An eight-year-old Syrian girl whose plight touched the world after she was photographed using tuna cans to walk has received prosthetic limbs in Turkey. Maya Merhi, who was born with no legs because of a rare congenital condition, had been living with her father at a refugee camp after fighting forced them from their home in Aleppo province. After fleeing to the northwestern region of Idlib, Maya was photographed struggling to move on homemade prosthetics made from tubes and old tins of tuna. Designed by her father Mohammad, who suffers from the same congenital disorder, the improvised legs were created to protect her from the hot, dirty and dusty ground. With the impromptu prosthetics, Maya was able to walk outside of her tent and could even attend the camp's school.

Enam Chowdury is using his passion for technology to make a difference for small business owners across the world. I founded EkkBaz in March 2017 after more than 5 years in Microsoft. Through EkkBaz, we aim to empower and transform root level small businesses in any remote corner of the earth. EkkBaz will make available artificial intelligence and Blockchain powered sophisticated tools that small businesses can use with ease. The desire to help the small guys is rooted in my upbringing.

Vladimir Putin was not in attendance, but his loyal lieutenants were. On 14 July last year, the Russian prime minister, Dmitry Medvedev, and several members of his cabinet convened in an office building on the outskirts of Moscow. On to the stage stepped a boyish-looking psychologist, Michal Kosinski, who had been flown from the city centre by helicopter to share his research. "There was Lavrov, in the first row," he recalls several months later, referring to Russia's foreign minister. "You know, a guy who starts wars and takes over countries." Kosinski, a 36-year-old assistant professor of organisational behaviour at Stanford University, was flattered that the Russian cabinet would gather to listen to him talk. "Those guys strike me as one of the most competent and well-informed groups," he tells me. Kosinski's "stuff" includes groundbreaking research into technology, mass persuasion and artificial intelligence (AI) – research that inspired the creation of the political consultancy Cambridge Analytica. Five years ago, while a graduate student at Cambridge University, he showed how even benign activity on Facebook could reveal personality traits – a discovery that was later exploited by the data-analytics firm that helped put Donald Trump in the White House.

In the past, technology has destroyed jobs, but has created many other jobs and new industries along the way. This time may be different. Job destruction may outpace job creation, while we may also face a skills mismatch where many of the new jobs require specialist skills. Machines and algorithms are beginning to compete with brain power. Automation used to be about robots in factories or warehouses.

The drug development lifecycle is long and fraught with heavy risk -- it takes a staggering 10 – 15 years on average, with ultimately only 12 percent of drugs in clinical trials gaining approval by the U.S. Food and Drug Administration (FDA) [1]. To put this in perspective, 22.7 percent of all global research and development spending in 2017 was in the healthcare industry, second only to 23.1 percent spent in the computing and electronics industry, yet the product lifecycle and cost are much higher [2]. For example, the original iPhone took two and a half years to develop from concept to launch, and an estimated $150 million spent in research and development [3]. In contrast, the average cost of new drug and biologics is $2.87 billion when factoring in the post-approval research and development costs, according to figures released in May 2016 by The Tufts Center for the Study of Drug development (CSDD) [4]. For pharmaceutical companies that have launched more than four drugs, the median cost is closer to a staggering $5.3 billion according to analysis by industry expert Matthew Herper of Forbes [5].

Reliance has been going all out when it comes to AI and its implications for the future. Under the leadership of Akash Ambani, Reliance is making strides in AI this year. The young leader is hiring the brightest minds in the country to create an area of excellence that can impact the telecom market in a huge way. The team at Reliance is also looking at applications under machine learning and blockchain so that the company can benefit from its potential. The company is taking advantage of the multi-billion-dollar opportunity that lies in the AI space in India.

