nhaliday + computer-vision   41

classification - ImageNet: what is top-1 and top-5 error rate? - Cross Validated
Now, in the case of top-1 score, you check if the top class (the one having the highest probability) is the same as the target label.

In the case of top-5 score, you check if the target label is one of your top 5 predictions (the 5 ones with the highest probabilities).
nibble  q-n-a  overflow  machine-learning  deep-learning  metrics  comparison  ranking  top-n  classification  computer-vision  benchmarks  dataset  accuracy  error  jargon 
june 2019 by nhaliday
[1803.00085] Chinese Text in the Wild
We introduce Chinese Text in the Wild, a very large dataset of Chinese text in street view images.


We give baseline results using several state-of-the-art networks, including AlexNet, OverFeat, Google Inception and ResNet for character recognition, and YOLOv2 for character detection in images. Overall Google Inception has the best performance on recognition with 80.5% top-1 accuracy, while YOLOv2 achieves an mAP of 71.0% on detection. Dataset, source code and trained models will all be publicly available on the website.
nibble  pdf  papers  preprint  machine-learning  deep-learning  deepgoog  state-of-art  china  asia  writing  language  dataset  error  accuracy  computer-vision  pic  ocr  org:mat  benchmarks  questions 
may 2019 by nhaliday
AI-complete - Wikipedia
In the field of artificial intelligence, the most difficult problems are informally known as AI-complete or AI-hard, implying that the difficulty of these computational problems is equivalent to that of solving the central artificial intelligence problem—making computers as intelligent as people, or strong AI.[1] To call a problem AI-complete reflects an attitude that it would not be solved by a simple specific algorithm.

AI-complete problems are hypothesised to include computer vision, natural language understanding, and dealing with unexpected circumstances while solving any real world problem.[2]

Currently, AI-complete problems cannot be solved with modern computer technology alone, but would also require human computation. This property can be useful, for instance to test for the presence of humans as with CAPTCHAs, and for computer security to circumvent brute-force attacks.[3][4]


AI-complete problems are hypothesised to include:

Bongard problems
Computer vision (and subproblems such as object recognition)
Natural language understanding (and subproblems such as text mining, machine translation, and word sense disambiguation[8])
Dealing with unexpected circumstances while solving any real world problem, whether it's navigation or planning or even the kind of reasoning done by expert systems.


Current AI systems can solve very simple and/or restricted versions of AI-complete problems, but never in their full generality. When AI researchers attempt to "scale up" their systems to handle more complicated, real world situations, the programs tend to become excessively brittle without commonsense knowledge or a rudimentary understanding of the situation: they fail as unexpected circumstances outside of its original problem context begin to appear. When human beings are dealing with new situations in the world, they are helped immensely by the fact that they know what to expect: they know what all things around them are, why they are there, what they are likely to do and so on. They can recognize unusual situations and adjust accordingly. A machine without strong AI has no other skills to fall back on.[9]
concept  reduction  cs  computation  complexity  wiki  reference  properties  computer-vision  ai  risk  ai-control  machine-learning  deep-learning  language  nlp  order-disorder  tactics  strategy  intelligence  humanity  speculation  crux 
march 2018 by nhaliday
Information Processing: US Needs a National AI Strategy: A Sputnik Moment?
FT podcasts on US-China competition and AI: http://infoproc.blogspot.com/2018/05/ft-podcasts-on-us-china-competition-and.html

A new recommended career path for effective altruists: China specialist: https://80000hours.org/articles/china-careers/
Our rough guess is that it would be useful for there to be at least ten people in the community with good knowledge in this area within the next few years.

By “good knowledge” we mean they’ve spent at least 3 years studying these topics and/or living in China.

We chose ten because that would be enough for several people to cover each of the major areas listed (e.g. 4 within AI, 2 within biorisk, 2 within foreign relations, 1 in another area).

AI Policy and Governance Internship: https://www.fhi.ox.ac.uk/ai-policy-governance-internship/

Deciphering China’s AI Dream
The context, components, capabilities, and consequences of
China’s strategy to lead the world in AI

Europe’s AI delusion: https://www.politico.eu/article/opinion-europes-ai-delusion/
Brussels is failing to grasp threats and opportunities of artificial intelligence.

