DUBAI, UNITED ARAB EMIRATES – From flying taxis to Batman-style surveillance motorcycles, Dubai's GITEX expo this week showcased innovations that were symbols of the city-state's ambitions to be a metropolis of the future. Known for its futuristic skyline and artificial islands, Gulf emirate Dubai has carved out a place alongside cities like Singapore as a hub for innovative ideas. At this year's 37th Gulf Information Technology Exhibition (GITEX), which runs until Thursday, city authorities were keen to show off they remain on the cutting edge. The undisputed star of the expo, which has more than 4,000 companies from 71 countries participating, was Dubai's flying taxi project. Videos of the craft's first "concept" flight last month -- albeit without passengers -- generated widespread buzz on social media.

Recent news about growth of Chinese robotics and related AI indicate just how massive their investments are and how well they are paying off. For example, 90% of the personal robots on display at the IFA consumer electronics trade show held in Berlin in September were developed and manufactured by Chinese companies. Further, Preqin reported that Q3 venture-backed deals totaled $49 billion. Included in the top 10 deals were Uber-competitor Grab's raising $2 billion from SoftBank and Didi Chuxing and Alibaba's $1.1 bn investment in eBay-like Tokopedia and $.8 bn to Cainiao (see below). Half of the top 10 were in Asia; only three were for US-based companies.

Minimizing non-convex and high-dimensional objective functions is challenging, especially when training modern deep neural networks. In this paper, a novel approach is proposed which divides the training process into two consecutive phases to obtain better generalization performance: Bayesian sampling and stochastic optimization. The first phase is to explore the energy landscape and to capture the "fat" modes; and the second one is to fine-tune the parameter learned from the first phase. In the Bayesian learning phase, we apply continuous tempering and stochastic approximation into the Langevin dynamics to create an efficient and effective sampler, in which the temperature is adjusted automatically according to the designed "temperature dynamics". These strategies can overcome the challenge of early trapping into bad local minima and have achieved remarkable improvements in various types of neural networks as shown in our theoretical analysis and empirical experiments.

In this paper, we study the online learning algorithm without explicit regularization terms. This algorithm is essentially a stochastic gradient descent scheme in a reproducing kernel Hilbert space (RKHS). The polynomially decaying step size in each iteration can play a role of regularization to ensure the generalization ability of online learning algorithm. We develop a novel capacity dependent analysis on the performance of the last iterate of online learning algorithm. The contribution of this paper is two-fold. First, our nice analysis can lead to the convergence rate in the standard mean square distance which is the best so far. Second, we establish, for the first time, the strong convergence of the last iterate with polynomially decaying step sizes in the RKHS norm. We demonstrate that the theoretical analysis established in this paper fully exploits the fine structure of the underlying RKHS, and thus can lead to sharp error estimates of online learning algorithm.

This article analyzes the stochastic runtime of a Cross-Entropy Algorithm on two classes of traveling salesman problems. The algorithm shares main features of the famous Max-Min Ant System with iteration-best reinforcement. For simple instances that have a $\{1,n\}$-valued distance function and a unique optimal solution, we prove a stochastic runtime of $O(n^{6+\epsilon})$ with the vertex-based random solution generation, and a stochastic runtime of $O(n^{3+\epsilon}\ln n)$ with the edge-based random solution generation for an arbitrary $\epsilon\in (0,1)$. These runtimes are very close to the known expected runtime for variants of Max-Min Ant System with best-so-far reinforcement. They are obtained for the stronger notion of stochastic runtime, which means that an optimal solution is obtained in that time with an overwhelming probability, i.e., a probability tending exponentially fast to one with growing problem size. We also inspect more complex instances with $n$ vertices positioned on an $m\times m$ grid. When the $n$ vertices span a convex polygon, we obtain a stochastic runtime of $O(n^{3}m^{5+\epsilon})$ with the vertex-based random solution generation, and a stochastic runtime of $O(n^{2}m^{5+\epsilon})$ for the edge-based random solution generation. When there are $k = O(1)$ many vertices inside a convex polygon spanned by the other $n-k$ vertices, we obtain a stochastic runtime of $O(n^{4}m^{5+\epsilon}+n^{6k-1}m^{\epsilon})$ with the vertex-based random solution generation, and a stochastic runtime of $O(n^{3}m^{5+\epsilon}+n^{3k}m^{\epsilon})$ with the edge-based random solution generation. These runtimes are better than the expected runtime for the so-called $(\mu\!+\!\lambda)$ EA reported in a recent article, and again obtained for the stronger notion of stochastic runtime.

