The Business of Film & Television @ The Origin of Consciousness & Understanding is the first step to conquer stress @ We’re letting China win the 5G race. It’s time to catch up & Researchers break the geometric limitations of moiré pattern in graphene heterostructures @ The Exponential Guide to Artificial Intelligence & The Year in Math and Computer Science & Giant Chinese Telescope Joins the Search for Alien Radio Signals @ Patti Smith performs Bob Dylan’s “A Hard Rain’s A-Gonna Fall” – Nobel Prize Award Ceremony 2016 & Announcement of the Nobel Prize in Physics 2019 & Announcement of the Nobel Peace Prize 2019 & Nobel Prize Award Ceremony 2016 @ ´´An economy (from Greek οίκος – “household” and νέμoμαι – “manage”) is an area of the production, distribution and trade, as well as consumption of goods and services by different agents. Understood in its broadest sense, ‘The economy is defined as a social domain that emphasize the practices, discourses, and material expressions associated with the production, use, and management of resources’.[1]´´ @ Videos, links and images #time

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Pathol Res Pract. 2012 Jul 15;208(7):377-81. doi: 10.1016/j.prp.2012.04.006. Epub 2012 Jun 8.

The influence of physical activity in the progression of experimental lung cancer in mice

Renato Batista Paceli 1Rodrigo Nunes CalCarlos Henrique Ferreira dos SantosJosé Antonio CordeiroCassiano Merussi NeivaKazuo Kawano NagaminePatrícia Maluf Cury


GRUPO_AF1 – GROUP AFA1 – Aerobic Physical Activity – Atividade Física Aeróbia – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

GRUPO AFAN 1 – GROUP AFAN1 – Anaerobic Physical Activity – Atividade Física Anaeróbia – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

GRUPO_AF2 – GROUP AFA2 – Aerobic Physical Activity – Atividade Física Aeróbia – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

GRUPO AFAN 2 – GROUP AFAN 2 – Anaerobic Physical Activity – Atividade Física Anaeróbia – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

Slides – mestrado – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto



Avaliação da influência da atividade física aeróbia e anaeróbia na progressão do câncer de pulmão experimental – Summary – Resumo – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto


Lung cancer is one of the most incident neoplasms in the world, representing the main cause of mortality for cancer. Many epidemiologic studies have suggested that physical activity may reduce the risk of lung cancer, other works evaluate the effectiveness of the use of the physical activity in the suppression, remission and reduction of the recurrence of tumors. The aim of this study was to evaluate the effects of aerobic and anaerobic physical activity in the development and the progression of lung cancer. Lung tumors were induced with a dose of 3mg of urethane/kg, in 67 male Balb – C type mice, divided in three groups: group 1_24 mice treated with urethane and without physical activity; group 2_25 mice with urethane and subjected to aerobic swimming free exercise; group 3_18 mice with urethane, subjected to anaerobic swimming exercise with gradual loading 5-20% of body weight. All the animals were sacrificed after 20 weeks, and lung lesions were analyzed. The median number of lesions (nodules and hyperplasia) was 3.0 for group 1, 2.0 for group 2 and 1.5-3 (p=0.052). When comparing only the presence or absence of lesion, there was a decrease in the number of lesions in group 3 as compared with group 1 (p=0.03) but not in relation to group 2. There were no metastases or other changes in other organs. The anaerobic physical activity, but not aerobic, diminishes the incidence of experimental lung tumors.


Business >> Media

Introductory Producing

The Business of Film & Television

Ratings 4.22 / 5.00
Introductory Producing


Welcome to Introductory Producing.  Have you ever wondered, what does a producer do?  Or, I want to be a producer as they seem to be the ones creating incredible content.  Many directors are producers – so what would you have to learn to be a producer yourself?  

This course will take you through the initial steps necessary to understand and execute a proper business plan, a budget and schedule for a half hour drama, comedy or documentary.  You will also be introduced to the concept of finding the underlying creative IP (Intellectual property) that any producer needs to create a project.  

Or, you may want to be the line producer – responsible for the day to day running of the set.  Arranging for the crew, cameras, locations, transportation, meals and the myriad of details that go into every production regardless of the size of the budget. 

What You Will Learn!

  • By the end of the introductory course, you will be able to budget and schedule a 30-minute production. You will understand what is necessary to pitch a project to a production company or a television network. You will appreciate the different roles that a producer does in feature films vs. an executive producer in television.

Who Should Attend!

  • Anyone who has a desire to be in the film and television business. It is an introductory course, so no experience is necessary. But it will help anyone who is working below the line to understand what is involved in the world of creating intellectual properties and independent productions.



  • Filmmaking





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The science of consciousness


Understanding is the first step to conquer stress

Sorrowing old man by Vincent van Gogh

Stress is generally considered a state in which timely information processing is inhibited due to added expectation load. A perennial problem of our time, it occurs across the social spectrum and in almost all age groups. We know that it even affects children. In the short term stress leads to confusion, productivity problems, and over the long term hormonal and bodily changes weaken the immune system and can lead to high blood pressure, heart attack, and stroke, and even mental problems. Stress probably contributes to diseases as varied, like depression or cancer. This makes stress is a serious professional as well as a health issue. For this reason, it is immensely important to look in detail what anxiety is, what are its causes and how to alleviate or at least reduce its adverse effects.
Stress is connected to time pressure and to circumstances that are challenging, difficult or even impossible to manage. Everybody gets stressed at one time or another, and the same stressors affect people very differently. However, stress is situation dependent, which indicates the importance of the mental state during challenging situations. Chronic high brain frequencies correspond to detail oriented mental processing. The overwhelming, unnecessary, and superfluous information load leads to a perceived shortage of time,forming asense of deficiency, or difficulty.

The detail-oriented focus of high brain frequencies inhibits the ability to see clearly. For example, in research studies subjects have been shown to display more severe moral judgment after experiencing negative emotion (disgust), even if the decision in question is unrelated to the original feeling. People with guilty conscience overestimate their own weight and consider chores to be more difficult than those with clear consciences. This occurs because the brain always generates thoughts, which are in a similar vein, so thoughts and memories acquire the same flavor. In this case only threatening, pessimistic or failed experiences surface, which uncontrollably interferes with concentration and compromises problem-solving ability. Although the information is just a fragmented and distorted illusion, it nevertheless masquerades as objective reality, forming the center of conscious attention. The information overload, which naturally leads to aggravation or desperation, is recognized as the typical stress symptom. The stressed mind is on an inevitable path toward conflict (interaction). Like a released bow, conflicts relax the mind, and the brain frequencies return to normal levels. At these times we are resilient and emotionally stable. However, the emotional struggle and conflicts exact high personal and professional price: broken promises cause problems at work, and strain relationships in private life. Not surprisingly, long-term stress contributes to a host of health problems.