When the computer program AlphaGo beat the Chinese professional Go player Ke Jie in a three-part match, it didn’t take long for Beijing to realize the implications.

If algorithms can already surpass the abilities of a master Go player, it can’t be long before they will be similarly supreme in the activity to which the classic board game has always been compared: war.

As I’ve written before, the great conflict of our time is about who can control the next wave of technological development: the widespread application of artificial intelligence in the economic and military spheres.


If China’s ambitions sound plausible, that’s because the country’s achievements in deep learning are so impressive already. After Microsoft announced that its speech recognition software surpassed human-level language recognition in October 2016, Andrew Ng, then head of research at Baidu, tweeted: “We had surpassed human-level Chinese recognition in 2015; happy to see Microsoft also get there for English less than a year later.”


One obvious advantage China enjoys is access to almost unlimited pools of data. The machine-learning technologies boosting the current wave of AI expansion are as good as the amount of data they can use. That could be the number of people driving cars, photos labeled on the internet or voice samples for translation apps. With 700 or 800 million Chinese internet users and fewer data protection rules, China is as rich in data as the Gulf States are in oil.

How can Europe and the United States compete? They will have to be commensurately better in developing algorithms and computer power. Sadly, Europe is falling behind in these areas as well.


Chinese commentators have embraced the idea of a coming singularity: the moment when AI surpasses human ability. At that point a number of interesting things happen. First, future AI development will be conducted by AI itself, creating exponential feedback loops. Second, humans will become useless for waging war. At that point, the human mind will be unable to keep pace with robotized warfare. With advanced image recognition, data analytics, prediction systems, military brain science and unmanned systems, devastating wars might be waged and won in a matter of minutes.


The argument in the new strategy is fully defensive. It first considers how AI raises new threats and then goes on to discuss the opportunities. The EU and Chinese strategies follow opposite logics. Already on its second page, the text frets about the legal and ethical problems raised by AI and discusses the “legitimate concerns” the technology generates.

The EU’s strategy is organized around three concerns: the need to boost Europe’s AI capacity, ethical issues and social challenges. Unfortunately, even the first dimension quickly turns out to be about “European values” and the need to place “the human” at the center of AI — forgetting that the first word in AI is not “human” but “artificial.”

US military: "LOL, China thinks it's going to be a major player in AI, but we've got all the top AI researchers. You guys will help us develop weapons, right?"

US AI researchers: "No."

US military: "But... maybe just a computer vision app."

US AI researchers: "NO."

AI-risk was a mistake.
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february 2018 by nhaliday
[1604.03640] Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex
We discuss relations between Residual Networks (ResNet), Recurrent Neural Networks (RNNs) and the primate visual cortex. We begin with the observation that a shallow RNN is exactly equivalent to a very deep ResNet with weight sharing among the layers. A direct implementation of such a RNN, although having orders of magnitude fewer parameters, leads to a performance similar to the corresponding ResNet. We propose 1) a generalization of both RNN and ResNet architectures and 2) the conjecture that a class of moderately deep RNNs is a biologically-plausible model of the ventral stream in visual cortex. We demonstrate the effectiveness of the architectures by testing them on the CIFAR-10 dataset.
papers  preprint  neuro  biodet  interdisciplinary  deep-learning  model-class  identity  machine-learning  nibble  org:mat  computer-vision 
february 2017 by nhaliday
Performance Trends in AI | Otium
Deep learning has revolutionized the world of artificial intelligence. But how much does it improve performance? How have computers gotten better at different tasks over time, since the rise of deep learning?

In games, what the data seems to show is that exponential growth in data and computation power yields exponential improvements in raw performance. In other words, you get out what you put in. Deep learning matters, but only because it provides a way to turn Moore’s Law into corresponding performance improvements, for a wide class of problems. It’s not even clear it’s a discontinuous advance in performance over non-deep-learning systems.

In image recognition, deep learning clearly is a discontinuous advance over other algorithms. But the returns to scale and the improvements over time seem to be flattening out as we approach or surpass human accuracy.

In speech recognition, deep learning is again a discontinuous advance. We are still far away from human accuracy, and in this regime, accuracy seems to be improving linearly over time.

