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Analyst Predicts Next Bull Run Will Send Bitcoin to $150K and Ether to $9K – Cointelegraph

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The co-founder of cryptocurrency analysis company Blockfyre believes that a bull run will return, propelling Bitcoin (BTC) to a price of $150,000.

In a tweet on Thursday, Simon Dedic suggested that these gains will not be reflected across the entire cryptocurrency market, although the more solid altcoins should also see impressive price action.

Bitcoins dizzying ascent to its current all-time high of almost $20,000 in December 2017 came complete with a media frenzy around all things crypto.

Coupled with a boom in initial coin offeringsand fuelled by investor FOMO the fear of missing out money was thrown at literally any project in the hope that it would mirror the gains of Bitcoin.

This became a self-fulfilling prophecyand pretty much every altcoin posted a significant price increase during 2017.

While Dedic warned that he believes this wont happen again, he does envisiona Bitcoin bull run returning and pumping the few solid alts out there.

He even went as far as to make a number of price predictions, such as Bitcoin gaining over 1,400% from its current price of around $9,750 to reach his target of $150,000.

Ether (ETH) is set to fare even better according to Dedic, increasing more than 3,570% from current levels around $245 to a price of $9,000. And Binance Coin (BNB) is predicted to see a 2,750% rise to $500.

Bigger increases still are forecast for Chainlink (LINK) and Tezos (XTZ), both with a target price of $200, representing 4,450% and 6,800% gains, respectively.

But this all fades into insignificance compared with Dedics prediction for VeChain (VET), with a seemingly modest target price of $1. However, this marks a massive 14,100% increase on its current price of $0.007.

VeChain has recently partnered with fresh meat suppliers in China to improve traceabilityand Walmarts Chinese subsidiary to track food products.

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Analyst Predicts Next Bull Run Will Send Bitcoin to $150K and Ether to $9K - Cointelegraph

Alessio Rastani On The State Of U.S. Stocks And Bitcoin – Forbes

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Peter Schiff and Alessio Rastani

When the stock markets crashed in March, Alessio Rastani was buying into the S&P 500 and the UK FTSE 100, averaging down by going long as the S&P 500 dropped from 2600 to 2191. Even though most investors were bearish and anticipating another stock market crash, Rastani was bullish and profited from bearish sentiment.

Now, Rastani is looking to pull out of stocks as he believes that the U.S. stock market is about to enter, a risky zone. In this exclusive interview, Rastani shares his thoughts on why stocks in the near future carry risk, how this risky zone could impact bitcoins price and why he is bullish on bitcoins long-term picture.

Bullish on U.S. stocks

According to Rastani, as long as the S&P can remain above 2550, he will maintain his long-term bullish perspective on the stock markets. Rastani thinks that most investors considered the recent stock rally to be nothing more than a bear market rally or correction. He disagrees with this perspective for 3 reasons:

Stocks could enter risky waters

The U.S. stock market is getting very close to a 100% full recovery from the black swan crash of March, and because of this, Rastani calls it a risky zone for stocks. Rastani says, This rally is going to get overbought and over-extended soonand the dumb money is likely to enter at the worst possible timevery likely when the S&P gets to the 3150-3200 zone.

Rastani says that: At the time of writing, the stock market has already reached my first targets at 3150 to 3200 (the levels I mentioned in my video 2 weeks ago on 22nd May). I think the markets will likely pause here near 3200 for a pullback, while the potential still exists for this rally to fill the gap at 3300 in the next few weeks or months. He thinks that between the 3200 to 3300 levels is where the most risk exists, as shown on the chart scenarios below:

S&P 500 Weekly Chart

Rastani sees 2 potential scenarios playing out for the chart above:

However, in both scenarios, Rastani is still bullish for the long term, as we will likely see a multi-month rally above the highs of this year.

Rastani is essentially eluding to the idea that once the stock markets return back to their former glories, then this could potentially lead to a massive sell-off of stocks as the majority of companies are in the black and reporting losses. This means that if such a massive sell-off were to take place, then retail investors that buy into this hype could suffer the brunt.

Unfortunately, most people have been on the wrong side of this market - they were bullish at the worst time (in January-February 2020), bearish at the worst time (from the end of March to May 2020), and now greedy at the worst time (June 2020), as this chart below shows.

Dumb Money Sentiment

If this scenario on the stock markets played, bitcoin could also take a temporary nosedive. Rastani says, If the positive correlation between bitcoin and the S&P 500 continues, which is likely, then bitcoin could also see a retracement or correction at the same time when the S&P starts its corrective drop.But this correlation remains to be confirmed by price action.

Long-term bullish on bitcoin

Rastani says, I have been bullish on bitcoin since late March and early April 2020especially when I saw bitcoin holding the weekly 200 moving average (200 WMA). This was an important bullish clue, and as long as bitcoin remains above 7000, I will continue to remain bullish for the long-term.

