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.

I also arguelater that the challenge of the social–technical gap creates an opportunity to re-focus CSCW as a Simonian science of the artificial (where a science of the arti-ficial is suitably revised from Simon’s strictly empiricist grounds).

Исследование должно было дать ответ на 9 главных вопросов о том, что предпочтительнее: 1. сохранить жизнь человека или животного; 2. сохранить курс или свернуть; 3. сохранить жизнь пассажиров или пешеходов; 4. наибольшего количества людей или наименьшего; 5. мужчин или женщин; 6. молодых или стариков; 7. толстых или худых; 8. пешеходов, переходящих дорогу в соответствии с ПДД, или пешеходов-нарушителей; 9. людей с высоким или низким социальным статусом.

In December of this year, Stanford Libraries will co-host a conference on artificial intelligence with the National Library of Norway in Oslo. I

For all the talk about data and learning, Essa offered this blunt assessment: “Pretty much all edtech sucks. And machine learning is not going to improve edtech.” So what’s missing? “It’s not about the data, but how do we apply it. The reason why this technology sucks is because we don’t do good design. We need good design people to understand how this works.”

That’s Dr. Hunter, isn’t it? “By the Way do you mind if I ask you a personal question?

Hal, you have an enormous responsibility on this mission  perhaps the greatest responsibility of any single mission element. You’re the brain and central nervous system of the ship. Your responsibilities include watching over the men in hibernation. Does this ever cause you any lack of confidence?

– Do you know what happened? I’m sorry, Dave. I don’t have enough information.

Large computer networks (and their associated users) may “wake up” as superhumanly intelligent entities.

performance curves beginning to level off – because of our inability to automate the design work needed to support further hardware improvements. Wed end up with some very powerful hardware, but without the ability to push it further

The maximum possible effectiveness of a software system increases in direct proportion to the log of the effectiveness (i.e., speed, bandwidth, memory capacity) of the underlying hardware.

Habe ich einBuch, das für mich Verſtand hat, einen Seelſor¬ger, der für mich Gewiſſen hat, einen Arzt der fürmich die Diät beurtheilt, u. ſ. w. ſo brauche ich michja nicht ſelbſt zu bemühen. Ich habe nicht nöthigzu denken, wenn ich nur bezahlen kann; anderewerden das verdrießliche Geſchäft ſchon für michübernehmen.

What has changed, what remains the same, and what general patterns can be discerned from the past twenty years in the fast-changing field of edtech?

On the other hand, computers cannot read.

“In short, they have no history of supporting the machine learning research community and instead they are viewed as part of the disreputable ecosystem of people hoping to hype machine learning to make money.”

in search of a guiding philosophy

rather than to comprehend them

Philosophers and others in the field of the humanities who helped shape previous concepts of world order tend to be disadvantaged, lacking knowledge of AI’s mechanisms or being overawed by its capacities.

Who is responsible for the actions of AI? How should liability be determined for their mistakes? Can a legal system designed by humans keep pace with activities produced by an AI capable of outthinking and potentially outmaneuvering them?

algorithms to personalize results and make them available to other parties for political or commercial purposes

internet’s purpose is to ratify knowledge

Human cognition loses its personal character. Individuals turn into data, and data become regnant

order is now in upheaval

Would these machines learn to communicate with one another?

His machine, he said, learned to master Go by training itself through practice

Google's founding philosophy is that we don't know why this page is better than that one: If the statistics of incoming links say it is, that's good enough

Could the data lead colleges to rethink how they operate to serve students?

I can’t go on

Tim Urban

What kind of role could intelligent machines have in this ecosystem?

Đầu tiên mình nghĩ bạn cần nắm về machine learning và algorithm, bạn có thể bắt đầu bằng các khóa học trên mạng. Mình recommend khóa học Machine Learning của Andrew Ng, khóa học này được coi là kinh thánh cho data scientist. Sau đó bạn có thể bắt đầu với Python hoặc R và tham gia challenge trên Kaggle. Kaggle là một platform để Data Scientist tham gia, kiếm tiền thưởng và cạnh tranh thứ hạng với nhau. Nhiều người cũng nói với mình Kaggle là con đường tốt nhất và ngắn nhất để đến với Data Science.

when randomness is used, itis easy to lose accountability, since by definition any outcome which a randomized process couldhave produced is at least facially consistent with the design of that process

he power of computers is generally limited by a concept that computer scientists call noncomputability.58In short, certain types of problems cannot be solved by any computer program in any finite amount of time. There are many examples of noncomputable problems, but the most famous is Alan Turing’s “Halting Problem,” whichasks whether a given program will finish running (“halt”)

Testing of any kind is, however, a fundamentally limited approach to determining whether any fact about a computer system is true or untrue.

“black-box testing,” which considers only the inputs and outputs of a system or component, and “white-box testing,” in which the structure of the system’s internals is used to design test case

dynamic methods are limited by the finite number of inputs that can be tested or outputs that can be observed

Transparency advocatesoften claim that by reviewing a program’s disclosed source code, an analyst will be able to determine how a program behaves.47Indeed, the very idea that transparency allows outsiders to understand how a system functions is predicated on the usefulness of static analysis. But this claim is belied by the extraordinary difficulty of identifying even genuinely malicious code (“malware”), a task which has spawned a multibillion-dollar industry based largely on the careful review of code samples collected acrossthe internet.

