If a novel was good, would you care if it was created by artificial intelligence? – The Guardian

Roland Barthes was speaking metaphorically when he suggested in 1967 that the birth of the reader must be ransomed by the death of the author. But as artificial intelligence takes its first steps in fiction writing, it seems technology may one day start to make Barthes metaphor all too real.

AI is still some way off writing a coherent novel, as surreal experiments with Harry Potter show, but the future isnt so far away in Hollywood. According to Nadira Azermai, whose company ScriptBook is developing a screenwriting AI: Within five years well have scripts written by AI that you would think are better than human writing.

Self-promotion aside, if there is the possibility of a decent screenplay from ScriptBooks AI within five years, then a novel composed by machines cant be far behind. But its hard to shake the impression that, even if such novels eventually turn out to be better than human writing, something would be lost.

Perhaps the feeling comes from an idea that would be anathema to Barthes: the idea of literature as communication.

If a book is a heart that only beats in the chest of another, as Rebecca Solnit suggests, then it seems two parties are required: someone to write and someone to read. So when AI writes fiction there seems to be a missing piece, a void at the heart of the text where meaning should reside.

Barthes would have none of this, of course, insisting that it is language which speaks, not the author. In terms which strikingly anticipate the workings of software currently at the cutting edge of artificial writing, such as OpenAIs GPT-2, he argues that a text is not a line of words releasing a single meaning (the message of the Author-God), but instead a tissue of citations, resulting from the thousand sources of culture. The writer can only imitate a gesture forever anterior, never original, Barthes continues. If he wants to express himself the internal thing he claims to translate is itself only a readymade dictionary whose words can be explained (defined) only by other words, and so on ad infinitum.

And he must be on to something. Imagine yourself, some years in the future, pulling a novel by an unknown author off the shelves and finding that it is really good. Would you be any less moved by the story if you were then told it had been produced using groundbreaking AI? If all you had were the words in front of you on the page, how would you even know? Those who scoff at the idea that AI could ever pass this literary Turing test havent been paying attention for the past 50 years. Computers can now drive cars, recognise faces, translate between languages, fill in as your personal assistant, even beat the world champion at Go achievements that are often dismissed as just computation even though an expert of the 1970s would have classed any one of them as a signature ability of human intelligence.

Should publishers decide the future of literature is written in code, there may still be some hope for authors. A shift to AI-generated novels could only ever be a short-term strategy. As Barthes intuited and OpenAIs latest algorithm demonstrates, its certainly possible to assemble writing from other writing. But even if this patchwork prose becomes better than human writing, it would be only drawing on a finite well of inspiration. Train your AI on the sum total of human literature thus far and all youll get is a mass of references: a gesture forever anterior, never original. No one who witnessed the phenomenon that was the Fifty Shades of Grey series could doubt that imitation can be lucrative for a while. But when even an imitator as skilful or as lucky as EL James finds her sales on a downward curve its clear that no matter how feisty your stallion at first appears, flogging it will only get you so far.

Barthes belief in the primacy of the word, his dogged insistence that life can only imitate the book, leaves his recipe for literature missing a vital ingredient: the individual experience that any human writer facing the blank page cannot avoid. Without the raw input of the complicated business that is life, even the most talented AI can only rearrange the books it ingested in its training enough for a few good years in publishing, perhaps, but hardly a sustainable model for literary culture.

Maybe Im thinking too small. Maybe any publisher looking forward to the death of the author would only need to expand the training programme for their writing machines. Perhaps they could hook their AIs up to the daily news, wire them into Spotify, encourage them to make new friends on Twitter and feed it all back into the work. The resulting algorithms would be very different to human beings, of course. But perhaps they would be enough like thinking, feeling beings that their fiction would be communicating something rather marvellous after all.

Richard Lea writes for Guardian books

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If a novel was good, would you care if it was created by artificial intelligence? - The Guardian

Artificial Intelligence: Can It Improve Results of Cancer Screening… – The Doctor Weighs In

Imaging studies are an important part of screening and diagnosis for some cancers, lung, and breast in particular. Such studies have led to more lung and breast cancers being diagnosed at a smaller size compared to what was found prior to the advent of screening programs. One important research question that is currently being explored is whether the use of artificial intelligence to aid in diagnosis can improve the performance of radiologists alone. Lets take a look at what we know so far.