He further goes on to say, Once bitcoin escapes the 10K resistance potentially in the next few months, we are likely to see extreme bullish sentiment in the bitcoin community. This is especially the case as we likely approach the 11K to 12K levels. I would use that contrary signal as extreme optimisma likely indication that we are reaching the next turning point for bitcoin.

This would then likely be followed by a drop or correction (Wave 2) back down below the 10K level towards 9K by September or October of this year. However, this will likely be the last time bitcoin goes below 10K. This is because once the correction ends this year, I see a much bigger bullish multi-month rally (Wave 3) towards significantly higher levels in the near future I.e. towards 14K this year to 20K by next yearand then towards 30K to 50K in the next couple of years.

In the short-term

Due to the danger zone that stocks are currently about to enter right now, Rastani said in May that he is looking to exit stocks. I have been taking profits along the way in this rally, but I am not completely out yet. I am looking to exit the stock market and take the majority of my profits. Ill do this when the S&P 500 goes into the risky danger zone between 3150 to 3200, says Rastani (levels which have now been reached).

I am happy to remain in cash until a better opportunity develops to go long on the US stock markets again and potentially bitcoin. I will wait for a corrective drop back down to support before I buy again. Its of my opinion that sometimes the best position can be to do nothingI.e. stay in cash until a high probability chart setup appears.

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Alessio Rastani On The State Of U.S. Stocks And Bitcoin - Forbes

Bitcoin in the times of COVID-19 will we see a new reserve? – InvestorDaily – InvestorDaily

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However, what weve observed in the Macquarie Business School research entitled Why you dont pay for coffee with Bitcoin is that the size of bitcoin transactions has been substantially increasing through time.

In other words, people are not paying for their coffees in bitcoin. Rather, average transactions sizes being more than $10,000 have been skyrocketing.

Much like gold, bitcoin is not a convenient payment mechanism nor is it likely to replace the transactional utility of a typical currency.

Nobody wants to wait 10 minutes to know their bitcoin payment for their coffee has been approved particularly after the invention of PayPass!

Our research shows that there has been a significant shift in the types of transactions included in the network.

In the early years of bitcoin, the majority of transactions were worth less than $1, and more than 90 per cent were for less than $100.

With the introduction of institutional investors, the majority of transactions are now $10,000 plus, with over 20 per cent of all transactions being for more than US$100,000.

A recent trend has seen much larger million-dollar-plus holdings, with bitcoin acting more as a store of value than as a transactional currency.

These changes are driven by the limited capacity of the bitcoin network being approximately 2,500 transactions every 10 minutes. This allows larger transactions to crowd out smaller transactions in the network.

The increase in investment-style activity is likely driving demand for these larger transactions in the bitcoin network. Both holdings and transactions are increasingly dominated by large institutional investors (such as hedge funds) wanting to diversify their portfolios.

This diversification can be particularly important in times of extreme volatility, such as during the current COVID-19 epidemic. Bitcoin, similar to gold, has a low correlation (or beta) with the market. This makes it a perfect safe haven asset when traditional stores of wealth, such as equities, experience sharp declines in price.

And that brings me to the potential use of bitcoin by investors and governments in these uncertain times.

To counteract the economically damaging effects of COVID-19, many developed economies like Europe, Australia and the US are using quantitative easing bazookasto stimulate their flagging economies.

Effectively, these policies result in central banks printing money, distributing it within their economies through initiatives such as the federal governments JobKeeper and JobSeeker.

Printing more money reduces the value of a countrys currency, to the point where some economies now have negative interest rates.

Such economic stimulus is inflationary, and is likely to lead to an implicitdefault on government debt basically printing money to repay that which was spent during the COVID-19 crisis.

All this points to significant and ongoing inflation, devaluing currencies such as the Australian dollar, US dollar and European Euro.

To counteract inflation, many investors look for safe haven assets in which to store their wealth.

Gold has historically served as the perfect hedge against such uncertain times. Bitcoin might be another such asset.

Unlike fiat currencies, where central banks can, literally overnight, double the money supply (halving the value of existing dollars), bitcoin is an inherently deflationary asset, experiencing ongoing quantitative tightening.

To understand this deflationary nature, it is important to understand how bitcoins are created.

Miners race to solve complex mathematical puzzles, with the winner receiving the block reward or new bitcoins.

In 2009, a miner would receive 50 bitcoins for solving these problems (currently worth $600,000). However, approximately every four years, this block reward halves.

On 11 May, the third such halving event occurred, lowering the block reward from 12.5 to 6.25 bitcoins, every 10 minutes.

This change has halved the number of new bitcoins brought into existence every 10 minutes.

In fact, this halving process will continue until a total of 21 million bitcoins have been mined (around 2120).

In this way, bitcoin may represent an even scarcer resource than gold, given there is a hardwired maximum amount of this asset that can ever exist.

In an environment where we anticipate many new Australian dollars, US dollars and euros being generated, which will significantly reduce their value, we are seeing the opposite occurring in bitcoin with the number of new bitcoins generated everyday halving.

