Machine learning has a limited scope

AI is a bigger concept to create intelligent machines that can simulate human thinking capability and behavior, whereas, machine learning is an application or subset of AI that allows machines to learn from data without being programmed explicitly

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed

machines tend to be designed for the lowest possible risk and the least casualties

machines must weigh the consequences of any action they take, as each action will impact the end result

goals of artificial intelligence include learning, reasoning, and perception

refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions

You can now distribute your add-on. Note, however, that your add-on may still be subject to further review, if it is you’ll receive notification of the outcome of the review later.

As the largest Voronoi regions belong to the states on the frontier of the search, this means that the tree preferentially expands towards large unsearched areas.

inherently biased to grow towards large unsearched areas of the problem

Natural Language Processing with Python – Analyzing Text with the Natural Language Toolkit Steven Bird, Ewan Klein, and Edward Loper

How to setup and use Stanford CoreNLP Server with Python Khalid Alnajjar August 20, 2017 Natural Language Processing (NLP) Leave a CommentStanford CoreNLP is a great Natural Language Processing (NLP) tool for analysing text. Given a paragraph, CoreNLP splits it into sentences then analyses it to return the base forms of words in the sentences, their dependencies, parts of speech, named entities and many more. Stanford CoreNLP not only supports English but also other 5 languages: Arabic, Chinese, French, German and Spanish. To try out Stanford CoreNLP, click here.Stanford CoreNLP is implemented in Java. In some cases (e.g. your main code-base is written in different language or you simply do not feel like coding in Java), you can setup a Stanford CoreNLP Server and, then, access it through an API. In this post, I will show how to setup a Stanford CoreNLP Server locally and access it using python.

CoreNLP includes a simple web API server for servicing your human language understanding needs (starting with version 3.6.0). This page describes how to set it up. CoreNLP server provides both a convenient graphical way to interface with your installation of CoreNLP and an API with which to call CoreNLP using any programming language. If you’re writing a new wrapper of CoreNLP for using it in another language, you’re advised to do it using the CoreNLP Server.

Programming languages and operating systems Stanford CoreNLP is written in Java; recent releases require Java 1.8+. You need to have Java installed to run CoreNLP. However, you can interact with CoreNLP via the command-line or its web service; many people use CoreNLP while writing their own code in Javascript, Python, or some other language. You can use Stanford CoreNLP from the command-line, via its original Java programmatic API, via the object-oriented simple API, via third party APIs for most major modern programming languages, or via a web service. It works on Linux, macOS, and Windows. License The full Stanford CoreNLP is licensed under the GNU General Public License v3 or later. More precisely, all the Stanford NLP code is GPL v2+, but CoreNLP uses some Apache-licensed libraries, and so our understanding is that the the composite is correctly licensed as v3+.

Stanford CoreNLP provides a set of human language technology tools. It can give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and syntactic dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, extract particular or open-class relations between entity mentions, get the quotes people said, etc. Choose Stanford CoreNLP if you need: An integrated NLP toolkit with a broad range of grammatical analysis tools A fast, robust annotator for arbitrary texts, widely used in production A modern, regularly updated package, with the overall highest quality text analytics Support for a number of major (human) languages Available APIs for most major modern programming languages Ability to run as a simple web service

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code. The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc. OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 18 million. The library is used extensively in companies, research groups and by governmental bodies. Along with well-established companies like Google, Yahoo, Microsoft, Intel, IBM, Sony, Honda, Toyota that employ the library, there are many startups such as Applied Minds, VideoSurf, and Zeitera, that make extensive use of OpenCV. OpenCV’s deployed uses span the range from stitching streetview images together, detecting intrusions in surveillance video in Israel, monitoring mine equipment in China, helping robots navigate and pick up objects at Willow Garage, detection of swimming pool drowning accidents in Europe, running interactive art in Spain and New York, checking runways for debris in Turkey, inspecting labels on products in factories around the world on to rapid face detection in Japan. It has C++, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS. OpenCV leans mostly towards real-time vision applications and takes advantage of MMX and SSE instructions when available. A full-featured CUDAand OpenCL interfaces are being actively developed right now. There are over 500 algorithms and about 10 times as many functions that compose or support those algorithms. OpenCV is written natively in C++ and has a templated interface that works seamlessly with STL containers.

that can be partially automated but still require human oversight and occasional intervention

but then have a tool that will show you each of the change sites one at a time and ask you either to accept the change, reject the change, or manually intervene using your editor of choice.

