There's no question as to who the real technology star is now: it's you. Your voice is what hundreds of companies are vying to attract, with thousands of new products calling out for you to talk to them. Voice-activated technology has erupted over the last 12 months since Amazon's Alexa was informally crowned breakout technology champion of the CES 2017 consumer tech show. Seemingly by stealth, Amazon had snuck Alexa into a dizzying array of products and everywhere you turned, there she was. Alexa was the name on everyone's lips – literally – and Amazon had achieved this near-ubiquitous name- recognition without even having a stand at the gargantuan annual gadget-fest in Las Vegas.

KT will apply its artificial intelligence technologies to insurance and health care services in partnership with Lina Life Insurance, the mobile carrier said Tuesday. The two companies signed a memorandum of understanding Monday to improve Lina's digital health care services by adding KT's AI platform technologies at the insurer's headquarters in central Seoul. Under the agreement, KT's AI GiGA Genie speaker will offer users informative health care content insurance services provided by Lina, including dental care tips for kids, descriptions of medial terms and insurance bills. There are more than 600,000 GiGA Genie users as of this month, according to KT. Benjamin Hong, CEO of Lina Life Insurance (left), poses with Koo Hyun-mo, president of KT's corporate planning group after signing a MOU on artificial intelligence cooperation at Lina's head office in central Seoul on Monday. KT will also provide speech-to-text conversion and text analysis technologies to the insurer to help improve the company's call center system for customers.

Tokyo Stock Exchange operator Japan Exchange Group Inc. said Monday it has introduced artificial intelligence systems aimed at detecting market price manipulations and other misconduct. According to Japan Exchange Regulation, the group's self-regulatory body, the AI systems are designed to conduct preliminary surveillance to identify suspicious transactions. Surveillance personnel will analyze the results closely to determine whether the transactions should be reported to financial authorities. The AI systems are designed to help improve the quality of overall surveillance and speed up preliminary probes, giving staff more time to closely examine suspicious transactions, an executive of the self-regulatory body said. The group began research in August 2015 to see whether AI could be used for any of its operations, and later confirmed that systems developed by NEC Corp. and Hitachi Ltd. can detect unfair transactions with high accuracy.

To help its employees at its Noida R&D facility to upgrade skills in areas such as Artificial Intelligence (AI), Cloud computing and Machine Learning (ML), Samsung India on Monday signed a Memorandum of Understanding (MoU) with India's premier engineering institute BITS Pilani. As part of the initiative, the employees of the R&D centre will be able to pursue M.Tech in Software Systems, to further upgrade their skills, Samsung India said in a statement. "As technology evolves, skill sets must evolve too, especially for a company like Samsung that is focused on the next level of innovations," said Seounghoon Oh, Managing Director, Samsung R&D Institute India-Noida. "This MoU is in line with our vision to develop futuristic skill-sets aligned to the requirements of the fast evolving mobile and consumer electronics sectors," Oh said. As part of the initiative, every year a batch of 35 employees from Samsung R&D Institute India-Noida (SRI-Noida) will be sponsored for this two-year M.Tech programme.

IN 2016 Lee Sedol, one of the world's best players of Go, lost a match in Seoul to a computer program called AlphaGo by four games to one. It was a big event, both in the history of Go and in the history of artificial intelligence (AI). Go occupies roughly the same place in the culture of China, Korea and Japan as chess does in the West. After its victory over Mr Lee, AlphaGo beat dozens of renowned human players in a series of anonymous games played online, before re-emerging in May to face Ke Jie, the game's best player, in Wuzhen, China. Mr Ke fared no better than Mr Lee, losing to the computer 3-0.

A team of Microsoft researchers said March 14 that they believe they have created the first machine translation system that can translate sentences of news articles from Chinese to English with the same quality and accuracy as a person. Researchers in the company's Asia and US labs said that their system achieved human parity on a commonly used test set of news stories, called newstest2017, which was developed by a group of industry and academic partners and released at a research conference called WMT17 last year. To ensure the results were both accurate and on par with what people would have done, the team hired external bilingual human evaluators, who compared Microsoft's results to two independently produced human reference translations. Xuedong Huang (pix, above), a technical fellow in charge of Microsoft's speech, natural language and machine translation efforts, called it a major milestone in one of the most challenging natural language processing tasks. "Hitting human parity in a machine translation task is a dream that all of us have had," Huang said.