What COVID-19 has demonstrated is the fragility of the current system and the fiscal and monetary policy mechanisms that have historically been used to stabilise our currencies and economies.

Currencies like bitcoin really shine in these unstable times because they are not linked to any central bank, and their value cant be destroyed with the stroke of a pen, as central banks can do with fiat currencies by ever-increasing the total money supply.

Thats simply not possible in bitcoin and thats what many investors find attractive.

Governments, too, are viewing bitcoin with some interest, particularly with the movement of large investors into the network.

Canada and China have launched their own central bank digital currencies in an effort to emulate the ongoing success of bitcoin.

It might not be long before central banks with reserve assets like gold, US dollars and euros hold bitcoin as a new global reserve currency as well.

What you dont want as a central bank is to hold assets that are going to rapidly lose their value.

In an era where we are seeing high inflation in countries that have been hit by the coronavirus, such as the US and Europe, its likely that those US dollars or euros held by the central banks as reserve currencies will lose their value the longer they hold onto them.

So, for both the Reserve Bank and traditional investors, diversifying into assets such as gold may limit the inflationary losses which are about to be foisted upon us due to increasing levels of inflation.

Its very likely well see continued increase in demand for bitcoin not just because of halving and its deflationary nature, but because investors see bitcoin as a tool to diversify the returns in their portfolio in these uncertain times.

Associate Professor Sean Foley is from the Macquarie Business School at Macquarie University

Bitcoin in the times of COVID-19 will we see a new reserve?

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Bitcoin in the times of COVID-19 will we see a new reserve? - InvestorDaily - InvestorDaily

Does AI Still Pose A Threat to Humanity? – AI Daily

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In addition to the 57 percent who believe that advancements in artificial intelligence does indeed pose a threat, 43 percent disagree and believe it's not something to be worried about. Elaborating on this data further, the 57 percent who consider it a threat, approximately 16 percent of them doubled down even further and indicated they believe it's a 'very serious threat.'Rasmussen said that the number of respondents who consider AI a threat has risen three percent since November, stating hat younger voters are a bit more concerned about artificial intelligence than voters over fifty.

A 2017 Pew Research study showed that approximately 70 percent of Americans are not sure as to whether they should be concerned about the rise of robots and AI.The biggest giants on the planet such as Google, Apple and Amazon have continuously placed strong emphasis on the potential for these technologies to make our lives easier and improving efficiency, with other benefits.

Although artificial intelligence could potentially pose a threat to mankind, as of right now our understanding and research in artificial intelligence is not advanced enough to create machines capable of carrying out multiple tasks. AI, as of right now, helps to make peoples lives easier and improves efficiency in the work environment.

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Does AI Still Pose A Threat to Humanity? - AI Daily

Artificial intelligence and algorithms bring drone inspection breakthrough – – Splash 247

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June 9th, 2020 Sam Chambers Operations, Tech 1 comments

A drone has successfully inspected a 19.4 meter high oil tank onboard a Floating Production, Storage and Offloading vessel. The video shot by the drone was interpreted in real-time by an algorithm to detect cracks in the structure.

Scout Drone Inspection and class society DNV GL have been working together to develop an autonomous drone system to overcome the common challenges of tank inspections. For the customer, costs can run into hundreds of thousands of dollars as the tank is taken out of service for days to ventilate and construct scaffolding. The tanks are also tough work environments, with surveyors often having to climb or raft into hard to reach corners. Using a drone in combination with an algorithm to gather and analyse video footage can significantly reduce survey times and staging costs, while at the same time improving surveyor safety.

Weve been working with drone surveys since 2015, said Geir Fuglerud, director of ofshore classification at DNV GL Maritime. This latest test showcases the next step in automation, using AI to analyse live video. As class we are always working to take advantage of advances in technology to make our surveys more efficient and safer for surveyors, delivering the same quality while minimising our operational downtime for our customers.

The drone, developed by Scout Drone Inspection, uses LiDAR to navigate inside the tank as GPS-reception is not available in the enclosed space. A LiDAR creates a 3-D map of the tank and all images and video is accurately geo-tagged with position data.

During the test, the drone was controlled by a pilot using the drones flight assistance functions, but as the technology matures it will be able to navigate more and more autonomously. DNV GL has been developing artificial intelligence to interpret videos to spot any cracks and eventually the camera and algorithm will be able to detect anomalies below the surface such as corrosion and structural deformations.

This is another important step towards autonomous drone inspections, said Nicolai Husteli, CEO of Scout Drone Inspection.

Up until now the process has been completely analogue but technology can address the urgent need to make the process more efficient and safer.

Altera Infrastructure hosted the test on its Petrojarl Varg FPSO. The video was livestreamed via Scout Drone Inspections cloud-system back to Altera Infrastructures headquarters in Trondheim, where the footage was monitored by engineers.