Recognizing stress as a result of latent high brain frequencies can give us the tools to manage our lives better. Rehearsing stressful situations in advance whenever possible is empowering, so is using relaxation techniques before difficult conditions. The goal should not be to eliminate stress from our lives entirely because occasional stress enhances focus and concentration. However, long-term stressis incapacitating and dangerous.Get rid of grudges, hurts of any kind as soon as you have them. This is an emotionally challenging, and meticulous task, but well worth the effort! You will have to develop a personal method that you can regularly follow. Go for a walk; focus on a mantra, prayer or meditation, with which to liberate your mind from a negative mindset. When your soul is not handicapped by the after-effects of negative emotions, it is able to handle any challenge. Do not forget to regularly challenge your improved mind. Adversities allow us to grow and develop.

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Dec 23, 2019

We’re letting China win the 5G race. It’s time to catch up

Posted by Derick Lee in category: internet


This new “digital highway” centered on 5G will give rise to new industries and services previously unimagined. The United States must redouble its efforts to build such a digital infrastructure and make the commercialization of the Internet of Things a reality.

We’re on the verge of another industrial revolution. We can’t let the U.S. miss out.Read more


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Researchers break the geometric limitations of moiré pattern in graphene heterostructures

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Researchers break the geometric limitations of moiré pattern in graphene heterostructures

 December 23, 2019 , University of Manchester

Researcher’s break the geometric limitations of moiré pattern in graphene heterostructures
Credit: University of Manchester

Researchers at the University of Manchester have uncovered interesting phenomena when multiple two-dimensional materials are combined into van der Waals heterostructures (layered “sandwiches” of different materials).

These heterostructures are sometimes compared to Lego bricks, where the individual blocks represent different atomically thin crystals, such as graphene, and are stacked on top of each other to form new devices.

Published in Science Advances, the team focused on how the different crystals begin to alter one another’s fundamental properties when brought into such close proximity. Of particular interest is when two crystals closely match and a moiré pattern forms. This moiré pattern has been shown to affect a range of properties in an increasing list of 2-D materials. However, typically, the geometry of the moiré pattern places a restriction on the nature and size of the effect.

A moiré pattern is due to the mismatch and rotation between the layers of materials, which produces a geometric pattern similar to a kaleidoscope.

The team broke this restriction by combining moiré patterns into a composite “super-moiré” in graphene, both aligning to substrate and encapsulation hexagonal boron nitride. The researchers demonstrated the nature of these composite super-moiré lattices by showing band structure modifications in graphene in the low-energy regime. Furthermore, they suggest that the results could provide new directions for research and device fabrication.

Zihao Wang and Colin Woods, authors of the paper, said: “In recent years, moiré patterns have allowed the observation of many exciting physical phenomena, from new, long-lived excitonic states, Hofstadter’s butterfly, and superconductivity. Our results push through the geometric limitation for these systems and therefore present new opportunities to see more of such science, as well as new avenues for applications.”

More information: Zihao Wang et al. Composite super-moiré lattices in double-aligned graphene heterostructures, Science Advances (2019). DOI: 10.1126/sciadv.aay8897

Journal information: Science Advances

Provided by University of Manchester

LAMOST first data release provides fundamental parameters of nearly 30,000 M dwarfs 55 minutes ago reportMATRIEX imaging: Simultaneously seeing neurons in action in multiple regions of the brain 25 minutes ago featureWidening metal tolerance for hydrogels 1 hour agoScientists create a ‘crystal within a crystal’ for new electronic devices 1 hour agoArchaeological discoveries are happening faster than ever before, helping refine the human story 1 hour ago


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Singularity University

The Exponential Guide to Artificial Intelligence

“AI is here today; it’s not just the future of technology. It’s embedded in the fabric of your everyday life.” —Neil Jacobstein, Singularity University Chair, AI & Robotics

Today, it can be difficult to understand the significance and potential impact that artificial intelligence (AI) has for humanity. From Siri to IBM’s Watson to Hollywood portrayals of killer robots, it’s not clear what we should ultimately expect from this exponential technology.

What is clear: AI-powered products and services have made it into nearly every aspect of our personal and professional lives in just a few years. And as AI solutions continue to emerge and converge, that pace of change will only continue to accelerate. It’s easy to find scenarios of a utopian future of abundance where machines do all the hard work—as well as grim scenarios where unemployment soars as traditional workers are replaced by increasingly capable machines.

With such rapid progress, it’s difficult to make assumptions about the future of AI. But instead of focusing on the unknown, we can examine what we know about AI, its current applications, and potential future impact.

At Singularity University, we help organizations and individuals understand the disruptions and opportunities of exponential technologies like AI. Whether you’re an entrepreneur, Fortune 500 CEO, or simply a curious human who wants to understand where we’re going as a species, AI is significantly impacting all of our lives.

None of us can predict the future of AI. But if you’re looking for an accessible guide to help you understand this exciting technology, we offer this Exponential Guide to Artificial Intelligence. Read on for more!

What Is Artificial Intelligence?

AI is an “umbrella term” for a branch of computer science focused on creating machines capable of thinking and learning. Based on their experiences, AIs learn to make better decisions in the future. This ability to both learn and apply knowledge closely mimics the way human beings understand the world and allows machines to accomplish tasks that were once only possible with human minds.

Some of the human-like tasks AIs can do include:

  • Complex problem solving
  • Visual interpretation (computer vision)
  • Speech recognition (natural language processing)

These capabilities are accomplished via a collection of computer algorithms that use mathematics and logic to perform the AI’s assigned task. So although our most famous science fiction books and movies tend to portray AI in the form of human-like robots, AI is simply computer code running in software.

Unlike the human brain, these intelligent programs can be run in a variety of different hardware types, whether that’s your smartphone, a warehouse of web servers, or a self-driving Tesla.

This variety of use cases is what often makes AI so difficult to understand, but it’s also what makes it so powerful. The ability to add an AI layer on to nearly every technology means that as AI progresses, the world around us will increasingly seem to come alive. This “awakening” will drastically alter life as we know it, from leisure and business activities to our health and spirituality. To get an idea of how this might happen, let’s first take a look at how AI works.

“AI is perhaps the granddaddy of all exponential technologies—sure to transform the world and the human race in ways that we can barely wrap our heads around.”

–Jason Silva

How Does Artificial Intelligence Work?

Much like human intelligence, AI works by taking in large amounts of data, processing it through algorithms that have been adjusted by past experiences, and using the patterns found within that data to improve decision-making.

To simulate human intelligence in this way, AI engineers provide their machines with ability to:

  1. Perceive their surrounding environment (which may simply be data)
  2. Detect patterns in the environment
  3. Learn from the patterns and update experiential memory

Then, these steps are repeated until there’s enough data to confidently make predictions and support decision-making.

artificial intelligence diagram

What makes AI remarkable is the speed, accuracy, and endurance it brings to this human-like learning process. Humans have to eat, sleep, and tend to a variety of personal needs. We are also creatures of comfort, and quite stubborn—too much change makes us uncomfortable. And when presented with new information and experiences, humans tend to let our biases sway us from making the most reasonable and logical decisions.

