The modern world is changing more and more quickly with each passing year. If you don't pay attention to new changes or keep up the pace, it's easy to fall behind the times (and the market) while other companies beat you to the punch. To keep abreast of current changes – at least at a level of basic understanding. Adding to that, if you can't understand the buzzwords others are using in conversation, it's much harder to look smart while participating in that conversation. In this post, we're going to give you the 10 IT & technology buzzwords you won't be able to avoid in 2020 so that you can stay poised to take advantage of market opportunities and new conversations alike. The first in our definitive rundown of tech buzzwords 2020 is computer vision. Exciting and futuristic, the concept of computer vision is based on computing devices or programs gaining the ability to extract detailed information from visual images. In a computing context, 'vision' involves systems that can identify items, places, objects, or even humans from visuals mediums – images caught by a camera or sensor.

These are just a few of the things that come to mind when we think about artificial intelligence (AI). AI is the buzzword that seems to be on everyone's lips. To some, AI signals a utopian future, while others foresee a dystopian nightmare coming to control us all. But before you try to ignore AI or run for the hills for fear your job is on the line, let's dig a little deeper. Fact: AI is transforming business operations and increasingly becoming our interface with technology.

The ADAPT Centre at Trinity College Dublin has received funding to hire a start-up founder to spin-out the business start-up Darwin & Goliath http://darwingoliath.com in the field of recommendations-as-a-service. The position is to be filled with a product manager, machine-learning engineer, software engineer, or software architect. The person is expected to work together very closely with the project lead and Business Administrator. Both will be responsible for developing a recommender-system as-a-service that uses unique machine-learning technology, which is based on the research of Professor Joeran Beel who is the project lead. This position is flexible in the responsibilities and open to focus more on either software engineering / machine-learning or on the architectural and product management part.

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Instead, it relies primarily on a technique called federated learning, Apple's head of privacy, Julien Freudiger, told an audience at the Neural Processing Information Systems conference on December 8. Federated learning is a privacy-preserving machine-learning method that was first introduced by Google in 2017. It allows Apple to train different copies of a speaker recognition model across all its users' devices, using only the audio data available locally. It then sends just the updated models back to a central server to be combined into a master model. In this way, raw audio of users' Siri requests never leaves their iPhones and iPads, but the assistant continuously gets better at identifying the right speaker.

What do Google Assistant, Siri, Alexa, and Cortana have in common? They tell jokes of varying cleverness, most of which are the work of writing teams operating behind the scenes. They're entertaining, but preliminary research suggests they also play a part in making interactions with assistants engaging. In pursuit of assistants capable of tailoring jokes to individual users' tastes, Amazon researchers investigated joke selection methods that tap either a basic natural language processing model or a machine learning model. They say that when tested against production data, both approaches "positively" impacted user satisfaction and potentially improved joke-telling.

We propose a scalable Bayesian preference learning method for jointly predicting the preferences of individuals as well as the consensus of a crowd from pairwise labels. Peoples' opinions often differ greatly, making it difficult to predict their preferences from small amounts of personal data. Individual biases also make it harder to infer the consensus of a crowd when there are few labels per item. We address these challenges by combining matrix factorisation with Gaussian processes, using a Bayesian approach to account for uncertainty arising from noisy and sparse data. Our method exploits input features, such as text embeddings and user metadata, to predict preferences for new items and users that are not in the training set. As previous solutions based on Gaussian processes do not scale to large numbers of users, items or pairwise labels, we propose a stochastic variational inference approach that limits computational and memory costs. Our experiments on a recommendation task show that our method is competitive with previous approaches despite our scalable inference approximation. We demonstrate the method's scalability on a natural language processing task with thousands of users and items, and show improvements over the state of the art on this task. We make our software publicly available for future work.

Smart home devices are designed to make our lives easier, but they also make it easier for hackers to infiltrate our lives. The FBI has sent out a warning that'hackers can use those innocent devices to do a virtual drive-by of your digital life.' The US intelligence agency urges users to regularly change passwords, check for firmware updates and never have two devices on the same network. Digital assistants, smart watches, fitness trackers, home security devices, thermostats, refrigerators, and even light bulbs are all on the list of devices that can be infiltrated by cybercriminals. And if these devices, among other smart home technology, are not properly protected, they can be used by hackers to'do a virtual drive-by of your digital life.' Samsung are developing an interactive kitchen that includes a fridge, oven and TV.

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"Alexa" was just another female name. Uber hadn't taken anyone for a ride yet. And the buzz around Facebook had more to do with the fact that seemingly everyone you once knew was turning up on "The Social Network," and less about the numerous data and privacy scandals that would tarnish the company's reputation later on. The year was 2010, the dawn of a new decade. And while 10 years is a long time for most every industry, in consumer tech it might as well be a lifetime.