Sam Chambers

Starting out with the Informa Group in 2000 in Hong Kong, Sam Chambers became editor of Maritime Asia magazine as well as East Asia Editor for the worlds oldest newspaper, Lloyds List. In 2005 he pursued a freelance career and wrote for a variety of titles including taking on the role of Asia Editor at Seatrade magazine and China correspondent for Supply Chain Asia. His work has also appeared in The Economist, The New York Times, The Sunday Times and The International Herald Tribune.

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Artificial intelligence and algorithms bring drone inspection breakthrough - - Splash 247

Artificial Intelligence (AI) in Supply Chain Market is projected to reach $21.8 billion by 2027, Growing at a CAGR of 45.3% from 2019- Meticulous…

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London, June 03, 2020 (GLOBE NEWSWIRE) -- Artificial intelligence has emerged as the most potent technologies over the past few years, that is transitioning the landscape of almost all industry verticals. Although enterprise applications based on AI and machine learning (ML) are still in the nascent stages of development, they are gradually beginning to drive innovation strategies of the business.

In the supply chain and logistics industry, artificial intelligence is gaining rapid traction among industry stakeholders. Players operating in the supply chain and logistics industry are increasingly realizing the potential of AI to solve the complexities of running a global logistics network. Adoption of artificial intelligence in the supply chain is routing a new era or industrial transformation, allowing the companies to track their operations, enhance supply chain management productivity, augment business strategies, and engage with customers in digital world.

Theartificial intelligence in supply chain market is expected to grow at a CAGR of 45.3% from 2019 to 2027 to reach $21.8 billion by 2027. The growth in this market is mainly driven by rising awareness of artificial intelligence and big data & analytics and widening implementation of computer vision in both autonomous & semi-autonomous applications. In addition, consistent technological advancements in the supply chain industry, rising demand for AI-based business automation solutions, and evolving supply chain complementing growing industrial automation are further offering opportunities for vendors providing AI solutions in the supply chain industry. However, high deployment and operating costs and lack of infrastructure hinder the growth of the artificial intelligence in supply chain market.

In this study, the globalAI in supply chain market is segmented on the basis of component, application, technology, end user, and geography.

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Based on component, AI in supply chain market is broadly segmented into hardware, software, and services. The software segment commanded the largest share of the overall AI in supply chain market in 2019. This can be attributed to the increasing demand for AI-based platforms and solutions, as they offer supply chain visibility through software, which include inventory control, warehouse management, order procurement, and reverse logistics & tracking.

Based on technology, AI in supply chain market is broadly segmented into machine learning, computer vision, natural language processing, and context-aware computing. In 2019, the machine learning segment commanded the largest share of the overall AI in supply chain market. This growth can be attributed to the growing demand for AI-based intelligent solutions; increasing government initiatives; and the ability of AI solutions to efficiently handle and analyze big data and quickly scan, parse, and react to anomalies

Based on application, AI in supply chain market is broadly segmented into supply chain planning, warehouse management, fleet management, virtual assistant, risk management, inventory management, and planning & logistics. In 2019, the supply chain planning segment commanded the largest share of the overall AI in supply chain market. The growth of this segment can be attributed to the increasing demand for enhancing factory scheduling & production planning and the evolving agility and optimization of supply chain decision-making. In addition, digitizing existing processes and workflows to reinvent the supply chain planning model is also contributing to the growth of this segment.

Based on end user, artificial intelligence in supply chain market is broadly segmented into manufacturing, food & beverage, healthcare, automotive, aerospace, retail, and consumer packaged goods sectors. The retail sector commanded the largest share of the overall AI in supply chain market in 2019. This can be attributed to the increase in demand for consumer retail products.

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Based on geography, the global artificial intelligence in supply chain market is categorized into five major geographies, namely, North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. In 2019, North America commanded for the largest share of the global artificial intelligence in supply chain market, followed by Europe, Asia-Pacific, Latin America, and the Middle East & Africa. The large share of the North American region is attributed to the presence of developed economies focusing on enhancing the existing solutions in the supply chain space, and the existence of major players in this market along with a high willingness to adopt advanced technologies.

On the other hand, the Asia-Pacific region is projected to grow at the fastest CAGR during the forecast period. The high growth rate is attributed to rapidly developing economies in the region; presence of young and tech-savvy population in this region; and growing proliferation of internet of things (IoT); rising disposable income; increasing acceptance of modern technologies across several industries including automotive, manufacturing, and retail; and broadening implementation of computer vision technology in numerous applications. Furthermore, the growing adoption of AI-based solutions and services among supply chain operations, increasing digitalization in the region, and improving connectivity infrastructure are also playing a significant role in the growth of this market in the region.

