As technology becomes indispensable from everyday life, humanity and tech will merge to the point they are indistinguishable, predicts communication and marketing firm PHD. Its research into the forces shaping marketing's future produced Merge: The Closing Gap Between Technology And Us, a documentary and book on how technology and human evolution had progressed since the 1950s and how technological advances over the next 25 to 35 years would reshape society and the marketing industry. The research drew on experts' insights and foresights, including inventor and author Ray Kurzweil, Facebook COO Sheryl Sandberg, futurologist Dr Ian Pearson and Microsoft UK chief envisioning officer Dave Coplin. PHD Malaysia head Eileen Ooi said the year ahead would bring a host of new technologies that would challenge the industry but also present new opportunities for those willing to invest in innovation and take a bold first step. She cited four key trends: chatbots, sentient virtual personal assistants, next wave of wearables, and intelligent data layers that give you additional info when viewing things.

How much surveillance is too much? That is a question being asked in China after police in the country began using sunglasses equipped with fixed facial recognition cameras in order to help identify potential suspects, reports said Wednesday. Since the beginning of China's Lunar New Year travel season, police at Zhengzhou East Railway Station have already identified and taken into custody seven fugitives in connection with major criminal cases. They also identified 26 people attempting to travel using other people's IDs, according to Chinese state media reports. The smart glasses are said to be connected to an internal database of suspects, which means police officers can quickly scan crowds while looking for fugitives.

Controversially I think C coders will be replaced long before truck drivers. Autonomous vehicles are at the far end of AI. If a car needs to know how to drive in India, it needs to understand so many things about the road, like how an elephant moves in traffic!

New players and blurring sector borders are starting to influence the competitive outlook in a wide range of industries. Four emerging technology clusters will define how automotive manufacturers, suppliers, and digital attackers compete and cooperate for growth. In banking, enlarged platform spaces will offer customers access to a wide range of products and services through a single gateway. In both industries, established players will need to rethink strategy, either by joining existing ecosystems or forging their own. An analysis of mobility investments reveals how technologies and players are beginning to interact, and where new opportunities are starting to appear.

As indicated by the Finance Minister Arun Jaitley in his Union budget 2018 speech, the Indian government's think tank NITI Aayog has geared itself to create a roadmap for National AI program. The initiative came on heels of China's three-step roadmap to become the world leader in artificial intelligence by 2030. The Indian National Program for AI will also be geared towards developing new applications of the AI technology. Reportedly, the high-level committee to create the artificial intelligence research and development roadmap will be headed by NITI Aayog vice-chairman Rajiv Kumar and will be a mix of government, academia and industry officials. In January 2018, the government conducted a preliminary meeting wherein a number of people put forward their suggestions.

Investors know that robotics and artificial intelligence (NYSE:AI) are disrupting traditional paradigms. But they may be surprised by just how much disruptive technologies are impacting our daily lives. I know that I was a little taken aback when I looked at my day. Typically, I wake up and check my smartphone to see what's going on in the world through social media (SOCL). I get my caffeine fix via a Wi-Fi enabled coffee maker (SNSR).

If so, you probably won't want your phone to suggest a slasher movie or a thrash metal album. An app that works out how you are feeling could allow recommendation systems to only make suggestions that chime with that mood. Called MoodExplorer, the app has been designed by Wenzhong Li at Nanjing University, China, and his colleagues. They say that assessing someone's mood from moment to moment has been a key factor missing from personalised recommendation engines, often leading to inappropriate suggestions that people simply dismiss.

A new paradigm is beginning to emerge in Radiology with the advent of increased computational capabilities and algorithms. This has led to the ability of real time learning by computer systems of different lesion types to help the radiologist in defining disease. For example, using a deep learning network, we developed and tested a multiparametric deep learning (MPDL) network for segmentation and classification using multiparametric magnetic resonance imaging (mpMRI) radiological images. The MPDL network was constructed from stacked sparse autoencoders with inputs from mpMRI. Evaluation of MPDL consisted of cross-validation, sensitivity, and specificity. Dice similarity between MPDL and post-DCE lesions were evaluated. We demonstrate high sensitivity and specificity for differentiation of malignant from benign lesions of 90% and 85% respectively with an AUC of 0.93. The Integrated MPDL method accurately segmented and classified different breast tissue from multiparametric breast MRI using deep leaning tissue signatures.

Pretraining with expert demonstrations have been found useful in speeding up the training process of deep reinforcement learning algorithms since less online simulation data is required. Some people use supervised learning to speed up the process of feature learning, others pretrain the policies by imitating expert demonstrations. However, these methods are unstable and not suitable for actor-critic reinforcement learning algorithms. Also, some existing methods rely on the global optimum assumption, which is not true in most scenarios. In this paper, we employ expert demonstrations in a actor-critic reinforcement learning framework, and meanwhile ensure that the performance is not affected by the fact that expert demonstrations are not global optimal. We theoretically derive a method for computing policy gradients and value estimators with only expert demonstrations. Our method is theoretically plausible for actor-critic reinforcement learning algorithms that pretrains both policy and value functions. We apply our method to two of the typical actor-critic reinforcement learning algorithms, DDPG and ACER, and demonstrate with experiments that our method not only outperforms the RL algorithms without pretraining process, but also is more simulation efficient.

Brain Electroencephalography (EEG) classification is widely applied to analyze cerebral diseases in recent years. Unfortunately, invalid/noisy EEGs degrade the diagnosis performance and most previously developed methods ignore the necessity of EEG selection for classification. To this end, this paper proposes a novel maximum weight clique-based EEG selection approach, named mwcEEGs, to map EEG selection to searching maximum similarity-weighted cliques from an improved Fr\'{e}chet distance-weighted undirected EEG graph simultaneously considering edge weights and vertex weights. Our mwcEEGs improves the classification performance by selecting intra-clique pairwise similar and inter-clique discriminative EEGs with similarity threshold $\delta$. Experimental results demonstrate the algorithm effectiveness compared with the state-of-the-art time series selection algorithms on real-world EEG datasets.