It has been my experience that whenever a new craze appears in the field of information technology (IT), the industry begins to have varying levels of flirtation with the concept, and each firm's executives try to best other firms by jumping on the bandwagon and then proceeding to make wide-ranging pronouncements about how they are using the new craze to transform their industry. Just a few years ago, the craze was to have a global delivery centre in India, either through an outsourcing relationship with an IT service provider or by tapping directly into the technology labour pool in India. This was starkly obvious to me when we hosted more than one Western client at an industry or service provider event--the first metrics they gauged each other by while sizing up where each stood on the totem pole were usually: "number of people in India" and "number of trips taken to India". Today, this chatter has moved on to topics around automation, artificial intelligence (AI), machine learning (ML), deep learning (DL) and blockchain. The irony is that many of these disciplines are actually quite old, but they have only now become fashionable catchphrases.

While IoT, big data, AI and machine learning technologies are increasingly applied to improve production process and management, predictive and advance warning mechanisms can be integrated with AI algorithms to help manufacturers better upgrade their automation capabilities and overall operating efficiencies, according to Allen Chen, deputy general manager of SAS Taiwan. Chen said that in incorporating AI and IoT into their manufacturing operations, most enterprises hope to get better decision-making support from the combination of AI and production process management while also pursuing improvements in prediction optimization, computer image recognition, visual operation and display, and data integration and management, so as to reduce operating cost. In terms of smart manufacturing alone, the incorporation of software programs integrating AI, machine learning and IoT applications can help the manufacturing end predict yield rates and provide advance warnings in case of poor equipment conditions. It can also help collect product information and environment data including temperatures and humidity at the customer end, so as to offer valuable references for better after-sales services and product upgrades. As a result, this will help manufacturers carry out digital transformation and production optimization at a faster pace, Chen said.

DUBAI – Artificial intelligence could be the breakthrough that teachers have been waiting for. At the recently concluded GESS Dubai, experts showed a glimpse of the future for the teaching profession with the help of AI, and how it can contribute significantly to school improvement. Century Tech founder and CEO Priya Lakhani presented an AI platform for school improvement that presents real-time data on a student, entire class even a whole school to support timely and evidence-based interventions; as well as multimedia content that can be used in and out of the classroom with features that can also help automate certain tasks such as assessments and tracking of homework. "With teachers spending up to 60% of their time on administrative tasks and data management they need a solution which saves them time to do what they love: teach!" commented Lakhani, who also says the AI platform can also be used to improve outcome for learners as well as involve parents and guardians. Meanwhile, Sallyann dela Casa, lead Skills Hacker at GLEAC and head of Growing Leaders Foundation, says AI can be harnessed to develop outstanding schools.

Deep neural networks are representation learning techniques. During training, a deep net is capable of generating a descriptive language of unprecedented size and detail in machine learning. Extracting the descriptive language coded within a trained CNN model (in the case of image data), and reusing it for other purposes is a field of interest, as it provides access to the visual descriptors previously learnt by the CNN after processing millions of images, without requiring an expensive training phase. Contributions to this field (commonly known as feature representation transfer or transfer learning) have been purely empirical so far, extracting all CNN features from a single layer close to the output and testing their performance by feeding them to a classifier. This approach has provided consistent results, although its relevance is limited to classification tasks. In a completely different approach, in this paper we statistically measure the discriminative power of every single feature found within a deep CNN, when used for characterizing every class of 11 datasets. We seek to provide new insights into the behavior of CNN features, particularly the ones from convolutional layers, as this can be relevant for their application to knowledge representation and reasoning. Our results confirm that low and middle level features may behave differently to high level features, but only under certain conditions. We find that all CNN features can be used for knowledge representation purposes both by their presence or by their absence, doubling the information a single CNN feature may provide. We also study how much noise these features may include, and propose a thresholding approach to discard most of it. All these insights have a direct application to the generation of CNN embedding spaces.