At the beginning of 2017, Chinese tech company Baidu, the largest provider of Chinese language internet search as well as other digital products and services, committed to emerging business sectors such as artificial intelligence (AI) and machine learning. Since China has 731 million internet users, almost twice the U.S. population, Baidu's data set is capable of fueling AI algorithms to make them even better. With this focus on artificial intelligence, Baidu is exploring some very intriguing applications for artificial intelligence and machine learning including in their offices where facial recognition technology makes standard ID cards unnecessary and allows you to order tea from a vending machine. They have also recruited top AI talent including one of the world's most notable AI pioneers Lu Qi, who was previously a Microsoft executive before he became Baidu's COO in January 2017. Qi will step down in July 2018 for personal reasons.

Today's software industry requires individuals who are proficient in as many programming languages as possible. Structured query language (SQL), as an adopted standard, is no exception, as it is the most widely used query language to retrieve and manipulate data. However, the process of learning SQL turns out to be challenging. The need for a computer-aided solution to help users learn SQL and improve their proficiency is vital. In this study, we present a new approach to help users conceptualize basic building blocks of the language faster and more efficiently. The adaptive design of the proposed approach aids users in learning SQL by supporting their own path to the solution and employing successful previous attempts, while not enforcing the ideal solution provided by the instructor. Furthermore, we perform an empirical evaluation with 93 participants and demonstrate that the employment of hints is successful, being especially beneficial for users with lower prior knowledge.

Combining logic and probability has been a long stand- ing goal of AI research. Markov Logic Networks (MLNs) achieve this by attaching weights to formulas in first-order logic, and can be seen as templates for constructing features for ground Markov networks. Most techniques for learning weights of MLNs are domain-size agnostic, i.e., the size of the domain is not explicitly taken into account while learn- ing the parameters of the model. This often results in ex- treme probabilities when testing on domain sizes different from those seen during training. In this paper, we propose Domain Aware Markov logic Networks (DA-MLNs) which present a principled solution to this problem. While defin- ing the ground network distribution, DA-MLNs divide the ground feature weight by a scaling factor which is a function of the number of connections the ground atoms appearing in the feature are involved in. We show that standard MLNs fall out as a special case of our formalism when this func- tion evaluates to a constant equal to 1. Experiments on the benchmark Friends & Smokers domain show that our ap- proach results in significantly higher accuracies compared to existing methods when testing on domains whose sizes different from those seen during training.

Ventricular Fibrillation (VF), one of the most dangerous arrhythmias, is responsible for sudden cardiac arrests. Thus, various algorithms have been developed to predict VF from Electrocardiogram (ECG), which is a binary classification problem. In the literature, we find a number of algorithms based on signal processing, where, after some robust mathematical operations the decision is given based on a predefined threshold over a single value. On the other hand, some machine learning based algorithms are also reported in the literature; however, these algorithms merely combine some parameters and make a prediction using those as features. Both the approaches have their perks and pitfalls; thus our motivation was to coalesce them to get the best out of the both worlds. Sohel Rahman) Preprint submitted to Pattern Recognition July 10, 2018 a Support Vector Machine for efficient classification. VFPred turns out to be a robust algorithm as it is able to successfully segregate the two classes with equal confidence (Sensitivity 99.99%, Specificity 98.40%) even from a short signal of 5 seconds long, whereas existing works though requires longer signals, flourishes in one but fails in the other. Keywords: Electrocardiogram(ECG), Empirical Mode Decomposition, Heart Arrhythmia, Support Vector Machine, Ventricular Fibrillation(VF). 1. Introduction Ventricular Fibrillation (VF) is a type of cardiac arrhythmia which occurs when the heart quivers instead of pumping due to disturbance in electrical activity in the ventricles [1]. This arrhythmia may result in a cardiac arrest leaving the patient unconscious without any pulse. Ventricular Fibrillation is found initially in about 10% of people in cardiac arrest [2] and sudden cardiac arrest is responsible for approximately 6 million deaths in Europe and in the United States [3]. Therefore, fast and accurate detection of Ventricular Fibrillation can save a lot of lives.