Machines suffer from none of these shortcomings. For most purposes, they’re capable of running indefinitely, allowing AIs to process and detect patterns in massive amounts of data without mental fatigue.

AIs are constantly tweaking their understanding of their environment, updating their “perspective” of reality, and updating the probability of their predictions without clinging to any old ideas. Some people find this cold logic the most terrifying part of AI, however, it’s also what allows AIs to find solutions humans may not recognize.

The concept of AI has been around since 1955, but its growth has exploded in recent years because of three factors:

  1. Vastly increased computing power
  2. Large, inexpensive data sets
  3. Advancements in the field of machine learning

But computing power alone wouldn’t have accomplished much if not for two key technologies that support AI: big data and machine learning.

Big data, which provides massive data sets and user activity to greatly increase the quality of “education” AIs receive.
Machine learning is a method of data analysis that enables computers to learn without external instruction.
Deep learning is a branch of machine learning that uses computer simulations called artificial neural networks.

How Are AI, Big Data, Machine Learning, and Deep Learning Related?

As we’ve mentioned, AI covers a broad field of sciences involved in developing computer systems that think and learn in a way that’s similar to human intelligence. AI applications are often divided into “narrow AIs” that perform specific tasks such as playing chess, and “general AIs” that understand language, context, and emotions as humans do. Let’s take a closer look at the relationships between AI, big data, machine learning, and deep learning.

Big data

With the rapidly decreasing cost of sensors and the global growth of the Internet of Things (IoT), we have dramatically increased the number of smart and connected devices that are continuously measuring and recording data. Nearly every action we take is now recorded in a database somewhere. This includes mobile device activity, the purchase history on our credit cards, our online browsing activity, our social media feeds, and even our biological data.

Big data is the term for these massive collections of data that we’re all contributing to every day. Big data is the fuel that enables AIs to learn much more quickly. The abundance of data we collect supplies our AIs with the examples they need to identify differences, increase pattern recognition capabilities, and to discern the fine details within the patterns.

If you provided an AI with one picture of a dog and one picture of a cat to learn from, you will have an AI that’s terrible at the task of determining pet species. Feed that same algorithm millions of pet pictures, and the AI can quickly learn how to distinguish dogs from cats, and also determine the different breeds within the species.

Big data enables AIs to learn by example rather than by instructions provided by humans. And they’re able to learn this way because of the advances in machine learning.

Machine learning

Machine learning is a method of data analysis that learns from experience, enabling computers to find hidden insights without being explicitly programmed to do so. Machine learning analyzes data and learns from it to make decisions and predictions, and includes supervised (manual entry of data and solutions) and unsupervised learning.

Machine learning is a subset of the larger field of AI, and it is one of the many processes that enable the creation of AI. Many ways of creating AIs have been explored, but machine learning is important because it does not require human input or interaction. Rather than learning by instruction, machine learning AIs learn by exposure to examples found in data. Through machine learning, AI is able to take advantage of the enormous data sets generated by our daily activities. To learn without human involvement, machine learning works largely by implementing statistical methods into the learning process.

Deep learning

Deep learning is part of the broader field of machine learning that uses artificial neural networks, which are computer simulations patterned after a human brain. Deep learning includes aspects of machine learning algorithms, neural networks, and AI.

The artificial neural networks created from these components are where the field of AI comes closest to modeling the workings of the human brain. Improved mathematical formulas and increased computer processing power are enabling the development of more sophisticated deep learning applications than ever before. Deep learning—also called structured learning and hierarchical learning—is the kind of machine intelligence used to create AIs that beat humans at games of Go and chess.

Keep Exploring!

Watch Singularity University Co-Founder and Chancellor Ray Kurzweil talk about deep learning and the path to artificial general intelligence in this captivating video.

How Does AI Affect Our Lives?

Some of the most powerful and prevalent applications of AI are the ones we often take for granted. These include the AIs that handle your Google searches, deflect spam from your inbox, and select the ads you see across the digital landscape. AIs identify people in your Facebook pictures, and recommend the products you buy from Amazon.

No matter where you live and work, one thing is certain: more and more of our society’s technical infrastructure is powered by AI. While many AIs are easy to overlook because they don’t talk to us like Siri or perform physical tasks like driving our Teslas, they constantly work behind the scenes, performing crucial functions like pattern recognition, problem solving, reporting, and optimization.

AI technology is making its way into nearly aspect of our lives. It’s helping to keep us alive through its integration with healthcare, and influencing our economies via its integration with finance.

Learn more about the powerful potential of AI in medicine at Singularity Hub and join us at Exponential Medicine in San Diego, California in November 2019.

What Are Some Examples of How AI Is Impacting Healthcare?

With the fundamental importance of health in our lives, it should be no surprise that we’re seeing a massive integration of AI throughout healthcare and medicine, from cybersecurity for patient records to AI-assisted surgeries. Here are some examples:

  • One study showed how virtual assistants running natural language AI systems are saving doctors and nurses 17-20 percent of the time by cutting back on unnecessary visits and workflow overhead.
  • New AI implementations are being used to discover gaps in patient care, protecting against oversights for scheduling and treatments, which helps hospitals improve care and potentially prevent malpractice lawsuits. It has been estimated that using AI to streamline general administrative workflow at hospitals might provide an annual $18 billion in savings.
  • Diagnostic practices are benefiting from the ability of AIs to quickly and accurately analyze samples.
  • In pharmaceutical research, AI is being used to massively speed up the process of drug discovery.

From helping human healthcare employees work more efficiently, to improving diagnoses and discovering new drugs, AI stands to revolutionize an industry that, became the largest U.S. employer in 2017.

The majority of women treated for late-stage breast cancer receive the wrong treatment in the first year because the only way to see if one of 30 FDA-approved drugs will work is for the patient to try it to see what happens.

Ourotech, a Singularity University Portfolio Company, is doing something about it. Learn about a major breakthrough that has led to a revolutionary way to treat late-stage breast cancer, thanks to AI.

Read the case study

Test tubes

What Are Some Examples of How AI Is Impacting Financial Services?

The strengths of AI are a good match for the challenges facing financial services firms around the world. AI has generated a lot of excitement and attention in recent years because of its huge potential to add value to all kinds of financial services transactions. Banks and investment firms are exploring the power of AI to improve customer experience, automate cumbersome tasks, cut costs, and help uncover new opportunities for future growth.

For example, the ability of AI to detect and analyze patterns in big data makes it a powerful tool for wealth management and investments. One of the key ways we’re seeing this partnership today is via AI-powered “roboadvisors” that are taking on many aspects of financial portfolio management for clients.

Companies like Betterment that use a combination of human and AI expertise are leading the charge in this growing trend. The company helps customers set up a portfolio, choose, and maintain investments for a fixed annual fee. Betterment’s approach has gained popularity in recent years and the company currently oversees more than $10 billion in assets for over 250,000 customers.

