Welcome to the 4th industrial revolution. This is where automation & artificial intelligence are transforming the way we all live, work, and connect. We've written extensively on the impact of AI in HR, staffing, and recruiting. Just about everything we do is being impacted by artificial intelligence. Farming, retail, healthcare, mental health, construction, IT…pick any industry you like.

Recent surveys, studies, forecasts and other quantitative assessments of the progress and impact of AI highlight the growing respect for data and its uses by businesses everywhere and the increasingly positive--but still mixed--attitudes towards AI by US consumers. The second wave of AI, right now, is soon going to fail because too much trickery and even self-trickery is used"--Simone Teufel, University of Cambridge "What's happening right now is not'AI.' That was an intellectual aspiration and that's still alive today as an aspiration… the dreams and aspirations are five hundred years from now--that's like the Greeks sitting there and saying it would be neat to get to the moon someday. We have no clue how the brain does computation"--Michael I. Jordan, University of California, Berkeley

In Data-driven marketing, there are 5 ways in which AI can be a game changer. I would like to share some insight on what we have learned at SnatchBot for the past few years, thanks to collaboration from various marketing teams around the world. The majority of experienced marketers have by now become aware of how "artificial intelligence marketing" is deemed to be the latest innovation of data-driven marketing strategy and how it is impacting the digital world. Marketers are now able to make use of artificial intelligence and this allows them to simulate highly personalized consumer experiences which is significantly cheaper than the more conventional large investment campaigns. The benefit is that absolutely every interaction which a consumer has with a solution or product is noted so that it can be used for future optimization.

However, the IoT is not a static force, and developers and users are constantly finding news ways to exploit the simple yet powerful concept of connected everything. In 2019, the industry should expect emerging trends to help set the trajectory of the IoT for years or even decades to come. Here are a few to keep an eye on next year. We often get caught up in the excitement of new technology, and low levels of regulation across the tech field mean it's easy to focus almost exclusively on innovation. The IoT, however, has posed a number of concerns, and 2019 will likely see various legislatures and regulatory entities start proposing and enforcing new regulations.

Over the last decade, crowdsourcing has been used to harness the power of human computation to solve tasks that are notoriously difficult to solve with computers alone, such as determining whether or not an image contains a tree, rating the relevance of a website, or verifying the phone number of a business. The machine learning and natural language processing communities were early to embrace crowdsourcing as a tool for quickly and inexpensively obtaining the vast quantities of labeled data needed to train systems. Once this data is collected, it can be handed off to algorithms that learn to make autonomous predictions or actions. Usually this handoff is where interaction with the crowd ends. The crowd provides the data, but the ultimate goal is to eventually take humans out of the loop.

This year has been truly remarkable in terms of technological development and as the sun sets on 2016 I believe we're heading towards a new and exciting era of computing. An era that will see a broad range of industries taking advantage of intelligent machines powered by artificial intelligence (AI) thanks to computers built with the processing power to learn and interact with people. We're already starting to see this technology come to life - driverless cars can now meander through country roads at night and AI powered robots are able to learn motor skills through trial and error. But how have we reached this point and what new developments are already on the horizon? Throughout the history of computing there have been a number of key moments that have defined each era.

Over the last decade, computer scientists have harnessed crowds of Internet users to solve tasks that are notoriously difficult to crack with computers alone, such as determining whether an image contains a tree, rating the relevance of websites, and verifying phone numbers. The machine learning community was early to embrace so-called crowdsourcing to quickly and inexpensively obtain the vast quantities of labeled data needed to train machine learning systems how to classify images or recognize speech, for example. Labeled data are essentially sets of teaching examples, such as pictures of cats that are tagged with the word "cat." Usually this handoff of labeled data is where interaction with the crowd ends. Are there better ways to make use of the crowd?