India's small and medium-sized software firms, wanting to ride the wave of Artificial Intelligence (AI) and the Internet of Things (IOT), could soon find a new home in China's northeast corner. Nasscom, India's IT industry body, has negotiated a deal with local authorities in Dalian, a famous port city on the Liaodong Peninsula, to establish an IT corridor at the Bio-diverse Emerging Science Technology (BEST) City, on the outskirts of the metropolis. In turn, it would allow small Indian cyber companies to register with the new IT cluster. The Chinese companies, who are not a part of the top 100 biggies, are also likely to benefit. "The story is a little more complicated," said Gagan Sabharwal, senior director with Nasscom, referring to his disruptive bottom-up model, which could script the next chapter of India's software story. "We want to marry Indian strengths, especially of small companies, in software with China's heft in hardware."

Dr. Chandrasekhar Subramanyam - Another good thing happening is a number of start up have come up in AI Space to solve complex problems in almost all areas. This is an indication that the AI Market is booming.,,

In the next stage of artificial intelligence adoption, banks will use AI to help understand the intentions and emotions of customers and enable better interactions, according to a new report from Accenture (NYSE: ACN). A new report from Accenture has revealed that banks are set to adopt artificial intelligence (AI) to better understand customers' intentions and emotions, with the aim of enabling better interactions. The report, Accenture Banking Technology Vision 2017, draws on the analysis of an advisory board of more than two dozen individuals, interviews with technology luminaries and industry experts, and results of a survey of 579 bank executives in 31 countries across North America, Europe, Asia Pacific, Africa and South America. The goal of the survey was to identify the key issues and priorities for technology adoption and investment. According to the report, more than three-quarters (78%) of bankers believe that AI will enable simpler user interfaces that will help banks create a more human-like customer experience.

The chatbot hype was mainly due to the argument that instant messaging channels have become the de facto user browser at Mobile over the last few years. With the high volume of active users, messaging platforms started integrating product and services from 3rd party partners. With the success of Wechat in China, it was easy to imagine a bright future of Chatbot. Quick forward to today, one year later after Facebook launched the Messenger platform for business, chatbots still haven't replaced apps. The main problem is the approach that Facebook and many other companies took: they were over optimistic about the state of AI technologies (mainly NLP), which are still too incipient for most business use cases.

My Startup World speaks to Talal Bayaa, the CEO and Co-Founder of Bayzat, about his company's plans for 2018 What were the major technology and market trends in 2017? The only factor consistent through all the trends is the rate of change created by technology. It is moving faster than anyone has ever predicted, making 2017 an eventful year for the tech sector. From Chatbots and deep learning to automation, technology has continued to impact every facet of life. User experience (UX), big data, and smart machines have proven big players in the business landscape this year.

A recent JLL-sponsored survey of CEOs across the Asia Pacific region reveals that 52.7 per cent CEOs are closely involved in making key strategic decisions about the future of work, particularly involving automation and artificial intelligence (AI). Given the potentially negative effects that automation and AI can bring to jobs, wages and people's livelihoods in the future, CEOs need to push their organisations to create an environment which encourages workers to not only embrace disruption, but also to create one. The survey also shows that only a small number of CEOs (11.2 per cent) think automation and AI will affect all jobs, while 41.6 per cent think only specific tasks within jobs will be affected. What this means is that business leaders understand that the impacts of automation and AI are complex; companies need to continuously train and retrain their employees, and business leaders play an important role in making sure that their companies provide the best environment for their employees in the face of disruption. An overwhelming 81.1 per cent of CEOs claim that they would lead by example and gladly automate parts of their job if AI was better at doing them than they are.

Miniature robots stage a group dance at a smart manufacturing conference in Nanjing, East China's Jiangsu Province on December 7. Photo: VCG. Some insiders claim that bubbles are already growing in China's artificial intelligence (AI) sector, following the rapid expansion of the industry.

India's largest home-grown e-commerce giant, Flipkart, is making a big push for artificial intelligence (AI). A new unit, AI For India, will use AI technologies to mine the consumer data that Flipkart has gathered over the last decade to strengthen its business offerings, a company blog post said on Dec. 21.

Multi-modal semantics, which aims to ground semantic representations in perception, has relied on feature norms or raw image data for perceptual input. In this paper we examine grounding semantic representations in raw auditory data, using standard evaluations for multi-modal semantics. After having shown the quality of such auditorily grounded representations, we show how they can be applied to tasks where auditory perception is relevant, including two unsupervised categorization experiments, and provide further analysis. We find that features transfered from deep neural networks outperform bag of audio words approaches. To our knowledge, this is the first work to construct multi-modal models from a combination of textual information and auditory information extracted from deep neural networks, and the first work to evaluate the performance of tri-modal (textual, visual and auditory) semantic models.

We study --both in theory and practice-- the use of momentum motions in classic iterative hard thresholding (IHT) methods. By simply modifying plain IHT, we investigate its convergence behavior on convex optimization criteria with non-convex constraints, under standard assumptions. In diverse scenaria, we observe that acceleration in IHT leads to significant improvements, compared to state of the art projected gradient descent and Frank-Wolfe variants. As a byproduct of our inspection, we study the impact of selecting the momentum parameter: similar to convex settings, two modes of behavior are observed --"rippling" and linear-- depending on the level of momentum.