In machine translation, neural nets seem to have made progress over conventional techniques, but it’s not yet clear if that’s a real phenomenon, or what the trends are.

In natural language processing, trends are positive, but deep learning doesn’t generally seem to do better than trendline.


The learned agent performs much better than the hard-coded agent, but moves more jerkily and “randomly” and doesn’t know the law of reflection. Similarly, the reports of AlphaGo producing “unusual” Go moves are consistent with an agent that can do pattern-recognition over a broader space than humans can, but which doesn’t find the “laws” or “regularities” that humans do.

Perhaps, contrary to the stereotype that contrasts “mechanical” with “outside-the-box” thinking, reinforcement learners can “think outside the box” but can’t find the box?

ratty  core-rats  summary  prediction  trends  analysis  spock  ai  deep-learning  state-of-art  🤖  deepgoog  games  nlp  computer-vision  nibble  reinforcement  model-class  faq  org:bleg  shift  chart  technology  language  audio  accuracy  speaking  foreign-lang  definite-planning  china  asia  microsoft  google  ideas  article  speedometer  whiggish-hegelian  yvain  ssc  smoothness  data  hsu  scitariat  genetics  iq  enhancement  genetic-load  neuro  neuro-nitgrit  brain-scan  time-series  multiplicative  iteration-recursion  additive  multi  arrows 
january 2017 by nhaliday
China invents the digital totalitarian state | The Economist
PROGRAMMING CHINA: The Communist Party’s autonomic approach to managing state security: https://www.merics.org/sites/default/files/2017-12/171212_China_Monitor_44_Programming_China_EN__0.pdf
- The Chinese Communist Party (CCP) has developed a form of authoritarianism that cannot be measured through traditional political scales like reform versus retrenchment. This version of authoritarianism involves both “hard” and “soft” authoritarian methods that constantly act together.
- To describe the social management process, this paper introduces a new analytical framework called China’s “Autonomic Nervous System” (ANS). This approach explains China’s social management process through a complex systems engineering framework. This framework mirrors the CCP’s Leninist way of thinking.
- The framework describes four key parts of social management, visualized through ANS’s “self-configuring,” “self-healing,” “self-optimizing” and “self-protecting” objectives.

China's Social Credit System: An Evolving Practice of Control: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3175792

The Chinese government is not the only entity that has access to millions of faces + identifying information. So do Google, Facebook, Instagram, and anyone who has scraped information from similar social networks (e.g., US security services, hackers, etc.).

In light of such ML capabilities it seems clear that anti-ship ballistic missiles can easily target a carrier during the final maneuver phase of descent, using optical or infrared sensors (let alone radar).

China goes all-in on technology the US is afraid to do right.
US won't learn its lesson in time for CRISPR or AI.

Artificial intelligence is developing fast in China. But is it likely to enable the suppression of freedoms? One of China's most successful investors, Neil Shen, has a short answer to that question. Also, Chinese AI companies now have the potential to overtake their Western rivals -- we explain why. Anne McElvoy hosts with The Economist's AI expert, Tom Standage

the dude just stonewalls when asked at 7:50, completely zipped lips

What you’re looking at above is the work of SenseTime, a Chinese computer vision startup. The software in question, called SenseVideo, is a visual scenario analytics system. Basically, it can analyse video footage to pinpoint whether moving objects are humans, cars, or other entities. It’s even sophisticated enough to detect gender, clothing, and the type of vehicle it’s looking at, all in real time.


Even China’s Backwater Cities Are Going Smart: http://www.sixthtone.com/news/1001452/even-chinas-backwater-cities-are-going-smart

remember that tweet with the ML readout of Chinese surveilance cameras? Get ready for the future (via @triviumchina)

XI praised the organization and promised to help it beef up its operations (China
- "China will 'help ... 100 developing countries build or upgrade communication systems and crime labs in the next five years'"
- "The Chinese government will establish an international law enforcement institute under the Ministry of Public Security which will train 20,000 police for developing nations in the coming five years"

The Chinese connection to the Zimbabwe 'coup': http://www.cnn.com/2017/11/17/africa/china-zimbabwe-mugabe-diplomacy/index.html

