Beauty brands are using everything from artificial intelligence (AI) to augmented reality (AR) to keep their customers engaged in a fiercely competitive market. But do such innovations actually work or are they simply marketing hype? When L'Oreal said last year it no longer wanted to be the number one beauty firm in the world, but "the number one beauty tech company", it was clear things in the industry had changed. "Women have had the same beauty concerns for 30 to 40 years, but technology has created a more demanding consumer," explains Guive Balooch, global vice president of L'Oreal's Technology Incubator. "They want more personalised and precise products, and we have to respond."

In modern times, talking about artificial intelligence is no longer science fiction; it is a reality more than palpable. The daily life of people is mostly supervised by artificial intelligence, whether it is found in social networks or the one used by governments as national security systems, to mention two. However, how can artificial intelligence influence today's economy?, Can this change the direction of the world economy?, We can start answering these answers today with certain echoes that occur around the world, as a week ago AICoin invested in a pioneer startup in Artificial Intelligence technologies for health in the United States of America, since AIcoin uses the profits of cryptocurrency operations to acquire capital holdings in Artificial Intelligence (AI) startups. The deal is as follows, AICoin, through Tessier-Ash pool PTE Ltd. is the main investor in this round of financing of US $ 2,000,000 and will acquire a position on the Sevamob board of directors. Sevamob, a provider of platforms enabled by artificial intelligence for medical care in organizations, and part of the Innovations in Healthcare program at Duke University, is one of the new companies that uses technologies such as artificial intelligence and telemedicine in clinics emerging from light assets, to provide superior primary medical care to marginalized populations at a cost up to 50% lower.

Dr. Frey currently serves as executive director and senior futurist at the DaVinci Institute in Colorado, which he founded. He is also Google's top-rated Futurist Speaker and has built an enormous following around the world based on his ability to develop accurate visions of the future and describe the opportunities ahead. We asked him what his vision of the cultured meat sector is for the next couple of decades. Will industrial livestock and meat production find itself at a significant cost disadvantage? This will also give rise to a new snake oil era where people will claim their special concoction of cultured meats are a cure for everything from melanoma, to depression, to dyslexia.

Digital transformation is rapidly disrupting current industry models. The adoption of new technologies is particularly accelerating in the logistics and manufacturing sector due to the benefits it offers enterprises, and is resulting in wider implementation of smart industry solutions. As the manufacturing and logistics sectors undergo major transformation, digital twins, artificial intelligence, the industrial internet of things, and warehouse robotization rank among the leading smart industry trends for 2019 and the coming years. Digital transformation of industry continues to move forward. A study by the German branch of the company PwC indicates that 91% of the industrial companies that participated in the research are investing or plan to invest in digital factories in Europe.

SoftBank Group Corp.'s early-stage venture capital arm is setting up a second investment fund dedicated to unearthing promising startups in artificial intelligence, propelling founder Masayoshi Son's ambition of staking out a position in the nascent technology. Deepcore Inc. is preparing to form a new AI investment fund in two to three years as it expands its core startup incubation business, Chief Executive Officer Katsumasa Niki said in an interview. The company aims to find promising companies and nurture the next generation of up-and-comers, enroute to addressing Japan's deficit of global AI firms. Deepcore's second fund will surpass the 6 billion yen ($55 million) raised for the first, Niki said without elaborating. The effort is separate from SoftBank's much better-known Vision Fund, the $100 billion giant that has made large bets on industries from ride-hailing and autonomous driving to co-working spaces.

Artificial Intelligence will bring massive new capabilities as well as disruption to businesses as well as society. The term isn't new, and has been around for decades, but AI today has gone far beyond the realm of science fiction. AI comes into play each time you use a smartphone, or when a bank decides to pitch a new financial product to you, or for personalised medicine. And AI usage will only grow, which is what strikes fear among some too. From successful use cases, sectors with massive potential for AI-driven transformation, job-loss fears, and what India needs to do to catch up with the US & China in AI, training AI for bias, etc., Ivor Soans, Editor-Special Features at BloombergQuint, discusses all these and more with Sanchit Vir Gogia, CEO of Greyhound Research, and one of India's finest navigators of technology trends.

Many mutations in DNA that contribute to disease are not in actual genes but instead lie in the 99% of the genome once considered "junk." Even though scientists have recently come to understand that these vast stretches of DNA do in fact play critical roles, deciphering these effects on a wide scale has been impossible until now. Using artificial intelligence, a Princeton University-led team has decoded the functional impact of such mutations in people with autism. The researchers believe this powerful method is generally applicable to discovering such genetic contributions to any disease. Publishing May 27 in the journal Nature Genetics, the researchers analyzed the genomes of 1,790 families in which one child has autism spectrum disorder but other members do not.

Neuromorphic Computing is a nascent research field in which models and devices are designed to process information by emulating biological neural systems. Thanks to their superior energy efficiency, analog neuromorphic systems are highly promising for embedded, wearable, and implantable systems. However, optimizing neural networks deployed on these systems is challenging. One main challenge is the so-called timescale mismatch: Dynamics of analog circuits tend to be too fast to process real-time sensory inputs. In this thesis, we propose a few working solutions to slow down dynamics of on-chip spiking neural networks. We empirically show that, by harnessing slow dynamics, spiking neural networks on analog neuromorphic systems can gain non-trivial performance boosts on a battery of real-time signal processing tasks.

We study the parameterized complexity of the optimal defense and optimal attack problems in voting. In both the problems, the input is a set of voter groups (every voter group is a set of votes) and two integers $k_a$ and $k_d$ corresponding to respectively the number of voter groups the attacker can attack and the number of voter groups the defender can defend. A voter group gets removed from the election if it is attacked but not defended. In the optimal defense problem, we want to know if it is possible for the defender to commit to a strategy of defending at most $k_d$ voter groups such that, no matter which $k_a$ voter groups the attacker attacks, the outcome of the election does not change. In the optimal attack problem, we want to know if it is possible for the attacker to commit to a strategy of attacking $k_a$ voter groups such that, no matter which $k_d$ voter groups the defender defends, the outcome of the election is always different from the original (without any attack) one.

We explore the behavior of a standard convolutional neural net in a setting that introduces classification tasks sequentially and requires the net to master new tasks while preserving mastery of previously learned tasks. This setting corresponds to that which human learners face as they acquire domain expertise, for example, as an individual reads a textbook chapter-by-chapter. Through simulations involving sequences of ten related tasks, we find reason for optimism that nets will scale well as they advance from having a single skill to becoming domain experts. We observed two key phenomena. First, _forward facilitation_---the accelerated learning of task $n+1$ having learned $n$ previous tasks---grows with $n$. Second, _backward interference_---the forgetting of the $n$ previous tasks when learning task $n+1$---diminishes with $n$. Amplifying forward facilitation is the goal of research on metalearning, and attenuating backward interference is the goal of research on catastrophic forgetting. We find that both of these goals are attained simply through broader exposure to a domain.