Archive for the ‘Artificial Intelligence’ Category

Opinion | The artificial intelligence frontier of economic theory – Livemint

Until recently, two big impediments limited what research economists could learn about the world with the powerful methods that mathematicians and statisticians, starting in the early 19th century, developed to recognize and interpret patterns in noisy data: Data sets were small and costly, and computers were slow and expensive. So it is natural that as gains in computing power have dramatically reduced these impediments, economists have rushed to use big data and artificial intelligence to help them spot patterns in all sorts of activities and outcomes.

Data summary and pattern recognition are big parts of the physical sciences as well. The physicist Richard Feynman once likened the natural world to a game played by the gods: You dont know the rules of the game, but youre allowed to look at the board from time to time, in a little corner, perhaps. And from these observations, you try to figure out what the rules are."

Feynmans metaphor is a literal description of what many economists do. Like astrophysicists, we typically acquire non-experimental data generated by processes we want to understand. The mathematician John von Neumann defined a game as (1) a list of players; (2) a list of actions available to each player; (3) a list of how pay-offs accruing to each player depend on the actions of all players; and (4) a timing protocol that tells who chooses what when. This elegant definition includes what we mean by a constitution" or an economic system": a social understanding about who chooses what when.

Like Feynmans metaphorical physicist, our task is to infer a game"which for economists is the structure of a market or system of marketsfrom observed data.

But then we want to do something that physicists dont: Think about how different games" might produce improved outcomes. That is, we want to conduct experiments to study how a hypothetical change in the rules of the game or in a pattern of observed behaviour by some players" (say, government regulators or a central bank) might affect patterns of behaviour by the remaining players.

Thus, structural model builders" in economics seek to infer from historical patterns of behaviour a set of invariant parameters for hypothetical (often historically unprecedented) situations in which a government or regulator follows a new set of rules. The government has strategies, and the people have counter-strategies, according to a Chinese proverb.

Structural models" seek such invariant parameters in order to help regulators and market designers understand and predict data patterns under historically unprecedented situations. The challenging task of building structural models will benefit from rapidly developing branches of artificial intelligence (AI) that dont involve more than pattern recognition. A great example is AlphaGo. The team of computer scientists that created the algorithm to play the Chinese game Go combined a suite of tools that had been developed by specialists in statistics, simulation, decision theory, and game theory communities.

Many of the tools used in just the right proportions to make an outstanding artificial Go player are also economists bread-and-butter tools for building structural models to study macroeconomics and industrial organization.

Of course, economics differs from physics in a crucial respect. Whereas Pierre-Simon Laplace regarded the present state of the universe as the effect of its past and the cause of its future," the reverse is true in economics: what we expect other people to do later causes what we do now.

We typically use personal theories about what other people want to forecast what they will do. When we have good theories of other people, what they are likely to do determines what we expect them to do. This line of reasoning, sometimes called rational expectations", reflects a sense in which the future causes the present" in economic systems. Taking this into account is at the core of building structural" economic models.

For example, I will join a run on a bank if I expect that other people will. Without deposit insurance, customers have incentives to avoid banks vulnerable to runs. With deposit insurance, customers dont care and wont run. On the other hand, if governments insure deposits, bank owners will want their assets to become as big and as risky as possible, while depositors wont care.

There are similar trade-offs with unemployment and disability insuranceinsuring people against bad luck may weaken their incentive to provide for themselvesand for official bailouts of governments and firms.

More broadly, my reputation is what others expect me to do. I face choices about whether to confirm or disappoint those expectations. Those choices will affect how others behave in the future. Central bankers think about that a lot.

Like physicists, we economists use models and data to learn. We dont learn new things until we appreciate that our old models cannot explain new data. We then construct new models in light of how their predecessors failed.

This explains how we have learned from past depressions and financial crises. And with big data, faster computers and better algorithms, we might see patterns where once we heard only noise.

*Thomas J. Sargent is professor of economics at New York University and senior fellow at the Hoover Institution

2019/project syndicate

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Opinion | The artificial intelligence frontier of economic theory - Livemint

Seminar – Artificial Intelligence and its Impact on Young People – Council of Europe

By its present and future impact on social life and organisation or by its reliance on young people to programme and fine-tune AI technologies, AI is very closely related to young people. Yet, there is relatively little research and information about how AI will impact on young people as young citizens in transition to autonomy regarding their well-being, possibilities to participate and shape society and their access to rights, including social rights.