Humans can no longer compete with AI in chess. They should not be without AI in litigation either.

Just as chess players marshall their 16 chess pieces in a battle of wits, attorneys must select from millions of cases in order to present the best legal arguments.

Attorney IO, a provider of an artificial intelligence-driven service that helps attorneys manage their legal documents

Now that we’re making breakthroughs in artificial intelligence, there’s a deeply cemented belief that the human brain works as a deterministic, mathematical process that can be replicated exactly by a Turing machine.

It doesn’t.What it does do is teach AI to recognize various things and fool you into thinking you’re getting better security.When you get something for free, you are the product.

Overestimating robots and AI underestimates the very people who can save us from this pandemic: Doctors, nurses, and other health workers, who will likely never be replaced by machines outright. They’re just too beautifully human for that.

the demand for products will keep climbing as well, as we’re seeing with this hiring bonanza.

Last week, Amazon officials announced that in response to the coronavirus they were hiring 100,000 additional humans to work in fulfillment centers and as delivery drivers, showing that not even this mighty tech company can do without people.

Why haven’t the machines saved us yet?

And that’s all because of the vulnerabilities of the human worker.

Ai Weiwei,

The system has been criticised due to its method of scraping the internet to gather images and storing them in a database. Privacy activists say the people in those images never gave consent. “Common law has never recognised a right to privacy for your face,” Clearview AI lawyer Tor Ekeland said in a recent interview with CoinDesk. “It’s kind of a bizarre argument to make because [your face is the] most public thing out there.”

Enligt Polismyndighetens riktlinjer ska en konsekvensbedömning göras innan nya polisiära verktyg införs, om de innebär en känslig personuppgiftbehandling. Någon sådan har inte gjorts för det aktuella verktyget.

le nuove tecnologie sono presenti nella vita di tutti, sia lavorativa sia quotidiana. Spesso non ci rendiamo neanche conto che interagiamo con sistemi automatici o che disseminiamo sulla rete dati che riguardano la nostra identità personale. Per cui si produce una grave asimmetria tra chi li estrae (per i propri interessi) e chi li fornisce (senza saperlo). Per ottenere certi servizi, alcuni siti chiedono a noi di precisare che non siamo un robot, ma in realtà la domanda andrebbe capovolta

«È necessario che l’etica accompagni tutto il ciclo della elaborazione delle tecnologie: dalla scelta delle linee di ricerca fino alla progettazione, la produzione, la distribuzione e l’utente finale. In questo senso papa Francesco ha parlato di “algoretica”»

However, there is skepticism about AI’s ability to replace human teaching in activities such as judging writing style, and some have expressed concern that policy makers could use AI to justify replacing (young) human labor.

He was sitting on large crate containing Boston Dynamics’ robot dog, Spot.

Cut and erase artwork Transform your artwork by cutting and erasing content.

Transform artwork Learn how to transform artwork with the Selection tool, Transform panel, and various transform tools.

command

Scale objects Scaling an object enlarges or reduces it horizontally (along the x axis), vertically (along the y axis), or both. Objects scale relative to a reference point which varies depending on the scaling method you choose. You can change the default reference point for most scaling methods, and you can also lock the proportions of an object.

The underlying guiding idea of a “trustworthy AI” is, first and foremost, conceptual nonsense. Machines are not trustworthy; only humans can be trustworthy (or untrustworthy). If, in the future, an untrustworthy corporation or government behaves unethically and possesses good, robust AI technology, this will enable more effective unethical behaviour.

Hans Moravec argued in 1976 that computers were still millions of times too weak to exhibit intelligence. He suggested an analogy: artificial intelligence requires computer power in the same way that aircraft require horsepower. Below a certain threshold, it's impossible, but, as power increases, eventually it could become easy.[79] With regard to computer vision, Moravec estimated that simply matching the edge and motion detection capabilities of human retina in real time would require a general-purpose computer capable of 109 operations/second (1000 MIPS).[80] As of 2011, practical computer vision applications require 10,000 to 1,000,000 MIPS. By comparison, the fastest supercomputer in 1976, Cray-1 (retailing at $5 million to $8 million), was only capable of around 80 to 130 MIPS, and a typical desktop computer at the time achieved less than 1 MIPS.

