Thursday, May 31, 2018

Moore’s Law

Source: https://gigaom.com/2018/05/31/moores-law/
May 31, 2018 at 05:15PM

The following is an excerpt from GigaOm publisher Byron Reese’s new book, The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity. You can purchase the book here.

The Fourth Age explores the implications of automation and AI on humanity, and has been described by Ethernet inventor and 3Com founder Bob Metcalfe as framing “the deepest questions of our time in clear language that invites the reader to make their own choices. Using 100,000 years of human history as his guide, he explores the issues around artificial general intelligence, robots, consciousness, automation, the end of work, abundance, and immortality.”

In this excerpt from The Fourth Age, Byron Reese explores the concept Moore’s Law and how more space, more speed, and more processor power impacts advancements in technology.


The scientific method supercharged technological development so much that it revealed an innate but mysterious property of all sorts of technology, a consistent and repeated doubling of its capabilities over fixed periods.

Our discovery of this profound and mysterious property of technology began modestly just half a century ago when Gordon Moore, one of the founders of Intel, noticed something interesting: the number of transistors in an integrated circuit was doubling about every two years. He noticed that this phenomenon had been going on for a while, and he speculated that the trend could continue for another decade. This observation became known as Moore’s law.

Doubling the number of transistors in an integrated circuit doubles the power of the computer. If that were the entire story, it would be of minor interest. But along came Ray Kurzweil, who made an amazing observation: computers have been doubling in power from way before transistors were even invented.

Kurzweil found that if you graph the processing power of computers since 1890, when simple electromechanical devices were used to help with the US census, computers doubled in processing power every other year, regardless of the underlying technology. Think about that: the underlying technology of the computer went from being mechanical, to using relays, then to vacuum tubes, then to transistors, and then to integrated circuits, and all along the way, Moore’s law never hiccupped. How could this be?

Well, the short answer is that no one knows. If you figure it out, tell me and we can split the Nobel money. How could the abstraction, the speed of the device, obey such a rigid law? Not only does no one really know, there aren’t even many ideas. But it appears to be some kind of law of the universe, that it takes a certain amount of technology to get to a place, and then once you have it, you’re able to use that technology to double that again.

Moore’s law continues to this day, well past the ten years Moore himself guessed it would hold up. And although every few years you see headlines like “Is this the End of Moore’s Law?” as is the case with almost all headlines phrased as a question, the answer is no. There are presently all manner of candidates that promise to keep the law going, from quantum computers to single-atom transistors to entirely new materials.

But—and here is the really interesting part—almost all types of technology, not just computers, seems to obey a Moore’s law of their own. The power of a given technology may not double every two years, but it doubles in something every n years. Anyone who has bought laptops or digital cameras or computer monitors over time has experienced this firsthand. Hard drives can hold more, megapixels keep rising, and screen resolutions increase.

There are even those who maintain that multicellular life behaves this way, doubling in complexity every 376 million years. This intriguing thesis, offered by the geneticists Richard Gordon and Alexei Sharov, posits that multicellular life is about ten billion years old, predating earth itself, implying . . . well, implying all kinds of things, such as that human life must have originated somewhere else in the galaxy, and through one method or another, made its way here.

The fact that technology doubles is a big deal, bigger than one might first suspect. Humans famously underestimate the significance of constant doubling because nothing in our daily lives behaves that way. You don’t wake up with two kids, then four kids, then eight, then sixteen. Our bank balances don’t go from $100 to $200 to $400 to $800, day after day.

To understand just how quickly something that repeatedly doubles gets really big, consider the story of the invention of chess. About a thousand years ago, a mathematician in what is today India is said to have brought his creation to the ruler, and showed him how the game was played. The ruler, quite impressed, asked the mathematician what he wanted for a reward. The mathematician responded that he was a humble man and his needs were few. He simply asked that a single grain of rice be placed on the first square of the chessboard. Then two on the second, four on the third, each square doubling along the way. All he wanted was the rice that would be on the sixty-fourth square.

So how much rice do you think this is? Given my setup to the story you know it will be a big number. But just imagine what that much rice would look like. Would it fill a silo? A warehouse? It is actually more rice than has been cultivated in the entire history of humanity. By the way, when the ruler figured it out, he had the mathematician put to death, so there is another life lesson to be learned here.

Think also of a domino rally, in which you have a row of dominos lined up and you push one and it pushes the next one, and so on. Each domino can push over a domino 50 percent taller than itself. So if you set up thirty-two dominos, each 50 percent bigger than the first, that last domino could knock over the Empire State Building. And that is with a mere 50 percent growth rate, not doubling.

If you think we have seen some pretty amazing technological advances in our day, then fasten your seat belt. With computers, we are on the sixtieth or sixty-first square of our chess board, metaphorically, where doubling is a pretty big deal. If you don’t have the computing power to do something, just wait two years and you will have twice as much. Sure, it took us thousands of years to build the computer on your desk, but in just two more years, we will have built one twice as powerful. Two years after that, twice as powerful again. So while it took us almost five thousand years to get from the abacus to the iPad, twenty-five years from now, we will have something as far ahead of the iPad as it is ahead of the abacus. We can’t even imagine or wrap our heads around what that thing will be.

The combination of the scientific method and Moore’s mysterious law is what has given us the explosion of new technology that is part and parcel of our daily life. It gave us robots, nanotech, the gene editing technology CRISPR-Cas9, space travel, atomic power, and a hundred other wonders. In fact, technology advances at such a rate that we are, for the most part, numb to the wonder of it all. New technology comes with such rapidity that it has become almost mundane. We carry supercomputers in our pockets that let us communicate instantly with almost anyone on the planet. These devices are so ubiquitous that even children have them and they are so inexpensive as to be free with a two-year cellular contract. We have powers that used to be attributed to the gods, such as seeing events as they happen from a great distance. We can change the temperature of the room in which we are sitting with the smallest movement of our fingers. We can fly through the air six miles above the Earth at the speed of sound, so safely that statistically one would have to fly every day for over 100,000 years to get in an accident. And yet somehow we can manage to feel inconvenienced when they run out of the turkey wrap and we have to eat the Cobb salad.