And for those of us who are concerned with the security of our personal bank accounts and assets, we can expect more sophisticated, AI-powered fraud protection in the future. And for those of us who have endured cumbersome and unhelpful phone support from our banks, we can look forward to advances in AI service bots that promise to be much more efficient at problem-solving and providing quick responses.

What Are the Risks and Benefits Associated with AI?

“People are really too focused on ‘evil AI,’ and not focused enough on human intent.“

—Neil Jacobstein

There is a popular argument that tools like AI essentially are neutral, and can be used for good or evil, depending on the user’s intentions. While AI is unique in that we’re building it to be capable of developing its own learning and “intentions,” it’s realistic to expect that for the foreseeable future, AI will be shaped by the direction of its human creators.

We can say with certainty that AI is such a profound tool that its impact marks a true global paradigm shift, similar to the revolutions brought about by the development of agriculture, writing, and manufacturing.

While the future changes that AI will bring are almost impossible to imagine, we have identified three key benefits and three key risks worth keeping in mind:

Risks of AI

  • Drastic changes to our lives
  • AI created with bad intention
  • AI created with good intention goes bad

Benefits of AI

  • Increased efficiency
  • Solving problems for humanity
  • Liberate humans to do what they do best

What Are the Benefits of AI, in Greater Detail?

In an ideal world, AI represents a win-win scenario by providing strengths that humans don’t possess. Advanced pattern recognition, computing speed, and nonstop productivity courtesy of AI allow humans to increase efficiency and offload mundane tasks—and potentially solve problems that have evaded human insight for thousands of years. Let’s look at some benefits of AI in more detail.

AI offers increased efficiency

We are human, and so we make mistakes and get tired. We can only perform competent work for a limited time before fatigue takes over and our focus and accuracy deteriorate. We require time to unplug, unwind, and sleep.

AIs have no biological body, side-gig, or family to pull their attention away from work. And while humans struggle to keep focus after a while, AIs stay as accurate whether they work one hour or 1,000 hours. While they work, these AIs can also be accurately recording data that will, in turn, provide more fuel for their own learning and pattern recognition.

For this reason, AI is transforming every industry. The amount of time and energy companies have to invest in repetitive manual work will diminish exponentially, freeing up time and money, which in turn allows for more research and more breakthroughs for each industry.

Keep exploring: Learn how to build an enterprise AI capability in eight steps.

AI is solving problems for humanity

As AIs gain greater capabilities and are deployed in different capacities, we can expect to see many of the problems that have plagued government, schools, and corporations to be solved. AIs will also be able to help improve our justice system, healthcare, social issues, economy, governance, and other aspects of our society.

These critical systems are rife with challenges, bottlenecks, and outright failures. In each realm, human bureaucracy and unpredictability seem to slow down and sometimes even break the system. When AIs gain traction in these important domains, we can expect much more rational, fair, and thorough examinations of data, and improved policy decisions should soon follow.

AI is liberating humans to do what they do best

As AIs become more mainstream and take over mundane and menial tasks, humans will be freed up to do what they do best—to think critically and creatively and to imagine new possibilities. It’s likely this critical thought and creativity will be augmented and improved by AI tools. In the future, more emphasis will be placed on co-working situations in which tasks are divided between humans and AIs, according to their abilities and strengths.

Perhaps the most important task humans will focus on is creating meaningful relationships and connections. As AIs manage more and more technical tasks, we may see a higher value placed on uniquely human traits like kindness, compassion, empathy, and understanding.

What Are the Risks of AI, in Greater Detail?

Will AI change our current way of life? Absolutely. Do we know exactly how? Absolutely not.

AI already is affecting nearly every aspect of our personal and professional lives. Every human institution—businesses, governments, academia, and non-profits—is already experiencing the accelerating pace of change. And although AI is often portrayed in terms of solutions to solve problems in healthcare, transportation, and business productivity, there is also a darker side to consider.

There are concerns that AI will replace human workers, and some people fear the ultimate outcome will be that superintelligent AI-powered machines will eventually replace humans entirely. While this is a possibility, many experts believe that it’s more likely that AIs will enhance, not replace, humanity and that eventually, we might merge with AIs.

It’s essential to think about what might happen when a tool as powerful as AI malfunctions or is used with malicious intent. Consider the following two scenarios:

Scenario 1: AI created with bad intentions

Those who insist that technology is neutral will point out that a hammer can be used to build a home or to hit someone over the head. As with any technology in the wrong hands, AI could be created to help humans commit horrible acts. This might be an autonomous weapon programmed by the military, or a malevolent algorithm set loose by an individual hacker.

Fear associated with AI—a technology that is intelligent and capable of self-learning—is not unfounded. But it’s important to remember that humans also are highly intelligent and capable of rapid learning and improvement.

Moreover, it’s also worth remembering that harmful AI capabilities aren’t created in a vacuum. While one person or group is attempting to create something harmful, there is often an equal or greater amount of energy being invested to stop that harm and create countermeasures that limit risk and impact.

Scenario 2: AI created with good intentions goes bad

Another scenario is the runaway AI, in which a machine that was built with good intentions turns bad—a staple of classic Sci-Fi films like “Blade Runner” and “2001 Space Odyssey.” Indeed, when the sentient computer HAL turned against astronauts in the 1968 Stanley Kubrick film, many viewers found the premise to be unrealistic. With the widespread use of AI, as well as its growing capabilities, this scenario may no longer seem as far-fetched.

Addressing concerns over whether AI will drive massive job displacement, Singularity University Co-Founder and Chancellor Ray Kurzweil explains that while certain jobs will be lost, new jobs and careers will be created as we build new capabilities.

Kurzweil notes that AI will benefit humans and that AI is less likely to be threatening than beneficial to us, and it benefits us in many ways already. In Kurzweil’s view, a robot takeover is less likely than a co-existence, where machines reinforce human abilities and accelerate our progress.

Resources for Additional Learning


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What Are Some Leading Trends in AI?

As the development and application of AI continues to evolve at an unprecedented rate, a handful of important trends have begun to emerge. Perhaps the most significant trends involve deep learning applications that have demonstrated outstanding performance competing against human contestants in games like Jeopardy and Go. The job market also reflects this growth clearly. From 2015 to 2017, for example, we saw a 35x increase in posted jobs that require deep learning development skills. And in 2019, the demand continues to increase.

One reason for AI’s powerful growth is its convergence with other technologies. We’re seeing a massive increase in AIs’ integration with the Internet of Things (IoT), and with edge computing, a strategy designed to increase performance by moving computing power out of data centers and closer to local devices. The purpose is to enable devices to respond faster by processing more information locally, rather than sending the communications back and forth to the cloud. The integration of AI, the IoT and edge computing will be a driving force as businesses seek to improve the speed and performance of their solutions and services.

Another important trend is the development of specialized processors that are engineered to optimize AI performance. Some of the world’s premier chip manufacturers, including Nvidia, Intel, AMD, Qualcomm, and ARM are all working on their own versions of high-performance chips that will enable AI’s deep integration into everyday products and the IoT.