This is not a new idea. It is based on the vision expounded by Vannevar Bush in his 1945 essay “As We May Think,” which conjured up a “memex” machine that would remember and connect information for us mere mortals. The concept was refined in the early 1960s by the Internet pioneer J. C. R. Licklider, who wrote a paper titled “Man-Computer Symbiosis,” and the computer designer Douglas Engelbart, who wrote “Augmenting Human Intellect.” They often found themselves in opposition to their colleagues, like Marvin Minsky and John McCarthy, who stressed the goal of pursuing artificial intelligence machines that left humans out of the loop.

Thompson’s point is that “artificial intelligence” — defined as machines that can think on their own just like or better than humans — is not yet (and may never be) as powerful as “intelligence amplification,” the symbiotic smarts that occur when human cognition is augmented by a close interaction with computers.

Four databases of citizen science and crowdsourcing projects —  SciStarter, the Citizen Science Association (CSA), CitSci.org, and the Woodrow Wilson International Center for Scholars (the Wilson Center Commons Lab) — are working on a common project metadata schema to support data sharing with the goal of maintaining accurate and up to date information about citizen science projects.  The federal government is joining this conversation with a cross-agency effort to promote citizen science and crowdsourcing as a tool to advance agency missions. Specifically, the White House Office of Science and Technology Policy (OSTP), in collaboration with the U.S. Federal Community of Practice for Citizen Science and Crowdsourcing (FCPCCS),is compiling an Open Innovation Toolkit containing resources for federal employees hoping to implement citizen science and crowdsourcing projects. Navigation through this toolkit will be facilitated in part through a system of metadata tags. In addition, the Open Innovation Toolkit will link to the Wilson Center’s database of federal citizen science and crowdsourcing projects.These groups became aware of their complementary efforts and the shared challenge of developing project metadata tags, which gave rise to the need of a workshop.  

What’s the Difference Between AI, Machine Learning and Data Science?

Tech Literacy Resources

However, PIPA is the agency's first standalone bot, meaning it can be used across multiple government agencies. Crucially, the bot can be embedded within web and mobile apps, as well as within third-party personal assistants, such as Google Home and Alexa.  According to Keenan, the gang of five digital assistants released so far by the DHS have answered "more than 2.3 million questions, reducing the need for people to have to pick up a phone or come into a service centre for help.” “This is what our digital transformation program is all about – making life simpler and easier for all Australians.”

uman Services has a number of public-facing chatbots already. The newest of them is ‘Charles’, launched last year, which offers support for the government’s MyGov service.Others include ‘Sam’ and ‘Oliver’, both of which launched in 2017. The department’s customer-facing digital assistants have so far answered more than 2.3 million questions. Human Services also uses a number of staff-facing chatbots. In November Keenan revealed that the department had launched an Augmented Intelligence Centre of Excellence, which the minister said would boost collaboration with industry, academia and other government entities.

The federal government has decided that all Commonwealth entities would benefit from having a chatbot, with the Department of Human Services (DHS) announcing it was working on the development of one that will be ready by the end of 2019.The Platform Independent Personal Assistant -- PIPA -- is expected to "significantly improve the customer experience for users of online government services", according to Minister for Human Services and Digital Transformation Michael Keenan.

Before implementing Alex 2.5 years ago, IP Australia staffers were taking 12,000 calls per month."Now I'm not saying Alex was the only intervention we had, but it was one of the main ones. Acting on the insights we were getting from Alex, we're now down to 5,000 calls per month and still dropping," Stokes said. "The value for money and return on investment is quite good."

In 2001, AI founder Marvin Minsky asked "So the question is why didn't we get HAL in 2001?"[167] Minsky believed that the answer is that the central problems, like commonsense reasoning, were being neglected, while most researchers pursued things like commercial applications of neural nets or genetic algorithms. John McCarthy, on the other hand, still blamed the qualification problem.[168] For Ray Kurzweil, the issue is computer power and, using Moore's Law, he predicted that machines with human-level intelligence will appear by 2029.[169] Jeff Hawkins argued that neural net research ignores the essential properties of the human cortex, preferring simple models that have been successful at solving simple problems.[170] There were many other explanations and for each there was a corresponding research program underway.