To read more of GigaOm publisher Byron Reese’s new book, The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity, you can purchase it here.

Tech Tip: Prune and Save Web Pages

Source: https://www.nytimes.com/2018/05/31/technology/personaltech/pages-pdf-print.html?partner=rss&emc=rss
May 31, 2018 at 04:00PM

Your computer most likely includes free tools for creating or printing stripped-down versions of web articles without ads, videos or other distractions.

Voices in AI – Episode 46: A Conversation with Peter Cahill

Lottery Wins on Christmas Were a Glitch, So South Carolina Won’t Pay

Source: https://www.nytimes.com/2018/05/31/us/south-carolina-lottery-glitch.html?partner=rss&emc=rss
May 31, 2018 at 02:49PM

The state lottery says the winning tickets resulted from a vendor’s error, so millions of dollars will not be distributed to would-be winners.

‘I Don’t Feel Superhuman. I Feel Like a Mom Who Has a Career.’

Source: https://www.nytimes.com/2018/05/31/technology/government-mom.html?partner=rss&emc=rss
May 31, 2018 at 01:00PM

Rebecca Slaughter, a new commissioner at the F.T.C., brings her infant to work in Washington, a city whose work culture has been stubbornly slow to change.

Prediksi Skor Real Madrid Vs Liverpool 27 Mei 2018

Source: http://feedproxy.google.com/~r/PandoBlog/~3/1Ug6HCkMw_k/
May 31, 2018 at 12:25PM

 Real Madrid vs Liverpool

Prediksi Bol Real Madrid vs Liverpool, Bursa Taruhan Real Madrid vs Liverpool, Prediksi Real Madrid vs Liverpool, Prediksi Real Madrid vs Liverpool, Hasil Skor Real Madrid vs Liverpool, Real Madrid vs Liverpool [ 003] – yang akan di adakan pada tanggal 27 Mei 2018 Pada Pukul 01: 45 WIB Di Stadion NSK Olimpijsky (Kyiv (Kiev))

Prediksi Real Madrid vs Liverpool – UEFA Champhionships akan hadir di hari ini hari Permainan ini dimainkan di paruh pertama pertandingan, dan sudah waktunya bagi tim untuk memainkan tontonan spektakuler tim, dan sekarang tim sepak bola nyata, Real Madrid, Spanyol, dan tim Melawan Liverpool Liverpool dari Inggris dan berada di garis terdepan Spektakuler yang spektakuler. Mampukah Si Putih menang Melawan Tim Merah nanti? Real Madrid Real Madrid adalah tim kayu asal Spaniol yang kami tidak perlu di ragukan lagi menadi tim pengepakan RAKSASA asal Spanyol yang saat ini menduduki posisi 5 besar klasemen Utama Liga Spanyol yaitu La Liga hari di harpa juha putra La Liga dan selatan UEFA Champhions nanti. Tim Sepakbola Real Madrid ” width=”640″ height=”360″ srcset=”http://www.prediksibola303.net/wp-content/uploads/2018/05/Real-Madrid-Football-Team.jpg 640w, http://www.prediksibola303.net/wp-content/uploads/2018/05/Real-Madrid-Football-Team-300×169.jpg 300w” sizes=”(max-width: 640px) 100vw, 640px”/>

Liverpool Tim Sepakbola Real Madrid ” width=”640″ height=”360″ srcset=”http://www.prediksibola303.net/wp-content/uploads/2018/05/Real-Madrid-Football-Team.jpg 640w, http://www.prediksibola303.net/wp-content/uploads/2018/05/Real-Madrid-Football-Team-300×169.jpg 300w” sizes=”(max-width: 640px) 100vw, 640px”/>

– 010] Liverpool adalah jam dari beberapa timebomb Asal Inggris dan kami sekarang memimpin terburu-buru untuk bermain di tengah hari dan di selatan kesenjangan antara berhala-berhala Liga Premier junior dan selatan. Kejuaraan 5-hari akan dimainkan dalam dua putaran, satu pertandingan dan dua bagian yang akan dimainkan oleh Chelsea skuad Chelsea untuk hasil imbang 1-1 di Chelsea. Real Madrid vs Liverpool ” width=”530″ height=”351″ srcset=”http://www.prediksibola303.net/wp-content/uploads/2018/05/Liverpool-Football-Team.jpg 530w, http://www.prediksibola303.net/wp-content/uploads/2018/05/Liverpool-Football-Team-300×199.jpg 300w” sizes=”(max-width: 530px) 100vw, 530px”/>

Real Madrid vs Liverpool

Pertemuan ke dua tim di 4 Real Madrid vs Liverpool:

11/05/2014 Real Madrid 1-0 Liverpool
23/10/14 Liverpool 0 – 3 Real Madrid
3/3/209 Liverpool 4-0 Real Madrid
26/02/09 Real Madrid Liverpool

5 Pertandingan Terakhir Bermain Real Madrid:

26/04/18 Bayern Munich 1-2 Real Madrid
28/04/18 Real Madrid 2-1 Leganés
2/5/2018 Nyata Madrid 2-2 Bayern Munich
7/5/2018 Barcelona 2-2 Real Madrid
10/5/2018 Sevilla 3-2 Real Madrid

5 laga Terakhir Bermain Liverpool:

21/04/18 West Bromwich Albion 2-2 Liverpool
25/04/18 Liverpool 5-2 Roma
28/04/18 Liverpool 0 – 0 Stoke City
3/5/2018 Roma 4-2 Liverpool
5/6/2018 Chelsea 1-0 Liverpool

Real Madrid:

Real Madrid: Kiko Casilla – Jesús Vallejo – M. Kovačić – Lucas Vázquez – K. Benzema – 3co Asensio – Dani Ceballos – T. Hernández – Casemiro – Sergio Ramos – Nacho

Liverpool: L. Karius – V. van Dijk – A. Robertson – G. Wijnaldum – S. Mané – Roberto Firmino – Mohamed Salah – T. Alexander-Arnold – J. Milner – D. Lovren – N. Clyne

Bursa Asian Handicap: –
Over / Under: –
Prediksi Real Madrid vs Liverpool oleh: Prediksi Jitu
Tips: Pada babak kedua kali kami memprediksikan Real Madrid Vs Liverpool akan berakhir dengan Scor 2: 1

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Self-Service Master Data Management

Source: https://gigaom.com/2018/05/30/self-service-master-data-management/
May 30, 2018 at 03:00PM

Once data is under management in its best-fit leveragable platform in an organization, it is as prepared as it can be to serve its many callings. It is in position to be used for purposes operationally and analytically and across the spectrum of need. Ideas emerge from business areas no longer encumbered with the burden of managing data, which can be 60% – 70% of the effort to bring the idea to reality. Walls of distrust in data come down and the organization can truly excel with an important barrier to success removed.

