Ml nordic management partner
The promising Nordic PropTech scene has been on the rise for several years and has attracted the watchful eye of industry experts. Assiduous PropTech companies are busy with creating smart solutions ready to answer the market demand for smart homes and smart buildings. Hyperight spoke to Fredrik Strand about their collaborative ecosystem for digital real estate, the PropTech areas that hold potential for improvement, the challenges with legacy systems, as well as the outlooks for the Nordic PropTech industry. Hyperight : Hello Fredrik, we are happy to have you with us at the 5th Celebrate edition of the Data Innovation Summit. To begin with, please tell us a bit about yourself and the company you are representing.SEE VIDEO BY TOPIC: Mobile Legends ❤ Best Romantic Couples 2019 ❤
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The term artificial intelligence AI refers to the collection of techniques machines use to solve problems that historically had been restricted to human capabilities. AI relies on both breadth and depth of data to learn underlying patterns and make correlations, enabling computer systems to engage in, for example, complex decision making, behavior propensity, visual perception, speech recognition, and language translation. AI has become increasingly relevant owing to recent advancements in computer processing power and statistical techniques such as deep learning, coupled with the decreasing cost of storing and managing data and the impressive expansion of open-source technology—technology that is accessible and available to the general public.
Because of its unprecedented power to change the way humans relate to technology and to each other, AI has become a topic of discussion at the tables of politicians, business leaders, and academics.
Many industries have made promising progress using AI to transform supply chains and manufacturing, operations and support, marketing and sales, and customer service.
Investment in AI is now an imperative. The question for senior executives is no longer, Should we invest? The question today is, When and how should we invest? To answer that question, business leaders must first engage in an in-depth exploration of AI. Many business leaders have made significant strides in learning about and applying AI. At the moment, few companies are successfully deploying AI, and because it is in a nascent stage, even small gains in understanding AI can lead to large rewards in the future.
As this imbalance rebalances, and more companies learn how to get the most out of AI, the returns from AI investment will diminish. Reviewing the current impact of AI in the business landscape, we see two types of companies.
In industries, such as insurance and finance that have historically relied on the heavy use of statistics to analyze data sets, many incumbents are leading AI development to enhance their existing capabilities.
For example, showcasing the adoption of AI by incumbents to deliver what they already do more effectively, the insurance giant Allianz and financial services conglomerate Citibank have enhanced their existing fraud detection algorithms by harnessing deep swaths of increasingly relevant data. And investment funds such as Two Sigma and Bridgewater Associates have shifted toward a more nuanced and quantitative approach to trading. Other industries, such as transportation and health care, are using available data in completely new ways.
Startups and tech giants are finding avenues to augment the status quo while incumbents are playing catch up. Use big data analytics to understand customer needs. Their requirements are complicated by such factors as high variability, stock availability, product-to-region pair selection, vendor lead times, and supply chain logistics. To solve the demand-forecasting challenge, the team at JD.
Connect customers directly and seamlessly to products and services. The team understood that retail success was about delivering the right product to the right customer at the right place and right time. The challenge was to match all customer variables—demographic background, location, activities, and social connections—with physical, digital, service, and content product variables through the appropriate context: browsing, search, recommendation, advertising, social, and content-to-mobile delivery.
The team evaluated content-based models, collaborative filtering models, and DL-based models, testing them to see which would be the best application for their business. Serve customer requirements with a shorter and more efficient supply chain. Develop an AI ecosystem by providing in-house AI capabilities as services for third-party small and midsize enterprises.
The AI platform supports point-of-purchase merchants with extensive pricing and market analysis, explores market opportunities and industrial trends to boost sales volume, optimizes inventory decisions on the basis of big data and operations research, and monitors market trends and public opinions on social media to improve PR.
Thus, the role of C-suite officers is shifting toward understanding a trifecta of technology, data, and analytics: senior leaders not only have to drive adoption of data and AI and to stay up-to-date on the ways that AI will change their industry and organizations, they also, for example, have to learn how to retrain their employees and hire talent with new skill sets.
The failure to realize value can be traced across many types of organizations in various stages of AI maturity.
In organizations that are passively exploring AI, there is a clear lack of direction and commitment, and they are forfeiting value while the leaders that leverage AI are becoming bigger, bolder, and faster.