According to the American Cancer Society (ACS), approximately one in eight women will be diagnosed with breast cancer in their lifetime. It is the second leading cause of death from cancer in women.

Breast cancer screening is commonly performed on patients who have no obvious signs of disease. Many of these women are not at high risk for the disease. Nor do they have a family history.

Although many preventive health guidelines recommend screening mammograms, concerns have been raised. For example, one in five abnormal mammograms is a false positive. That means the mammogram was read as positive by a radiologist but proved not to be cancer on biopsy.

Over the span of ten years, about half of women are given a false-positive result. This usually leads to further testing, anxiety, distress, and sometimes unnecessary procedures or treatment.

Experts from Google Health and its subsidiary, Alphabets DeepMind unit, recently worked with Northwestern University, Cancer Research UK Imperial Center, and Royal Surrey County Hospital to examine aspects of radiographic breast cancer diagnosis. In particular, they wanted to better understand the reasons for inaccuracies in the diagnosis of breast cancer. And, they wanted to determine if artificial intelligence could help.

In order to comprehend how AI can be used to improve the results of breast imaging moving forward, it is important to have a basic understanding of how this artificial intelligence system works. This is a type of system known as Deep Learning which involves a three-dimensional model:

The results of this research were recently published in the journal Nature in an article titled International evaluation of an AI system for breast cancer screening. The study compared the results of mammography readings in an artificial intelligence model to those read by radiologists. There were close to 26,000 women from the UK and over 3,000 women in the United States in the study.

The researchers found that the artificial intelligence model reduced both false positives (when patients are told they have cancer when they dont) and false negatives (when the disease is present, but not diagnosed).

Although in this early testing the AI caught cancers missed by radiologists, there were also cases in which it missed cancer that was caught by radiologists. This suggests that AI alone may not be the sole solution moving forward.

With approximately 160,000 deaths in 2018 due to lung cancer, it is the most common cause of cancer death in the United States. The U.S Preventive Services Task Forces (USPSTF) new guidelines for the use of low dose computed tomography has recently been updated for individuals at high risk of having lung cancer.

Lung cancer screening using this type of computed tomography testing has been shown to reduce death by 20-40%. However, similar to breast cancer screening, one ongoing issue with the use of this screening exam has been the high rate of false positives (a result that indicates that a person has a disease when they actually do not). Although low-dose lung CTs have helped immensely in early detection, it has been found that about one-quarter of the suspected nodules are actually not cancerous.

To determine if this could be improved upon, doctors at Northwestern University and Stanford, teamed up with Google to determine if the same type of artificial intelligence, called Deep Learning, could help improve upon our current methods with lung cancer.

Researchers from Google used more than 42,000 CT scans to train this artificial intelligence system to detect cancerous lung nodules on radiology imaging. The study, titled End-to-end lung cancer screening with three-dimensional deep learning on low dose chest computed tomography was published in Natureas well.

Over 6,000 National Lung Cancer Screening Trial cases were tested in this study. In addition, there was an independent evaluation of a set of over a thousand cases. The performance of the artificial intelligence system was compared against radiologists who had evaluated low-dose chest computed tomography scans for patients several of which had confirmation of cancer by biopsy within a year.

This deep-learning artificial intelligence system produced fewer false negatives (a result that indicates that a person does not have a disease when they actually do) as well as fewer false positives. When prior imaging was available, the model performed better than the radiologists (six of them) with an 11% reduction in false positives and a 5% reduction in false negatives.

The Nature study was a retrospective study that examined past cases. This type of study design is not as strong as prospective studies with randomization. Mozziyar Etemadi, MD, Ph.D., one of the authors of the study has said that the next step is to perform a prospective study to see if the tool, when used by a radiologist, can lead to earlier and more accurate diagnosis of cancer.

Another caveat is that it may be some time before AI with deep learning is routinely used in hospital and free-standing radiology suites. The algorithm that is the backbone of the AI-deep learning system is very sophisticated and will undoubtedly require some painstaking work to fully integrate into hospital computer systems. Further, the variability of many cancers could make new scenarios difficult for the deep learning system to interpret if they have not been seen before.

We also need to consider that although AI with deep learning improves some aspects of cancer screening diagnoses, it is not (yet) perfect. It may be that the best way to introduce AI into imaging analysis is to add it to the workflow of radiologists. This is because both have the potential to not catch something or make mistakes.