The globalAI in supply chain market is fragmented in nature and is characterized by the presence of several companies competing for the market share. Some of the leading companies in the artificial intelligence in supply chain market are from the core technology background. These include IBM Corporation (U.S.), Microsoft Corporation (U.S.), Google LLC (U.S.), and Amazon.com, Inc. (U.S.). These companies are leading the market owing to their strong brand recognition, diverse product portfolio, strong distribution & sales network, and strong organic & inorganic growth strategies. The other key players in the global artificial intelligence in supply chain market are Intel Corporation (U.S.), Nvidia Corporation (U.S.), Oracle Corporation (U.S.), Samsung (South Korea), LLamasoft, Inc. (U.S.), SAP SE (Germany), General Electric (U.S.), Deutsche Post DHL Group (Germany), Xilinx, Inc. (U.S.), Micron Technology, Inc. (U.S.), FedEx Corporation (U.S.), ClearMetal, Inc. (U.S.), Dassault Systmes (France), and JDA Software Group, Inc. (U.S.), among others.

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Amidst this crisis, Meticulous Researchis continuously assessing the impact of COVID-19 pandemic on various sub-markets and enables global organizations to strategize for the post-COVID-19 world and sustain their growth. Let us know if you would like to. assess the impact of COVID-19 on any industry here-https://www.meticulousresearch.com/custom-research.php

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Meticulous Research was founded in 2010 and incorporated as Meticulous Market Research Pvt. Ltd. in 2013 as a private limited company under the Companies Act, 1956. Since its incorporation, with the help of its unique research methodologies, the company has become the leading provider of premium market intelligence in North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa regions.

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Artificial Intelligence (AI) in Supply Chain Market is projected to reach $21.8 billion by 2027, Growing at a CAGR of 45.3% from 2019- Meticulous...

Coronavirus impact on dental practices: Imaging software capabilities and artificial intelligence (Video) – Dentistry IQ

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As a young dentist, Dr. David Gane learned quickly that photographic imaging records were a terrific tool for his dental practice in many ways. Within 10 years he segued into his own imaging software company, and today hes the CEO of Apertyx Imaging.

In this discussion, he and Dr. Pamela Maragliano-Muniz agree that April was a challenging month for dentists and dental companies. But theyre encouraged by recent data from the Health Policy Institute and the American Dental Association that indicates dentistry is beginning to bounce back successfully and will continue to do so. Surveys from the HPI also show that most patients want to return to their dentists as soon as they're able.

Dr. Maragliano Muniz notes that to continue to be successful, dentists are investing more in themselves. She and Dr. Gane believe that an efficient imaging system is an excellent tool for dentists to spend their money on. Dr. Gane explains the advantages of Apteryx.

Editor's note:To viewDentistryIQ's full coverage of the COVID-19 pandemic, including original news articles and video interviews with dental thought leaders,visit theDentistryIQCOVID-19 Resource Center.

Pamela Maragliano-Muniz, DMD,is the chief editor ofDentistryIQ.Based in Salem, Massachusetts, Dr. Maragliano-Muniz began her clinical career as a dental hygienist. She went on to attend Tufts University School of Dental Medicine, where she earned her doctorate in dental medicine. She then attended the University of California, Los Angeles, School of Dental Medicine, where she became board-certified in prosthodontics. Dr. Maragliano-Munizowns a private practice, Salem Dental Arts, and lectures on a variety of clinical topics.

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Coronavirus impact on dental practices: Imaging software capabilities and artificial intelligence (Video) - Dentistry IQ

Global Artificial Intelligence in Big Data Analytics and IoT Market 2020 Industry Analysis, Key Players, Type and Application, Regions, Forecast to…

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Researchstore.biz has recently announced a research report Global Artificial Intelligence in Big Data Analytics and IoT Market 2020 by Company, Regions, Type and Application, Forecast to 2025 which elaborates the industry coverage, current market competitive status, and market outlook and forecast by 2025. It evaluates global Artificial Intelligence in Big Data Analytics and IoT market size, product sales volume, value, as well as market dynamics such as opportunities, challenges, threats, and issues the market is facing. It categorizes the global market by key players, product types, applications, and regions, etc. The report shows the competitive landscape, future trends, volume, manufacturing cost, and investment strategy. Comprehensive analysis of consumption, market share, and growth rate of each application is offered.

Some of the key players operating in this market include: Amazon , NVIDIA Corporation , Microsoft Corporation , Google Inc. , Infineon Technologies AG , IBM Corporation , Apple Inc. , Intel Corporation , CISCO Systems Inc. , Veros Systems Inc.

NOTE: This report takes into account the current and future impacts of COVID-19 on this industry and offers you an in-dept analysis of Artificial Intelligence in Big Data Analytics and IoT market.

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Customer-Based Analysis:

For the customer-based market, the report classifies market maker data to better comprehend who the customers are, their purchasing patterns and behavior. Segments of the global Artificial Intelligence in Big Data Analytics and IoT market are analyzed on the basis of market share, production, consumption, revenue, CAGR, market size, and more factors. The market report provides all data with easily absorbable information to assist every businessmans future innovation and move the business forward.

