Written by Philip Boucher and Carl Pierer. The European Commission published the much-anticipated Artificial Intelligence Act (AIA), an ambitious cross-sectoral attempt to regulate artificial intelligence (AI) applications on 21 April 2021. Its aim is to ensure that all European citizens can trust AI by providing proportionate and flexible rules – harmonised across the single market – to address the specific risks posed by AI systems and set the highest standards worldwide. The proposal sets out a risk-based approach to regulating AI applications: those presenting an'unacceptable risk' would be banned, those presenting a'high-risk' would be subjected to additional requirements before entering the market, and others, such as chatbots and'deep fakes', would be subject to new transparency requirements. Applications presenting'low or minimal risk' – the vast majority of AI applications – could enter the market without restrictions, although voluntary codes of conduct may be developed. Other proposed measures include a European AI Board to monitor implementation and regulatory sandboxes to facilitate innovation.

While the country continues to struggle convincing people it is safe and smart to get vaccinated against Covid-19 despite emergency authorization and advocacy from the U.S. Federal Drug Administration (FDA), other growing numbers of people are willing to jump in with both feet when it comes to trusting their lives to Artificial Intelligence (AI), which has no standards or organizational oversight. At this time, only "guidelines" exist from the U.S. Federal Trade Commission (FTC) for AI. I will take you through an example of how even impactful applications of AI still need a human assistant to ensure trustworthy, explainable, and unbiased decision making. Using AI for automating manufacturing and improving our work streams, such as providing robust CMMS (computerized maintenance management systems) is an excellent application for AI. CMMS is a proactive methodology to keep systems running and to optimize maintenance operations.

Artificial intelligence is a double-edged sword when it comes to cybersecurity, with defenders using it to respond to and predict threats and attackers using it to launch even more refined attacks. For example, AI algorithms can send'spear phishing' tweets (personalized tweets sent to targeted users to trick them into sharing sensitive information) six times faster than a human and with twice the success. The enlargement of attack surface and the increased sophistication of attacks has made AI a key weapon in thwarting cyberattacks, Capgemini found. Cyber analysts are finding it increasingly difficult to effectively monitor current levels of data volume, velocity, and variety across firewalls, prompting organizations to turn to artificial intelligence. In fact, Capgemini found that 61 percent of organizations acknowledge they wouldn't be able to identify critical threats without AI.

We use Reinforcement Meta Learning to optimize an adaptive guidance system suitable for the approach phase of a gliding hypersonic vehicle. Adaptability is achieved by optimizing over a range of off-nominal flight conditions including perturbation of aerodynamic coefficient parameters, actuator failure scenarios, and sensor noise. The system maps observations directly to commanded bank angle and angle of attack rates. These observations include a velocity field tracking error formulated using parallel navigation, but adapted to work over long trajectories where the Earth's curvature must be taken into account. Minimizing the tracking error keeps the curved space line of sight to the target location aligned with the vehicle's velocity vector. The optimized guidance system will then induce trajectories that bring the vehicle to the target location with a high degree of accuracy at the designated terminal speed, while satisfying heating rate, load, and dynamic pressure constraints. We demonstrate the adaptability of the guidance system by testing over flight conditions that were not experienced during optimization. The guidance system's performance is then compared to that of a linear quadratic regulator tracking an optimal trajectory.

The last few years have seen an increasing wave of attacks with serious economic and privacy damages, which evinces the need for accurate Network Intrusion Detection Systems (NIDS). Recent works propose the use of Machine Learning (ML) techniques for building such systems (e.g., decision trees, neural networks). However, existing ML-based NIDS are barely robust to common adversarial attacks, which limits their applicability to real networks. A fundamental problem of these solutions is that they treat and classify flows independently. In contrast, in this paper we argue the importance of focusing on the structural patterns of attacks, by capturing not only the individual flow features, but also the relations between different flows (e.g., the source/destination hosts they share). To this end, we use a graph representation that keeps flow records and their relationships, and propose a novel Graph Neural Network (GNN) model tailored to process and learn from such graph-structured information. In our evaluation, we first show that the proposed GNN model achieves state-of-the-art results in the well-known CIC-IDS2017 dataset. Moreover, we assess the robustness of our solution under two common adversarial attacks, that intentionally modify the packet size and inter-arrival times to avoid detection. The results show that our model is able to maintain the same level of accuracy as in previous experiments, while state-of-the-art ML techniques degrade up to 50% their accuracy (F1-score) under these attacks. This unprecedented level of robustness is mainly induced by the capability of our GNN model to learn flow patterns of attacks structured as graphs.