An important goal of the information management function in an organization is to get all data under management by this definition, and to keep it under management as systems come and go over time.

Master Data Management (MDM) is one of these key leveragable platforms. It is the elegant place for data with widespread use in the organization. It becomes the system of record for customer, product, store, material, reference and all other non-transactional data. MDM data can be accessed directly from the hub or, more commonly, mapped and distributed widely throughout the organization. This use of MDM data does not even account for the significant MDM benefit of efficiently creating and curating master data to begin with.

MDM benefits are many, including hierarchy management, data quality, data governance/workflow, data curation, and data distribution. One overlooked benefit is just having a database where trusted data can be accessed. Like any data for access, the visualization aspect of this is important. With MDM data having a strong associative quality to it, the graph representation works quite well.

Graph traversals are a natural way for analyzing network patterns. Graphs can handle high degrees of separation with ease and facilitate visualization and exploration of networks and hierarchies. Graph databases themselves are no substitute for MDM as they provide only one of the many necessary functions that an MDM tool does. However, when graph technology is embedded within MDM, such as what IBM is doing in InfoSphere MDM – similar to AI (link) and blockchain (link) – it is very powerful.

Graph technology is one of the many ways to facilitate self-service to MDM. Long a goal of business intelligence, self-service has significant applicability to MDM as well. Self-service is opportunity oriented. Users may want to validate a hypothesis, experiment, innovate, etc. Long development cycles or laborious process between a user and the data can be frustrating.

Historically, the burden for all MDM functions has fallen squarely on a centralized, development function. It’s overloaded and, as with the self-service business intelligence movement, needs disintermediation. IBM is fundamentally changing this dynamic with the next release of Infosphere MDM. Its self-service data import, matching, and lightweight analytics allows the business user to find, share and get insight from both MDM and other data.

Then there’s Big Match. Big Match can analyze structured and unstructured customer data together to gain deeper customer insights. It can enable fast, efficient linking of data from multiple sources to grow and curate customer information. The majority of the information in your organization that is not under management is unstructured data. Unstructured data has always been a valuable asset to organizations, but it can be difficult to manage. Emails, documents, medical records, contracts, design specifications, legal agreements, advertisements, delivery instructions, and other text-based sources of information do not fit neatly into tabular relational databases. Most BI tools on MDM data offer the ability to drill down and roll up data in reports and dashboards, which is good. But what about the ability to “walk sideways” across data sources to discover how different parts of the business interrelate?

Using unstructured data for customer profiling allows organizations to unify diverse data from inside and outside the enterprise—even the “ugly” stuff; that is, dirty data that is incompatible with highly structured, fact-dimension data that would have been too costly to combine using traditional integration and ETL methods.

Finally, unstructured data management enables text analytics, so that organizations can gain insight into customer sentiment, competitive trends, current news trends, and other critical business information. In text analytics, everything is fair game for consideration, including customer complaints, product reviews from the web, call center transcripts, medical records, and comment/note fields in an operational system. Combining unstructured data with artificial intelligence and natural language processing can extract new attributes and facts for entities such as people, location, and sentiment from text, which can then be used to enrich the analytic experience.

All of these uses and capabilities are enhanced if they can be provided using a self-service interface that users can easily leverage to enrich data from within their apps and sources. This opens up a whole new world for discovery.

With graph technology, distribution of the publishing function and the integration of al data including unstructured data, MDM can truly have important data under management, empower the business user, be the cornerstone to digital transformation and truly be self-service.

The Case For and Against AGI

Source: https://gigaom.com/2018/05/29/case-for-and-against-agi/
May 29, 2018 at 05:15PM

The following is an excerpt from GigaOm publisher Byron Reese’s new book, The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity. You can purchase the book here.

The Fourth Age explores the implications of automation and AI on humanity, and has been described by Ethernet inventor and 3Com founder Bob Metcalfe as framing “the deepest questions of our time in clear language that invites the reader to make their own choices. Using 100,000 years of human history as his guide, he explores the issues around artificial general intelligence, robots, consciousness, automation, the end of work, abundance, and immortality.”

One of those deep questions of our time:

Is an artificial general intelligence, or AGI, even possible? Most people working in the field of AI are convinced that an AGI is possible, though they disagree about when it will happen. In this excerpt from The Fourth Age, Byron Reese considers it an open question and explores if it is possible.


The Case for AGI

Those who believe we can build an AGI operate from a single core assumption. While granting that no one understands how the brain works, they firmly believe that it is a machine, and therefore our mind must be a machine as well. Thus, ever more powerful computers eventually will duplicate the capabilities of the brain and yield intelligence. As Stephen Hawking explains:

I believe there is no deep difference between what can be achieved by a biological brain and what can be achieved by a computer. It therefore follows that computers can, in theory, emulate human intelligence—and exceed it.

As this quote indicates, Hawking would answer our foundational question about the composition of the universe as a monist, and therefore someone who believes that AGI is certainly possible. If nothing happens in the universe outside the laws of physics, then whatever makes us intelligent must obey the laws of physics. And if that is the case, we can eventually build something that does the same thing. He would presumably answer the foundational question of “What are we?” with “machines,” thus again believing that AGI is clearly possible. Can a machine be intelligent? Of course! You are just such a machine.