Market research supported product sort includes: Machine Learning, Deep Learning Platform, Voice Recognition, Artificial Neural Network, Others

Market research supported application coverage: Smart Machine, Self Driving Vehicles, Cyber Security Intelligence, Others

Region-Wise Analysis of the Market as follows:

Geographic penetration also shows the market potential, market risk, industry trends, and opportunities. Reportedly, the global Artificial Intelligence in Big Data Analytics and IoT market region is dominating this industry in the forthcoming year. On a regional basis, the global market can be segmented into: North America (United States, Canada and Mexico), Europe (Germany, France, UK, Russia and Italy), Asia-Pacific (China, Japan, Korea, India and Southeast Asia), South America (Brazil, Argentina, etc.), Middle East& Africa (Saudi Arabia, Egypt, Nigeria and South Africa)

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Moreover, the analytical tools used during the research include Porters five forces analysis, SWOT analysis, and investment feasibility and returns analysis. In short, this document provides the global Artificial Intelligence in Big Data Analytics and IoT industry outline with growth study and past & futuristic cost, revenue, demand and supply analysis. The conclusion part highlights market drivers, opportunities and challenges, sales channels, distributors, customers, research findings & conclusion, appendix & data source and Porters Five Forces Analysis.

Table of Contents:

Chapter 1, Scope of The Report: Market introduction, research objectives, market research methodology, data source

Chapter 2, Executive Summary: Market overview, consumption, segment by type, consumption by type, segment by application, consumption by application

Chapter 3, Global Artificial Intelligence in Big Data Analytics and IoT By Company: Sales market share, revenue, sale price, manufacturing base distribution, sales area, type, concentration rate analysis, competition landscape analysis, new products, and potential entrants, mergers & acquisitions, expansion

Chapter 4, 5, 6, 7, 8, By Regions: Consumption growth, consumption by countries, value by countries, consumption by type, consumption by application, consumption by regions, value by regions

Chapter 9, Market Drivers, Challenges and Trends: Market drivers and impact, growing demand from key regions, growing demand from key applications and potential industries, market challenges and impact, market trends

Chapter 10, Marketing, Distributors, and Customer: Sales channel, direct channels, indirect channels, distributors, and customer

Chapter 11, Global Market Forecast: Consumption forecast, consumption forecast, forecast by regions, value forecast by regions, market forecast

Chapter 12, Key Players Analysis: Company information, sales, revenue, price, and gross margin, business overview, latest developments, company information

Chapter 13, Research Findings and Conclusion

Customization of the Report:This report can be customized to meet the clients requirements. Please connect with our sales team ([emailprotected]), who will ensure that you get a report that suits your needs. You can also get in touch with our executives on +1-201-465-4211 to share your research requirements.

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Global Artificial Intelligence in Big Data Analytics and IoT Market 2020 Industry Analysis, Key Players, Type and Application, Regions, Forecast to...

The Promise and Risks of Artificial Intelligence: A Brief History – War on the Rocks

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Editors Note: This is an excerpt from a policy roundtable Artificial Intelligence and International Security from our sister publication, the Texas National Security Review. Be sure to check out the full roundtable.

Artificial intelligence (AI) has recently become a focus of efforts to maintain and enhance U.S. military, political, and economic competitiveness. The Defense Departments 2018 strategy for AI, released not long after the creation of a new Joint Artificial Intelligence Center, proposes to accelerate the adoption of AI by fostering a culture of experimentation and calculated risk taking, an approach drawn from the broader National Defense Strategy. But what kinds of calculated risks might AI entail? The AI strategy has almost nothing to say about the risks incurred by the increased development and use of AI. On the contrary, the strategy proposes using AI to reduce risks, including those to both deployed forces and civilians.

While acknowledging the possibility that AI might be used in ways that reduce some risks, this brief essay outlines some of the risks that come with the increased development and deployment of AI, and what might be done to reduce those risks. At the outset, it must be acknowledged that the risks associated with AI cannot be reliably calculated. Instead, they are emergent properties arising from the arbitrary complexity of information systems. Nonetheless, history provides some guidance on the kinds of risks that are likely to arise, and how they might be mitigated. I argue that, perhaps counter-intuitively, using AI to manage and reduce risks will require the development of uniquely human and social capabilities.

A Brief History of AI, From Automation to Symbiosis

The Department of Defense strategy for AI contains at least two related but distinct conceptions of AI. The first focuses on mimesis that is, designing machines that can mimic human work. The strategy document defines mimesis as the ability of machines to perform tasks that normally require human intelligence for example, recognizing patterns, learning from experience, drawing conclusions, making predictions, or taking action. A somewhat distinct approach to AI focuses on what some have called human-machine symbiosis, wherein humans and machines work closely together, leveraging their distinctive kinds of intelligence to transform work processes and organization. This vision can also be found in the AI strategy, which aims to use AI-enabled information, tools, and systems to empower, not replace, those who serve.

Of course, mimesis and symbiosis are not mutually exclusive. Mimesis may be understood as a means to symbiosis, as suggested by the Defense Departments proposal to augment the capabilities of our personnel by offloading tedious cognitive or physical tasks. But symbiosis is arguably the more revolutionary of the two concepts and also, I argue, the key to understanding the risks associated with AI.