Other important trends driving the growth of AI include computer vision, voice assistants, and a push for more standardization and ethics.

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What Is the Future of AI?

The Singularity is often defined as the point at which exponential technology crosses the threshold of “strong AI” and machines possess a broad intelligence that exceeds human levels. It’s a concept that’s understandably hard for many of us to accept, because the Singularity also represents a point where human intelligence and AI merge.

On the way to such a merger, human intelligence will undergo an extensive integration with AI, forming a symbiotic relationship where AIs are empowered by human talent for creative, lateral thinking, and humans are empowered by AI’s near-infallible memory and rapid computing. So not only is AI likely to be integrated into nearly every electronic system—but also into nearly every person as well.

None of us can predict the future, nor can we stand against the wave of change driven by AI and other exponential technologies. Instead, we can do our best to learn about these technologies, understand their inherent opportunities, and apply them to solving our biggest global challenges. Perhaps the biggest mistake we can make with AI is to underestimate its impact and rapid growth.

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The Year in Math and Computer Science



NEXT: 2019 IN REVIEWThe Year in Biology



The Year in Math and Computer Science

Mathematicians and computer scientists made big progress in number theory, graph theory, machine learning and quantum computing, even as they reexamined our fundamental understanding of mathematics and neural networks.


Olena Shmahalo/Quanta MagazineBill AndrewsSenior Editor

December 23, 2019

VIEW PDF/PRINT MODE2019 In ReviewComputer ScienceMachine LearningMathematicsNumber TheoryQuantum Computing

Alice and Bob Meet the Wall of Fire - The Biggest Ideas in Science from Quanta – AVAILABLE NOW!

For mathematicians and computer scientists, this was often a year of double takes and closer looks. Some reexamined foundational principles, while others found shockingly simple proofs, new techniques or unexpected insights in long-standing problems. Some of these advances have broad applications in physics and other scientific disciplines. Others are purely for the sake of gaining new knowledge (or just having fun), with little to no known practical use at this time.

Quanta covered the decade-long effort to rid mathematics of the rigid equal sign and replace it with the more flexible concept of “equivalence.” We also wrote about emerging ideas for a general theory of neural networks, which could give computer scientists a coveted theoretical basis to understand why deep learning algorithms have been so wildly successful.

Meanwhile, ordinary mathematical objects like matrices and networks yielded unexpected new insights in short, elegant proofs, and decades-old problems in number theory suddenly gave way to new solutions. Mathematicians also learned more about how regularity and order arise from chaotic systems, random numbers and other seemingly messy arenas. And, like a steady drumbeat, machine learning continued to grow more powerful, altering the approach and scope of scientific research, while quantum computers (probably) hit a critical milestone.

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Ana Porta for Quanta Magazine

Building a Bedrock of Understanding

What if the equal sign — the bedrock of mathematics — was a mistake? A growing number of mathematicians, led in part by Jacob Lurie at the Institute for Advanced Study, want to rewrite their field, replacing “equality” with the looser language of “equivalence.” Currently, the foundations of mathematics are built with collections of objects called sets, but decades ago a pair of mathematicians began working with more versatile groupings called categories, which convey more information than sets and more possible relationships than equality. Since 2006, Lurie has produced thousands of dense pages of mathematical machinery describing how to translate modern math into the language of category theory.

More recently, other mathematicians have begun establishing the foundational principles of a field with no prevailing dogma to cast aside: neural networks. The technology behind today’s most successful machine learning algorithms is becoming increasingly indispensable in science and society, but no one truly understands how it works. In January, we reported on the ongoing efforts to build a theory of neural networks that explains how structure could affect a network’s abilities.

Neutrinos and matrices
Maciej Rebisz for Quanta Magazine

A New Look at Old Problems

Just because a path is familiar doesn’t mean it can’t still hold new secrets. Mathematicians, physicists and engineers have worked with mathematical terms called “eigenvalues” and “eigenvectors” for centuries, using them to describe matrices that detail how objects stretch, rotate or otherwise transform. In August, three physicists and a mathematician described a simple new formula they’d stumbled upon that relates the two eigen-terms in a new way – one that made the physicists’ work studying neutrinos much simpler while yielding new mathematical insights. After the article’s publication, the researchers learned that the relationship had been discovered and neglected multiple times before.

The familiar also gave way to novel insights in computer science, when a mathematician abruptly solved one of the biggest open problems in the field by proving the “sensitivity” conjecture, which describes how likely you are to affect the output of a circuit by changing a single input. The proof is disarmingly simple, compact enough to be summarized in a single tweet. And in the world of graph theory, another spartan paper (this one weighing in at just three pages) disproved a decades-old conjecture about how best to choose colors for the nodes of a network, a finding that affects maps, seating arrangements and sudokus.DVDP for Quanta Magazine

The Signal in the Noise

Mathematics often involves an imposition of order on disorder, a wresting of hidden structures out of the seemingly random. In May, a team used so-called magic functions to show that the best ways of arranging points in eight- and 24-dimensional spaces are also universally optimal – meaning they solve an infinite number of problems beyond sphere packing. It’s still not clear exactly why these magic functions should be so versatile. “There are some things in mathematics that you do by persistence and brute force,” said the mathematician Henry Cohn. “And then there are times like this where it’s like mathematics wants something to happen.”

Others also found patterns in the unpredictable. Sarah Peluse proved that numerical sequences called “polynomial progressions” are inevitable in large enough collections of numbers, even if the numbers are chosen randomly. Other mathematicians showed that under the right conditions, consistent patterns emerge from the doubly random process of analyzing in a random way the shapes produced by random means. Further cementing the link between disorder and meaning, Tim Austin proved in March that all mathematical descriptions of change are, ultimately, a mix of orderly and random systems – and even the orderly ones need a trace of randomness in them. Finally, in the real world, physicists have been working toward understanding when and how chaotic systems, from blinking fireflies to firing neurons, can synchronize and beat as one.

Art for "Mathematicians Discover the Perfect Way to Multiply"
Mengxin Li for Quanta Magazine

Playing With Numbers

We all learned how to multiply in elementary school, but in March, two mathematicians described an even better, faster method. Rather than multiply every digit with every other digit, which quickly grows untenable with big enough numbers, would-be multipliers can combine a series of techniques that includes adding, multiplying and rearranging digits to arrive at a product after significantly fewer steps. This may, in fact, be the most efficient possible way to multiply large numbers.

Other fun insights into the world of numbers this year include finally discovering a way to express 33 as the sum of three cubes, proving a long-standing conjecture about when you can approximate irrational numbers like pi and deepening the connections between the sums and products of a set of numbers.

Rachel Suggs for Quanta Magazine

Machine Learning’s Growing Pains

Scientists are increasingly turning to machines for help not just in acquiring data, but also in making sense of it. In March, we reported on the ways machine learning is changing how science is done. A process called generative modeling, for example, may be a “third way” to formulate and test hypotheses, after the more traditional means of observations and simulations – though many still see it as merely an improved method of processing information. Either way, Dan Falk wrote, it’s “changing the flavor of scientific discovery, and it’s certainly accelerating it.”