Consider this thought experiment: What if we built a mechanical neuron that worked exactly like the organic kind. And what if we then duplicated all the other parts of the brain mechanically as well. This isn’t a stretch, given that we can make other artificial organs. Then, if you had a scanner of incredible power, it could make a synthetic copy of your brain right down to the atomic level. How in the world can you argue that won’t have your intelligence?

The only way, the argument goes, you get away from AGI being possible is by invoking some mystical, magical feature of the brain that we have no proof exists. In fact, we have a mountain of evidence that it doesn’t. Every day we learn more and more about the brain, and not once have the scientists returned and said, “Guess what! We discovered a magical part of the brain that defies all laws of physics, and which therefore requires us to throw out all the science we have based on that physics for the last four hundred years.” No, one by one, the inner workings of the brain are revealed. And yes, the brain is a fantastic organ, but there is nothing magical about it. It is just another device.

Since the beginning of the computer age, people have come up with lists of things that computers will supposedly never be able to do. One by one, computers have done them. And even if there were some magical part of the brain (which there isn’t), there would be no reason to assume that it is the mechanism by which we are intelligent. Even if you proved that this magical part is the secret sauce in our intelligence (which it isn’t), there would be no reason to assume we can’t find another way to achieve intelligence.

Thus, this argument concludes, of course we can build an AGI. Only mystics and spiritualists would say otherwise.

The Case against AGI

Let’s now explore the other side.

A brain, as was noted earlier, contains a hundred billion neurons with a hundred trillion connections among them. But just as music is the space between the notes, you exist not in those neurons, but in the space between them. Somehow, your intelligence emerges from these connections.

We don’t know how the mind comes into being, but we do know that computers don’t operate anything at all like a mind, or even a brain for that matter. They simply do what they have been programmed to do. The words they output mean nothing to them. They have no idea if they are talking about coffee beans or cholera. They know nothing, they think nothing, they are as dead as fried chicken.

A computer can do only one simple thing: manipulate abstract symbols in memory. So what is incumbent on the “for” camp is to explain how such a device, no matter how fast it can operate, could, in fact, “think.”

We casually use language about computers as if they are creatures like us. We say things like, “When the computer sees someone repeatedly type in the wrong password, it understands what this means and interprets it as an attempted security breach.”

But the computer does not actually “see” anything. Even with a camera mounted on top, it does not see. It may detect something, just like a lawn system uses a sensor to detect when the lawn is dry. Further, it does not understand anything. It may compute something, but it has no understanding.

We use language that treats computers as alive colloquially, but we should keep in mind it is not really true. It is important now to make the distinction, because with AGI we are talking about machines going from computing something to understanding something.

Joseph Weizenbaum, an early thinker about AI, built a simple computer program in 1966, ELIZA, which was a natural language program that roughly mirrored what a psychologist might say. You make a statement like “I am sad” and ELIZA would ask, “What do you think made you sad?” Then you might say, “I am sad because no one seems to like me.” ELIZA might respond “Why do you think that no one seems to like you?” And so on. This approach will be familiar to anyone who has spent much time with a four-year-old who continually and recursively asks why, why, why to every statement.

When Weizenbaum saw that people were actually pouring out their hearts to ELIZA, even though they knew it was a computer program, he turned against it. He said that in effect, when the computer says “I understand,” it tells a lie. There is no “I” and there is no understanding.

His conclusion is not simply linguistic hairsplitting. The entire question of AGI hinges on this point of understandingsomething. To get at the heart of this argument, consider the thought experiment offered up in 1980 by the American philosopher John Searle. It is called the Chinese room argument. Here it is in broad form:

There is a giant room, sealed off, with one person in it. Let’s call him the Librarian. The Librarian doesn’t know any Chinese. However, the room is filled with thousands of books that allow him to look up any question in Chinese and produce an answer in Chinese.

Someone outside the room, a Chinese speaker, writes a question in Chinese and slides it under the door. The Librarian picks up the piece of paper and retrieves a volume we will call book 1. He finds the first symbol in book 1, and written next to that symbol is the instruction “Look up the next symbol in book 1138.” He looks up the next symbol in book 1138. Next to that symbol he is given the instruction to retrieve book 24,601, and look up the next symbol. This goes on and on. When he finally makes it to a final symbol on the piece of paper, the final book directs him to copy a series of symbols down. He copies the cryptic symbols and passes them under the door. The Chinese speaker outside picks up the paper and reads the answer to his question. He finds the answer to be clever, witty, profound, and insightful. In fact, it is positively brilliant.

Again, the Librarian does not speak any Chinese. He has no idea what the question was or what the answer said. He simply went from book to book as the books directed and copied what they directed him to copy.

Now, here is the question: Does the Librarian understand Chinese?

Searle uses this analogy to show that no matter how complex a computer program is, it is doing nothing more than going from book to book. There is no understanding of any kind. And it is quite hard to imagine how there can be true intelligence without any understanding whatsoever. He states plainly, “In the literal sense, the programmed computer understands what the car and the adding machine understand, namely, exactly nothing.”

Some try to get around the argument by saying that the entire system understands Chinese. While this seems plausible at first, it doesn’t really get us very far. Say the Librarian memorized the contents of every book, and further could come up with the response from these books so quickly that as soon as you could write a question down, he could write the answer. But still, the Librarian has no idea what the characters he is writing mean. He doesn’t know if he is writing about dishwater or doorbells. So again, does the Librarian understand Chinese?

So that is the basic argument against the possibility of AGI. First, computers simply manipulate ones and zeros in memory. No matter how fast you do that, that doesn’t somehow conjure up intelligence. Second, the computer just follows a program that was written for it, just like the Chinese Room. So no matter how impressive it looks, it doesn’t really understand anything. It is just a party trick.

It should be noted that many people in the AI field would most likely scratch their heads at the reasoning of the case against AGI and find it all quite frustrating. They would say that of course the brain is a machine—what else could it be? Sure, computers can only manipulate abstract symbols, but the brain is just a bunch of neurons that send electrical and chemical signals to each other. Who would have guessed that would have given us intelligence? It is true that brains and computers are made of different stuff, but there is no reason to assume they can’t do the same exact things. The only reason, they would say, that we think brains are not machines is because we are uncomfortable thinking we are only machines.