The performance of the deep learning system shows that there can be a beneficial role of artificial intelligence in cancer screening moving forward. In fact, the use of algorithms that incorporate co-morbidities and risk factors in medicine is not uncommon today. However, the use of such a sophisticated one on its own will most certainly take time. It will also require well-designed prospective studies that follow patients over time. Nonetheless, there is no denying that there will be an important role of artificial intelligence in cancer screening moving forward.

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How Artificial Intelligence (AI) Is Revolutionizing the Real Estate industry – PR Web

Without the help of AI, the agent has to go through the entire process of marketing, gathering leads, finding the right buyer and following up. Now, the reason for stressing on how time-consuming and tedious this job is, is that the restorative has been found - TruConnectRE.Com.

FALLS CHURCH, Va. (PRWEB) January 27, 2020

Artificial intelligence, as we know it, is going to revolutionize the real estate sector and give it groundbreaking innovation. It is clear that this phenomenal technology is going to impact the future of real estate in a huge way through its ability to gather, analyze and extract information more efficiently, speeding up processes. Other than efficiency, it saves heaps of money in real estate transactions. Experts have foreseen that the advancement of AI in the real estate sector will increase the weight of technical solutions and alter the pertinence of human involvement. In the future, artificial intelligence will, in particular, automate facility management, i.e. the management of the real estate. At this plane, it's not instantly visible how real estate is influenced by artificial intelligence but it is. This impact is on the buying, selling, and maintenance of the Real estate.

With the worldwide market size of $280.6 trillion and nearly $9.6 billion in technology investments, the real estate industry has become one of the most prominent industries in the world. At that rate, one would that this industry would be the most technologically enlightened. However, until very recently, real estate agents would go out into their neighborhoods, knock on doors and introduce themselves to prospective home buyers. Or they would buy lists of contacts and start cold calling. Fortunately for realtors, those days are long gone. The biggest strength of Artificially Intelligent systems lies in having access to tons of data and being able to find patterns in that data, generating insights and inferences while augmenting peoples ability to make decisions based on that data.

Recently, research proved that up to 98% of home buyers now start their research online before they get in touch with a real estate professional and 71% of them settle for the first agent they connect with. With both, the buyer and seller online, one would assume that all that needs to be done now is to find the right buyer and sell to them. Nope. Its not that simple. Without the help of AI, the agent has to go through the entire process of marketing, gathering leads, finding the right buyer and following up. Now, the reason for stressing on how time-consuming and tedious this job is, is that the restorative has been found - TruConnectRE.Com.

TruConnect Real Estate is deeply vested in the agents win. It not only furnishes solid leads for the realtor but also offers a turn-key solution to the problems at hand. With its data-driven, human-powered approach, TruConnectRE.Com provides the realtor with advanced Artificial Intelligence software to manage and track the leads provided. The best part? TruConnectRE charges only a 25% referral fee succeeding in a closing! A powerful lead generation system uses both, inbound and outbound marketing to engage buyers and take them towards conversion. To expedite that, TruConnect also tailors and executes marketing material, establishing an all-in-one platform for the realtor.

With its developing strategy, TruConnect Real Estate does all the work for the realtor by generating thousands of real estate leads from around the world. And it doesnt stop there. They call the turnover, strain and ensure follow-ups until they find the perfect client for an agent to connect with. All the while, they not only increase performance and cut down costs but also rid the agents of the tiresome task of calling leads. This frees up time for the realtor to focus on the buyer, closing 2 to 5 times the deals they normally do. Agents are introduced to screened and effectuate clients and are left to form a relationship and come to an agreement.

Artificial intelligence proves to be very advantageous in streamlining the glacial real estate processes. TruConnectRE.Com with its powerful AI software can now aid the human-led processes to make them accurate and more efficient, helping them serve their client better.

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Artificial Intelligence Used to Fight Against Mosquitoes – Unite.AI

Mosquitos cause serious problems throughout the globe, spreading diseases such as malaria, dengue, and zika. According to the World Health Organization (WHO), they are responsible for more than 1 million deaths every year. Now, artificial intelligence (AI) is being used as a way to fight back. This comes at a time when the effects of mosquitoes continues to worsen, especially due to the consequences of climate change.

Back in the summer of 2018, Europe faced a growing threat of the West Nile virus infection. The increased threat was due to the rise of temperatures and wet weather, conditions that are preferred by the insect.

