On Inauguration Day, fans of the All-In Podcast gathered in a billiards room in Washington DC to watch Donald Trump's swearing-in – and a few miles away, the podcast co-host and PayPal Mafia alum David Sacks prepared to ascend to his role as Trump's AI and crypto czar. Very popular in Silicon Valley, All-In is fiercely pro-capitalism and enthusiastic about the world of tech start-ups and investments. Last summer, its co-hosts, Sacks and Jason Calacanis in particular, became vocal in their support for Trump and attempted to rally other tech leaders, including their listeners, behind the candidate. Now, Sacks has a seat at the table in the White House, as do many others in tech, including a former Uber executive, a senior adviser at Palantir, and a PayPal co-founder, who was picked to be ambassador to Denmark (Greenland, a territory Trump wants to seize, is part of Denmark). It's a watershed moment for relationships between Silicon Valley and Washington and, more broadly, what's often described as the tech right.

The industry thrives on its futuristic image, worships boy-CEOs and renders the past obsolete at a frightening pace. Even in the eight years I've sat on the Guardian's technology desk, the field I cover is frequently unrecognisable from what it was when I started – a world where self-driving cars were just around the corner, where virtual reality was an impressive technology that had failed to catch on with normal people, and where the world was starting to tire of the like-clockwork appearance of a new iPhone every 12 months. Well, fine, but some things really have changed in that time. Just before I started at the paper, the Guardian broke the news that the NSA had been spying on Americans – and the rest of the world – through the tech sector, with more revelations to come thanks to the whistleblowing efforts of Edward Snowden. It was the first sign that the lustre had started to come off the sector, an inkling of what was to follow a few years later as the "techlash" saw first Facebook, then the rest of the industry, fall from grace.

Burn-In: A Novel of the Real Robotic Revolution is a technothriller that follows the hunt for a terrorist through the streets of a future Washington, DC. More than 300 factual explanations and predictions (with endnotes) are baked into the story, and the research for it ranged from assembling the latest job automation reports to interviews with AI scientists and water-system cybersecurity experts. This is the first chapter, where we meet the main character, FBI special agent Lara Keegan, who is responding to an emergency alert at Washington's Union Station. Soon Keegan will be assigned to test out a robotic policing tool and launched into a conspiracy whose mastermind is using cutting-edge tech to tear the nation apart. The man's greasy red beard and braided Viking-style Mohawk had likely not been washed in a couple weeks, but the way that he cradled his AR-15 assault rifle made it clear he took care of what most mattered to him.

RealtyAds is a digital marketing platform that is using AI to help the commercial real estate industry more successfully market their buildings, brokers, and brands to key, target audiences. Trevor Marticke: After graduating from Colgate University with a focus in Political Science and Economics, Trevor moved to Chicago and began his real estate career. He worked as a Tenant Advisor for Transwestern Commercial Services representing companies looking for office space. After 3 years, Trevor and his team were recruited to Cushman & Wakefield to grow their practice globally and continue representing firms as their real estate advisors. Over his career Trevor worked on transactions as far reaching as Sydney, Australia and sourced / executed more than $350mm in total transaction value.

How many times have you applied for a job, waited anxiously for an offer or a refusal and never heard anything back? But could a robot streamline the recruitment process, and is a rejection letter from a robot better than no letter at all? This year, the Washington Post reported that Ikea Moscow used Robot Vera to conduct preliminary interviews and provide feedback to applicants. Anne Keegan, professor of human resource management at the College of Business, University College Dublin, explains Robot Vera. "Robot Vera is a video-enhanced chatbot that uses machine learning to continuously refine her conversational skills based on more and more practice with applicants. Robot Vera recognises emotions and gathers data based on the answers given by job applicants to her questions. She then distinguishes applicants based on this information to make predictions about job suitability. She also sends follow-up letters to applicants," she says.

Adversarial behavioral has been observed in many different contexts. In this paper we address the problem of adversarial behavior in the context of clandestine networks. We use data from a massively multiplayer online role playing game to illustrate the behavioral and structural signatures of deviant players change over time as a response to "policing" activities of the game administrators. Preliminary results show that the behavior of the deviant players and their affiliates show co-evolutionary behavior and the timespan within the game can be divided into different epochs based on their behaviors. Feature sets derived from these results can be used for better predictive machine learning models for detecting deviants in clandestine networks.

Gold farming and real money trade refer to a set of illicit practices in massively multiplayer online games (MMOGs) whereby players accumulate virtual resources to sell for “real world” money. Prior work has examined trade relationships formed by gold farmers but not the trust relationships which exist between members of these organizations. We adopt a hypergraph approach to model the multi-modal relationships of gold farmers granting other players permission to use and modify objects they own. We argue these permissions reflect underlying trust relationships which can be analyzed using network analysis methods. We compare farmers’ trust networks to the trust networks of both unidentified farmers and typical players. Our results demonstrate that gold farmers’ networks are different from trust networks of normal players whereby farmers trust highly-central non-farmer players but not each other. These findings have implications for augmenting detection methods and re-evaluating theories of clandestine behavior.