Your customer support team answers "How do I reset my password?" Your website traffic is growing every month, but lead conversions aren't following suit. Your customer service reps have too many messaging apps to monitor. If any of these ring true for you, then it might be time your business considers "hiring" a chatbot. And you wouldn't be alone.

Huawei Technologies has unveiled a new high-end artificial intelligence (AI) chip for servers, bidding to grow its share of the booming cloud services market even as the world's largest network equipment vendor battles a US trade ban. Ascend 910, an AI processor first mentioned by Huawei in Shanghai last year, is the "world's most powerful AI processor" targeted at AI model training, the company said in a press release on Friday. Huawei added that it sees AI as a "general purpose technology" that can be used in almost every sector of the economy. "The Ascend 910 has performed much better than initial expectations," said Eric Xu Zhijun, Huawei's rotating chairman at the media launch in Shenzhen. "Without doubt, it has more computing power than any other AI processor in the world."

In one corner, there's a newbie, 5G-- higher bandwidth networks promising super-fast speed and increased responsiveness and reliability. And in the other, artificial intelligence (AI)--technology that can analyze massive amounts of data and learn from previous actions. But combined together, 5G networks and AI have the potential to pack a significantly more powerful punch. Some industry experts argue that the key differentiator among mobile networks will be the quality of the AI in their systems. "Together, AI and 5G will make possible capabilities that never existed before," says Prakash Sangam, principal and founder of Tantra Analyst, a high-tech research, analysis and strategic advisory firm.

Author of many technical papers about various telecommunications subjects including the published reports "Yes 2 Prepay" and "Data on SS7" as well as co-author of the books "Wireless Intelligent Networking" and "Mobile Positioning and Location Management". Increasingly integrated with and/or supporting various aspects of computing and networking, Artificial Intelligence (AI) is anticipated to become increasingly more important in terms of support for digital assets as well as physical infrastructure. Accordingly, forward-thinking ICT engineers and managers are no longer thinking of AI as merely algorithms and software in support of smart products and services but rather as a functional component of networking, systems, communications and applications as well as digital content and commerce. The potential economic and social benefits of generation networking AI in support of virtualized infrastructure and programmable networks cannot be overstated as networks will achieve an entirely new level of self-awareness, self-configuration, self-optimization, self-healing, and self-protection. Mind Commerce has gone on record to state that "AI in combination with Software-Defined Networking (SDN) and advanced analytics, is poised to take autonomous, intelligent network operation and control to an entirely new level" Existing carrier systems such as Self-organizing Networks (used largely to optimize radio infrastructure for cellular service providers) will benefit greatly through AI based decision making, developed through machine learning of network characteristics and customer usage patterns.

The technology exists to properly detect firearms in places they just should not be present. Then in less than 3 seconds, notify principals, security, law enforcement and more. All with the ability to notify law enforcement and first responders in 3 seconds with location of the shooter(s) and types of weapons. Generally, our first responders don't have enough situational awareness when they arrive on scene because of a lack of information. Consequently, they walk into unimaginable situations without knowledge of where threats are or how many people are involved. Using existing cameras, this AI can can identify the number of shooters, as well as pinpoint the active shooter's location(s) directly to security, law enforcement and other first responders.

The telecommunication industry is riding the waves of the tech revolution and digital transformation to offer a wider variety of services to its consumers. However, consumers in today's digital world are not going to be happy with run-of-the-mill products and services – they also demand a better quality of services and more responsive service providers. Solutions powered by artificial intelligence and machine learning can help telecom companies to fulfill these expectations. Artificial Intelligence is transforming the way the telecommunication industry operates. The adoption of AI technology based solutions has grown, especially to drive efficiencies and to fulfill the consumers' demand for contextualized experiences.

We have been making steady progress since we announced our AI strategy in October last year,

As Western powers continue to grapple with if or how to fit Huawei's 5G networks into their societies, reports have revealed the Chinese telecom giant is already well into researching 6G mobile technology. Presently, that network is slowly being rolled out in cities around the globe, and in Australia, access to the service has been slow, with coverage so far being provided by just Telstra and Optus. However, this week, tech website The Logic reported that Huawei was the latest company to join a small list of companies and universities commencing 6G's research and development. Huawei's research will happen at the company's Canadian lab, and Song Zhang, Huawei Canada's vice-president of research strategy and partnerships, told Logic the company was "in talks with Canadian university researchers" about the network's development. Yang Chaobin, the president of Huawei's 5G products, said that 6G would not be viable until 2030.

--As more end devices are getting connected, the Internet will become more congested. Various congestion control techniques have been developed either on transport or network layers. Active Queue Management (AQM) is a paradigm that aims to mitigate the congestion on the network layer through active buffer control to avoid overflow. However, finding the right parameters for an AQM scheme is challenging, due to the complexity and dynamics of the networks. On the other hand, the Explicit Congestion Notification (ECN) mechanism is a solution that makes visible incipient congestion on the network layer to the transport layer. In this work, we propose to exploit the ECN information to improve AQM algorithms by applying Machine Learning techniques. Our intelligent method uses an artificial neural network to predict congestion and an AQM parameter tuner based on reinforcement learning. The evaluation results show that our solution can enhance the performance of deployed AQM, using the existing TCP congestion control mechanisms. Thanks to the proliferation of smart devices and the paradigm of Internet of Things (IoT), the demand for connections to the Internet is dramatically growing.

Combining the capabilities of the programmability of networks by SDN and discovering patterns by machine learning are utilized in security, traffic classification, QoS prediction, and network performance and has attracted the attention of researchers. In this work, we propose HyMER: a novel hybrid machine learning framework for traffic aware energy efficient routing in SDN which has supervised and reinforcement learning components. The supervised learning component consists of feature extraction, training, and testing. The reinforcement learning component learns from existing data or from scratch by iteratively interacting with the network environment. The framework is developed on POX controller and is evaluated on Mininet using Abiline, GEANT, and Nobel-Germany real-world topologies and dynamic traffic traces. Experimental results show that the supervised component achieves up to 70% feature size reduction and more than 80% accuracy in parameter prediction. The refine heuristics algorithm increases the accuracy of the prediction to 100% with 14X to 25X speedup as compared to the brute force method. The reinforcement learning module converges from 100 to 275 iterations and converges twice faster if applied on top of the supervised component. Moreover, HyMER achieves up to 10 watts per switch power saving, 30% link saving, 2 hops decrease in average path length.