The Institute of Agrifood Research and Technology (IRTA) in Catalonia, Spain is now using artificial intelligence (AI), sensors, and satellite communication in order to automate the process of trapping mosquitoes. The technology also classifies them according to species, sex, age, and if they can cause infection.

IRTA is owned by the government of Catalonia, and they have developed the EU-funded VECTRACK project. Remote sensing and spatial modeling techniques will be adopted, and these will create special maps to help with risk surveillance and assessments.

The project will rely on the Earth Observation Satellite Sentinel service. It will also use ground nodes with optoelectronic sensors in order to remotely count and classify the mosquitos. It is set to be the first transnational and automated vector surveillance system.

VECTRACK was developed by Irideon, a Spanish-German company based in Spain. The company sells sensor-based products to different sectors.

Trapping has been used by countries for a long time in order to control mosquitoes, and manual trap inspections are done by researchers or technicians. These inspections take a lot of time and resources, especially when it comes to classifying mosquitoes accurately by eye.

The test phases with the special traps are being done by the Barcelona Public Health Agency (ASPB). They are responsible for controlling and monitoring mosquitoes in the city.

What makes the traps different from those already in use is the addition of optoelectronic sensors. This enables remote and automated counting and classification of targeted mosquitoes.

According to researcher Dr. Carles Aranda, mosquitoes are attracted to the traps. The traps emit carbon dioxide as an entree and then suck the specimens inwards so that they do not escape.

The sensors collect data for various different parameters including temperature and humidity related to the GPS location of the trap. A digital fingerprint of the mosquitoes is then created with the morphological, physiological, and flight kinetics of the insect.

The VECTRACK sensors are able to be installed throughout most of the globe. They are compatible with many different communication protocols such as 2G, 3G, 4G, Wi-Fi, LPWAN technologies NB-IoT and LoRA, and satellite IoT.

After the sensors collect the data, it is sent to the cloud to be analyzed by algorithms. It is then processed in geographic information systems that are provided by Avia-GIS Software, a Belgian company. The European Space Agency (ESA) provides satellite information which the data is integrated into.

The real-time risk maps that are developed by the researchers through the process can be provided to regional, national, and international public-health bodies like the European Center for Disease Control.

The new technology can be extremely helpful in preventing disease and controlling epidemics.

The Protocol of the Surveillance and Control of mosquito-borne diseases began in 2014. 507 cases of dengue have been detected in Catalonia, highlighting the importance of this technology.

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Emotional Intelligence and Its Connection with Artificial Intelligence – Techiexpert.com – TechiExpert.com

With the emergence of Artificial Intelligence (AI) and Machine Learning, technology has become an integral seat of thought, regardless of the industry. With such integration, it seems scary to see the effects of these on our social and inter-personal lives. Indeed, when it comes to managing tasks, AI is without a doubt the most useful technology.

However, the impact it has on our neuralarchitecture leads to loopholes in our lives and especially in our careers. According to hbr.org, 37% of executives say that managers lack ofunderstanding of cognitive technologies hampers AI adoption. As theseautomation technologies keep on achieving more, we need to acquire skills thatbuild our neural architecture to stop the neural hijacking from the advent ofthese technologies.

In general, there are a lot of jobs thatmachines can perform with the utmost efficiency in comparison to humans.Especially, that needs absolute accuracy like the accumulation of data, examiningthe data, illustrating the outcomes, figuring out the best-suited course ofaction, and enforcing this action. However, the seat of planning and organizingactions towards a goal still lies in the hands of humans.

To create an integration process between the automation work process and human authorization, one has to understand the synergy of big data Hadoop training. Chatbots Magazine cites, 41% of executives believe AIs most important benefit is providing data that can be used to make informed decisions. Well, this says everything even when the task is done by automation technology, the final stroke is made by humans only, i.e. decision making.

IBM Watson is solving medical cases with the help of automation, leaving doctors baffled. The investors are providing funds without any hesitation for letting the automation perform better. This means the most top paid career options are already at a major risk for humans; especially those that do not involve harmony between human emotions and thoughts. Therefore, to master the art of understanding AI for a brighter future, an aspiring data scientist ought to have a fintech degree as it provides him the added benefit of discovering this abode better.

