The research on improving Artificial Intelligence (A.I.) has been ongoing for decades. However, it wasn't until recently that developers were finally able to create smart systems that closely resemble the A.I. capabilities of humans. The main reason for this breakthrough in technology is advancements in Big Data. Recent developments in Big Data have allowed us the capability to organize a very large amount of information into structured components that can be very quickly processed by computers. Another technology that has the potential for rapidly advancing and transforming Artificial Intelligence is the Blockchain.

Today, the amount of data that can be retrieved from communications networks is extremely high and diverse (e.g., data regarding users behavior, traffic traces, network alarms, signal quality indicators, etc.). Advanced mathematical tools are required to extract useful information from this large set of network data. In particular, Machine Learning (ML) is regarded as a promising methodological area to perform network-data analysis and enable, e.g., automatized network self-configuration and fault management. In this survey we classify and describe relevant studies dealing with the applications of ML to optical communications and networking. Optical networks and system are facing an unprecedented growth in terms of complexity due to the introduction of a huge number of adjustable parameters (such as routing configurations, modulation format, symbol rate, coding schemes, etc.), mainly due to the adoption of, among the others, coherent transmission/reception technology, advanced digital signal processing and to the presence of nonlinear effects in optical fiber systems. Although a good number of research papers have appeared in the last years, the application of ML to optical networks is still in its early stage. In this survey we provide an introductory reference for researchers and practitioners interested in this field. To stimulate further work in this area, we conclude the paper proposing new possible research directions.

Considered as a data-driven approach, Fingerprinting Localization Solutions (FPSs) enjoy huge popularity due to their good performance and minimal environment information requirement. This papers addresses applications of artificial intelligence to solve two problems in Received Signal Strength Indicator (RSSI) based FPS, first the cumbersome training database construction and second the extrapolation of fingerprinting algorithm for similar buildings with slight environmental changes. After a concise overview of deep learning design techniques, two main techniques widely used in deep learning are exploited for the above mentioned issues namely data augmentation and transfer learning. We train a multi-layer neural network that learns the mapping from the observations to the locations. A data augmentation method is proposed to increase the training database size based on the structure of RSSI measurements and hence reducing effectively the amount of training data. Then it is shown experimentally how a model trained for a particular building can be transferred to a similar one by fine tuning with significantly smaller training numbers. The paper implicitly discusses the new guidelines to consider about deep learning designs when they are employed in a new application context.

Most Android phones will have to wait until 2019 to duplicate the 3D sensing feature behind Apple s Face ID security, three major parts producers have told Reuters, handicapping Samsung and others on a technology that is set to be worth billions in revenue over the next few years. The development of new features for the estimated 1.5 billion smart phones shipped annually has been at the heart of the battle for global market share over the past decade, with Apple, bolstered by its huge R&D budget, often leading. When the iPhone 5S launched with a fingerprint-sensing home button in September 2013, for example, it took its biggest rival Samsung until just April of the next year to deliver its own in the Galaxy S5, with others following soon after. Most Android phones will have to wait until 2019 to duplicate the 3D sensing feature behind Apple s Face ID security, three major parts producers have told Reuters. The 3D sensing technology is expected to enhance the next generation of phones, enabling accurate facial recognition as well as secure biometrics for payments, gesture sensing, and immersive shopping and gaming experiences.

If a new report is to be believed, Samsung and other Android phone makers would need more time to catch up with Apple's pretty advanced 3D-sensing technology that the Cupertino giant debuted with last year's iPhone X. Tim Cook's company is said to have secured a two-year lead when it comes to this technology, so Apple's rivals won't likely be capable of duplicating Face ID until next year. Reuters reported Tuesday that most Android phone makers will have to wait until 2019 before they could be able to deliver a technology that's up to par with Apple's 3D-sensing, which is powering the Face ID security feature of the iPhone X. This is seen as a setback on the Android manufacturers' end because 3D-sensing technology is expected to be worth billions in revenue in the next few years. Reuters obtained data from parts suppliers, and the publication feels that Huawei and Xiaomi will be among the Android brands that will be capable of matching Apple's 3D-sensing next year. This is because 3D-sensing parts suppliers will be capable of reaching production levels for worldwide adoption by that time.