SAN MATEO, Calif., July 29, 2021 (GLOBE NEWSWIRE) -- Chooch AI, the leading computer vision AI platform, has been cited for accelerating adoption of computer vision–powered solutions across industry verticals by leading research company IDC. Chooch AI models are ready to deploy now both in the cloud and on edge devices. Clients include Fortune 500 companies and the US Government. Partners include NVIDIA, Intel, Dell, Deloitte, Convergint and Vantiq. IDC states that, "Chooch AI's horizontal- and vertical-agnostic platform supports rapid data set generation capabilities using machine labeling techniques such as smart annotation, data augmentation, and use of synthetic data, along with pretrained ready-to-use models. They believe this will accelerate adoption and time to value computer vision–powered solutions across industry verticals."

Artificial intelligence has shown incredible potential in countless applications and has simplified today's life seamlessly. It has already been proven that it has unlimited potential in different applications and industries. While maintaining its imperative and optimistic potential, AI further advances its way through cybersecurity to help protect organizations from existing cyber threats and identify newer types of malware. In this world of cyber threats, in which antivirus software and firewalls are taken as antiquity tools, companies are now looking for more advanced technological means to protect their data, confidential and sensitive information. This is where AI comes in to offer protection against digital threats around the world.

All the sessions from Transform 2021 are available on-demand now. According to a new report released by the Pew Research Center and Elon University's Imaging the Internet Center, experts doubt that ethical AI design will be broadly adopted within the next decade. In a survey of 602 technology innovators, business and policy leaders, researchers, and activists, a majority worried that the evolution of AI by 2030 will continue to be primarily focused on optimizing profits and social control and that stakeholders will struggle to achieve a consensus about ethics. Implementing AI ethically means different things to different companies. For some, "ethical" implies adopting AI -- which people are naturally inclined to trust even when it's malicious -- in a manner that's transparent, responsible, and accountable. For others, it means ensuring that their use of AI remains consistent with laws, regulations, norms, customer expectations, and organizational values.

What's New: Intel in collaboration with the Central Board of Secondary Education (CBSE), Ministry of Education today announced the launch of the AI For All initiative with the purpose of creating a basic understanding of artificial intelligence (AI) for everyone in India. Based on Intel's AI For Citizens program, AI For All is a 4-hour, self-paced learning program that demystifies AI in an inclusive manner. It is as applicable to a student, a stay-at-home parent as it is to a professional in any field or even a senior citizen. The program aims to introduce AI to 1 million citizens in its first year. "AI has the power to drive faster economic growth, address population-scale challenges and benefit the lives and livelihoods of people. The AI For All initiative based on Intel's AI For Citizens program aims to make India AI-ready by building awareness and appreciation of AI among everyone. The program further strengthens Intel's commitment to collaborating with the Government of India to reach the full potential of AI and further the vision of a digitally-empowered India."

"With the support of the Canadian Space Agency, Canadian scientists and engineers will be able to participate in near-term missions to the lunar surface," said Ewan Reid, president and chief executive of Mission Control. Reem Mohammed/The National The Emirates Lunar Mission logo as revealed by Sheikh Hamdan bin Mohammed, Crown Prince of Dubai. UAE's lunar mission also aims to study lunar soil, as well as dust. Reem Mohammed/The National The Mohammed bin Rashid Space Centre is carrying out the Emirates Lunar Mission. The Emirates Lunar Mission will also be provided with wired communication and power during the cruise phase and wireless communication on the lunar surface by iSpace.