However,human interference will be an integral part of organizations where skills likemotivation, emotional understanding, and interaction between humans are high indemand. That is the reason why pwc.comstates, 76% of CEOs are mostconcerned with AI adoptions lack of transparency and potential for bias.A piece of intellectual machinery might provide a better diagnose than a doctorand a feasible solution to it.

But, only a human can connect with you onan emotional level and understand your undetectable conditions such asfinances, quality of thoughts and emotions, connection with family and provideyou an optimal treatment solution. Another fact stated by pwc.com defines it all for us,77% of CEOs say vulnerability anddisruption to their businesses could increase with AI and automation.

ToConclude:

Persuasion,empathy, social insights, understanding human emotions are the skills that willdifferentiate humans from machines. However, when it comes to technical fields,without a doubt, Machine Learning and Artificial Intelligence will rule thoseover humans.

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Artificial Intelligence in Medicine Market Advanced Technology and New Innovations by 2025 InSilico Medicine, Globavir Biosciences – Media Releases -…

Market Growth Insight has announced the addition of the Global Artificial Intelligence in Medicine Market Research Report 2018-2025 The report focuses on global major leading industry players with information such as company profiles, product picture and specification.

The global artificial intelligence (AI) in medicine market was valued at $719 million in 2017 and is estimated to reach $18,119 million at a CAGR of 49.6% from 2018 to 2025. AI is an intelligent system that applies various human intelligence-based functions such as reasoning, learning, and problem-solving skills. AI technology uses software and different algorithms in the field of pharmaceuticals to support the decision-making processes for existing drugs and repurposing drugs to treat other conditions, along with accelerating the clinical trials process by finding the right patients from several data sources.

Shortage of skilled healthcare professionals and increase in the processing power of AI systems that is projected to help improve the efficiency of drug discovery and management of clinical trials majorly drive the growth of the global artificial intelligence in medicine market. Furthermore, the growth in importance of precision medicine and rise in funding of the R&D activities for the use of AI technology in the field of medicine are expected to fuel the market growth. However, limited acceptance from healthcare professionals and limitations of AI decision-making can impede the market growth. Untapped market opportunities available in developing regions such as India and China help to open new avenues for the growth of the artificial intelligence in medicine market in future.

Major Key Players of the Artificial Intelligence in Medicine Market are:InSilico Medicine, Globavir Biosciences, GNS Healthcare, Flatiron Health, Benevolent AI, Atomwise, Verge Genomics, Cloud Pharmaceuticals, and Recursion Pharmaceuticals.

Get sample copy of Artificial Intelligence in Medicine Market at: http://bit.ly/38ISZeC

The global artificial intelligence in medicine market is segmented based on product type, technology, application, and region. Based on product type, the market is segmented into hardware, software, and service. Based on technology, the market is classified into deep learning, querying method, natural language processing, and context aware processing. Based on application, the market is categorized into drug discovery & repurposing, clinical research trial, personalized medicine, and others. Based on region, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA.

Major Types of Artificial Intelligence in Medicine Market covered are:Deep Learning, Querying MethodNatural Language ProcessingContext Aware Processing

Major Applications of Artificial Intelligence in Medicine Market covered are:Drug Discovery & RepurposingClinical Research TrialPersonalized Medicine, and Others

Research objectives:-

To study and analyze the global Artificial Intelligence in Medicine consumption (value & volume) by key regions/countries, product type and application, history data. To understand the structure of the Artificial Intelligence in Medicine market by identifying its various sub-segments. Focuses on the key global Artificial Intelligence in Medicine manufacturers, to define, describe and analyze the sales volume, value, market share, market competitive landscape, SWOT analysis, and development plans in the next few years. To analyze the Artificial Intelligence in Medicine with respect to individual growth trends, future prospects, and their contribution to the total market. To share detailed information about the key factors influencing the growth of the market (growth potential, opportunities, drivers, industry-specific challenges and risks).