INTERVIEW: Artificial Intelligence (AI) will be vital to unlocking the "true potential" of IoT, believes Chris Penrose, AT&T's president of IoT solutions (pictured), as he suggested experience will help the operator gain a competitive edge in the segment. Speaking to Mobile World Live, Penrose said it "almost becomes impossible" to use the data generated by connected devices and make it into something actionable, given its volume, without some sort of AI assistance. Using data will ultimately "unlock predictability", he said, and will enable an evolution for the industry from being able to "sense information to being able to predict things". "We will know that a factory floor or a machine might be going wrong ahead of time and can be altered, or for a car battery we will be notified that its going to fail before it does using the power of AI." Penrose also said the operator was confident in the potential of its consumer and enterprise IoT offering, despite increased competition from rival operators in the US. At the start of the year, T-Mobile US launched its Magenta NB-IoT tariff for businesses, while Verizon rolled out a similar offering for its LTE-M network.

It was great catching up with Ericsson last week in San Francisco at the inaugural Mobile World Congress Americas conference. Ericsson is doing incredible work to advance innovation by partnering with operators globally around IoT and 5G deployments, ranging from testing new radio technology, like advanced MIMO, to new core 5G systems for providing network slicing, to applications like Autonomous vehicles. Ericsson's radio access network was also featured at Sprint's booth where the first 2.5 GHz Massive MIMO field tests were conducted using Sprint's spectrum and Ericsson's radios reaching peak speeds of more than 300 Mbps using a single 20 MHz channel! A great new use case for 5G was intelligent video streaming with Verizon for security and smart city applications, with streams coming to a central, video optimized repository in the core of the 5G network. This 5G overlay to an existing 4G network will provide benefits across multiple applications at the edge of the network from video cameras to drones to industrial control endpoints.

NTT DoCoMo has launched an artificial intelligence (AI) engine that analyzes shelf allocation in stores and warehouses using photos taken with smartphones and other common devices. The image recognition engine employs DoCoMo's AI technology and constitutes part of NTT Group's corevo AI technology. Object-detection technology detects individual items in an image with over 98% accuracy and object-recognition technology identifies specific products with over 95% accuracy by matching them with images stored in a database. Currently, shelf-analysis technology requires products to be placed in the front row and facing forward to ensure high-precision recognition. DoCoMo says its new engine can recognize products on shelves without special arrangement, even when they are packed tightly together.

Artificial intelligence could emulate human bias, including sexism, if there is no oversight on data used to create it, experts at the world's largest mobile phone fair in Barcelona warned Thursday. "We're all very aware the machines will learn the same bias as those who coded them," Emma McGuiguan, in charge of technology at consultants Accenture, said at the Mobile World Congress. AI is the science of programming machines or computers to reproduce human processes, like learning and decision making. Julie Woods-Moss, chief innovation officer at Indian mobile operator Tata Communications, said that in order to do this, a large amount of human-led data was needed. "We have to be very careful that we don't encourage AI to be biased," she said, calling on professionals in the sector to find ways to identify these biases.

SenseTime, a leading Chinese startup specialized in artificial intelligence (AI) research and development, has established an alliance with Massachusetts Institute of Technology (MIT) to promote the further application of the technology widely utilized in facial recognition. The Chinese AI researcher and developer, currently valued at around $3 billion, said the cooperation aims to explore new avenues across MIT in areas like computer vision, human-intelligence inspired algorithms, medical imaging, and robotics. Tang Xiao'ou, a founder of SenseTime who is also a PhD '96 MIT alumnus specialized in computer vision and deep learning, said he expects the cooperation between the world's best and brightest talents will further promote AI's development and benefit society. Founded in 2014, the Chinese startup is currently working with a number of well-known Chinese brands including China Mobile, UnionPay, Sina Weibo, as well as major smartphone companies in China to provide machine learning technology. SenseTime's advanced facial recognition expertise has also helped attract leading investments from Qualcomm and CDH Investments.