The seminars programme will explore the issues, role and possible contributions of the youth sector to ensure that AI is responsibly used in democratic societies and that young people have a say about matters that concern their present and future.The seminar will bring together some 50 youth leaders and experts in AI from the business and academic sectors.

More info, including the programme, at https://www.coe.int/en/web/youth/artificial-intelligence.

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Seminar - Artificial Intelligence and its Impact on Young People - Council of Europe

Facebook Artificial Intelligence to take the place of its Content Moderators – Digital Information World

Facebook is continuously facing privacy and security issues, and the users exposure to sensitive data on the platform just adds salt to the wounds. Facebook is famous for the spread of misinformation along with offensive content that could lead to wrong messages. Even though these are serious allegations, still Facebook says that they try to remove harrowing content as much as they can.

Facebook has thousands of content moderators all over the world including their always-on artificial intelligence service to detect offensive content. Since technology has taken over our lives, Facebook is also making its way towards it.

Now, most of the content moderation on Facebook is done by machine-learning systems. In this way, the moderators do not have to review a lot of content themselves, instead, artificial intelligence is doing all the work for them.

Facebook claims that they detect 98% of terrorist photos and videos before users can even see them. This is how far Facebook has come in content moderation, much appreciated!

Currently, Facebook is training its machine learning systems to identify objects as dangerous in its videos by labeling them. They are using neural networks to identify objects based on their behaviors and features and label them with confidence and percentage.

Right now, Facebook is training these networks on a variety of videos along with pre-labeled videos. The networks have the capability to identify the whole scenario in the picture and highlight any flags (if any).

This is a wise step taken by Facebook, but it is still struggling to automate the machines understanding of language, meaning, and nuance. It is because of the machines inability that Facebook highly depends on content moderators to review harassment and bullying content on the platform. AI systems do not have the capability to identify much content as of now, but it might in the future.

Read next: Facebook to Start Suggesting Moderators for Groups

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Facebook Artificial Intelligence to take the place of its Content Moderators - Digital Information World

Asia-Pacific Digital Transformation Markets 2019-2024: Focus on 5G, Artificial Intelligence, Internet of Things, and Smart Cities -…

DUBLIN--(BUSINESS WIRE)--The "Digital Transformation Asia Pacific: 5G, Artificial Intelligence, Internet of Things, and Smart Cities in APAC 2019 - 2024" report has been added to ResearchAndMarkets.com's offering.

This report identifies market opportunities for deployment and operations of key technologies within the Asia Pac region.

While the biggest markets China, Korea, and Japan often get the most attention, it is important to also consider the fast growing ASEAN region including Indonesia, Malaysia, Philippines, Singapore, Thailand, Brunei, Laos, Myanmar, Cambodia, and Vietnam. In fact, many lessons learned in leading Asia Pac countries will be applied to the ASEAN region.

By way of example, H3C Technologies Co. is planning to offer a comprehensive digital transformation platform within Thailand that includes core cloud and edge computing, big data, interconnectivity, information security, IoT, AI, and 5G solutions.

From predicting what will happen with 5G technology in the next few years to identifying how 5G will transform business, Digital Transformation Asia Pacific: 5G, Artificial Intelligence, Internet of Things, and Smart Cities in APAC 2019 - 2024 is must-have research for any ICT company looking to expand business within the region. This report represents the most comprehensive research available focused on the role and impact of 5G, AI, and IoT technologies in Asia Pac. It also provides analysis about how these technologies will have a positive feedback loop effect with smart cities.

The AI segment is currently very fragmented, characterized with most companies focusing on silo approaches to solutions. Longer-term, researchers see many solutions involving multiple AI types as well as integration across other key areas such as the Internet of Things (IoT) and data analytics. AI is expected to have a big impact on data management. However, the impact goes well beyond data management as we anticipate that these technologies will increasingly become part of every network, device, application, and service.