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Table of Content

1 Report Overview1.1 Study Scope1.2 Key Market Segments1.3 Players Covered1.4 Market Analysis by Type1.5 Market by Application1.6 Study Objectives1.7 Years Considered

2 Global Growth Trends2.1 Artificial Intelligence in Medicine Market Size2.2 Artificial Intelligence in Medicine Growth Trends by Regions2.3 Industry Trends

3 Market Share by Key Players3.1 Artificial Intelligence in Medicine Market Size by Manufacturers3.2 Artificial Intelligence in Medicine Key Players Head office and Area Served3.3 Key Players Artificial Intelligence in Medicine Product/Solution/Service3.4 Date of Enter into Artificial Intelligence in Medicine Market3.5 Mergers & Acquisitions, Expansion Plans

4 Breakdown Data by Product4.1 Global Artificial Intelligence in Medicine Sales by Product4.2 Global Artificial Intelligence in Medicine Revenue by Product4.3 Artificial Intelligence in Medicine Price by Product

5 Breakdown Data by End User5.1 Overview5.2 Global Artificial Intelligence in Medicine Breakdown Data by End User

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In the end, Artificial Intelligence in Medicine industry report specifics the major regions, market scenarios with the product price, volume, supply, revenue, production, and market growth rate, demand, forecast and so on. This report also presents SWOT analysis, investment feasibility analysis, and investment return analysis.

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Bitcoin Is A Leading Indicator Of The Coronavirus Outbreak – Forbes

The number of cases of coronavirus has risen to more than 2,700 in mainland China. Photo by Betsy ... [+] Joles

I have maintained for a long time now that China is a very significant driver of bitcoin (BTC). I have also maintained that bitcoin is a leading indicator of troubles in China because it is a safe haven asset and flight capital.

I have repeated the assertion that you cant easily fly out of a country with cash or gold and for countries with capital controls BTC is the only way to get out of town with significant amounts of capital.

Like it or not, the people are never the first to know when trouble is brewing. That is the whole reason insider information is illegal to act upon. The first to know in these emergency situations are the first to act.

So if you were a rich Chinese person and you heard about this outbreak, what would you do? You would certainly consider packing the family off for a bit of a holiday. You might stick around to look after the business, but you would definitely get your loved ones out. You see this in the Riviera. The Russian oligarchs keep their families safe in France while they ply their trade in Moscow. Who would not do the same in that situation?

However, you have to fund a long stay abroad and the best way to go, unless you are very well prepared, is to grab some bitcoin because once you have those crypto wallet keys the money is anywhere you want it. You might also buy it as a hedge against the worst.

The coronavirus outbreaks started in early December, so lets take a look at bitcoin:

Bitcoin's movement since the Coronavirus outbreak in Decwember

Remember we are not looking to the future, we are looking to the past, a past where things are happening before we know about them. Bitcoins recent rally aligns with the assumption that Chinese demand for bitcoin because of coronavirus has driven the price.

However, what are we looking for? We are looking for an early signal that things are going to blow over or get worse and I believe you can look at bitcoin for that temperature. As such, the chart suggests the situation is stabilizing, but of course it cannot predict the future, only the current situation.

This is what we are looking for in the Bitcoin chart

With the media screaming plague at the top of its toxic voice, the stock market could well take a tumble, but unless BTC shoots above $10,000 and heads for the moon I will be holding and looking to buy.

I wrote a fiction novel about global plague called The First Horseman so Im pre-sensitized to think the worse, but people on the ground will know what is really going on and will react like rational economic actors; if they think the game is up the word will spread far faster than the infection and BTC is the instrument that will react sharply and send up the SELL flare and have me pulling up my drawbridge.

I think we are going to get a buying opportunity in the market, not a global catastrophe, but thats simply a barely informed guess. Instead I believe bitcoin will provide early warning of good or bad news and we will have a reasonably clear picture one way or the other within ten days.

As such, Im going to be watching the bitcoin price like a hawk.

From Forbes: Stay informed and ahead of the crowd with Forbes Crypto Confidential, a free weekly e-letter delivered to your inbox. Sign up today.

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Clem Chambers is the CEO of private investors websiteADVFN.com and author of 101 Ways to Pick Stock Market Winners and Trading Cryptocurrencies: A Beginners Guide.

Chambers won Journalist of the Year in the Business Market Commentary category in the State Street U.K. Institutional Press Awards in 2018.

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Bitcoin Is A Leading Indicator Of The Coronavirus Outbreak - Forbes

Bitcoin Price Indicator That Called 2019 Bull Run Flashes Green Again – Cointelegraph

Bitcoin (BTC) plans to move higher and further squeeze bears in the short term, several price indicators suggest.

As the week begins, a group of measurements some surprisingly accurate historically are combining to make traders firmly bullish on BTC.

Leading the positive signs is a useful but somewhat forgotten indicator dubbed the Guppy. This is a collection of exponential moving averages which has flashed green on the daily chart for the first time in around 300 days.