Both approaches to AI are quite old. Machines have been taking over tasks that otherwise require human intelligence for decades, if not centuries. In 1950, mathematician Alan Turing proposed that a machine can be said to think if it can persuasively imitate human behavior, and later in the decade computer engineers designed machines that could learn. By 1959, one researcher concluded that a computer can be programmed so that it will learn to play a better game of checkers than can be played by the person who wrote the program.

Meanwhile, others were beginning to advance a more interactive approach to machine intelligence. This vision was perhaps most prominently articulated by J.C.R. Licklider, a psychologist studying human-computer interactions. In a 1960 paper on Man-Computer Symbiosis, Licklider chose to avoid argument with (other) enthusiasts for artificial intelligence by conceding dominance in the distant future of cerebration to machines alone. However, he continued: There will nevertheless be a fairly long interim during which the main intellectual advances will be made by men and computers working together in intimate association.

Notions of symbiosis were influenced by experience with computers for the Semi-Automatic Ground Environment (SAGE), which gathered information from early warning radars and coordinated a nationwide air defense system. Just as the Defense Department aims to use AI to keep pace with rapidly changing threats, SAGE was designed to counter the prospect of increasingly swift attacks on the United States, specifically low-flying bombers that could evade radar detection until they were very close to their targets.

Unlike other computers of the 1950s, the SAGE computers could respond instantly to inputs by human operators. For example, operators could use a light gun to select an aircraft on the screen, thereby gathering information about the airplanes identification, speed, and direction. SAGE became the model for command-and-control systems throughout the U.S. military, including the Ballistic Missile Early Warning System, which was designed to counter an even faster-moving threat: intercontinental ballistic missiles, which could deliver their payload around the globe in just half an hour. We can still see the SAGE model today in systems such as the Patriot missile defense system, which is designed to destroy short-range missiles those arriving with just a few minutes of notice.

SAGE also inspired a new and more interactive approach to computing, not just within the Defense Department, but throughout the computing industry. Licklider advanced this vision after he became director of the Defense Departments Information Processing Technologies Office, within the Advanced Research Projects Agency, in 1962. Under Lickliders direction, the office funded a wide range of research projects that transformed how people would interact with computers, such as graphical user interfaces and computer networking that eventually led to the Internet.

The technologies of symbiosis have contributed to competitiveness not primarily by replacing people, but by enabling new kinds of analysis and operations. Interactive information and communications technologies have reshaped military operations, enabling more rapid coordination and changes in plans. They have also enabled new modes of commerce. And they created new opportunities for soft power as technologies such as personal computers, smart phones, and the Internet became more widely available around the world, where they were often seen as evidence of American progress.

Mimesis and symbiosis come with somewhat distinct opportunities and risks. The focus on machines mimicking human behavior has prompted anxieties about, for example, whether the results produced by machine reasoning should be trusted more than results derived from human reasoning. Such concerns have spurred work on explainable AI wherein machine outputs are accompanied by humanly comprehensible explanations for those outputs.

By contrast, symbiosis calls attention to the promises and risks of more intimate and complex entanglements of humans and machines. Achieving an optimal symbiosis requires more than well-designed technology. It also requires continual reflection upon and revision of the models that govern human-machine interactions. Humans use models to design AI algorithms and to select and construct the data used to train such systems. Human designers also inscribe models of use assumptions about the competencies and preferences of users, and the physical and organizational contexts of use into the technologies they create. Thus, like a film script, technical objects define a framework of action together with the actors and the space in which they are supposed to act. Scripts do not completely determine action, but they configure relationships between humans, organizations, and machines in ways that constrain and shape user behavior. Unfortunately, these interactively complex sociotechnical systems often exhibit emergent behavior that is contrary to the intentions of designers and users.

Competitive Advantages and Risks

Because models cannot adequately predict all of the possible outcomes of complex sociotechnical systems, increased reliance on intelligent machines leads to at least four kinds of risks: The models for how machines gather and process information, and the models of human-machine interaction, can both be inadvertently flawed or deliberately manipulated in ways not intended by designers. Examples of each of these kinds of risks can be found in past experiences with smart machines.

First, changing circumstances can render the models used to develop machine intelligence irrelevant. Thus, those models and the associated algorithms need constant maintenance and updating. For example, what is now the Patriot missile defense system was initially designed for air defense but was rapidly redesigned and deployed to Saudi Arabia and Israel to defend against short-range missiles during the 1991 Gulf War. As an air defense system it ran for just a few hours at a time, but as a missile defense system it ran for days without rebooting. In these new operating conditions, a timing error in the software became evident. On Feb. 25, 1991, this error caused the system to miss a missile that struck a U.S. Army barracks in Dhahran, Saudi Arabia, killing 28 American soldiers. A software patch to fix the error arrived in Dhahran a day too late.