Data analytics at the edge of networks is very different than centralized cloud computing as data is contextual (example: collected and computed at a specific location) and may be processed in real-time (e.g. streaming data) via big data analytics technologies. Edge Computing represents an important ICT trend in which computational infrastructure is moving increasingly closer to the source of data processing needs. This movement to the edge does not diminish the importance of centralized computing such as is found with many cloud-based services. Instead, computing at the edge offers many complementary advantages including reduced latency for time sensitive data, lower capital costs and operational expenditures due to efficiency improvements.

For both core cloud infrastructure and edge computing equipment, the use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks. Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic. In many cases, the data itself, and actionable information will be the service.

Many industry verticals will be transformed through AI integration with enterprise, industrial, and consumer product and service ecosystems. It is destined to become an integral component of business operations including supply chains, sales, and marketing processes, product and service delivery and support models. The term for AI support of IoT (or AIoT) is just beginning to become part of the ICT lexicon as the possibilities for the former adding value to the latter are only limited by the imagination.

AI, IoT, and 5G will provide the intelligence, communications, connectivity, and bandwidth necessary for highly functional and sustainable smart cities market solutions. The combination of these technologies are poised to produce solutions that will dramatically transform all aspects of ICT and virtually all industry verticals undergoing transformed through AI integration with enterprise, industrial, and consumer product and service ecosystems. The convergence of these technologies will attract innovation that will create further advancements in various industry verticals and other technologies such as robotics and virtual reality.

In addition, these technologies are destined to become an integral component of business operations including supply chains, sales, and marketing processes, product and service delivery and support models. There will be a positive feedback loop created and sustained by leveraging the interdependent capabilities of AI, IoT, and 5G (e.g. a term coined as AIoT5G). For example, AI will work in conjunction with IoT to substantially improve smart city supply chains. Metropolitan area supply chains represent complex systems of organizations, people, activities, information, and resources involved in moving a product or service from supplier to customer.

Smart cities in particular represent a huge market for Asia Pac digital transformation through a combination of solutions deployed urban environments that are poised to transform the administration and support of living and working environments. Accordingly, Information and Communications Technologies (ICT) are transforming at a rapid rate, driven by urbanization, industrialization of emerging economies, and the specific needs of various smart city initiatives. Smart city development is emerging as a focal point for growth drivers in several key ICT areas including 5G, AI, IoT, and the convergence of AI and IoT known as the Artificial Intelligence of Things or simply AIoT.

Sustainable smart city technology deployments depend upon careful planning and execution as well as monitoring and adjustments as necessary. For example, feature/functionality must be blended to work efficiently across many different industry verticals as smart city address the needs of disparate market segments with multiple overlapping and sometimes mutually exclusive requirements. This will stimulate the need for both cross-industry coordination as well as orchestration of many different capabilities across several important technologies.

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For more information about this report visit https://www.researchandmarkets.com/r/w67926

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Asia-Pacific Digital Transformation Markets 2019-2024: Focus on 5G, Artificial Intelligence, Internet of Things, and Smart Cities -...

Triple your CX impact with artificial intelligence and these five tactics – CXNetwork

A practical guide with guest presenters and case studies from Microsoft and Sonos.

It is not enough to simply claim to be customer obsessed. In a climate where a moment of inconvenience could be enough to push customers to switch to your competitor, brands have no choice but to deliver what customers want. To do this with accuracy, brands need to consistently plug themselves into various sources of customer feedback.

But the reality is 91 per cent customer feedback is not properly used today, with many businesses overwhelmed by the task of processing the high volumes of insights and the soaring costs when deployed at scale.

This webinar, featuring case studies from the likes of Microsoft and Sonos, is a step-by-step guide on what it takes to drive value from unstructured CX feedback, providing insights on the set-up needed to allow text analytics to thrive.

Attend this webinar for practical insights to apply in your business on:

Frank Buckler, PhD., CX Pioneer, Book Author, Keynote Speaker and Founder & CEO Success Drivers

Rajul Jain, PhD., Senior Research ManagerMicrosoft

David Feick, PhD,Former Head of Customer InsightsSonos

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Triple your CX impact with artificial intelligence and these five tactics - CXNetwork