p*d
2 楼
【 以下文字转载自 JobHunting 讨论区 】
发信人: phantomkid (), 信区: JobHunting
标 题: 求助！！OPT被deny，因为SEVIS里把学位搞错了
关键字: OPT denial,motion to reopen,sevis
发信站: BBS 未名空间站 (Mon Oct 3 14:24:15 2016, 美东)
已经有了offer可是OPT被deny。。。
四月份申请的，七月收到拒信，信上的理由是不能用同一个学位申请两次OPT。但是我
上一次申请用的是MS学位，现在用的是PhD学位，完全符合要求，莫名其妙被拒了。拒
信上说可以申请Motion to Reopen，学校国际学生办公室帮我一起弄了申请，然后发现
SEVIS系统里面我原本的MS学位变成了PhD。。。。申请的时候附上了之前的I20和成绩
单什么的，第一次申请OPT的时候只有MS学位。
Reopen的申请是8月十号收到的，看起来遥遥无期。因为有了offer我去申请加急但是失
败了。。。律师建议我用Ombudsman或者找congressman，USCIS客服建议我去field
office。
请问有人遇到类似情况吗？大家有没有什么好建议？不知道什么时候才能解决，offer
希望我十一月就开始工作，好着急。。。我想问这个算不算USCIS的错误呢？另外加急
是不是只能打电话给USCIS？
发信人: phantomkid (), 信区: JobHunting
标 题: 求助！！OPT被deny，因为SEVIS里把学位搞错了
关键字: OPT denial,motion to reopen,sevis
发信站: BBS 未名空间站 (Mon Oct 3 14:24:15 2016, 美东)
已经有了offer可是OPT被deny。。。
四月份申请的，七月收到拒信，信上的理由是不能用同一个学位申请两次OPT。但是我
上一次申请用的是MS学位，现在用的是PhD学位，完全符合要求，莫名其妙被拒了。拒
信上说可以申请Motion to Reopen，学校国际学生办公室帮我一起弄了申请，然后发现
SEVIS系统里面我原本的MS学位变成了PhD。。。。申请的时候附上了之前的I20和成绩
单什么的，第一次申请OPT的时候只有MS学位。
Reopen的申请是8月十号收到的，看起来遥遥无期。因为有了offer我去申请加急但是失
败了。。。律师建议我用Ombudsman或者找congressman，USCIS客服建议我去field
office。
请问有人遇到类似情况吗？大家有没有什么好建议？不知道什么时候才能解决，offer
希望我十一月就开始工作，好着急。。。我想问这个算不算USCIS的错误呢？另外加急
是不是只能打电话给USCIS？
a*g
3 楼
By Roland Moore-Colyer
Mon Nov 16 2015, 07:20
http://www.theinquirer.net/inquirer/feature/2434242/facebook-s-
FACEBOOK IS A COMPANY known primarily for its social feed of emotional
statuses, endless emojis, pictures of 'hols with the ladz', and, of course,
a big blue thumbs-up.
Normally associated with tech giants like IBM, Google and Apple, or some
disruptive Tech City startup, artificial intelligence (AI) is not the first
thing to spring to mind when pondering Zuckerberg's 1.5 billion-strong
social network.
Yet alongside solar-powered drones, virtual reality headsets and a wealth of
coding tech, Facebook is also building its own deep learning and cognitive
computing AI.
mike-schroepfer"The core goal here is to build systems that can better
understand and perceive the world the way we as people do, so it can help us
manage that world," said Mike Schroepfer (pictured) Facebook's chief
technical officer.
This may sound like Facebook is just making another virtual assistant with a
few smart moves and dry witticisms. But the social networking giant
actually appears to be pushing the boundaries of AI tech that could leave
Siri and Cortana scratching their digital heads.
Memory Networks, the moniker Facebook has given its AI technology, is what
Schroepfer sees as the key to unlocking the door that separates deep
learning machines which need to be taught, from intelligent systems that
learn by themselves.
"[Memory] is in my opinion a fundamentally missing component of AI; there's
no way we could view AI systems that can do the sorts of things we want
without the capability to learn and retain new facts that they've never seen
before," he said.