They would also be quick to offer rebuttals of the Chinese room argument. There are several, but the one most pertinent to our purposes is what I call the “quacks like a duck” argument. If it walks like a duck, swims like a duck, and quacks like a duck, I am going to assume it is a duck. It doesn’t really matter if in your opinion there is no understanding, for if you can ask it questions in Chinese and it responds with good answers in Chinese, then it understands Chinese. If the room can act like it understands, then it understands. End of story. This was in fact Turing’s central thesis in his 1950 paper on the question of whether computers can think. He states, “May not machines carry out something which ought to be described as thinking but which is very different from what a human does?” Turing would have seen no problem at all in saying the Chinese room can think. Of course it can. It is obvious. The idea that it can answer questions in Chinese but doesn’t understand Chinese is self-contradictory.


To read more of GigaOm publisher Byron Reese’s new book, The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity, you can purchase it here.

Voices in AI – Episode 45: A Conversation with Stephen Wolfram

Source: https://gigaom.com/2018/05/29/voices-in-ai-episode-45-a-conversation-with-stephen-wolfram/
May 29, 2018 at 05:10PM

In this episode, Byron and Stephen discuss computational intelligence and what’s happening in the brain.

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Byron Reese: This is Voices in AI, brought to you by GigaOm. I’m Byron Reese. Today my guest is Stephen Wolfram. Few people can be said to literally need no introduction, but he is one of them. Anyway, as a refresher, Stephen Wolfram exploded into the world as a child prodigy who made all kinds of contributions to physics. He worked with Richard Feynman for years. But, unlike many prodigies, he didn’t peak at 18, or 28, or 38, or 48. In fact, he probably hasn’t peaked at all right now. He went on to create Mathematica, which is the closest thing the world has to math as a language. He wrote his magnum opus, a book called ‘A New Kind of Science.’ And he created Wolfram Alpha, an answer engine that grows better and better every day. Welcome to the show, Stephen.

Stephen Wolfram: Thanks.

I usually start off by asking, what is artificial intelligence? But I want to ask you a different question. What is intelligence?

It’s a complicated and slippery concept. It’s useful to start, maybe, in thinking about what may be an easier concept, what is life? You might think that was an easy thing to define. Here on Earth, you can pretty much tell whether something is alive or not. You dig down, you look in a microscope, you figure out does it have RNA, does it have cell membrane? Does it have all those kinds of things that are characteristic of life as we know it on Earth? The question is, what abstractly is something like life? And, we just don’t really know. I remember when I was a kid, there were these spacecrafts sent to Mars, and they would dig up soil, and they had this definitive test for life at some point which was, you feed it sugar and you see whether it metabolizes it. I doubt that in an abstract sense that’s a good, fundamental definition of life. In the case of life on Earth, we kind of have a definition because it’s connected by this, sort of, thread of history. All life is, kind of, connected by a thread of history. It’s sort of the same thing with intelligence. If you ask, what is the fundamental essence of intelligence? Well, in the case of the intelligence that we know with humans and so on, it’s all connected by a thread of history. If we ask, what is intelligence abstractly? That’s a much harder question, and it’s one I’ve thought about for a long time. What’s necessary to say that something is intelligent is for it to be capable of some level of sophisticated computation. If all the thing does is to, kind of, add two numbers together, and that’s the only thing it can do, we’re not going to likely consider it intelligent.

But your theory is that hurricanes are computational and icicles, and DNA.

Absolutely.

And so they’re all intelligent?

Wednesday, May 30, 2018

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Source: http://www.pandoblog.com/2018/05/30/agen-judi-bola-agen-bola-piala-dunia-2018/
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Tech Tip: How to Watch Live Sports Online

Source: https://www.nytimes.com/2018/05/30/technology/personaltech/how-live-sports-streaming.html?partner=rss&emc=rss
May 30, 2018 at 04:00PM

Several streaming services offer multiple channels for athletic events, games and matches — which can make ditching the monthly cable bill even easier for some fans.

Self-Service Master Data Management

Source: https://gigaom.com/2018/05/30/self-service-master-data-management/
May 30, 2018 at 03:00PM

Once data is under management in its best-fit leveragable platform in an organization, it is as prepared as it can be to serve its many callings. It is in position to be used for purposes operationally and analytically and across the spectrum of need. Ideas emerge from business areas no longer encumbered with the burden of managing data, which can be 60% – 70% of the effort to bring the idea to reality. Walls of distrust in data come down and the organization can truly excel with an important barrier to success removed.

An important goal of the information management function in an organization is to get all data under management by this definition, and to keep it under management as systems come and go over time.

Master Data Management (MDM) is one of these key leveragable platforms. It is the elegant place for data with widespread use in the organization. It becomes the system of record for customer, product, store, material, reference and all other non-transactional data. MDM data can be accessed directly from the hub or, more commonly, mapped and distributed widely throughout the organization. This use of MDM data does not even account for the significant MDM benefit of efficiently creating and curating master data to begin with.

MDM benefits are many, including hierarchy management, data quality, data governance/workflow, data curation, and data distribution. One overlooked benefit is just having a database where trusted data can be accessed. Like any data for access, the visualization aspect of this is important. With MDM data having a strong associative quality to it, the graph representation works quite well.

Graph traversals are a natural way for analyzing network patterns. Graphs can handle high degrees of separation with ease and facilitate visualization and exploration of networks and hierarchies. Graph databases themselves are no substitute for MDM as they provide only one of the many necessary functions that an MDM tool does. However, when graph technology is embedded within MDM, such as what IBM is doing in InfoSphere MDM – similar to AI (link) and blockchain (link) – it is very powerful.

Graph technology is one of the many ways to facilitate self-service to MDM. Long a goal of business intelligence, self-service has significant applicability to MDM as well. Self-service is opportunity oriented. Users may want to validate a hypothesis, experiment, innovate, etc. Long development cycles or laborious process between a user and the data can be frustrating.

Historically, the burden for all MDM functions has fallen squarely on a centralized, development function. It’s overloaded and, as with the self-service business intelligence movement, needs disintermediation. IBM is fundamentally changing this dynamic with the next release of Infosphere MDM. Its self-service data import, matching, and lightweight analytics allows the business user to find, share and get insight from both MDM and other data.