On the simplest level, some programming languages are designed to prevent certain classes of mistakes. For example, some are designed in such a way that it is impossible to make the mistake that caused the Heartbleed bug.45These techniques have also been deployed in the aviation industry, for example, to ensure that the software that provides guidance functionality on rockets, airplanes, satellites, and scientific probes does not ever crash, as software failures have caused the losses of several vehicles in the past

static methods on their own say nothing about how a program interacts with its environment

Code can be complicated or obfuscated, and even expert analysis often misses eventual problems with the behavior of the program.

two testing methodologies

Test Driven Development (TDD) is a software engineering methodology practiced by many major software companies

Larry analyzes your historical and real-time data to create an entire social media strategy for you.

这张图给出了谷歌在2015年提出的Inception-v3模型。这个模型在ImageNet数据集上可以达到95%的正确率。然而，这个模型中有2500万个参数，分类一张图片需要50亿次加法或者乘法运算。

AlphaGo is going out on top. After beating Ke Jie, the world’s best player of the ancient Chinese board game Go, for the third time today at the Future of Go Summit in Wuzhen, Google’s DeepMind unit announced that it would be the last event match the AI plays.

Unanimous A.I. used a technology called “swarm intelligence” to coordinate a group of racing fans to correctly predict the Kentucky Derby superfecta (the first four places, in order). The swarm beat 540-to-1 odds, along with the most-trusted handicappers in the world.

She and her colleagues are using neural networks—complex mathematical systems for identifying patterns in data—to recognize diabetic retino­pathy, a leading cause of blindness among US adults.

Previously, Alexa lived inside speakers. Now, it’s in a camera.

The first, say, one hour and thirty-five minutes of The Circle are enormously powerful, in an intelligent, worry-inducing kind of way. The film’s last fifteen minutes, which feel rushed, don’t quite measure up. The ending is ambiguous, confusing, and strangely open-ended. But maybe that’s only appropriate. It feels the most like reality.

How Artificial Intelligence Will Transform Education

areas where deep learning is currently being poorly utilized

highly automated tools for training deep learning models

The best way we can help these people is by giving them the tools and knowledge to solve their own problems, using their own expertise and experience.

Elon Musk’s neural lace project could turn us all into cyborgs, and he says that it’s only four or five years away.

Corporate thought leaders have now realized that it is a much greater challenge to actually apply that data. The big takeaways in this topic are that data has to be seen to be acknowledged, tangible to be appreciated, and relevantly presented to have an impact. Connecting data on the macro level across an organization and then bringing it down to the individual stakeholder on the micro level seems to be the key in getting past the fact that right now big data is one thing to have and quite another to unlock.

The tech company is still preparing itself for what’s next—whether it’s virtual reality or a Matrix-like pill with which one could hallucinate entertainment.

What emerged was a prototype for a feature that could let blind or visually impaired people put their fingers over an image and have their phones read them a description of what’s happening.

“Facebook today cannot exist without AI. Every time you use Facebook or Instagram or Messenger, you may not realize it, but your experiences are being powered by AI.”

Perspective was created by Jigsaw and Google’s Counter Abuse Technology team in a collaborative research project called Conversation-AI.

In a world of rapidly evolving science and technology, imagination and moral foresight are our greatest human advantage.

Learn more about AMC’s Humans and tune in to the season two premiere on Monday, February 13, at 10/9c.

there are now popular game playing competitions and platforms which drive AI research

Yes, they discussed the possibility of a superintelligence that could somehow escape human control

So, they descended on a coastal retreat called Asilomar, a name that became synonymous with the guidelines they laid down at this meeting—a strict ethical framework meant to ensure that biotechnology didn’t unleash the apocalypse.

For all the talk of AI, it always seems that gossip is faster than progress. But it could be that within this century, we will fully realize the visions science fiction has promised us, says Dr. Ben Goertzel – for better or worse.

Social Out, VR In: We Analyzed Thousands Of Early-Stage Startup Descriptions To See Where Tech Is Headed Next

Microsoft Confirms Its Chinese-Language Chatbot Filters Certain Topics

Big Data

DNC stores 'temporal links' to keep track of the order things were written in

changes scare many people, whereas in fact they contain the potential to free us,

The Tesla accident in May, researchers say, was not a failure of computer vision. But it underscored the limitations of the science in applications like driverless cars despite remarkable progress in recent years, fueled by digital data, computer firepower and software inspired by the human brain.

A few cognitive scientists – notably Anthony Chemero of the University of Cincinnati, the author of Radical Embodied Cognitive Science (2009) – now completely reject the view that the human brain works like a computer. The mainstream view is that we, like computers, make sense of the world by performing computations on mental representations of it, but Chemero and others describe another way of understanding intelligent behaviour – as a direct interaction between organisms and their world.

Misleading headlines notwithstanding, no one really has the slightest idea how the brain changes after we have learned to sing a song or recite a poem. But neither the song nor the poem has been ‘stored’ in it. The brain has simply changed in an orderly way that now allows us to sing the song or recite the poem under certain conditions. When called on to perform, neither the song nor the poem is in any sense ‘retrieved’ from anywhere in the brain, any more than my finger movements are ‘retrieved’ when I tap my finger on my desk. We simply sing or recite – no retrieval necessary.

most important democratizing technology on the horizon

The top players, it turns out, can’t fully access their own knowledge about how they’re able to perform so well. This self-ignorance is common to many human abilities, from driving a car in traffic to recognizing a face. This strange state of affairs was beautifully summarized by the philosopher and scientist Michael Polanyi, who said, “We know more than we can tell.” It’s a phenomenon that has come to be known as “Polanyi’s Paradox.”

OpenAI is a non-profit artificial intelligence research company. Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return.

Big Sur is our newest Open Rack-compatible hardware designed for AI computing at a large scale. In collaboration with partners, we've built Big Sur to incorporate eight high-performance GPUs

artificial intelligence

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