As for what the machines are helping us learn, researchers announced pattern-finding algorithms that have the potential to predict earthquakes in the Pacific Northwest, and a multidisciplinary team is decoding how vision works by creating a mathematical model based on brain anatomy. But there’s still far to go: A team in Germany announced that machines often fail at recognizing pictures because they focus on textures rather than on shapes, and a neural network nicknamed BERT learned to beat humans at reading comprehension tests, only for researchers to question whether the machine was truly comprehending or just getting better at test-taking.

James O’Brien for Quanta Magazine

Next Steps for Quantum Computers

After years of suspense, researchers finally achieved a major quantum computing milestone this year – though as with all things quantum, it’s a development suffused with uncertainty. Regular, classical computers are built from binary bits, but quantum computers instead use qubits, which exploit quantum rules to enhance computational power. In 2012, John Preskill coined the term “quantum supremacy” to describe the point at which a quantum computer outperforms a classical one. Reports of increasingly fast quantum systems led many insiders to suspect we would reach that point this year, and in October Google announced that the moment had finally arrived. A rival tech company, IBM, disagreed, however, arguing that Google’s claim deserved “a large dose of skepticism.” Nevertheless, the clear progress in building viable quantum computers over the years has also motivated researchers like Stephanie Wehner to build a next-generation, quantum internet.

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Giant Chinese Telescope Joins the Search for Alien Radio Signals

Will it help us find an answer to the Fermi Paradox, or even those puzzling UFOs?

FAST telescope.png
The Five-hundred-meter Aperture Spherical Telescope (FAST) in southwest China’s Guizhou Province. (NAO/FAST)

By Dirk Schulze-MakuchAIRSPACEMAG.COM
DECEMBER 10, 20194901612

The world’s largest single-dish radio telescope—the Five Hundred-Meter Aperture Spherical Telescope (FAST) in southern China—is about to start operation after more than three years of testing and commissioning. While the trials have focused on detecting neutron stars, one of the goals during the telescope’s operational lifetime will be to search for signals generated by intelligent extraterrestrials. Since Chinese officials claim that FAST is already three times as sensitive as the Arecibo observatory in Puerto Rico, the second-largest single-dish telescope in the world, we surely expect new discoveries.

We don’t know, of course, whether the giant telescope will detect signs of extraterrestrial technology. But we’d love to have an explanation for what’s been called the Great Silence, also known as the Fermi Paradox: If there is intelligent life out there, why don’t we see any evidence of it?

There are many possible answers, including the idea that we live in a kind of designated nature preserve, or zoo. Or, if you like Star Trek, maybe the aliens are applying their version of the prime directive and trying not to interfere with life on Earth.

When I was in New Mexico recently for a workshop on extant life on Mars, I also visited the International UFO Museum and Research Center in Roswell. While the museum did have some interesting exhibits, including artwork and depictions of aliens in science fiction movies, its focus on the famous 1947 Roswell UFO incident seemed to suggest a government cover-up of an alien visitation. Never mind that the Roswell event likely has a much more mundane explanation: the crash of a high altitude balloon like the ones used by the U.S. Air Force for Project Mogul.

The problem with many so-called alien encounters is that the claims are based on eyewitness reports that are not always reliable. While I think scientists sometimes too easily dismiss such accounts, the underlying truth is that any science investigations have to be based, by their very nature, on experiments and reproducibility. And when it comes to alien visitations, those standards can be difficult or impossible to apply.

The credibility of UFO reports is not helped by the fact that 99 percent of them can be easily explained as natural phenomena. Others are outright hoaxes. As for the rest of the one percent, some events are stubbornly difficult to explain, which is why the government has long investigated UFOs. Not that the investigators turned up evidence of aliens. Atmospheric phenomena like sprites—which produce dancing flashes of bright light when lightning is exciting the electrical field above a storm—are still routinely mistaken for UFOs.

Where does this leave us? I still think UFO claims deserve serious scientific investigation. Even if we find no alien spacecraft, it will benefit science to discover previously unknown natural phenomena. And considering that we still have no answer for the Great Silence, we have to leave open the possibility that aliens have been visiting Earth, or are close by. Scientists have to keep an open mind.

Even though standard scientific methods have trouble evaluating claimed encounters, we should not stigmatize the research. From a practical viewpoint, science may be better suited to analyzing alien artifacts or possible alien objects in space, like the recent debate about the interstellar asteroid ʻOumuamua. As our observatories improve, and better telescopes like FAST come online, we may find ourselves with many more such mysteries to solve.Like this article? SIGN UP for our newsletter  

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About Dirk Schulze-Makuch
Dirk Schulze-Makuch

Dirk Schulze-Makuch is a Professor at the Technical University Berlin, Germany, and an Adjunct Professor at Arizona State University and Washington State University. He has published eight books and nearly 200 scientific papers related to astrobiology and planetary habitability. His latest books are The Cosmic Zoo: Complex Life on Many Worlds and the 3rd edition of Life in the Universe: Expectations and Constraints.Read more from this author4901612PREVIOUS ARTICLELife Recovers in a Geological Blink of an Eye after an Armageddon EventNEXT ARTICLEA Stellar Mystery: How Could 100 Stars Just Vanish?


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From Wikipedia, the free encyclopediaJump to navigationJump to searchFor the field of study, see Economics.For other uses, see Economy (disambiguation).

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An economy (from Greek οίκος – “household” and νέμoμαι – “manage”) is an area of the productiondistribution and trade, as well as consumption of goods and services by different agents. Understood in its broadest sense, ‘The economy is defined as a social domain that emphasize the practices, discourses, and material expressions associated with the production, use, and management of resources’.[1] Economic agents can be individuals, businesses, organizations, or governments. Economic transactions occur when two groups or parties agree to the value or price of the transacted good or service, commonly expressed in a certain currency. However, monetary transactions only account for a small part of the economic domain. Economic activity is spurred by production which uses natural resources, labor and capital. It has changed over time due to technology (automation, accelerator of process, reduction of cost functions), innovation (new products, services, processes, expanding markets, diversification of markets, niche markets, increases revenue functions) such as, that which produces intellectual property and changes in industrial relations (most notably child labor being replaced in some parts of the world with universal access to education). A given economy is the result of a set of processes that involves its culture, values, education, technological evolution, history, social organization, political structure and legal systems, as well as its geography, natural resource endowment, and ecology, as main factors. These factors give context, content, and set the conditions and parameters in which an economy functions. In other words, the economic domain is a social domain of human practices and transactions. It does not stand alone.

market-based economy is one where goods and services are produced and exchanged according to demand and supply between participants (economic agents) by barter or a medium of exchange with a credit or debit value accepted within the network, such as a unit of currency. A command-based economy is one where political agents directly control what is produced and how it is sold and distributed. A green economy is low-carbon, resource efficient and socially inclusive. In a green economy, growth in income and employment is driven by public and private investments that reduce carbon emissions and pollution, enhance energy and resource efficiency, and prevent the loss of biodiversity and ecosystem services.[2] A gig economy is one in which short-term jobs are assigned or chosen via online platforms.[3] New economy is a term referred to the whole emerging ecosystem where new standards and practices were introduced, usually as a result of technological innovations.