Then there’s Big Match. Big Match can analyze structured and unstructured customer data together to gain deeper customer insights. It can enable fast, efficient linking of data from multiple sources to grow and curate customer information. The majority of the information in your organization that is not under management is unstructured data. Unstructured data has always been a valuable asset to organizations, but it can be difficult to manage. Emails, documents, medical records, contracts, design specifications, legal agreements, advertisements, delivery instructions, and other text-based sources of information do not fit neatly into tabular relational databases. Most BI tools on MDM data offer the ability to drill down and roll up data in reports and dashboards, which is good. But what about the ability to “walk sideways” across data sources to discover how different parts of the business interrelate?

Using unstructured data for customer profiling allows organizations to unify diverse data from inside and outside the enterprise—even the “ugly” stuff; that is, dirty data that is incompatible with highly structured, fact-dimension data that would have been too costly to combine using traditional integration and ETL methods.

Finally, unstructured data management enables text analytics, so that organizations can gain insight into customer sentiment, competitive trends, current news trends, and other critical business information. In text analytics, everything is fair game for consideration, including customer complaints, product reviews from the web, call center transcripts, medical records, and comment/note fields in an operational system. Combining unstructured data with artificial intelligence and natural language processing can extract new attributes and facts for entities such as people, location, and sentiment from text, which can then be used to enrich the analytic experience.

All of these uses and capabilities are enhanced if they can be provided using a self-service interface that users can easily leverage to enrich data from within their apps and sources. This opens up a whole new world for discovery.

With graph technology, distribution of the publishing function and the integration of al data including unstructured data, MDM can truly have important data under management, empower the business user, be the cornerstone to digital transformation and truly be self-service.

How a Pentagon Contract Became an Identity Crisis for Google

Source: https://www.nytimes.com/2018/05/30/technology/google-project-maven-pentagon.html?partner=rss&emc=rss
May 30, 2018 at 12:00PM

A $9 million deal for the use of artificial intelligence technology has fractured the internet giant’s work force and risks driving away top engineering talent.

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Source: http://www.pandoblog.com/2018/05/30/agen-bola-piala-dunia-agen-bola-piala-dunia/
May 30, 2018 at 12:16PM

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The Case For and Against AGI

Source: https://gigaom.com/2018/05/29/case-for-and-against-agi/
May 29, 2018 at 05:15PM

The following is an excerpt from GigaOm publisher Byron Reese’s new book, The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity. You can purchase the book here.

The Fourth Age explores the implications of automation and AI on humanity, and has been described by Ethernet inventor and 3Com founder Bob Metcalfe as framing “the deepest questions of our time in clear language that invites the reader to make their own choices. Using 100,000 years of human history as his guide, he explores the issues around artificial general intelligence, robots, consciousness, automation, the end of work, abundance, and immortality.”

One of those deep questions of our time:

Is an artificial general intelligence, or AGI, even possible? Most people working in the field of AI are convinced that an AGI is possible, though they disagree about when it will happen. In this excerpt from The Fourth Age, Byron Reese considers it an open question and explores if it is possible.


The Case for AGI

Those who believe we can build an AGI operate from a single core assumption. While granting that no one understands how the brain works, they firmly believe that it is a machine, and therefore our mind must be a machine as well. Thus, ever more powerful computers eventually will duplicate the capabilities of the brain and yield intelligence. As Stephen Hawking explains:

I believe there is no deep difference between what can be achieved by a biological brain and what can be achieved by a computer. It therefore follows that computers can, in theory, emulate human intelligence—and exceed it.

As this quote indicates, Hawking would answer our foundational question about the composition of the universe as a monist, and therefore someone who believes that AGI is certainly possible. If nothing happens in the universe outside the laws of physics, then whatever makes us intelligent must obey the laws of physics. And if that is the case, we can eventually build something that does the same thing. He would presumably answer the foundational question of “What are we?” with “machines,” thus again believing that AGI is clearly possible. Can a machine be intelligent? Of course! You are just such a machine.

Consider this thought experiment: What if we built a mechanical neuron that worked exactly like the organic kind. And what if we then duplicated all the other parts of the brain mechanically as well. This isn’t a stretch, given that we can make other artificial organs. Then, if you had a scanner of incredible power, it could make a synthetic copy of your brain right down to the atomic level. How in the world can you argue that won’t have your intelligence?

The only way, the argument goes, you get away from AGI being possible is by invoking some mystical, magical feature of the brain that we have no proof exists. In fact, we have a mountain of evidence that it doesn’t. Every day we learn more and more about the brain, and not once have the scientists returned and said, “Guess what! We discovered a magical part of the brain that defies all laws of physics, and which therefore requires us to throw out all the science we have based on that physics for the last four hundred years.” No, one by one, the inner workings of the brain are revealed. And yes, the brain is a fantastic organ, but there is nothing magical about it. It is just another device.

Since the beginning of the computer age, people have come up with lists of things that computers will supposedly never be able to do. One by one, computers have done them. And even if there were some magical part of the brain (which there isn’t), there would be no reason to assume that it is the mechanism by which we are intelligent. Even if you proved that this magical part is the secret sauce in our intelligence (which it isn’t), there would be no reason to assume we can’t find another way to achieve intelligence.

Thus, this argument concludes, of course we can build an AGI. Only mystics and spiritualists would say otherwise.

The Case against AGI

Let’s now explore the other side.

A brain, as was noted earlier, contains a hundred billion neurons with a hundred trillion connections among them. But just as music is the space between the notes, you exist not in those neurons, but in the space between them. Somehow, your intelligence emerges from these connections.

We don’t know how the mind comes into being, but we do know that computers don’t operate anything at all like a mind, or even a brain for that matter. They simply do what they have been programmed to do. The words they output mean nothing to them. They have no idea if they are talking about coffee beans or cholera. They know nothing, they think nothing, they are as dead as fried chicken.

A computer can do only one simple thing: manipulate abstract symbols in memory. So what is incumbent on the “for” camp is to explain how such a device, no matter how fast it can operate, could, in fact, “think.”

