In March 2004, the U.S. Defense Advanced Research Projects Agency (DARPA) organized a special Grand Challenge event to test out the promise -- or lack thereof -- of current-generation self-driving cars. Entrants from the world's top A.I. labs competed for a $1 million prize; their custom-built vehicles trying their best to autonomously navigate a 142-mile route through California's Mojave Desert. The "winning" team managed to travel just 7.4 miles in several hours before shuddering to a halt. A decade-and-a-half, a whole lot has changed. Self-driving cars have successfully driven hundreds of thousands of miles on actual roads.

Then Alexa takes over basic household functions. And now a robot may be conducting your job interview. That's right--portions of corporate America are now using artificial intelligence ("AI") to conduct interviews of job applicants. How does this work, what are the risks and has there been a legislative response? And how would the Luddites respond to this?

Artificial Intelligence (AI): The fundamental change in our daily work routine, the way we live and interact with others, is all going to be represented by the fourth industrial revolution. Moving from the third to the fourth industrial revolution is going to open a new chapter in human development – incorporating the extraordinary technological advances. These advanced technologies are emerging and will continue to merge in the business world. We see the fourth industrial revolution changing the digital, physical, and biological worlds. It is creating novel opportunities and promises of a better future. On the other hand, the evolution of technology will become the reason for potential risks and dangers.

TEMPO.CO, Jakarta - The Indonesian Government is eyeing to complete its national strategy to develop artificial intelligence (AI) in 2020 after the Research and Technology Minister Bambang Brodjonegoro admitted that it was not yet included in the national roadmap but was only part of the country's preparation toward the industrial revolution 4.0.

The high frequency of road accidents makes driver safety one of the biggest challenges facing Fleet Management each day. In the US alone, 6 million car accidents every year happen every year, with more than 40,000 motor vehicle accident-related deaths in 2017. Several factors come into play when looking at the cause of traffic accidents. It could be the weather, changing road conditions, or the fault of other road users such as another driver or pedestrian. Apart from the risks posed by accidents to drivers, companies face significant losses when such accidents and traffic violations occur.

The technology industry and policymakers need to address public concerns about artificial intelligence (AI) which are "not the fault of AI" itself, a tech executive said Tuesday. "It is the fault of developers, so we need to solve this problem," said Song Zhang, managing director for China at global software consultancy, ThoughtWorks. Consumer worries relating to AI include concerns about personal privacy and how the systems may get out of control, said Zhang during a panel discussion discussing the "Future of AI" at CNBC's East Tech West conference in the Nansha district of Guangzhou, China. It is the duty of the tech industry and policymakers to focus on, discuss and solve such problems, said Zhang in Mandarin, according to a CNBC translation. Indeed, while consumers are curious about AI when they first come into contact with the technology, their mindset changes over time, said Rong Luo, chief financial officer of TAL Education Group.

BOOSTING TRANSFORMATION TOWARDS A DIGITAL THAILAND This initiative is in line with the Government's digital roadmap laid out in 4 phases. Phase 1 includes investing and building a digital foundation. Phase 2 ensures everyone can reap the benefits of digital technology. Phase 3 drives the country towards digital technology and innovation while the final phase will help Thailand become a developed country. This is a one of a kind gathering of 300 pre-qualified CIOs, CEOs, CTOs, Heads of AI, Chief Digital Officers, Heads of Innovation and International AI & ML experts among others who will be a part of powerful keynotes, workshops, government and enterprise use-case presentations, product exhibitions, panel discussions and tech talks.

Texts like news, encyclopedias, and some social media strive for objectivity. Yet bias in the form of inappropriate subjectivity - introducing attitudes via framing, presupposing truth, and casting doubt - remains ubiquitous. This kind of bias erodes our collective trust and fuels social conflict. To address this issue, we introduce a novel testbed for natural language generation: automatically bringing inappropriately subjective text into a neutral point of view ("neutralizing" biased text). We also offer the first parallel corpus of biased language. The corpus contains 180,000 sentence pairs and originates from Wikipedia edits that removed various framings, presuppositions, and attitudes from biased sentences. Last, we propose two strong encoder-decoder baselines for the task. A straightforward yet opaque CONCURRENT system uses a BERT encoder to identify subjective words as part of the generation process. An interpretable and controllable MODULAR algorithm separates these steps, using (1) a BERT-based classifier to identify problematic words and (2) a novel join embedding through which the classifier can edit the hidden states of the encoder. Large-scale human evaluation across four domains (encyclopedias, news headlines, books, and political speeches) suggests that these algorithms are a first step towards the automatic identification and reduction of bias.

Artificial Intelligence (AI) has become an integral part of modern-day security solutions for its capability of learning very complex functions and handling "Big Data". However, the lack of explainability and interpretability of successful AI models is a key stumbling block when trust in a model's prediction is critical. This leads to human intervention, which in turn results in a delayed response or decision. While there have been major advancements in the speed and performance of AI-based intrusion detection systems, the response is still at human speed when it comes to explaining and interpreting a specific prediction or decision. In this work, we infuse popular domain knowledge (i.e., CIA principles) in our model for better explainability and validate the approach on a network intrusion detection test case. Our experimental results suggest that the infusion of domain knowledge provides better explainability as well as a faster decision or response. In addition, the infused domain knowledge generalizes the model to work well with unknown attacks, as well as open the path to adapt to a large stream of network traffic from numerous IoT devices.

We introduce a new black-box attack achieving state of the art performances. It only requires to have access to the logits of the classifier without any other information which is a more realistic scenario. Not only we introduce a new objective function, we extend previous works on black box adversarial attacks to a larger spectrum of evolution strategies and other derivative-free optimization methods. We also highlight a new intriguing property that deep neural networks are not robust to single shot tiled attacks. Our models achieve, with a budget limited to 10, 000 queries, results up to 99 .2% of success rate against InceptionV3 classifier with 630 queries to the network on average in the untargeted attacks setting, which is an improvement by 90 queries of the current state of the art. In the targeted setting, we are able to reach, with a limited budget of 100, 000, 100% of success rate with a budget of 6, 662 queries on average, i.e. we need 800 queries less than the current state of the art. Despite their success, deep learning algorithms have shown vulnerability to adversarial attacks (Big-gio et al., 2013; Szegedy et al., 2014), i.e. small imperceptible perturbations of the inputs, that lead the networks to misclassify the generated adversarial examples. Since their discovery, adversarial attacks and defenses have become one of the hottest research topics in the machine learning community as serious security issues are raised in many critical fields. They also question our understanding of deep learning behaviors. Designing new and stronger attacks helps building better defenses, hence the motivation of our work. First attacks were generated in a setting where the attacker knows all the information of the network (architecture and parameters).