In spite of recovering major ground after the Great Recession, the construction industry is still facing troubling skilled labor shortages, with a lack of qualified candidates stepping up to take over the positions once held by industry veterans nearing retirement age. The construction industry lost 2.3 million jobs between 2006-2011, and today there are a million fewer residential construction jobs than before 2006, according to Tradesmen International. The Bureau of Labor Statistics Job Openings and Labor Turnover Survey shows nearly 200,000 unfilled construction industry jobs nationwide. This gap between available positions and skilled workers ready to fill them puts added pressure on developers, contractors and owners. Even in the face of a worker shortage, construction is booming.

Telecom companies are currently scrambling to implement fifth-generation cellular network technology. But the world of 5G is a world where all objects are wired and constantly communicating data to one another. The dark truth is that the development of 5G networks and the various networked products that they will give rise to in the global smart city infrastructure, represent the greatest threat to freedom in the history of humanity. STEVE MOLLONKOPF: 5G will upgrade the human experience at home and across industries as we connect virtually everything. By 2020, analysts estimate that there will be more than 20 billion installed IoT devices around the world, generating massive amounts of data. With access to this kind of information, industries of all kinds will be able to reach new levels of efficiency as they add products, services, and capabilities. As you may have heard by now, telecom companies are currently scrambling to implement fifth-generation cellular network technology.

Tuesday of this past week was quite fortuitous: In my Data Science Cohort at Lambda School, we are working a predictive maintenance competition on Kaggle regarding Water pumps in Tanzania. And, I went to a Data Science networking event at a defense contractor who spoke of the importance of Predictive Maintenance Solutions -- in their case, they were predicting the failure rates of parts of the F35 Joint Strike Fighter. According to IoT world, The Predictive Maintenance report forecasts a compound annual growth rate for Predictive Maintenance of 39% between 2016–2022, with annual technology spending reaching US$10.96 This has a large positive impact on Data Science and Machine Learning if the industry can keep up with the needs of predictive maintenance problems. What is predictive maintenance and why is it so important to different domains?

Artificial intelligence (AI) holds tremendous promise to benefit nearly all aspects of society, including the economy, healthcare, security, the law, transportation, even technology itself. On February 11, 2019, the President signed Executive Order 13859, Maintaining American Leadership in Artificial Intelligence. This order launched the American AI Initiative, a concerted effort to promote and protect AI technology and innovation in the United States. The Initiative implements a whole-of-government strategy in collaboration and engagement with the private sector, academia, the public, and like-minded international partners. Among other actions, key directives in the Initiative call for Federal agencies to prioritize AI research and development (R&D) investments, enhance access to high-quality cyberinfrastructure and data, ensure that the Nation leads in the development of technical standards for AI, and provide education and training opportunities to prepare the American workforce for the new era of AI.

AI-generated faces might be more than just novelties -- they could also be used as espionage tools. Experts talking to the AP believe that spies used AI to create a'photo' of Katie Jones, a non-existent person used in an attempt to infiltrate the American political scene. While the snapshot may have looked plausible with a cursory look, there were telltale clues like a blurry earring and hetero-chromatic eyes that didn't quite line up. And crucially, that AI fakery might have been enough to fool some important political figures. The imaginary Jones had LinkedIn connections to a number of American officials or political influencers, including economist Paul Winfree (considered for a Federal Reserve seat), a deputy assistant secretary of state and a senator's senior aide.

Teams of UGVs patrolling harsh and complex 3D environments can experience interference and spatial conflicts with one another. Neglecting the occurrence of these events crucially hinders both soundness and reliability of a patrolling process. This work presents a distributed multi-robot patrolling technique, which uses a two-level coordination strategy to minimize and explicitly manage the occurrence of conflicts and interference. The first level guides the agents to single out exclusive target nodes on a topological map. This target selection relies on a shared idleness representation and a coordination mechanism preventing topological conflicts. The second level hosts coordination strategies based on a metric representation of space and is supported by a 3D SLAM system. Here, each robot path planner negotiates spatial conflicts by applying a multi-robot traversability function. Continuous interactions between these two levels ensure coordination and conflicts resolution. Both simulations and real-world experiments are presented to validate the performances of the proposed patrolling strategy in 3D environments. Results show this is a promising solution for managing spatial conflicts and preventing deadlocks.

Given a huge set of applicants, how should a firm allocate sequential resume screenings, phone interviews, and in-person site visits? In a tiered interview process, later stages (e.g., in-person visits) are more informative, but also more expensive than earlier stages (e.g., resume screenings). Using accepted hiring models and the concept of structured interviews, a best practice in human resources, we cast tiered hiring as a combinatorial pure exploration (CPE) problem in the stochastic multi-armed bandit setting. The goal is to select a subset of arms (in our case, applicants) with some combinatorial structure. We present new algorithms in both the probably approximately correct (PAC) and fixed-budget settings that select a near-optimal cohort with provable guarantees. We show on real data from one of the largest USbased computer science graduate programs that our algorithms make better hiring decisions or use less budget than the status quo. '... nothing we do is more important than hiring and developing people. At the end of the day, you bet on people, not on strategies." - Lawrence Bossidy, The CEO as Coach (1995)

Input distribution monitoring: Recent model input data may be compared with model training data to determine whether incoming credit applications are significantly different from model training data. The more that live data differs from training data, the less accurate the model is likely to be. This data comparison is typically done by looking at variable distributions and ensuring recent data is drawn from a similar distribution as occurred in the model training data. For ML models, multivariate input variable distributions should be monitored to identify input data where combinations of values that were unlikely to appear together during model development are now occurring in production. Systems for monitoring model inputs should trigger alerts to monitors or validators when they spot anomalies or shifts that exceed pre-defined safe bounds.

In experiments the technique was at least 92% accurate in spotting several variations of deepfakes, including face swaps and ones in which an impersonator is using a digital puppet. It was also able to deal with artifacts in the files that come from recompressing a video, which can confuse other detection techniques. The researchers plan to improve the technique by accounting for characteristics of a person's speech as well. The research, which was presented at a computer vision conference in California this week, was funded by Google and DARPA, a research wing of the Pentagon. DARPA is funding a program to devise better detection techniques.

Is Facial Recognition a valuable public-safety tool or is it an infringement of our civil liberties? Also, name our new Podcast & win a prize! Links in the episode: STUDY: Facial feature discovery for ethnicity recognition San Francisco just banned facial-recognition technology SF Ban on Face Recognition – Acquisition of Surveillance Technology Facial recognition data collected by U.S. customs agency stolen by hackers Facial Recognition Software Wrongly Identifies 28 Lawmakers As Crime Suspects Does object recognition work for everyone? A new method to assess bias in CV systems Don't smile for surveillance: Why airport face scans are a privacy trap U.S. Customs and Border Protection says photos of travelers were taken in a data breach Chickens Prefer Attractive People