We casually use language about computers as if they are creatures like us. We say things like, “When the computer sees someone repeatedly type in the wrong password, it understands what this means and interprets it as an attempted security breach.”

But the computer does not actually “see” anything. Even with a camera mounted on top, it does not see. It may detect something, just like a lawn system uses a sensor to detect when the lawn is dry. Further, it does not understand anything. It may compute something, but it has no understanding.

We use language that treats computers as alive colloquially, but we should keep in mind it is not really true. It is important now to make the distinction, because with AGI we are talking about machines going from computing something to understanding something.

Joseph Weizenbaum, an early thinker about AI, built a simple computer program in 1966, ELIZA, which was a natural language program that roughly mirrored what a psychologist might say. You make a statement like “I am sad” and ELIZA would ask, “What do you think made you sad?” Then you might say, “I am sad because no one seems to like me.” ELIZA might respond “Why do you think that no one seems to like you?” And so on. This approach will be familiar to anyone who has spent much time with a four-year-old who continually and recursively asks why, why, why to every statement.

When Weizenbaum saw that people were actually pouring out their hearts to ELIZA, even though they knew it was a computer program, he turned against it. He said that in effect, when the computer says “I understand,” it tells a lie. There is no “I” and there is no understanding.

His conclusion is not simply linguistic hairsplitting. The entire question of AGI hinges on this point of understandingsomething. To get at the heart of this argument, consider the thought experiment offered up in 1980 by the American philosopher John Searle. It is called the Chinese room argument. Here it is in broad form:

There is a giant room, sealed off, with one person in it. Let’s call him the Librarian. The Librarian doesn’t know any Chinese. However, the room is filled with thousands of books that allow him to look up any question in Chinese and produce an answer in Chinese.

Someone outside the room, a Chinese speaker, writes a question in Chinese and slides it under the door. The Librarian picks up the piece of paper and retrieves a volume we will call book 1. He finds the first symbol in book 1, and written next to that symbol is the instruction “Look up the next symbol in book 1138.” He looks up the next symbol in book 1138. Next to that symbol he is given the instruction to retrieve book 24,601, and look up the next symbol. This goes on and on. When he finally makes it to a final symbol on the piece of paper, the final book directs him to copy a series of symbols down. He copies the cryptic symbols and passes them under the door. The Chinese speaker outside picks up the paper and reads the answer to his question. He finds the answer to be clever, witty, profound, and insightful. In fact, it is positively brilliant.

Again, the Librarian does not speak any Chinese. He has no idea what the question was or what the answer said. He simply went from book to book as the books directed and copied what they directed him to copy.

Now, here is the question: Does the Librarian understand Chinese?

Searle uses this analogy to show that no matter how complex a computer program is, it is doing nothing more than going from book to book. There is no understanding of any kind. And it is quite hard to imagine how there can be true intelligence without any understanding whatsoever. He states plainly, “In the literal sense, the programmed computer understands what the car and the adding machine understand, namely, exactly nothing.”

Some try to get around the argument by saying that the entire system understands Chinese. While this seems plausible at first, it doesn’t really get us very far. Say the Librarian memorized the contents of every book, and further could come up with the response from these books so quickly that as soon as you could write a question down, he could write the answer. But still, the Librarian has no idea what the characters he is writing mean. He doesn’t know if he is writing about dishwater or doorbells. So again, does the Librarian understand Chinese?

So that is the basic argument against the possibility of AGI. First, computers simply manipulate ones and zeros in memory. No matter how fast you do that, that doesn’t somehow conjure up intelligence. Second, the computer just follows a program that was written for it, just like the Chinese Room. So no matter how impressive it looks, it doesn’t really understand anything. It is just a party trick.

It should be noted that many people in the AI field would most likely scratch their heads at the reasoning of the case against AGI and find it all quite frustrating. They would say that of course the brain is a machine—what else could it be? Sure, computers can only manipulate abstract symbols, but the brain is just a bunch of neurons that send electrical and chemical signals to each other. Who would have guessed that would have given us intelligence? It is true that brains and computers are made of different stuff, but there is no reason to assume they can’t do the same exact things. The only reason, they would say, that we think brains are not machines is because we are uncomfortable thinking we are only machines.

They would also be quick to offer rebuttals of the Chinese room argument. There are several, but the one most pertinent to our purposes is what I call the “quacks like a duck” argument. If it walks like a duck, swims like a duck, and quacks like a duck, I am going to assume it is a duck. It doesn’t really matter if in your opinion there is no understanding, for if you can ask it questions in Chinese and it responds with good answers in Chinese, then it understands Chinese. If the room can act like it understands, then it understands. End of story. This was in fact Turing’s central thesis in his 1950 paper on the question of whether computers can think. He states, “May not machines carry out something which ought to be described as thinking but which is very different from what a human does?” Turing would have seen no problem at all in saying the Chinese room can think. Of course it can. It is obvious. The idea that it can answer questions in Chinese but doesn’t understand Chinese is self-contradictory.


To read more of GigaOm publisher Byron Reese’s new book, The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity, you can purchase it here.

Voices in AI – Episode 45: A Conversation with Stephen Wolfram

Source: https://gigaom.com/2018/05/29/voices-in-ai-episode-45-a-conversation-with-stephen-wolfram/
May 29, 2018 at 05:10PM

In this episode, Byron and Stephen discuss computational intelligence and what’s happening in the brain.

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Byron Reese: This is Voices in AI, brought to you by GigaOm. I’m Byron Reese. Today my guest is Stephen Wolfram. Few people can be said to literally need no introduction, but he is one of them. Anyway, as a refresher, Stephen Wolfram exploded into the world as a child prodigy who made all kinds of contributions to physics. He worked with Richard Feynman for years. But, unlike many prodigies, he didn’t peak at 18, or 28, or 38, or 48. In fact, he probably hasn’t peaked at all right now. He went on to create Mathematica, which is the closest thing the world has to math as a language. He wrote his magnum opus, a book called ‘A New Kind of Science.’ And he created Wolfram Alpha, an answer engine that grows better and better every day. Welcome to the show, Stephen.

