BERLIN – German industrial robot-maker Hahn Automation plans to invest millions of euros in new factories in China over the next three years, keen to capitalize on an economy that's rebounding more rapidly than others from the COVID-19 crisis. "If we want to grow with the Chinese market, we have to manufacture on the ground," Chief Executive Frank Konrad said of the investment drive, intended to skirt Chinese export hurdles in what Beijing views as a strategic sector. "Our goal is to make up to 25% of our sales in China by 2025," he said, up from roughly 10% now. But while the Chinese recovery may be good news for companies like Hahn, it is complicating efforts by Chancellor Angela Merkel's government to diversify trade relations and become less dependent on Asia's rising superpower. Despite Berlin's concerns, German industry is deepening ties with China, which battled the pandemic with stricter measures than other countries, moved out of a first lockdown earlier and saw demand rebound more quickly. Olaf Kiesewetter, CEO of car sensor supplier UST in Thuringia in eastern Germany, shares the same ambition of making 25% of sales in China.

Rapid technological advancements in Artificial Intelligence (AI), as well as the growing deployment of intelligent technologies in new application domains, have generated serious anxiety and a fear of missing out among different stake-holders, fostering a racing narrative. Whether real or not, the belief in such a race for domain supremacy through AI, can make it real simply from its consequences, as put forward by the Thomas theorem. These consequences may be negative, as racing for technological supremacy creates a complex ecology of choices that could push stake-holders to underestimate or even ignore ethical and safety procedures. As a consequence, different actors are urging to consider both the normative and social impact of these technological advancements, contemplating the use of the precautionary principle in AI innovation and research. Yet, given the breadth and depth of AI and its advances, it is difficult to assess which technology needs regulation and when. As there is no easy access to data describing this alleged AI race, theoretical models are necessary to understand its potential dynamics, allowing for the identification of when procedures need to be put in place to favour outcomes beneficial for all. We show that, next to the risks of setbacks and being reprimanded for unsafe behaviour, the time-scale in which domain supremacy can be achieved plays a crucial role. When this can be achieved in a short term, those who completely ignore the safety precautions are bound to win the race but at a cost to society, apparently requiring regulatory actions. Our analysis reveals that imposing regulations for all risk and timing conditions may not have the anticipated effect as only for specific conditions a dilemma arises between what is individually preferred and globally beneficial. Similar observations can be made for the long-term development case. Yet different from the short-term situation, conditions can be identified that require the promotion of risk-taking as opposed to compliance with safety regulations in order to improve social welfare. These results remain robust both when two or several actors are involved in the race and when collective rather than individual setbacks are produced by risk-taking behaviour. When defining codes of conduct and regulatory policies for applications of AI, a clear understanding of the time-scale of the race is thus required, as this may induce important non-trivial effects. This article is part of the special track on AI and Society.  

This work provides documentation for a suite of R functions contained in gonogo.R. The functions provide sensitivity testing practitioners and researchers with an ability to conduct, analyze and simulate various sensitivity experiments involving binary responses and a single stimulus level (e.g., drug dosage, drop height, velocity, etc.). Included are the modern Neyer and 3pod adaptive procedures, as well as the Bruceton and Langlie. The latter two benchmark procedures are capable of being performed according to generalized up-down transformed-response rules. Each procedure is designated phase-one of a three-phase experiment. The goal of phase-one is to achieve overlapping data. The two additional (and optional) refinement phases utilize the D-optimal criteria and the Robbins-Monro-Joseph procedure. The goals of the two refinement phases are to situate testing in the vicinity of the median and tails of the latent response distribution, respectively.

Turns out, computers do too. When US and European researchers fed pictures of members of Congress to Google's cloud image recognition service, the service applied three times as many annotations related to physical appearance to photos of women as it did to men. The top labels applied to men were "official" and "businessperson"; for women they were "smile" and "chin." The researchers administered their machine vision test to Google's artificial intelligence image service and those of rivals Amazon and Microsoft. Crowdworkers were paid to review the annotations those services applied to official photos of lawmakers and images those lawmakers tweeted.

Words for health and the human body often make their way into the language we use to describe IT. Computers get viruses; companies manage their security hygiene; incident response teams train on their cyber fitness. Framing IT concepts in terms of health can also be useful when looking at security operations centers (SOCs) and jobs in cybersecurity. For many businesses and other entities today, SOCs are not the healthiest they could be. Jobs in cybersecurity can be stressful and overwhelming due to the volume of alerts.

CLASSIC DOGFIGHTS, in which two pilots match wits and machines to shoot down their opponent with well-aimed gunfire, are a thing of the past. Guided missiles have seen to that, and the last recorded instance of such duelling was 32 years ago, near the end of the Iran-Iraq war, when an Iranian F-4 Phantom took out an Iraqi Su-22 with its 20mm cannon. But memory lingers, and dogfighting, even of the simulated sort in which the laws of physics are substituted by equations running inside a computer, is reckoned a good test of the aptitude of a pilot in training. And that is also true when the pilot in question is, itself, a computer program. So, when America's Defence Advanced Research Projects Agency (DARPA), an adventurous arm of the Pentagon, considered the future of air-to-air combat and the role of artificial intelligence (AI) within that future, it began with basics that Manfred von Richthofen himself might have approved of.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A Hollywood man who operated a drone that crashed into a police helicopter, forcing an emergency landing, is facing a federal charge. Andrew Rene Hernandez, 22, was arrested by FBI agents Thursday and charged with one count of unsafe operation of an unmanned aircraft, the Justice Department said. The criminal case is believed to be the first in the nation stemming from a drone collision.

Having been privileged to witness the evolution of the data science and artificial intelligence (AI) scene in the Middle East for the past 10 years and having spoken at one of the first big data events in Dubai back in 2013, it is clear to me that there are considerable opportunities for AI in this vibrant region. Recently, I got the chance to present on the top 10 AI challenges of companies in the Gulf Cooperation Council (GCC) region, at Virtual Executive Boardroom: Key Insights on Becoming a Data-Driven Enterprise, which took place at DigiConnect (UAE) and was delivered to top C-level executives and senior data managers from the most relevant companies in the GCC region. In this post, I will not get into each of these ten challenges. However, I will focus on a common issue that came up as a top priority for them in a quick live poll during the session: The issue of trusting decisions made by AI. Interpreting deep learning networks takes place in a tough playground, so making AI interpretable serves one specific goal, and that is to trust the decisions made by AI models.

Both the private and public sectors need to embrace data science as part of developing policies and procedures to better manage a crisis.

In mid-January, Toby Ord, a philosopher and senior research fellow at Oxford University, was reviewing the final proofs for his first book, "The Precipice: Existential Risk and the Future of Humanity." Ord works in the university's Future of Humanity Institute, which specializes in considering our collective fate. He had noticed that a few of his colleagues--those who worked on "bio-risk"--were tracking a new virus in Asia. Occasionally, they e-mailed around projections, which Ord found intriguing, in a hypothetical way. Among other subjects, "The Precipice" deals with the risk posed to our species by pandemics both natural and engineered.