"One of the challenges with AI systems is many of the existing systems are
dumb pattern matchers; you ask it a question, it gives you an answer, it
doesn't learn as it goes.
"So one of the challenges with Memory Networks is can we take a neural net,
this thing that you train, and can we attach a short-term memory to it so
that it can take in data and answer questions based on that data."
Schroepfer described standard deep learning systems as just ways to create a
black box of data for pattern matching after lengthy training.
But where Memory Networks differs is the ability to ingest new data and use
machine learning to effectively get incrementally smart over time, rather
than rely on being taught by a human.
In practice, this works by having a deep learning neural network to act as a
‘reasoning' system, which uses logical techniques like deduction, applied
to data in a separate memory to turn it into knowledge that can be used to
answer questions.
Through a form of associative memory - the ability to learn and remember the
relationship between unrelated items such as the name of a place and its
appearance - Memory Networks can then store and retrieve internal answers,
observations and knowledge, thus getting smarter.
Schroepfer used the example of feeding Memory Networks the basic script of a
film. Through natural language comprehension, Memory Networks can reason
the movie's events and timeline to answer general questions without being
specifically taught to answer exact queries.
In effect, the neural network is applying logic and reasoning to its
memories, learning from knowledge and experience, not unlike our own fleshy
human brains.
Photographic memory
Robot artificial intelligence
Applying Memory Networks to natural text-based language is only one half of
Facebook's AI research.
Schroepfer explained that adding image recognition into the mix is the way
to help Memory Networks better perceive the world, or most likely pictures
uploaded onto social media.
Facebook's image recognition tech analyses photos at a pixel level, and has
been trained to recognise patterns among them to better distinguish separate
different objects in a photo even if they overlap. This process of
segmentation then allows the AI tech to identify each object in the picture.
Schroepfer noted Facebook's image recognition system can do this 30 percent
faster than most other systems and through using 10 times less training data.
But the magic happens when image recognition is combined with Memory
Networks. This produces Visual Q&A, an AI system that answers questions
posed to it via manual or voice inputs by people with impaired vison who
want to know what a picture is composed of and what is happening in it.
Think smart, look sharp
AI brain
Schroepfer highlighted how the company's AI research was exploring how it
can use image recognition to teach neural networks to perceive whether
something is going to happen from observation, rather than have an innate
understanding of the situation. This is similar to how children work out
when something is going to fall without understanding the physics behind it.
"That's how people learn; they learn by messing around with the world and
seeing what happens. And we have computer systems now that are brilliant on
a lot of things but don't understand basic physics and don't understand
operations of the world because they haven't been able to observe it," he
explained.
"So one of the other things we're trying to do is to teach computers some
basic common sense about the world, and one way we are doing this is by
stacking blocks together and showing an image of that to a computer and
asking it to determine is this stack of blocks going to fall in this case or
stand up."
According to Schroepfer, Facebook's techs were able to build a classifier
that is over 90 percent accurate at identifying when said block was going to
fall, and can in fact beat most humans at the task.
"It's one of many different ways we're trying to help systems understand
what's going to happen in the future and help us think about not just
reacting to what's happened but helping me plan things in the future,"
Schroepfer added. He noted how the elements of this AI research were being
added into the M assistant to make it more capable or understanding complex
requests.
Beating the human
inside-brain-mind
You could question why Facebook is effectively looking to create an AI with
'common sense' rather than relying on strict logic systems, which can often
work their way through all possible outcomes to come up with the right
answer.
As ever, Schroepfer provided a compelling reason, backed up by an example;
in this case, pitting a computer against a human in the Chinese board game
Go, a game people consistently win over computers.
This is because unlike chess, where a computer can trump humans by working
through all the possibilities of board configurations, Go has significantly
more moves. For example, after the first two moves in chess there are 400
possible next moves; with Go there are 130,000. This is too much information
for AI to crunch without bursting into a silicon sweat.
So Facebook looked to combine traditional AI tech designed to apply deep
learning methods with Go and connect this with image recognition.
Schroepfer said that this approach gave the AI the ability to work out from
patterns on the board what's a good move, instead of crunching thousands
upon thousands of potential moves. In short, Facebook created a Go AI that
has intuition.