Stephen Wolfram: Thanks.

I usually start off by asking, what is artificial intelligence? But I want to ask you a different question. What is intelligence?

It’s a complicated and slippery concept. It’s useful to start, maybe, in thinking about what may be an easier concept, what is life? You might think that was an easy thing to define. Here on Earth, you can pretty much tell whether something is alive or not. You dig down, you look in a microscope, you figure out does it have RNA, does it have cell membrane? Does it have all those kinds of things that are characteristic of life as we know it on Earth? The question is, what abstractly is something like life? And, we just don’t really know. I remember when I was a kid, there were these spacecrafts sent to Mars, and they would dig up soil, and they had this definitive test for life at some point which was, you feed it sugar and you see whether it metabolizes it. I doubt that in an abstract sense that’s a good, fundamental definition of life. In the case of life on Earth, we kind of have a definition because it’s connected by this, sort of, thread of history. All life is, kind of, connected by a thread of history. It’s sort of the same thing with intelligence. If you ask, what is the fundamental essence of intelligence? Well, in the case of the intelligence that we know with humans and so on, it’s all connected by a thread of history. If we ask, what is intelligence abstractly? That’s a much harder question, and it’s one I’ve thought about for a long time. What’s necessary to say that something is intelligent is for it to be capable of some level of sophisticated computation. If all the thing does is to, kind of, add two numbers together, and that’s the only thing it can do, we’re not going to likely consider it intelligent.

But your theory is that hurricanes are computational and icicles, and DNA.

Absolutely.

And so they’re all intelligent?

Tech Tip: Converting High Efficiency Image File Photos for Windows

Source: https://www.nytimes.com/2018/05/29/technology/personaltech/convert-heic-heif-windows.html?partner=rss&emc=rss
May 29, 2018 at 04:00PM

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Monday, May 28, 2018

Prediksi Skor Inggris Vs Nigeria 2 Juni 2018

Source: http://feedproxy.google.com/~r/PandoBlog/~3/5KOzeEVTsv0/
May 28, 2018 at 06:02PM

 Inggris VS Nigeria (prediksibola303)

Prediksi Bola Inggris Vs Nigeria, Bursa Taruhan Inggris Vs Nigeria, Prediksi Inveri Vs Nigeria, Prediksi Pertandingan Inggris Vs Nigeria, Hasil Scor Inggris Vs Nigeria, Inggris Vs Nigeria – yang akan di adakan pada tanggal 2 Juni 2018 Pada Pukul 23: 15 WIB Stadion Di Stadion Wembley (London)

Prediksi Inggris Vs Nigeria masih di musim ini kami akan menghadapi banyak laga persahabatan yang akan di mulai sebelum pertandingan sepakbola Piala dunia Rusia 2018 yang nantinya akan di mainkan oleh tim sepakbola asal Inggris melawan tim sepakbola asal Nigeria yang nanti akan di mainkan di Inggris di mana tim sepakbola Nigeria akan menjadi tim tamu di laga kali ini. Mampukah tim inglés Inggris yang bisa di katakan di atas Nigeria menang nanti?. 006] Inggris – Inggris, salah satu negara yang mana Bahasa Inggris adalah Bahasa Internacional, dan juga di kenal sebagai salah satu negara yang di mana hampir 90 % rakyat dia merupakan olahraga sepakbola. Di kancah Internasional sendiri tim ini dikenal sebagai penyuplai pemain berbakat seperti Rooney, Ryan Giggsdan lainnya. 008] Tim Sepakbola Inggris (2) ” width=”615″ height=”409″ srcset=”http://www.prediksibola303.net/wp-content/uploads/2018/05/England-Football-Team-2-.jpg 615w, http://www.prediksibola303.net/wp-content/uploads/2018/05/England-Football-Team-2–300×200.jpg 300w” sizes=”(max-width: 615px) 100vw, 615px”/>

Nigeria – Nigeria, tim ini di Nigeria, tim ini diego kenal sebagai tim sepakbola yang diunjukkan oleh CAF yaitu badan asosiasi Afrika. Untuk membuat Super Eagles, Anda harus melihat logo tim Timeless Timbers di Elang, di mana Anda akan menemukan Warna Hijau dari logo Seragam Pertamanya. Di Laga kali ini di mana tim ini menjadi Tim Tamu mampukah The Eagles nunti menunjukkan cakar nya di Eropa nanti? saksikan lah pertandingan nya nanti.

 Tim Sepakbola Nigeria (20

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Statistic Inggris Vs Nigeria

Pertemuan ke dua tim di 5 Pertandingan terakhir Inggris Vs Nigeria:

12/6/202 Nigeria 0 – 0 Inggris
16/11/94 Inggris 1-0 Nigeria

5 bola Terakhir Bermain Inggris:

8/10/2017 Lithuania 0 – 1 Inggris
11/11/2017 Inggris 0 – 0 Jerman
15/11/17 Inggris 0 – 0 Brasil
24/03/18 Belanda 0 – 1 Inggris
28/03/18 Inggris 1-1 Italia

5 pertandingan Terakhir Bermain Nigeria:

28/01/18 Nigeria E 1- 2 Angola
1/2/2018 Sudan 0 – 1 Nigeria
5/2/2018 Maroko 4-0 Nigeria
24/03/18 Polandia 0 – 1 Nigeria
28/03/18 Nigeria 0 – 2 Serbia

Susanan Pemain Inggris Vs Nigeria:

J. Butland – J. Tarkowski – A. Oxlade-Chamberlain – R. Sterling – J. Vardy – J. Linga rd – E. Dier – J. Stones – A. Young – K. Walker – K. Trippier

Nigeria: F. Uzoho – T. Ebuehi – J. Obi – O. Onazi – A. Musa – A. Iwobi – W. Ndidi – C. Awaziem – V. Moses – W. Troost-Ekong – B. Idowu

Bursa Asian Handicap: –

Prediksi Skor Inggris vs Nigeria oleh: Prediksi Jitu 011 ] Tips: Pada babak pertama kali ini kami memprediksikan Inggris Vs Nigeria akan berakhir dengan Scor 1: 0

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