Second, the models upon which machines are designed to operate can be exploited for deceptive purposes. Consider, for example, Operation Igloo White, an effort to gather intelligence on and stop the movement of North Vietnamese supplies and troops in the late 1960s and early 1970s. The operation dropped sensors throughout the jungle, such as microphones, to detect voices and truck vibrations, as well as devices that could detect the ammonia odors from urine. These sensors sent signals to overflying aircraft, which in turn sent them to a SAGE-like surveillance center that could dispatch bombers. However, the program was a very expensive failure. One reason is that the sensors were susceptible to spoofing. For example, the North Vietnamese could send empty trucks to an area to send false intelligence about troop movements, or use animals to trigger urine sensors.

Third, intelligent machines may be used to create scripts that enact narrowly instrumental forms of rationality, thereby undermining broader strategic objectives. For example, unpiloted aerial vehicle operators are tasked with using grainy video footage, electronic signals, and assumptions about what constitutes suspicious behavior to identify and then kill threatening actors, while minimizing collateral damage. Operators following this script have, at times, assumed that a group of men with guns was planning an attack, when in fact they were on their way to a wedding in a region where celebratory gun firing is customary, and that families praying at dawn were jihadists rather than simply observant Muslims. While it may be tempting to dub these mistakes operator errors, this would be too simple. Such operators are enrolled in a deeply flawed script one that presumes that technology can be used to correctly identify threats across vast geographic, cultural, and interpersonal distances, and that the increased risk of killing innocent civilians is worth the increased protection offered to U.S. combatants. Operators cannot be expected to make perfectly reliable judgments across such distances, and it is unlikely that simply deploying the more precise technology that AI enthusiasts promise can bridge the very distances that remote systems were made to maintain. In an era where soft power is inextricable from military power, such potentially dehumanizing uses of information technology are not only ethically problematic, they are also likely to generate ill will and blowback.

Finally, the scripts that configure relationships between humans and intelligent machines may ultimately encourage humans to behave in machine-like ways that can be manipulated by others. This is perhaps most evident in the growing use of social bots and new social media to influence the behavior of citizens and voters. Bots can easily mimic humans on social media, in part because those technologies have already scripted the behavior of users, who must interact through liking, following, tagging, and so on. While influence operations exploit the cognitive biases shared by all humans, such as a tendency to interpret evidence in ways that confirm pre-existing beliefs, users who have developed machine-like habits reactively liking, following, and otherwise interacting without reflection are all the more easily manipulated. Remaining competitive in an age of AI-mediated disinformation requires the development of more deliberative and reflective modes of human-machine interaction.

Conclusion

Achieving military, economic, and political competitiveness in an age of AI will entail designing machines in ways that encourage humans to maintain and cultivate uniquely human kinds of intelligence, such as empathy, self-reflection, and outside-the-box thinking. It will also require continual maintenance of intelligent systems to ensure that the models used to create machine intelligence are not out of date. Models structure perception, thinking, and learning, whether by humans or machines. But the ability to question and re-evaluate these assumptions is the prerogative and the responsibility of the human, not the machine.

Rebecca Slayton is an associate professor in the Science & Technology Studies Department and the Judith Reppy Institute of Peace and Conflict Studies, both at Cornell University. She is currently working on a book about the history of cyber security expertise.

Image: Flickr (Image by Steve Jurvetson)

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The Promise and Risks of Artificial Intelligence: A Brief History - War on the Rocks

Meet STACI: your interactive guide to advances of AI in health care – STAT

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Artificial intelligence has become its own sub-industry in health care, driving the development of products designed to detect diseases earlier, improve diagnostic accuracy, and discover more effective treatments. One recent report projected spending on health care AI in the United States will rise to $6.6 billion in 2021, an 11-fold increase from 2014.

The Covid-19 pandemic underscores the importance of the technology in medicine: In the last few months, hospitals have used AI to create coronavirus chatbots, predict the decline of Covid-19 patients, and diagnose the disease from lung scans.

Its rapid advancement is already changing practices in image-based specialties such as radiology and pathology, and the Food and Drug Administration has approved dozens of AI products to help diagnose eye diseases, bone fractures, heart problems, and other conditions. So much is happening that it can be hard for health professionals, patients, and even regulators to keep up, especially since the concepts and language of AI are new for many people.

The use of AI in health care also poses new risks. Biased algorithms could perpetuate discrimination along racial and economic lines, and lead to the adoption of inadequately vetted products that drive up costs without benefiting patients. Understanding these risks and weighing them against the potential benefits requires a deeper understanding of AI itself.

Its for these reasons that we created STACI: the STAT Terminal for Artificial Computer Intelligence. She will walk you through the key concepts and history of AI, explain the terminology, and break down its various uses in health care. (This interactive is best experienced on screens larger than a smartphones.)

Remember, AI is only as good as the data fed into it. So if STACI gets something wrong, blame the humans behind it, not the AI!

This is part of a yearlong series of articles exploring the use of artificial intelligence in health care that is partly funded by a grant from the Commonwealth Fund.

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Meet STACI: your interactive guide to advances of AI in health care - STAT


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