There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.

Network traffic classification that is widely applicable and highly accurate is valuable for many network security and management tasks. A flexible and easily configurable classification framework is ideal, as it can be customized for use in a wide variety of networks. In this paper, we propose a highly configurable and flexible machine learning traffic classification method that relies only on statistics of sequences of packets to distinguish known, or approved, traffic from unknown traffic. Our method is based on likelihood estimation, provides a measure of certainty for classification decisions, and can classify traffic at adjustable certainty levels. Our classification method can also be applied in different classification scenarios, each prioritizing a different classification goal. We demonstrate how our classification scheme and all its configurations perform well on real-world traffic from a high performance computing network environment.

Editors Note: This article was originally published in Robot Report, a sister publication to Robotics Business Review. Qualcomm has been a pioneer in wireless telecommunications for 30-plus years. To maintain its spirit of innovation, the San Diego-based company now spends approximately $5 billion per year on R&D. Under the stewardship of Dev Singh the last four years, Qualcomm has made major in-roads with the robotics development community. But today it took another major step in hopes of becoming the de facto development platform for robotics companies.

Collectively, we've moved from the telegraph, to the telephone, to the handheld cell phone, in less than 200 years. Since its inception, telecommunications has been the backbone of business around the world. As business and technology advances, telecommunications and communication service providers need to advance with it. This is not without the support of a multitude of startups offering new solutions. B-Yond is one of these startups, bringing modern-day solutions to communication service providers through artificial intelligence.

This paper investigates the problem of providing ultra-reliable and energy-efficient virtual reality (VR) experiences for wireless mobile users. To ensure reliable ultra-high-definition (UHD) video frame delivery to mobile users and enhance their immersive visual experiences, a coordinated multipoint (CoMP) transmission technique and millimeter wave (mmWave) communications are exploited. Owing to user movement and time-varying wireless channels, the wireless VR experience enhancement problem is formulated as a sequence-dependent and mixed-integer problem with a goal of maximizing users' feeling of presence (FoP) in the virtual world, subject to power consumption constraints on access points (APs) and users' head-mounted displays (HMDs). The problem, however, is hard to be directly solved due to the lack of users' accurate tracking information and the sequence-dependent and mixed-integer characteristics. To overcome this challenge, we develop a parallel echo state network (ESN) learning method to predict users' tracking information by training fresh and historical tracking samples separately collected by APs. With the learnt results, we propose a deep reinforcement learning (DRL) based optimization algorithm to solve the formulated problem. In this algorithm, we implement deep neural networks (DNNs) as a scalable solution to produce integer decision variables and solving a continuous power control problem to criticize the integer decision variables. Finally, the performance of the proposed algorithm is compared with various benchmark algorithms, and the impact of different design parameters is also discussed. Simulation results demonstrate that the proposed algorithm is more 4.14% energy-efficient than the benchmark algorithms.

Softbank Robotics Europe, the group behind two of the more recognizable robots, is laying off 40% of its workforce. On July 7, the developer of the famous Nao and Pepper robots will reduce its Paris-based workforce that had 330 employees as of March 2021. The Robot Report confirmed this news, which was first reported by French media outlet Le Journal du Net. Softbank Robotics Europe lost \$38 million in its fiscal 2019-2020 year and more than \$119 million over the last three years, according to Le Journal du Net. Despite their worldwide fame, the Nao and Pepper robots never achieved financial success.

In this paper, we investigate the parameter identification problem in dynamical systems through a deep learning approach. Focusing mainly on second-order, linear time-invariant dynamical systems, the topic of damping factor identification is studied. By utilizing a six-layer deep neural network with different recurrent cells, namely GRUs, LSTMs or BiLSTMs; and by feeding input-output sequence pairs captured from a dynamical system simulator, we search for an effective deep recurrent architecture in order to resolve damping factor identification problem. Our study results show that, although previously not utilized for this task in the literature, bidirectional gated recurrent cells (BiLSTMs) provide better parameter identification results when compared to unidirectional gated recurrent memory cells such as GRUs and LSTM. Thus, indicating that an input-output sequence pair of finite length, collected from a dynamical system and when observed anachronistically, may carry information in both time directions for prediction of a dynamical systems parameter.

Automatic network management driven by Artificial Intelligent technologies has been heatedly discussed over decades. However, current reports mainly focus on theoretic proposals and architecture designs, works on practical implementations on real-life networks are yet to appear. This paper proposes our effort toward the implementation of knowledge graph driven approach for autonomic network management in software defined networks (SDNs), termed as SeaNet. Driven by the ToCo ontology, SeaNet is reprogrammed based on Mininet (a SDN emulator). It consists three core components, a knowledge graph generator, a SPARQL engine, and a network management API. The knowledge graph generator represents the knowledge in the telecommunication network management tasks into formally represented ontology driven model. Expert experience and network management rules can be formalized into knowledge graph and by automatically inferenced by SPARQL engine, Network management API is able to packet technology-specific details and expose technology-independent interfaces to users. The Experiments are carried out to evaluate proposed work by comparing with a commercial SDN controller Ryu implemented by the same language Python. The evaluation results show that SeaNet is considerably faster in most circumstances than Ryu and the SeaNet code is significantly more compact. Benefit from RDF reasoning, SeaNet is able to achieve O(1) time complexity on different scales of the knowledge graph while the traditional database can achieve O(nlogn) at its best. With the developed network management API, SeaNet enables researchers to develop semantic-intelligent applications on their own SDNs.

Infovista, the global provider in network lifecycle automation, announced the availability of its Artificial Intelligence Model (AIM), the commercially available AI-based propagation model that changes the way wireless networks can be planned and optimised. "Operators are at different stages within the 5G rollout, but the majority are still faced with the massive task of selecting, testing and commissioning new sites," says Regis Lerbour, VP product & R&D, RAN engineering at Infovista. "Our AI-based propagation model, successfully introduced to our customers at Infovista RAN Summit, is, by design, cloud-ready and scalable to increase agility and the ability to adapt the network more dynamically, thus offering a way to automate and accelerate the planning and roll-out of 5G networks." Infovista's AIM has been built around state-of-the-art machine learning frameworks such as TensorFlow to focus on training and inference of deep neural networks. The project utilised over 10 million data points collected by the company during the last 15 years and spans multiple sub-6 GHz and millimetre wave bands, geographic locations, antenna heights, weather conditions, seasonal foliage variations and hundreds of additional variables across urban, mixed and rural environments. The AI-model predictions have been extensively validated against real-world measurement sampling data and are proven to deliver network plans that are 25% more accurate compared to those delivered using traditional propagation models.

The internet is now our nervous system. We are constantly streaming and buying and watching and liking, our brains locked into the global information matrix as one universal and coruscating emanation of thought and emotion. What happens when the machine stops though? It's a question that E.M. Forster was intensely focused on more than a century ago in a short story called, rightly enough, "The Machine Stops," about a human civilization connected entirely through machines that one day just turn off. Those fears of downtime are not just science fiction anymore.