According to Verified Market Research, the Global Smart Robot Market was valued at USD 4.83 Billion in 2018 and is projected to reach USD 26.25 Billion by 2026, growing at a CAGR of 23.6% from 2019 to 2026. Smart robots are defined as the robots that have been enhanced with advanced technologies such as artificial intelligence (AI) and IoT. These robots are capable of learning from its environment and further building its capabilities based on that knowledge. Smart robots act like a man's substitution in executing the tasks that are either dangerous or repetitive, where man is incapable of performing due to body limitations, or tasks that occur in extreme environments. Moreover, these smart robots are designed to carry out specific tasks for personal, professional, and industrial applications such as elderly assistance, pool cleaning, and robotic pets among others.

Veteran perfumer David Apel works on the AI-designed fragrance.IBM and Symrise Artificial intelligence, a buzzword across several sectors, may be about to shake up the fragrance industry. IBM Research and Symrise -- a major global producer of flavors and fragrances that counts among its clients Estee Lauder, Coty and Victoria's Secret parent L Brands -- have created what they described as the industry's first AI-designed perfume for sale, after the two parties came together over a year ago. The AI tool, named Philyra, uses a machine-learning algorithm to study Symrise's database of some 1.7 million formulas and can identify "white space" before suggesting not only formulas that may resonate with consumers but also combinations that perfumers may not have thought of before. For instance, when asked to come up with the "most creative" interpretation of a fragrance created 12 years ago, the AI system generated one formula that removed an outdated material and upped the dosage of a popular sandalwood scent. It also unexpectedly introduced to the mix cedar wood, another ingredient popular with today's consumers, said David Apel, Symrise's VP and senior perfumer of fine fragrance.

"We are proud of our cars and so we should be," Merkel said, adding, however, that many were built in the United States and exported to China. "If that is viewed as a security threat to the United States, then we are shocked," she told the Munich Security Conference to applause from the audience.

The landscape of formal education in India is based on a relatively archaic model. Over the last 150 years, not much has evolved. The students still attend brick-and-mortar establishments for schools in order to educate themselves. The system is largely exam-driven, theoretical and impractical. The emphasis is on scoring rather than learning and subsequent application of the knowledge.

Flight departures from Dubai International, which handled around 90 million passengers last year, were suspended between 10:13 a.m. and 10:45 a.m. The incident, a Dubai official said, was caused by "a guy in the desert" operating a drone. It wasn't immediately clear if the person was apprehended. Although operating a drone without a license is illegal, individuals often fly their drones in the open space of the desert, in part to take pictures in the often scenic setting. The suspension came only weeks after U.S. regulators halted flights at Newark Liberty International Airport after a drone was spotted near another New Jersey airport.

"Machine learning is the big one," Duncan Stewart, the director of technology, media, and telecommunications research at Deloitte Canada, told CNBC. The dominant tech trend in places like Southeast Asia will not be artificial intelligence as a whole, but rather it will be the more specific AI field of machine learning, according to a researcher from Deloitte. "Machine learning is the big one, not AI. AI's a very broad field, we are talking about the very narrow field of machine learning," Duncan Stewart, the director of technology, media, and telecommunications research at Deloitte Canada, told CNBC. Deloitte predicts that the next 12 months will see significant progress in augmented reality, mobile device usage, and increasingly sophisticated chips.

In the United States, the average patient sees a physician just three times per year, according to the CDC, for visits lasting just 20 minutes each. Doctors, for their part, put more time into administrative tasks than face-to-face care: every hour spent with patients takes about two hours at the desk for documentation and other tasks, according to a Stanford study. More than half of all doctors report symptoms of burnout. In the United States, the average patient sees a physician just three times per year, for visits lasting just 20 minutes each. Voice-driven artificial intelligence (AI) can help cure the time shortage on both ends of the spectrum.

Researchers at the non-profit AI research group OpenAI just wanted to train their new text generation software to predict the next word in a sentence. It blew away all of their expectations and was so good at mimicking writing by humans they've decided to pump the brakes on the research while they explore the damage it could do. Elon Musk has been clear that he believes artificial intelligence is the "biggest existential threat" to humanity. Musk is one of the primary funders of OpenAI and though he has taken a backseat role at the organization, its researchers appear to share his concerns about opening a Pandora's box of trouble. This week, OpenAI shared a paper covering their latest work on text generation technology but they're deviating from their standard practice of releasing the full research to the public out of fear that it could be abused by bad actors.

We consider a context-based dynamic pricing problem of online products which have low sales. Sales data from Alibaba, a major global online retailer, illustrate the prevalence of low-sale products. For these products, existing single-product dynamic pricing algorithms do not work well due to insufficient data samples. To address this challenge, we propose pricing policies that concurrently perform clustering over products and set individual pricing decisions on the fly. By clustering data and identifying products that have similar demand patterns, we utilize sales data from products within the same cluster to improve demand estimation and allow for better pricing decisions. We evaluate the algorithms using the regret, and the result shows that when product demand functions come from multiple clusters, our algorithms significantly outperform traditional single-product pricing policies. Numerical experiments using a real dataset from Alibaba demonstrate that the proposed policies, compared with several benchmark policies, increase the revenue. The results show that online clustering is an effective approach to tackling dynamic pricing problems associated with low-sale products. Our algorithms were further implemented in a field study at Alibaba with 40 products for 30 consecutive days, and compared to the products which use business-as-usual pricing policy of Alibaba. The results from the field experiment show that the overall revenue increased by 10.14%.

Proximal gradient method has been playing an important role to solve many machine learning tasks, especially for the nonsmooth problems. However, in some machine learning problems such as the bandit model and the black-box learning problem, proximal gradient method could fail because the explicit gradients of these problems are difficult or infeasible to obtain. The gradient-free (zeroth-order) method can address these problems because only the objective function values are required in the optimization. Recently, the first zeroth-order proximal stochastic algorithm was proposed to solve the nonconvex nonsmooth problems. However, its convergence rate is $O(\frac{1}{\sqrt{T}})$ for the nonconvex problems, which is significantly slower than the best convergence rate $O(\frac{1}{T})$ of the zeroth-order stochastic algorithm, where $T$ is the iteration number. To fill this gap, in the paper, we propose a class of faster zeroth-order proximal stochastic methods with the variance reduction techniques of SVRG and SAGA, which are denoted as ZO-ProxSVRG and ZO-ProxSAGA, respectively. In theoretical analysis, we address the main challenge that an unbiased estimate of the true gradient does not hold in the zeroth-order case, which was required in previous theoretical analysis of both SVRG and SAGA. Moreover, we prove that both ZO-ProxSVRG and ZO-ProxSAGA algorithms have $O(\frac{1}{T})$ convergence rates. Finally, the experimental results verify that our algorithms have a faster convergence rate than the existing zeroth-order proximal stochastic algorithm.