Companies that have been experimenting and investigating AI use cases have managed to crack the code with a few successful AI pilots, but they lack the internal capabilities to scale up their solutions across the broader organization.
The challenge is not only in scaling the technology but also in having the people and capabilities to serve many use cases simultaneously.
Even organizations that have pioneered AI efforts face challenges in ramping up platforms to serve AI algorithms, in building algorithms that can repeatedly serve various parts of the value chain, and in managing the changes in the processes, decision making, culture, and skills required to deliver value at scale. Essentially, this is because AI is a new topic for businesses, and it presents immeasurable implications for the workforce, regulation and compliance, and traditional value creation levers.
BCG then conducted a localized survey to secure 78 Nordic responses. These 78 responses were then merged with the responses from Nordic organizations in the global survey. Overall, the Nordic responses represent various functions of Nordic companies in a variety of industries and functions, from board-level and C-suite to senior and junior managers.
We compared the results of the Nordic survey with the findings from the global version, which included 26 countries and garnered responses from more than 3, business executives, managers, and analysts in 29 industries.
There is simply no precedent model to which business leaders and advisors can turn for decision-making guidance and strategy. The process of navigating toward long-term goals to harness the benefits of AI involves three imperatives: think big, start small, and scale fast. Very few have started to tackle the two remaining steps successfully. Currently in the discovery phase, Nordic companies are actively engaged in understanding and implementing AI. The Nordics have a rich history of innovation—from the era when magnetic compasses, long boats with keels, and tents gave Vikings the competitive edge to today, when Denmark, Sweden, and Finland all rank in the top 10, and Norway and Iceland not included in this study are in the top 25 of the Global Innovation Index.
See Exhibit 1. We reported in that Nordic companies were falling short on execution of digital strategies. By contrast, our more recent research indicates that in AI, Nordic companies are actively experimenting and willing to lead in a field that is quickly evolving.
To evaluate how and to what level companies are engaging in AI, BCG and MIT Sloan Management Review have classified respondents into the following four maturity clusters on the basis of their level of understanding and engagement in AI-related activity:.
See Exhibit 2. Investigators and experimenters reported the biggest changes in opinion: roughly 53 percentage point differences. See Exhibit 3. See Exhibit 4. Digging deeper, we found that many companies are starting to expect the value from revenue increases rather than cost reductions. A majority had believed that AI would reduce costs in the past three years, and now a majority expect AI will increase revenues over the next five years.
Experimenters lag: the number of those who expect higher revenues rather than cost reductions increased by only 4 percentage points. See Exhibit 5. As they gain increasingly specific understanding of the effects of AI on functional parts of their business, small and midsize enterprises expect significant changes in go-to-market functions, customer service, and products and services. Although go-to-market functions, customer service, and products and services may offer opportunities for reducing costs, many of the AI use cases today indicate that pricing and marketing offer clear revenue-increasing opportunities.
More of the cost reduction opportunities come from implementing AI in support processes—the functional area in which very large enterprises expect a significant impact.
See Exhibit 6. See Exhibit 7. Investments in AI include sourcing and hiring AI talent, acquiring and developing AI technology, acquiring and man- aging the data required, as well as implementing the new processes for training AI algorithms. See Exhibit 8. Many Nordic companies have a decent understanding of AI, expect significant value, and are increasing investments in AI, but most companies—in all maturity clusters—reported some to very low adoption of AI.
Many of the respondents reported that they had started pilots but had been unable to scale their AI solutions or drive the necessary change at scale. In this section of the report, we take a deeper look into how companies are thinking about data and how that can impact their success with AI, as well as other challenges companies are facing. Only slightly more than half of all pioneers, investigators, and experimenters reported central ownership and management—a key success factor for launching AI at scale—as a top pick.
Without central ownership and management, companies would find it difficult to implement any data and AI strategy—even as a top agenda topic and viewed as a corporate asset. For the most part, data is located in silos rather than in a centralized data lake, creating inefficiencies for AI use cases in processing data ingest and egress.
See Exhibit 9. Asked to name the top three barriers to their successful adoption of AI, companies listed a host of challenges, but the most widely noted barriers were attracting, acquiring, and developing AI talent; dealing with competing investment priorities; having an unclear or no business case for AI applications; and dealing with cultural resistance to AI approaches.