The interval is significant the last flip from red to green for Guppy was on April 9, 2019, coinciding with Bitcoins rapid rise to highs of $13,800.

Before that, Guppy also turned bullish on Jan. 14, 2018, when Bitcoin briefly rose above $9,000 on the way down from the all-time high a month earlier.

Bitcoins Guppy indicator bull and bear phases. Source: Hsaka/ Twitter

A second sign that bullish momentum is building for Bitcoin lies in the so-called Puell Multiple.

Used to identify the cryptocurrencys price cycles, the tool allows traders to tell from a miners perspective when the value of newly-mined Bitcoins is historically too high or too low.

Puell spiked during the 2017 highs, bottoming a year later in January 2019 when BTC/USD traded at under $4,000.

At present, the indicator suggests Bitcoin is significantly closer to the too low area than its lifetime highs.

Bitcoin Puell Multiple with peaks and troughs highlighted. Source: Glassnode/ Twitter

Zooming in, steady enthusiasm is already creeping into traders forecasts once again. For regular Cointelegraph contributor Michal van de Poppe, current action means $8,000 has now formed a fresh support level.

BTC/USD has gained around 3.8% since Friday, having bounced off local lows around $8,200.

Nice breakthrough of $8,600 level and we're back in the range. This means that the $8,000-8,100 level has now flipped as support, he summarized in a Twitter update on Jan. 27.

Van de Poppe continued:

Eyeing to see a retest of $8,500. Holding that and we can aim for $8,900.

A classic guidance signal for Bitcoin comes in the form of the Mayer Multiple, which is also firmly supportive of Bitcoin as a buying opportunity this week.

The brainchild of Proof of Keys organizer, Trace Mayer, the Mayer Multiple divines to what extent it is profitable to buy Bitcoin at a particular time.

To arrive at its conclusions, it uses the current Bitcoin price versus its 200-day moving average. When the multiple is below 2.4, Mayer says, long-term Bitcoin buys saw the best long-term results.

The current multiple is 0.97 and has been higher 63% of the time since Bitcoin was created eleven years ago.

Bitcoin Mayer Multiple with 2.4 boundary highlighted. Source: Mayermultiple.info

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Bitcoin Price Indicator That Called 2019 Bull Run Flashes Green Again - Cointelegraph

Different Type of Shakeout Trader Says Bitcoin Unlikely to Hit $6K – Cointelegraph

Bitcoin (BTC) hitting $6,000 again is not only unlikely but would be concerning, a well-known commentator has told Cointelegraph.

Speaking in a market discussion with Cointelegraph, EzeeTrader partner Charlie Burton said that should current market behavior continue, those waiting to buy in closer to $6,000 will face disappointment.

...I think well have upside and then well have downside again, just to the point where a number of players will just get bored and move on, he said. Burton continued:

And then therell be a fast move thatll come, and a lot of people will say, Oh my God, why was I not on that move?

BTC/USD was trading at around $8,600 on Monday, having gained almost 4% over the weekend.

As Cointelegraph reported, a number of price indicators are flipping bullish for Bitcoin under current conditions, providing strong suggestions of bullish momentum on both a short and long-term basis.

I think the market has done a good job of shaking out a load of people into 2018 and 2019, but I think its probably a different type of shakeout now, Burton continued.

The comments broadly echoed previous market discussion guest, Peter Brandt, who also argued that buyers planning to enter at $6,000 had already missed their opportunity.

The weak hands are out the strong hands own it, he famously summarized last weekend.

Fellow guest YouTuber and Twitch regular Eric Krown appeared to agree. Based on technical analysis, he suggested that it would be poetic if Bitcoin denied the lower levels demanded by some traders.

Cointelegraph regularly produces Market Discussions, Interviews and Documentaries. To watch more of our videos, subscribe to Cointelegraphs YouTube channel.

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Different Type of Shakeout Trader Says Bitcoin Unlikely to Hit $6K - Cointelegraph

Bitcoin Breaks 7-Month Downtrend But Must Clear These Hurdles to $10K – Cointelegraph

The price of Bitcoin (BTC) found strong support at $8,200 last week, after which it started to rally toward $8,800 earlier today.

Alongside with that, the total market capitalization of crypto found a support at $215 billion and starting to look bullish. Will this mean that the correction is over, and crypto is trending upwards?