Eventually the earliest successful expert systems, such as XCON, proved too expensive to maintain. They were difficult to update, they could not learn, they were "brittle" (i.e., they could make grotesque mistakes when given unusual inputs), and they fell prey to problems (such as the qualification problem) that had been identified years earlier. Expert systems proved useful, but only in a few special contexts

The neats: logic and symbolic reasoning[edit source] Logic was introduced into AI research as early as 1958, by John McCarthy in his Advice Taker proposal.[100] In 1963, J. Alan Robinson had discovered a simple method to implement deduction on computers, the resolution and unification algorithm. However, straightforward implementations, like those attempted by McCarthy and his students in the late 1960s, were especially intractable: the programs required astronomical numbers of steps to prove simple theorems.[101] A more fruitful approach to logic was developed in the 1970s by Robert Kowalski at the University of Edinburgh, and soon this led to the collaboration with French researchers Alain Colmerauer and Philippe Roussel who created the successful logic programming language Prolog.[102] Prolog uses a subset of logic (Horn clauses, closely related to "rules" and "production rules") that permit tractable computation. Rules would continue to be influential, providing a foundation for Edward Feigenbaum's expert systems and the continuing work by Allen Newell and Herbert A. Simon that would lead to Soar and their unified theories of cognition.[103] Critics of the logical approach noted, as Dreyfus had, that human beings rarely used logic when they solved problems. Experiments by psychologists like Peter Wason, Eleanor Rosch, Amos Tversky, Daniel Kahneman and others provided proof.[104] McCarthy responded that what people do is irrelevant. He argued that what is really needed are machines that can solve problems—not machines that think as people do.[105] The scruffies: frames and scripts[edit source] Among the critics of McCarthy's approach were his colleagues across the country at MIT. Marvin Minsky, Seymour Papert and Roger Schank were trying to solve problems like "story understanding" and "object recognition" that required a machine to think like a person. In order to use ordinary concepts like "chair" or "restaurant" they had to make all the same illogical assumptions that people normally made. Unfortunately, imprecise concepts like these are hard to represent in logic. Gerald Sussman observed that "using precise language to describe essentially imprecise concepts doesn't make them any more precise."[106] Schank described their "anti-logic" approaches as "scruffy", as opposed to the "neat" paradigms used by McCarthy, Kowalski, Feigenbaum, Newell and Simon.[107] In 1975, in a seminal paper, Minsky noted that many of his fellow "scruffy" researchers were using the same kind of tool: a framework that captures all our common sense assumptions about something. For example, if we use the concept of a bird, there is a constellation of facts that immediately come to mind: we might assume that it flies, eats worms and so on. We know these facts are not always true and that deductions using these facts will not be "logical", but these structured sets of assumptions are part of the context of everything we say and think. He called these structures "frames". Schank used a version of frames he called "scripts" to successfully answer questions about short stories in English.[108] Many years later object-oriented programming would adopt the essential idea of "inheritance" from AI research on frames.

Bolt, Beranek and Newman (BBN) developed its own Lisp machine, named Jericho,[7] which ran a version of Interlisp. It was never marketed. Frustrated, the whole AI group resigned, and were hired mostly by Xerox. So, Xerox Palo Alto Research Center had, simultaneously with Greenblatt's own development at MIT, developed their own Lisp machines which were designed to run InterLisp (and later Common Lisp). The same hardware was used with different software also as Smalltalk machines and as the Xerox Star office system.

In 1979, Russell Noftsker, being convinced that Lisp machines had a bright commercial future due to the strength of the Lisp language and the enabling factor of hardware acceleration, proposed to Greenblatt that they commercialize the technology.[citation needed] In a counter-intuitive move for an AI Lab hacker, Greenblatt acquiesced, hoping perhaps that he could recreate the informal and productive atmosphere of the Lab in a real business. These ideas and goals were considerably different from those of Noftsker. The two negotiated at length, but neither would compromise. As the proposed firm could succeed only with the full and undivided assistance of the AI Lab hackers as a group, Noftsker and Greenblatt decided that the fate of the enterprise was up to them, and so the choice should be left to the hackers. The ensuing discussions of the choice divided the lab into two factions. In February 1979, matters came to a head. The hackers sided with Noftsker, believing that a commercial venture fund-backed firm had a better chance of surviving and commercializing Lisp machines than Greenblatt's proposed self-sustaining start-up. Greenblatt lost the battle.