This, perhaps, should signal passives and experimenters that they should expect further challenges in finding the right AI talent as their maturity develops. We do see, however, that pioneers understand the core technological competencies required to enable AI.
Companies have to learn how to walk before they run: their technology must have a strong foundation upon which they can build a more advanced technology such as AI. See Exhibit One implicit understanding is that many companies recognize that AI talent will be scarce in the years to come, so there will be a need to develop capabilities internally.
Many are employed in knowledge-intensive services, and, given that AI has historically been seen as taking over tasks that can be automated, these workers may be nonchalant about the impact of AI on their jobs.
Nordic companies have a higher AI adoption rate than their global peers. Compared with the rest of the world, they have more pioneers and experimenters and fewer passives. Nordic investigator and experimenter companies have, in the past year, increased their investments in AI more than investigator and experimenter companies in the rest of the world.
Nordic investigators and experimenters have also responded more frequently to expected AI-related business model shifts. Nordic companies could be acting preemptively about the impact of AI on their businesses and are investing in AI to actively shape the outcome.
In surprising contrast to Nordic companies, more global peers across maturity clusters reported believing that AI will reduce costs, rather than drive revenues, compared with the past three years.
This trend is the inverse of the opinion in Nordic companies, most of which reported that they expect revenue increases rather than cost reductions. Pioneers around the globe report mostly similar barriers toward AI adoption: attracting, acquiring, and developing the right talent; cultural resistance to AI approaches; limited general-technology capabilities; and competing investment priorities. The telling detail is the largest gap between Nordic and global pioneers. This is interesting in light of the fact that the Nordic companies surveyed comprised a larger proportion of pioneers than the rest of the world.
The learning and experience that global pioneers had acquired over the past three years may have provided them with valuable insight into developing more coherent business cases for AI applications. Why did almost half of the pioneers report that unclear or no business case for AI applications is a barrier?
We noted above that Nordic companies generally expect revenue increases from AI, whereas global peers expect cost reductions. Common business sense suggests that it is easier to realize cost savings and efficiencies than to raise revenues, so perhaps the global pioneers have found strong business cases in cutting costs, and the Nordic pioneers find it difficult to substantiate business cases that anticipate an upside in revenues.
Most companies are just starting their AI journey. As they mature, they are bound to discover an increasing number of possible applications, as well as the hurdles that they need to overcome before they can turn ambitions into reality. In short, we advise companies to think big, start small, and scale fast. To do this, companies must be mindful of several imperatives.
Focus on business value over technology. Once the high-value use cases have been identified and deemed feasible, companies can avoid fragmenting scarce resources by focusing resources on a select few use cases.
We advise companies to focus on business value over technology: all too many companies spend years and lots of money building analytics platforms and setting up data lakes before they have a clear idea of which high-value use cases they will address. There are plenty of open-source standards and easy-to-use tools that companies can use to get started at relatively low cost.
AWS provides a broad portfolio of managed services for data analytics, along with a vast partner community to help you build virtually any big data application in the Cloud. However, customers often ask us to show them the big picture: How to put all these pieces together so that they can explain it to all their stakeholders. The video will show you how to use the poster to build a narrative that can be used in cross-functional meetings involving both technical and business stakeholders — from developers and data scientists to line of business and senior executives.
Nordic Place Research is an interdisciplinary group of researchers providing research-informed contributions towards developing, managing, marketing, and making places better. Places contribute to our identity, and we, in turn, contribute to the identity of a place, its nature, culture and society. For the Nordic region and beyond, the responsible development of urban zones, towns, villages, resorts and wild places becomes increasingly more challenging as society changes under pressures from globalisation, population growth and technological advancement. The Nordic region, including the countries of Iceland, Norway, Sweden, Finland, and Denmark, are generally lightly populated, but increasingly comer under tourism pressure and must respond to calls for responsible, sustainable development for the good of all. Places define, sustain and challenge us.