A robot masseuse specializing in back and knee massages has started work in Singapore today. Named Emma, short for Expert Manipulative Massage Automation, it mimics the human palm and thumb to replicate therapeutic massages such as shiatsu and physiotherapy. The company's technology aims to address workforce shortages and challenges with quality consistency in the healthcare industry. Emma massaging a patient autonomously on the left while physician Calista Lim treats another patient on the right. Emma, short for Expert Manipulative Massage Automation, specializes in back and knee massages, mimicking the human palm and thumb to replicate therapeutic massages such as shiatsu and physiotherapy.

Reskilling is the buzzword in the IT sector. With the sector seeing huge churn due to automation and protectionism in the western markets, industry lobby group Nasscom's president R Chandrashekhar told employees in May: Re-skill or perish. The sector is seeing layoffs and voluntary severances. Companies' hiring is on the decline. One estimate even puts the likely job loss at a whopping 2 lakh over the next three years. And in that, the sector is class agnostic.

Gartner, Inc. today revealed its top predictions for 2018 and beyond. Gartner's top predictions will enable organizations to move beyond thinking about mere notions of technology adoption to focus on the issues that surround what it really means to be human in the digital world. "Technology-based innovation is arriving faster than most organizations can keep up with. Before one innovation is implemented, two others arrive," said Daryl Plummer, vice president and Gartner Fellow, Distinguished. "CIOs in end-user organizations will need to develop a pace that can be sustained no matter what the future holds. Our predictions provide insight into that future, but enterprises will still be required to develop a discipline around how pace can be achieved. Those who seek value from technology-based options must move faster as their digital business efforts move into high gear. Speed of change will require variability of skills and capabilities to address rising challenges."

"AI is going to be more impactful than the invention of the personal computer and the spread of mobile phones into your pocket," AI expert and Google Senior Fellow Jeff Dean told a TEDx Los Angeles crowd last December. So-called machine learning – where computers find their own insights without being directly programmed to do so – is set to fundamentally change the relationship between humans and robots. A reality that is both exhilarating and terrifying. As millions ponder whether AI will replace their jobs, Tesla CEO Elon Musk is warning AI could cause World War III, responding to Russian President Vladimir Putin's comment that AI's eventual leader "will become the ruler of the world." Against that backdrop, an AI arms race has been triggered between tech Goliaths such as Apple, Amazon and Google. McKinsey Global Institute, a leading think tank, estimates the tech giants invested as much as US$30 billion in artificial intelligence last year in a combination of R&D spending and startup acquisitions. McKinsey estimates venture capitalists and private equity investors plowed another US$9 billion into AI startups, particularly those focused on machine learning.

San Francisco, Oct 9 Google's artificial intelligence (AI)-based "Clips" camera has not impressed Tesla founder Elon Musk, a famed critic of AI. "Clips" does image recognition and AI processing on-device, deploying machine learning to automatically click best pictures for you. Musk, who thinks AI could trigger World War III and poses a far greater threat than North Korea, has now tweeted against "Clips" and its prowess. Musk took to Twitter with reference to a video of "Clips" posted by The Verge. "This doesn't even'seem' innocent," he tweeted. Google declined to comment specifically on Musk's tweet, CNET reported on Monday.