This map shows the gross domestic product (GDP) for every country (2015).

Today the range of fields of study examining the economy revolves around the social science of economics, but may include sociology (economic sociology), history (economic history), anthropology (economic anthropology), and geography (economic geography). Practical fields directly related to the human activities involving productiondistributionexchange, and consumption of goods and services as a whole are engineeringmanagementbusiness administrationapplied science, and finance.

All professions, occupations, economic agents or economic activities, contribute to the economy. Consumptionsaving, and investment are variable components in the economy that determine macroeconomic equilibrium. There are three main sectors of economic activity: primarysecondary, and tertiary.

Due to the growing importance of the economical sector in modern times,[4] the term real economy is used by analysts[5][6] as well as politicians[7] to denote the part of the economy that is concerned with the actual production of goods and services,[8] as ostensibly contrasted with the paper economy, or the financial side of the economy,[9] which is concerned with buying and selling on the financial markets. Alternate and long-standing terminology distinguishes measures of an economy expressed in real values (adjusted for inflation), such as real GDP, or in nominal values (unadjusted for inflation).[10]


The English words “economy” and “economics” can be traced back to the Greek word οἰκονόμος (i.e. “household management”), a composite word derived from οἶκος (“house;household;home”) and νέμω (“manage; distribute;to deal out;dispense”) by way of οἰκονομία (“household management”).

The first recorded sense of the word “economy” is in the phrase “the management of œconomic affairs”, found in a work possibly composed in a monastery in 1440. “Economy” is later recorded in more general senses, including “thrift” and “administration”.

The most frequently used current sense, denoting “the economic system of a country or an area”, seems not to have developed until the 1650s.[11]


Ancient times

Storage room, Palace of Knossos.See also: Palace economy

As long as someone has been making, supplying and distributing goods or services, there has been some sort of economy; economies grew larger as societies grew and became more complex. Sumer developed a large-scale economy based on commodity money, while the Babylonians and their neighboring city states later developed the earliest system of economics as we think of, in terms of rules/laws on debt, legal contracts and law codes relating to business practices, and private property.[12]

The Babylonians and their city state neighbors developed forms of economics comparable to currently used civil society (law) concepts.[13] They developed the first known codified legal and administrative systems, complete with courts, jails, and government records.

The ancient economy was mainly based on subsistence farming. The Shekel referred to an ancient unit of weight and currency. The first usage of the term came from Mesopotamia circa 3000 BC., and referred to a specific mass of barley which related other values in a metric such as silver, bronze, copper etc. A barley/shekel was originally both a unit of currency and a unit of weight, just as the British Pound was originally a unit denominating a one-pound mass of silver.

For most people, the exchange of goods occurred through social relationships. There were also traders who bartered in the marketplaces. In Ancient Greece, where the present English word ‘economy’ originated, many people were bond slaves of the freeholders. The economic discussion was driven by scarcity.

Middle ages

10 Ducats (1621), minted as circulating currency by the Fugger Family.

In Medieval times, what we now call economy was not far from the subsistence level. Most exchange occurred within social groups. On top of this, the great conquerors raised what we now call venture capital (from ventura, ital.; risk) to finance their captures. The capital should be refunded by the goods they would bring up in the New World. The discoveries of Marco Polo (1254–1324), Christopher Columbus (1451–1506) and Vasco da Gama (1469–1524) led to a first global economy. The first enterprises were trading establishments. In 1513, the first stock exchange was founded in Antwerpen. Economy at the time meant primarily trade.

Early modern times

The European captures became branches of the European states, the so-called colonies. The rising nation-states SpainPortugalFranceGreat Britain and the Netherlands tried to control the trade through custom duties and (from mercator, lat.: merchant) was a first approach to intermediate between private wealth and public interest. The secularization in Europe allowed states to use the immense property of the church for the development of towns. The influence of the nobles decreased. The first Secretaries of State for economy started their work. Bankers like Amschel Mayer Rothschild (1773–1855) started to finance national projects such as wars and infrastructure. Economy from then on meant national economy as a topic for the economic activities of the citizens of a state.

Industrial Revolution

Sächsische Maschinenfabrik in Chemnitz, Germany, 1868Main article: Industrial Revolution

The first economist in the true modern meaning of the word was the Scotsman Adam Smith (1723–1790) who was inspired partly by the ideas of physiocracy, a reaction to mercantilism and also later Economics student, Adam Mari.[14] He defined the elements of a national economyproducts are offered at a natural price generated by the use of competition – supply and demand – and the division of labor. He maintained that the basic motive for free trade is human self-interest. The so-called self-interest hypothesis became the anthropological basis for economics. Thomas Malthus (1766–1834) transferred the idea of supply and demand to the problem of overpopulation.

The Industrial Revolution was a period from the 18th to the 19th century where major changes in agriculturemanufacturingmining, and transport had a profound effect on the socioeconomic and cultural conditions starting in the United Kingdom, then subsequently spreading throughout EuropeNorth America, and eventually the world. The onset of the Industrial Revolution marked a major turning point in human history; almost every aspect of daily life was eventually influenced in some way. In Europe wild capitalism started to replace the system of mercantilism (today: protectionism) and led to economic growth. The period today is called industrial revolution because the system of Production, production and division of labor enabled the mass production of goods.

Recognition of the concept of “the economy”

The contemporary concept of “the economy” wasn’t popularly known until the American Great Depression in the 1930s.[15]

After the chaos of two World Wars and the devastating Great Depression, policymakers searched for new ways of controlling the course of the economy. This was explored and discussed by Friedrich August von Hayek (1899–1992) and Milton Friedman (1912–2006) who pleaded for a global free trade and are supposed to be the fathers of the so-called neoliberalism. However, the prevailing view was that held by John Maynard Keynes (1883–1946), who argued for a stronger control of the markets by the state. The theory that the state can alleviate economic problems and instigate economic growth through state manipulation of aggregate demand is called Keynesianism in his honor. In the late 1950s, the economic growth in America and Europe—often called Wirtschaftswunder (ger: economic miracle) —brought up a new form of economy: mass consumption economy. In 1958, John Kenneth Galbraith (1908–2006) was the first to speak of an affluent society. In most of the countries the economic system is called a social market economy.

Late 20th – beginning of 21st century

ESET (IT security company) headquarters in Bratislava, Slovakia.

