of the main challenges when building a research project, or a solution based on a published algorithm, is getting a concrete and reliable baseline that reproduces the algorithm's results, as reported by its authors.
"AI may compete with an employee able to earn $100 per hour sometime between 2027 and 2055." "The creativity of evolution is not limited to the natural world: artificial organisms evolving in computational environments have also elicited surprise and wonder from the researchers studying them". There is a great deal of buzz, but not a great deal of concrete information, on what artificial intelligence will mean for marketing roles over the next several years. Being a marketer myself I don't think I am exaggerating when I say I'm concerned that I may be part of a dying breed. There have been big shake-ups in marketing since the 1990s with the advent of digital, and again since the late 00s with social media; but marketers have for the most part been able to adapt. My gut – and everything I've read - tells me this is something far more disruptive.
"First we have to get our internal data in order". It's something we hear frequently. Why invest externally when there is an underutilized resource on hand? Today's article breaks down the dimensions of when to focus on external data and why, and discusses why the demand for external data is expanding fast. Commercial impact from analytics, i.e. by improvement over the status quo, is driven foundationally by data and algorithms.
Quantum computing is a relatively modern technology that pioneering scientists, researchers, and entrepreneurs worldwide are currently actively pursuing to commercialize. For example, recently at CES (Consumer Electronics Show) in January 2019, IBM debuted its "Q System One" as the first standalone quantum computer geared for scientific and commercial use. Making quantum computing accessible will help accelerate progress in artificial intelligence (AI). Speed up computing, and you enhance the performance of deep learning. For example, the parallel processing capabilities of GPUs (graphics processing unit) helped accelerate AI deep learning by providing greater computational power than serial processing CPUs (central processing unit) to process large amounts of big data used for machine learning.
Talking to voice assistants or chatbots are more like asking something to do with no emotions attached which is far different from talking to a human. With the advancements in artificial intelligence and machine learning, the tech giants are always looking to develop something out of the box. In an effort to make these assistants more human-like in nature, Microsoft has come up a chatbot that not only assists you whenever you want but also conveys it emotionally. AI-based chatbot, XiaoIce, translated to "little Ice" in Chinese is one of the ambitious projects of Microsoft, that was released by researchers in May 2014 at China. It's uniquely designed as an AI companion with an emotional connection to satisfy the need for human communication and is catering to over 660 million users.
Technology innovation is moving so fast you can be forgiven for thinking it's moving too fast. What was predicted one year ago to be the next big thing may have already been replaced with the latest and greatest concept. For entrepreneurs this presents a challenge as not all new technology ends up catching on, so it's important to be careful with R&D dollars and investments. Remember technology is a tool to enable business to run more efficiently and more economically. Three big trends for 2019 evolve around 5G, the Internet of Things (IoTs), and Artificial Intelligence (AI).
Chinese Internet giant Alibaba Group has acquired data Artisans, the German company behind the Apache Flink data processing framework. Alibaba is one of the biggest users of Flink, and plans to continue developing and Flink and contributing enhancements back to the open source project. Apache Flink originated from the Stratosphere research project at the Technical University of Berlin in 2009, and in 2015 became a top level project at the Apache Software Foundation. The software features a dataflow engine developed in Java and Scala that's designed to run batch and streaming analytic workloads on distributed systems, including Hadoop clusters and cloud-based systems. Flink is a contemporary of Apache Spark, and in fact boasted certain advantages over Spark, particularly when it comes to real-time streaming analytics, although Spark closed the gap last year when it replaced "micro-batch" streaming with a more continuous approach.
There's a new drinking game that is sweeping across after-work corporate watering holes. Everyone takes turns guessing how long it will be until their job is automated out of existence. After every guess, everyone drinks. There is a steady drumbeat of news and analysis that predict a certain demise of much of modern work. You could even put my last CIO article, "The'future of work' in the digital era may not be what you think," in that category.