We live in an age of paradox. Systems using artificial intelligence match or surpass human level performance in more and more domains, leveraging rapid advances in other technologies and driving soaring stock prices. Yet measured productivity growth has fallen in half over the past decade, and real income has stagnated since the late 1990s for a majority of Americans. Brynjolfsson, Rock, and Syverson describe four potential explanations for this clash of expectations and statistics: false hopes, mismeasurement, redistribution, and implementation lags. While a case can be made for each explanation, the researchers argue that lags are likely to be the biggest reason for paradox. The most impressive capabilities of AI, particularly those based on machine learning, have not yet diffused widely. More importantly, like other general purpose technologies, their full effects won't be realized until waves of complementary innovations are developed and implemented. The adjustment costs, organizational changes and new skills needed for successful AI can be modeled as a kind of intangible capital. A portion of the value of this intangible capital is already reflected in the market value of firms. However, most national statistics will fail to capture the full benefits of the new technologies and some may even have the wrong sign

espite the potential of emerging technologies to assist persons with cognitive disabilities,significant practical impediments remain to be overcome in commercialization, consumerabandonment, and in the design and development of useful products. Barriers also exist in terms of the financial and organizational feasibility of specific envisionedproducts, and their limited potential to reach the consumer market. Innovative engineeringapproaches, effective needs analysis, user-centered design, and rapid evolutionary developmentare essential to ensure that technically feasible products meet the real needs of persons withcognitive disabilities. Efforts must be made by advocates, designers and manufacturers to promote betterintegration of future software and hardware systems so that forthcoming iterations of personalsupport technologies and assisted care systems technologies do not quickly become obsolete.They will need to operate seamlessly across multiple real-world environments in the home,school, community, and workplace

Elon Musk.

No matter how well you design a system, humans will end up surprising you with how they use it. “We make it obvious that it’s a bot, a digital assistant, at the start. But sometimes customers overlook that. And they’ll say, ‘are you a bot? What’s going on here? Transfer me through!’ And they’ll get into it quite strongly,” explains David Grilli, AGL’s chatbot product owner

At the moment, GPT-2 uses a binary search algorithm, which means that its output can be considered a ‘true’ set of rules. If OpenAI is right, it could eventually generate a Turing complete program, a self-improving machine that can learn (and then improve) itself from the data it encounters. And that would make OpenAI a threat to IBM’s own goals of machine learning and AI, as it could essentially make better than even humans the best possible model that the future machines can use to improve their systems. However, there’s a catch: not just any new AI will do, but a specific type; one that uses deep learning to learn the rules, algorithms, and data necessary to run the machine to any given level of AI.

75 countries already using the technology

Machine learning is an approach to making many similar decisions that involves algorithmically finding patterns in your data and using these to react correctly to brand new data

Semantic dictionaries are powerful not just because they move away from meaningless indices, but because they express a neural network’s learned abstractions with canonical examples. With image classification, the neural network learns a set of visual abstractions and thus images are the most natural symbols to represent them. Were we working with audio, the more natural symbols would most likely be audio clips. This is important because when neurons appear to correspond to human ideas, it is tempting to reduce them to words. Doing so, however, is a lossy operation — even for familiar abstractions, the network may have learned a deeper nuance. For instance, GoogLeNet has multiple floppy ear detectors that appear to detect slightly different levels of droopiness, length, and surrounding context to the ears. There also may exist abstractions which are visually familiar, yet that we lack good natural language descriptions for: for example, take the particular column of shimmering light where sun hits rippling water.

AI relies upon a bet. It is the bet that if you get your syntax (mechanism) right the semantics (meaning) will take care of itself. It is the hope that if computer engineers get the learning feedback process right, a new transhuman intellect will emerge.

AI, especially in popular culture, is often a jumping-off point for dialogue with ourselves about what the future means, sometimes at the expense of understanding the present.

By comparison, Amazon’s Best Seller badges, which flag the most popular products based on sales and are updated hourly, are far more straightforward. For third-party sellers, “that’s a lot more powerful than this Choice badge, which is totally algorithmically calculated and sometimes it’s totally off,” says Bryant.