"We've built up some of the image recognition technology and connected that
together to some deep learning systems about possible good moves, and
basically in a short number of months we've built a Go AI that can beat some
of the AIs that were designed specifically for the purpose of playing Go,
and it's as good as a very good amateur player," said Schroepfer, without
sounding smug.
While the idea of beating humans may send a chill up some people's spines
and send others running deep into the internet-free zones of Wales while
screaming ‘Skynet', an army of Facebook-branded remembering, reasoning and
predicating AIs is some way off; 10 years or more according to Schroepfer.
But the social media company, which is now very much a major technology
industry player, and its AI research is a good indication of how neural
networks and smart systems in the near future will be developed.
"The lesson really here is that by combining the different technologies, you
could very rapidly build something that was better than the thing that
people have been working on for many, many years, and I think this will be
one of the many ways we will see advances in AI in the future," said
Schroepfer.
He concluded with Facebook's ultimate AI destiny: "When these AI systems get
good enough, we can afford to scale it to the entire planet; it's a super
power we can give to every person on the planet."
We only hope everyone remembers that with great power comes great
responsibility. μ
Mon Nov 16 2015, 07:20
http://www.theinquirer.net/inquirer/feature/2434242/facebook-s-
FACEBOOK IS A COMPANY known primarily for its social feed of emotional
statuses, endless emojis, pictures of 'hols with the ladz', and, of course,
a big blue thumbs-up.
Normally associated with tech giants like IBM, Google and Apple, or some
disruptive Tech City startup, artificial intelligence (AI) is not the first
thing to spring to mind when pondering Zuckerberg's 1.5 billion-strong
social network.
Yet alongside solar-powered drones, virtual reality headsets and a wealth of
coding tech, Facebook is also building its own deep learning and cognitive
computing AI.
mike-schroepfer"The core goal here is to build systems that can better
understand and perceive the world the way we as people do, so it can help us
manage that world," said Mike Schroepfer (pictured) Facebook's chief
technical officer.
This may sound like Facebook is just making another virtual assistant with a
few smart moves and dry witticisms. But the social networking giant
actually appears to be pushing the boundaries of AI tech that could leave
Siri and Cortana scratching their digital heads.
Memory Networks, the moniker Facebook has given its AI technology, is what
Schroepfer sees as the key to unlocking the door that separates deep
learning machines which need to be taught, from intelligent systems that
learn by themselves.
"[Memory] is in my opinion a fundamentally missing component of AI; there's
no way we could view AI systems that can do the sorts of things we want
without the capability to learn and retain new facts that they've never seen
before," he said.
"One of the challenges with AI systems is many of the existing systems are
dumb pattern matchers; you ask it a question, it gives you an answer, it
doesn't learn as it goes.
"So one of the challenges with Memory Networks is can we take a neural net,
this thing that you train, and can we attach a short-term memory to it so
that it can take in data and answer questions based on that data."
Schroepfer described standard deep learning systems as just ways to create a
black box of data for pattern matching after lengthy training.
But where Memory Networks differs is the ability to ingest new data and use
machine learning to effectively get incrementally smart over time, rather
than rely on being taught by a human.
In practice, this works by having a deep learning neural network to act as a
‘reasoning' system, which uses logical techniques like deduction, applied
to data in a separate memory to turn it into knowledge that can be used to
answer questions.
Through a form of associative memory - the ability to learn and remember the
relationship between unrelated items such as the name of a place and its
appearance - Memory Networks can then store and retrieve internal answers,
observations and knowledge, thus getting smarter.
Schroepfer used the example of feeding Memory Networks the basic script of a
film. Through natural language comprehension, Memory Networks can reason
the movie's events and timeline to answer general questions without being
specifically taught to answer exact queries.
In effect, the neural network is applying logic and reasoning to its
memories, learning from knowledge and experience, not unlike our own fleshy
human brains.
Photographic memory
Robot artificial intelligence
Applying Memory Networks to natural text-based language is only one half of
Facebook's AI research.