China to create national name-and-shame system for ‘deadbeat borrowers’: http://www.scmp.com/news/china/economy/article/2114768/china-create-national-name-and-shame-system-deadbeat-borrowers
Anyone who fails to repay a bank loan will be blacklisted and have their personal details made public

China Snares Innocent and Guilty Alike to Build World’s Biggest DNA Database: https://www.wsj.com/articles/china-snares-innocent-and-guilty-alike-to-build-worlds-biggest-dna-database-1514310353
Police gather blood and saliva samples from many who aren’t criminals, including those who forget ID cards, write critically of the state or are just in the wrong place

Many of the ways Chinese police are collecting samples are impermissible in the U.S. In China, DNA saliva swabs or blood samples are routinely gathered from people detained for violations such as forgetting to carry identity cards or writing blogs critical of the state, according to documents from a national police DNA conference in September and official forensic journals.

Others aren’t suspected of any crime. Police target certain groups considered a higher risk to social stability. These include migrant workers and, in one city, coal miners and home renters, the documents show.


In parts of the country, law enforcement has stored DNA profiles with a subject’s other biometric information, including fingerprints, portraits and voice prints, the heads of the DNA program wrote in the Chinese journal Forensic Science and Technology last year. One provincial police force has floated plans to link the data to a person’s information such as online shopping records and entertainment habits, according to a paper presented at the national police DNA conference. Such high-tech files would create more sophisticated versions of paper dossiers that police have long relied on to keep tabs on citizens.

Marrying DNA profiles with real-time surveillance tools, such as monitoring online activity and cameras hooked to facial-recognition software, would help China’s ruling Communist Party develop an all-encompassing “digital totalitarian state,” says Xiao Qiang, adjunct professor at the University of California at Berkeley’s School of Information.


A teenage boy studying in one of the county’s high schools recalled that a policeman came into his class after lunch one day this spring and passed out the collection boxes. Male students were told to clean their mouths, spit into the boxes and place them into envelopes on which they had written their names.


Chinese police sometimes try to draw connections between ethnic background or place of origin and propensity for crime. Police officers in northwestern China’s Ningxia region studied data on local prisoners and noticed that a large number came from three towns. They decided to collect genetic material from boys and men from every clan to bolster the local DNA database, police said at the law-enforcement DNA conference in September.

China is certainly in the lead in the arena of digital-biometric monitoring. Particularly “interesting” is the proposal to merge DNA info with online behavioral profiling.



This is the thing I find the most disenchanting about the current political spectrum. It's all reheated ideas that are a century old, at least. Everyone wants to run our iPhone society with power structures dating to the abacus.
Thank God for the forward-thinking Chinese Communist Party and its high-tech social credit system!


INSIDE CHINA'S VAST NEW EXPERIMENT IN SOCIAL RANKING: https://www.wired.com/story/age-of-social-credit/

The government thinks "social credit" will fix the country's lack of trust — and the public agrees.

To be Chinese today is to live in a society of distrust, where every opportunity is a potential con and every act of generosity a risk of exploitation. When old people fall on the street, it’s common that no one offers to help them up, afraid that they might be accused of pushing them in the first place and sued. The problem has grown steadily since the start of the country’s economic boom in the 1980s. But only recently has the deficit of social trust started to threaten not just individual lives, but the country’s economy and system of politics as a whole. The less people trust each other, the more the social pact that the government has with its citizens — of social stability and harmony in exchange for a lack of political rights — disintegrates.

All of which explains why Chinese state media has recently started to acknowledge the phenomenon — and why the government has started searching for solutions. But rather than promoting the organic return of traditional morality to reduce the gulf of distrust, the Chinese government has preferred to invest its energy in technological fixes. It’s now rolling out systems of data-driven “social credit” that will purportedly address the problem by tracking “good” and “bad” behavior, with rewards and punishments meted out accordingly. In the West, plans of this sort have tended to spark fears about the reach of the surveillance state. Yet in China, it’s being welcomed by a public fed up of not knowing who to trust.

It’s unsurprising that a system that promises to place a check on unfiltered power has proven popular — although it’s… [more]
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january 2017 by nhaliday
a search system for videos based on drawing
video  search  computer-vision  ai  worrydream  organization  software  skunkworks  SIGGRAPH 
march 2016 by nhaliday

bundles : acmframetechie

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