Previous research on the institutional structure of franchising networks Bri- ley et al. The latter includes not only residual income rights of franchised outlets but also residual income rights of franchisor-owned outlets. Previous studies primarily examines the incentive, signalling and screening effects of fees, royalties and other contractual pro- sions from the point of view of organizational economics see Dnes for a review without taking into account the interactions between residual decision and residual income rights as interrelated parts of the governance structure. This paper fills this gap in the literature. According to the property rights view, de- sion rights should be allocated according to the distribution of intangible kno- edge assets between the franchisor and franchisee and ownership rights should be assigned according to the residual decision rights. Since ownership rights are diluted in franchising networks, the dilution of residual income rights of fr- chised outlets is compensated by residual income rights of company-owned o- lets. Under a dual ownership structure, company-owned outlets compensate the disincentive effect of low royalties for the franchisor, and low royalties strengthen the investment incentives for the franchisee. Performance and Efficiency in Franchising. Governance Structure Issues. Multicase Study.
AWS Machine Learning Partner Solutions
The project questionnaire was filled in by the service providers. Results indicate that still some services are not free of charge and are not equally distributed geographically. In terms of safety, although most programmes contact the ex- partner at the beginning of the treatment, still half of the programmes do not contact the ex- partner during the treatment or at the end of it, moreover nearly half of the programmes do not use any risk assessment instrument. Outcome is measured by most of the programmes however partner and official reports should also be included.
We back Entrepreneurs and Investors with bold goals to make the world a bit better and more interesting. Together we will learn along the way! Our ideal investments are around million euros, following the standard terms in the Nordics like seriesseed.
The IoT piece in the growing Nordic PropTech puzzle
Request agenda. The agenda is suited to guide you through the process of extracting knowledge from data by using the latest methodologies, tools and algorithms. With domestic and international speakers on stage, workshops, interactive panel discussion and plenty of learning and networking activities in the exhibition area, the Nordic Data Science and Machine Learning Summit is the place to be for all professionals and organisations working with utilization of Data Science, Machine and Deep Learning, to innovate and improve their business. Domestic and International speakers.SEE VIDEO BY TOPIC: Best Love Couples in Mobile Legends - 2020 - All 16 Couples - -
But there is more. Find out what Cloud best suit your needs, or maybe multiple Clouds will be your choice. After eight successful years we are developing as a conference while continuing the key concept of the original conference: Less slides, more demos! With a determined focus on hands-on clinics and sessions we will show you how to solve the most common tasks and challenges, sharing all our experience, tips and tricks from the real world. The two-day event will focus on deep-dives and practical knowledge.
Вначале все шло гладко. Халохот, по всей видимости, настоящий профессионал. Но потом появилась группа людей, и Халохот не смог завладеть искомым предметом. Фонтейн кивнул. Агенты связались с ним, когда он находился в Южной Америке, и сообщили, что операция прошла неудачно, поэтому Фонтейн в общих чертах уже знал, что случилось. Тут вступил агент Колиандер: - Как вы приказали, мы повсюду следовали за Халохотом.
В морг он не пошел, поскольку в этот момент напал на след еще какого-то парня в пиджаке и галстуке, вроде бы штатского. - Штатского? - переспросил Фонтейн.
Черные атакующие линии начали исчезать. - Происходит восстановление! - кричал Джабба. - Все становится на свои места.
NORDIC MANAGEMENT PARTNER
Вовсе нет, - ответила Мидж. - Хотела бы, но шифровалка недоступна взору Большого Брата. Ни звука, ни картинки. Приказ Стратмора.
Scale-UP FUNDING WITH A JAPANESE TWIST
Красное лицо немца исказилось от страха. - Was willst du. Чего вы хотите. - Я из отдела испанской полиции по надзору за иностранными туристами.
Беккер отшвырнул пистолет и без сил опустился на ступеньку.
Хорошенькое зрелище, - подумал Беккер. - Где, черт возьми, регистратура. За едва заметным изгибом коридора Беккер услышал голоса.
Он пошел на звук и уткнулся в стеклянную дверь, за которой, судя по доносящемуся оттуда шуму и гвалту, происходило нечто вроде драки.
The Power of NOW
Там проблема с электричеством. - Я не электрик. Позвони в технический отдел. - В куполе нет света. - У тебя галлюцинации. Тебе пора отправляться домой.
В ушах у нее раздавался непрекращающийся звон, а все тело словно онемело. Хаос, царивший в комнате оперативного управления, воспринимался ею как отдаленный гул. Люди на подиуме не отрываясь смотрели на экран.