Humans act like a “liability sponge,” she says, absorbing all legal and moral responsibility in algorithmic accidents no matter how little or unintentionally they are involved.

a working station that has a visual display screen some three feet on a side; this is his working surface, and is controlled by a computer (his "clerk") with which he can communicate by means of a small keyboard and various other devices

Global AI Talent Report 2019

Amazon employs a system that not only tracks warehouse workers' productivity but also can automatically fire them for failing to meet expectations.

The agency is looking for industry vendors that can provide such a capability, which should also include “topic modeling; text categorization; text clustering; information extraction; named entity resolution; relationship extraction; sentiment analysis; and summarization,” and “may include statistical techniques that can provide a general understanding of the statutory and regulatory text as a whole.”

We often think about AI “replacing us” with a vision of robots literally doing our jobs, but it’s not going to shake out in quite that way. Look at radiology, for example: with the advances in computer vision, people sometimes talk about AI replacing radiologists. We probably won’t ever get to the point where there’s zero human radiologists. But a very possible future is one where, out of 100 radiologists now, AI lets the top 5 or 10 of them do the job of all the rest. If such a scenario plays out, where does that leave the other 90 or so doctors?

Machine learning techniques were originally designed for stationary and benign environments in which the training and test data are assumed to be generated from the same statistical distribution.

what EU leadership in AI could look like and what might be needed to get there.

“Meditations on Moloch,”

More people work in the shadow mines of content moderation than are officially employed by Facebook or Google. These are the people who keep our Disneyland version of the web spic and span.

Algorithms will privilege some forms of ‘knowing’ over others, and the person writing that algorithm is going to get to decide what it means to know… not precisely, like in the former example, but through their values. If they value knowledge that is popular, then knowledge slowly drifts towards knowledge that is popular.

Some questions to use when discussing why we shouldn’t replace humans with AI (artificial intelligence) for learning

AI Progress Measurement

The summation of human experience is being expanded at a prodigious rate

The conceptual framework we seek must orient us toward the real possibilities and problems associated with using modern technology to give direct aid to an individual in comprehending complex situations, isolating the significant factors, and solving problems.

executive capability.

augmentation means

But every single photo on the site has been created by using a special kind of artificial intelligence algorithm called generative adversarial networks (GANs).

AI Robots will be replacing the White Collar Jobs by 6% until 2021

假设另一种场景，我把它叫“运输机难题”——假设你是一场救灾行动的总指挥，正带着一支小队坐在一架装满物资的运输机中，这是唯一一架运输机，如果没准时到就会有上万灾民饿死病死，如果最终也没到那几十万人都活不成。但此时受恶劣天气影响飞机突然损坏了，承载不了这么大的重量，必须要有一半人跳下飞机（假设不能丢物资），否则可能机毁人亡，要不要让半支队伍跳下去？   这架运输机就是比特大陆，灾区的难民就是现在的币民。比特大陆若是完蛋，对行业造成的冲击又会让一大批币民破产出局。   试想一下，有一天比特大陆真的倒闭了，那么可以预见到，矿机将挥泪大甩卖，矿工将抛售手里的BTC、BCH，BCH奄奄一息，BTC跌到新一轮谷底。虽然还会有“灾后重建工作”，但一大批人都将倒在这场灾难里，看不到明天的太阳。   作为灾民，会不会因为心疼跳下去的半支队伍而甘愿饿死病死？作为币民，会不会因为心疼被裁掉的几百上千人而甘愿看着比特大陆倒闭，忍受自己哪怕只是短期的破产？

The chances that they might miscommunicate and collide will therefore be far smaller.

The technology that favored democracy is changing, and as artificial intelligence develops, it might change further.

By utilizing the Deeplearning4j library1 for model representation, learning and prediction, KNIME builds upon a well performing open source solution with a thriving community.

It is especially thanks to the work of Yann LeCun and Yoshua Bengio (LeCun et al., 2015) that the application of deep neural networks has boomed in recent years. The technique, which utilizes neural networks with many layers and enhanced backpropagation algorithms for learning, was made possible through both new research and the ever increasing performance of computer chips.

One of KNIME's strengths is its multitude of nodes for data analysis and machine learning. While its base configuration already offers a variety of algorithms for this task, the plugin system is the factor that enables third-party developers to easily integrate their tools and make them compatible with the output of each other.