Schroepfer explained that adding image recognition into the mix is the way
to help Memory Networks better perceive the world, or most likely pictures
uploaded onto social media.
Facebook's image recognition tech analyses photos at a pixel level, and has
been trained to recognise patterns among them to better distinguish separate
different objects in a photo even if they overlap. This process of
segmentation then allows the AI tech to identify each object in the picture.
Schroepfer noted Facebook's image recognition system can do this 30 percent
faster than most other systems and through using 10 times less training data.
But the magic happens when image recognition is combined with Memory
Networks. This produces Visual Q&A, an AI system that answers questions
posed to it via manual or voice inputs by people with impaired vison who
want to know what a picture is composed of and what is happening in it.
Think smart, look sharp
AI brain
Schroepfer highlighted how the company's AI research was exploring how it
can use image recognition to teach neural networks to perceive whether
something is going to happen from observation, rather than have an innate
understanding of the situation. This is similar to how children work out
when something is going to fall without understanding the physics behind it.
"That's how people learn; they learn by messing around with the world and
seeing what happens. And we have computer systems now that are brilliant on
a lot of things but don't understand basic physics and don't understand
operations of the world because they haven't been able to observe it," he
explained.
"So one of the other things we're trying to do is to teach computers some
basic common sense about the world, and one way we are doing this is by
stacking blocks together and showing an image of that to a computer and
asking it to determine is this stack of blocks going to fall in this case or
stand up."
According to Schroepfer, Facebook's techs were able to build a classifier
that is over 90 percent accurate at identifying when said block was going to
fall, and can in fact beat most humans at the task.
"It's one of many different ways we're trying to help systems understand
what's going to happen in the future and help us think about not just
reacting to what's happened but helping me plan things in the future,"
Schroepfer added. He noted how the elements of this AI research were being
added into the M assistant to make it more capable or understanding complex
requests.
Beating the human
inside-brain-mind
You could question why Facebook is effectively looking to create an AI with
'common sense' rather than relying on strict logic systems, which can often
work their way through all possible outcomes to come up with the right
answer.
As ever, Schroepfer provided a compelling reason, backed up by an example;
in this case, pitting a computer against a human in the Chinese board game
Go, a game people consistently win over computers.
This is because unlike chess, where a computer can trump humans by working
through all the possibilities of board configurations, Go has significantly
more moves. For example, after the first two moves in chess there are 400
possible next moves; with Go there are 130,000. This is too much information
for AI to crunch without bursting into a silicon sweat.
So Facebook looked to combine traditional AI tech designed to apply deep
learning methods with Go and connect this with image recognition.
Schroepfer said that this approach gave the AI the ability to work out from
patterns on the board what's a good move, instead of crunching thousands
upon thousands of potential moves. In short, Facebook created a Go AI that
has intuition.
"We've built up some of the image recognition technology and connected that
together to some deep learning systems about possible good moves, and
basically in a short number of months we've built a Go AI that can beat some
of the AIs that were designed specifically for the purpose of playing Go,
and it's as good as a very good amateur player," said Schroepfer, without
sounding smug.
While the idea of beating humans may send a chill up some people's spines
and send others running deep into the internet-free zones of Wales while
screaming ‘Skynet', an army of Facebook-branded remembering, reasoning and
predicating AIs is some way off; 10 years or more according to Schroepfer.
But the social media company, which is now very much a major technology
industry player, and its AI research is a good indication of how neural
networks and smart systems in the near future will be developed.
"The lesson really here is that by combining the different technologies, you
could very rapidly build something that was better than the thing that
people have been working on for many, many years, and I think this will be
one of the many ways we will see advances in AI in the future," said
Schroepfer.
He concluded with Facebook's ultimate AI destiny: "When these AI systems get
good enough, we can afford to scale it to the entire planet; it's a super
power we can give to every person on the planet."
We only hope everyone remembers that with great power comes great
responsibility. μ
l*g
4 楼
It has been like this for a long time.
h*c
5 楼
can it learn how human forgets
C*V
6 楼
不能merge了？
l*g
7 楼
Not as I know.
【在 C**V 的大作中提到】
: 不能merge了？
【在 C**V 的大作中提到】
: 不能merge了？
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