With the fall of the Iron Curtain and the transition of the countries of the Eastern Bloc towards democratic government and market economies, the idea of the post-industrial society is brought into importance as its role is to mark together the significance that the service sector receives instead of industrialization. Some attribute the first use of this term to Daniel Bell’s 1973 book, The Coming of Post-Industrial Society, while others attribute it to social philosopher Ivan Illich’s book, Tools for Conviviality. The term is also applied in philosophy to designate the fading of postmodernism in the late 90s and especially in the beginning of the 21st century.

With the spread of Internet as a mass media and communication medium especially after 2000-2001, the idea for the Internet and information economy is given place because of the growing importance of e-commerce and electronic businesses, also the term for a global information society as understanding of a new type of “all-connected” society is created. In the late 2000s, the new type of economies and economic expansions of countries like China, Brazil, and India bring attention and interest to different from the usually dominating Western type economies and economic models.

Economic phases of precedence

The economy may be considered as having developed through the following phases or degrees of precedence.

In modern economies, these phase precedences are somewhat differently expressed by the three-sector theory.[citation needed]

Other sectors of the developed community include :

  • the public sector or state sector (which usually includes: parliament, law-courts and government centers, various emergency services, public health, shelters for impoverished and threatened people, transport facilities, air/sea ports, post-natal care, hospitals, schools, libraries, museums, preserved historical buildings, parks/gardens, nature-reserves, some universities, national sports grounds/stadiums, national arts/concert-halls or theaters and centers for various religions).
  • the private sector or privately run businesses.
  • the social sector or voluntary sector.

Economic measures

There are a number of concepts associated with the economy, such as these:


The GDP (gross domestic product) of a country is a measure of the size of its economy. The most conventional economic analysis of a country relies heavily on economic indicators like the GDP and GDP per capita. While often useful, GDP only includes economic activity for which money is exchanged.

Informal economy

Black market peddler on graffiti, KharkivMain article: Informal economy

An informal economy is economic activity that is neither taxed nor monitored by a government, contrasted with a formal economy. The informal economy is thus not included in that government’s gross national product (GNP). Although the informal economy is often associated with developing countries, all economic systems contain an informal economy in some proportion.

Informal economic activity is a dynamic process which includes many aspects of economic and social theory including exchange, regulation, and enforcement. By its nature, it is necessarily difficult to observe, study, define, and measure. No single source readily or authoritatively defines informal economy as a unit of study.

The terms “underground”, “under the table” and “off the books” typically refer to this type of economy. The term black market refers to a specific subset of the informal economy. The term “informal sector” was used in many earlier studies, and has been mostly replaced in more recent studies which use the newer term.

The informal sector makes up a significant portion of the economies in developing countries but it is often stigmatized as troublesome and unmanageable. However the informal sector provides critical economic opportunities for the poor and has been expanding rapidly since the 1960s. As such, integrating the informal economy into the formal sector is an important policy challenge.

Economic research

Economic research is conducted in fields as different as economicseconomic sociologyeconomic anthropology, and economic history.

See also


  1. ^ James, Paul; with Magee, Liam; Scerri, Andy; Steger, Manfred B. (2015). Urban Sustainability in Theory and Practice: Circles of Sustainability. London: Routledge. p. 53.
  2. ^ “Archived copy” (PDF). Archived from the original (PDF) on November 11, 2013. Retrieved October 26, 2014.
  3. ^ “How governments should deal with the rise of the gig economy”The Economist. Retrieved October 8, 2018.
  4. ^ The volume of financial transactions in the 2008 global economy was 73.5 times higher than nominal world GDP, while, in 1990, this ratio amounted to “only” 15.3 (“A General Financial Transaction Tax: A Short Cut of the Pros, the Cons and a Proposal”Archived April 2, 2012, at the Wayback Machine, Austrian Institute for Economic Research, 2009)
  5. ^ “Meanwhile, in the Real Economy”Wall Street Journal, July 23, 2009
  6. ^ “Bank Regulation Should Serve Real Economy”Wall Street Journal, October 24, 2011
  7. ^ “Perry and Romney Trade Swipes Over ‘Real Economy'”Wall Street Journal, August 15, 2011
  8. ^ “Real Economy” Archived February 9, 2018, at the Wayback Machine definition in the Financial Times Lexicon
  9. ^ “Real economy” definition in the Economic Glossary
  10. ^ • Deardorff’s Glossary of International Economics, search for real.
       • R. O’Donnell (1987). “real and nominal quantities,” The New Palgrave: A Dictionary of Economics, v. 4, pp. 97-98.
  11. ^, “economy.” The American Heritage Dictionary of the English Language, Fourth Edition. Houghton MifflinCompany, 2004. October 24, 2009.
  12. ^ Sheila C. Dow (2005), “Axioms and Babylonian thought: a reply”, Journal of Post Keynesian Economics 27 (3), p. 385-391.
  13. ^ Charles F. Horne, Ph.D. (1915). “The Code of Hammurabi : Introduction”. Yale University. Retrieved September 14, 2007.
  14. ^ François Quesnay. An Encyclopedia of the Early Modern World- preview entry:Physiocrats & physiocracy. Charles Scribner & Sons. Retrieved February 24, 2014.
  15. ^ Goldstein, Jacob (February 28, 2014). “The Invention Of ‘The Economy'”NPR – Planet Money. Retrieved April 6, 2017.


  • Aristotle, Politics, Book I-IIX, translated by Benjamin Jowett,
  • Barnes, Peter, Capitalism 3.0, A Guide to Reclaiming the Commons, San Francisco 2006,
  • Dill, Alexander, Reclaiming the Hidden Assets, Towards a Global Freeware Index, Global Freeware Research Paper 01-07, 2007,
  • Fehr Ernst, Schmidt, Klaus M., The Economics Of Fairness, Reciprocity and Altruism – experimental Evidence and new Theories, 2005, Discussion PAPER 2005-20, Munich Economics,
  • Marx, Karl, Engels, Friedrich, 1848, The Communist Manifesto,
  • Stiglitz, Joseph E., Global public goods and global finance: does global governance ensure that the global public interest is served? In: Advancing Public Goods, Jean-Philippe Touffut, (ed.), Paris 2006, pp. 149/164,
  • Where is the Wealth of Nations? Measuring Capital for the 21st Century. Wealth of Nations Report 2006, Ian Johnson and Francois Bourguignon, World Bank, Washington 2006,

Further reading

Wikimedia Commons has media related to Economy.
Wikivoyage has a travel guide for Economy.
  • Friedman, Milton, Capitalism and Freedom, 1962.
  • Rothbard, Murray, Man, Economy, and State: A Treatise on Economic Principles, 1962.
  • Galbraith, John Kenneth, The Affluent Society, 1958.
  • Mises, Ludwig von, Human Action: A Treatise on Economics, 1949.
  • Keynes, John Maynard, The General Theory of Employment, Interest and Money, 1936.
  • Marx, Karl, Das Kapital, 1867.
  • Smith, Adam, An Inquiry into the Nature and Causes of the Wealth of Nations, 1776.
Authority control GND4066399-1NDL00616793


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