LinkedIn released its list of the top emerging jobs for 2020. These jobs have grown substantially in the last five years, and LinkedIn predicts they will continue to increase demand in the new year. Demand for artificial intelligence specialists grew 74% over the last five years. The job requires fluency in deep learning and machine learning. Cities hiring the most for artificial intelligence specialists include Boston and San Francisco.

OSAKA – This year, all attention will turn to the 2020 Olympics, with politicians, business leaders and the media talking about how to ensure its success, what it means for Japan domestically and internationally and how to avert a post-Olympic economic slump. Outside the seven prefectures in the Kanto region centered on Tokyo, however, the high drama of the games will be absent with the notable exceptions of Hokkaido (marathons and soccer), Fukushima (baseball and softball) and Miyagi (soccer). With that in mind, here are some issues that parts of the rest of Japan will face in 2020 and official plans to deal with them. From Hokkaido to Okinawa, governments are fighting to stay optimistic and energetic in the face of ever-increasing aging, declining populations and the flight of younger residents and businesses to Tokyo and other major cities. In late December, the government unveiled a five-year plan for regional revitalization aimed at easing the overconcentration of resources in Tokyo and the Kanto region by early 2025. Financial assistance will be available for some startups relocating outside of Tokyo, and the plan calls for the greater use of artificial intelligence in those areas of the country where it would be difficult to relocate or in which the needed number of employees can't be found.

Few are doing it well, however. In fact, nine out of ten companies have made some investment in AI, but 70 percent said they have seen minimal or no impact from AI thus far, according to the 2019 MIT SMR-BCG Artificial Intelligence Global Executive Study and Research Report. Looking ahead to 2020, CIOs will need to better assess the value of their AI bets and prove that ROI to the business, says Kara Longo Korte, director of product management at TetraVX. That's the headline for Forrester's AI prognostications as well: "We believe 2020 will be the year when companies become laser-focused on AI value, leap out of experimentation mode, and ground themselves in reality to accelerate adoption," Forrester analysts write. Here's a sobering stat: Fewer than two out of five companies reported business gains from AI in the past three years, according to the MIT AI survey.

Recently I participated in the ThinkX program sponsored by SAP Innovation Partnership Program and Singularity University. The origins of ThinkX touch the xPrize competitions and other design events which seek to leverage exponential learning. Over the past 5 years, SAP and SU have hosted over 800 SAP executives, leaders and employees to events around the world. In this second article of a three-part series I will explore the big trends, themes and take-aways - including how they may impact industry and society in the coming years. Advances in cognitive science, particularly in areas such as Artificial Intelligence (AI) and blockchain will increase at an accelerated rate.

We are often interested in clustering objects that evolve over time and identifying solutions to the clustering problem for every time step. Evolutionary clustering provides insight into cluster evolution and temporal changes in cluster memberships while enabling performance superior to that achieved by independently clustering data collected at different time points. In this paper we introduce evolutionary affinity propagation (EAP), an evolutionary clustering algorithm that groups data points by exchanging messages on a factor graph. EAP promotes temporal smoothness of the solution to clustering time-evolving data by linking the nodes of the factor graph that are associated with adjacent data snapshots, and introduces consensus nodes to enable cluster tracking and identification of cluster births and deaths. Unlike existing evolutionary clustering methods that require additional processing to approximate the number of clusters or match them across time, EAP determines the number of clusters and tracks them automatically. A comparison with existing methods on simulated and experimental data demonstrates effectiveness of the proposed EAP algorithm.

Financial markets were cheered recently by the news that the US and China have reached a "phase one" deal to prevent further escalation of their bilateral trade war. But there is actually very little to cheer about. In exchange for China's tentative commitment to buy more US agricultural (and some other) goods, and modest concessions on intellectual property rights and the yuan, the US agreed to withhold tariffs on another $160bn (£124bn) worth of Chinese exports, and to roll back some of the tariffs introduced on 1 September. The good news for investors is that the deal averted a new round of tariffs that could have tipped the US and the global economy into recession and crashed global stock markets. The bad news is that it represents just another temporary truce amid a much larger strategic rivalry encompassing trade, technology, investment, currency and geopolitical issues.

After San Francisco in May placed new controls, including a ban on facial recognition, on municipal surveillance, city employees began taking stock of what technology agencies already owned. They quickly learned that the city owned a lot of facial recognition technology--much of it in workers' pockets. City-issued iPhones equipped with Apple's signature unlock feature, Face ID, were now illegal--even if the feature was turned off, says Lee Hepner, an aide to supervisor Aaron Peskin, the member of the local Board of Supervisors who spearheaded the ban. Around the same time, police department staffers scurried to disable a facial recognition system for searching mug shots that was unknown to the public or Peskin's office. The department called South Carolina's DataWorks Plus and asked it to disable facial recognition software the city had acquired from the company, according to company vice president Todd Pastorini.

San Francisco quietly amends its municipal surveillance legislation to permit for Apple's Face ID, although the ban on facial recognition nonetheless applies. After San Francisco in Might positioned new controls, together with a ban on facial recognition, on municipal surveillance, metropolis workers started taking inventory of what know-how businesses already owned. They shortly realized that the town owned numerous facial recognition know-how--a lot of it in employees' pockets. Metropolis-issued iPhones geared up with Apple's signature unlock characteristic, Face ID, had been now unlawful--even when the characteristic was turned off, says Lee Hepner, an aide to supervisor Aaron Peskin, the member of the native Board of Supervisors who spearheaded the ban. Across the similar time, police division staffers scurried to disable a facial recognition system for looking out mug pictures that was unknown to the general public or Peskin's workplace.

ABOUT THE AUTHOR Usman Sheikh Founder and CEO xiQ, Inc.Usman Sheikh is founder and CEO of xiQ, Inc. xiQ has been developed on three principles: Leverage AI to reimagine the buyers journey and customer engagement Use of mobile technology to provide ubiquitous access to business critical information Design thinking to develop user-centric experiences Prior to founding xiQ, Usman was a Vice President with SAP, SE where he had first hand experience with ABM and B2B Sales. Usman has worked in over 40 countries and lived in Singapore, Germany and the United States. Currently he resides in the Silicon Valley, San Francisco Bay Area.

Multi-horizon forecasting problems often contain a complex mix of inputs -- including static (i.e. time-invariant) covariates, known future inputs, and other exogenous time series that are only observed historically -- without any prior information on how they interact with the target. While several deep learning models have been proposed for multi-step prediction, they typically comprise black-box models which do not account for the full range of inputs present in common scenarios. In this paper, we introduce the Temporal Fusion Transformer (TFT) -- a novel attention-based architecture which combines high-performance multi-horizon forecasting with interpretable insights into temporal dynamics. To learn temporal relationships at different scales, the TFT utilizes recurrent layers for local processing and interpretable self-attention layers for learning long-term dependencies. The TFT also uses specialized components for the judicious selection of relevant features and a series of gating layers to suppress unnecessary components, enabling high performance in a wide range of regimes. On a variety of real-world datasets, we demonstrate significant performance improvements over existing benchmarks, and showcase three practical interpretability use-cases of TFT.