Crypto market daily performance. Source: Coin360

Bitcoin is still trending upwards since the low at $6,500, as previous resistance zones have become support. A recent example is showing a bounce on the green area, which is the $8,200 level. This type of bullish support/resistance flips is a common occurrence in an uptrend market.

BTC USDT 1-day chart. Source: TradingView

A break below $8,200 would have demonstrated weakness, as that level would not have provided enough buying pressure and support. Losing such a level would usually have been followed by a continuation downwards. An example is found after the push to $10,000 in November 2019.

The chart is also showing a clear breakout from the 7-month downtrend. A retest was done at $7,600, after which the price of Bitcoin rallied towards $9,200 for temporary resistance.

BTC USDT 4-hour chart. Source: TradingView

The 4-hour chart of Bitcoin is showing a healthy support/resistance flip at $8,200, after which price broke through the $8,500 resistance. Currently, the price of Bitcoin is facing the next resistance at $8,800.

However, its quite unlikely to see an immediate breakthrough at this level as the indicators on smaller time frames show exhaustion of this upwards move.

Additionally, some significant resistances are shown on the chart, i.e. $9,000 and $9,200-9,400, which are two hurdles to overcome if the price of Bitcoin wants to continue moving upwards.

On the support side, a retest of $8,500 looks quite healthy for confirmation of new support. Range-bound movements are now likely to happen if price cant break through $8,800 or drop below $8,500.

Total market capitalization cryptocurrency chart. Source: TradingView

The total market capitalization of cryptocurrencies is showing an essential bounce from the blue zone (level around $217-218 billion). A retest there was quite healthy as anticipated in a recent article.

This retest is now completed and shows intense buying pressure as the total market capitalization has already rallied up to $238 billion. This retest also indicates confirmation of the uptrend with the total market cap breaking the 7-month downtrend as well.

The first hurdle to overcome now is the $247 billion level. If that is broken, continuation towards $270 and $300 billion is likely to occur.

The total market capitalization chart of altcoins is looking healthy The market cap rallied from $52 billion to $80 billion. Only a slight retracement occurred to $71 billion, which means that it is stuck in a narrow range.

Total altcoin market capitalization chart. Source: TradingView

If we check the rest of the chart, we can spot many tests of the $80 billion level in recent months. Around three tests have happened prior to this latest one, which means that the resistance should become weaker.

Remember, the more times a resistance gets tested, the more exhausted sellers will get, and the weaker a resistance becomes. On the other hand, this also happens with support zones. The $6,000 support of Bitcoin in 2018 was tested many times before it broke down.

Given that these tests of the $80 billion level occurred quite frequently, a breakout to the upside is the most likely scenario at this point, meaning that the altcoin market cap could rally towards $120 billion.

BTC USDT 4-hour chart bullish scenario. Source: TradingView

The most bullish scenario would be a clear breakout of $8,800 and a continuation from there. However, as stated earlier, I find it unlikely to see such a move occur in one go.

A retest and consolidation would be more likely including a likely retest of the $8,500. This is healthy and would be almost required before the price of Bitcoin can continue to face higher resistance levels.

If Bitcoin can hold the $8,500 area for support, I see a breakthrough of the $8,800 and $9,000 as likely, after which $10,000 will become the primary target. Moreover, clearing $10,000 could bring the price of Bitcoin towards $11,000 as well.

BTC USDT 4-hour chart bearish scenario. Source: TradingView

Typically, the bearish scenario has a similar pattern in the beginning, as BTC needs to be rejected at the $8,800 level. However, the difference is in the subsequent pattern.

If the price of Bitcoin is to make lower highs with weak bounces, the downward trend is likely to resume. If this occurs, Id be aiming for bearish retest (support/resistances flips) of the $8,500 level as a potential short opportunity. The main target would then be the $7,600 area.

But first, the price needs to be rejected at $8,800-9,000 to get these scenarios going. Overall, the $8,100 support/resistance flip doesnt say that were bearish at this point. Especially, since that price has broken at a 7-month downtrend.

The views and opinions expressed here are solely those of the author and do not necessarily reflect the views of Cointelegraph. Every investment and trading move involves risk. You should conduct your own research when making a decision.

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Bitcoin Breaks 7-Month Downtrend But Must Clear These Hurdles to $10K - Cointelegraph