分類
人工智慧 區塊鏈

區塊鍊技術與人工智慧的結合將會成為未來的趨勢

人工智慧與區塊鏈為近年最熱門的話題,若將兩者的技術結合在一起,將如何顛覆我們的未來?

分辨真偽
美國每年因假貨所造成公司損失的成本超過6000億美元,分辨真偽的技術不斷推陳出新。日前,IBM研究部門發表了
一款以人工智慧為基礎的膺品檢測器,名為Crypto Anchor Verifier。

這款檢測器的特色是結合手機的相機配合區塊鏈
技術來驗證物品的真偽。

操作的方式其實很簡單,使用者只需拿出手機並打開應用程序對要檢測的物品拍照,即可完成物品鑑定。

據悉,操作的原理是採用影像處理技術,將物體與區塊鏈中的分散式賬本中的資料庫進行交叉比對。目前這項檢測的技術已確定可用於分辨鑽石、紙幣及紅酒的真偽。

醫療分析
Google DeepMind透過區塊鏈技術開發出一套醫療資料審計系統,而透過人工智慧的運用,醫務人員可從病人的簡介
中獲取相關的資料進行醫療分析,得出更精確的判斷。

數位版權
對於創作合約管理而言,區塊鏈技術為藝術家和創作者提供即時支付的方式。人工智慧則運用在學習規則以識別那些這反國際版權法的人。此外,Verizon和Ujo Music有意使用區塊鏈來稽核許可證與經銷產品,如Ujo Music已透過以太坊區塊鏈平臺來播放歌曲。

※本文章屬於TNZE天擇集團所有嚴禁轉載※

 

分類
人工智慧

基金公司以人工智能為手段,大膽向體育博彩市場叩關!

知名量化公司SIG (Susquehanna International Group)近期積極布局體育博彩市場,成立的新部門將
以發展人工智能技術針對籃球、橄欖球、足球等項目進行分析。事實上,借助人工智能分析體育博彩已非
新鮮事。據英國金融時報報導,Stratagen Technologies公司已在今年推出體育博彩基金,利用人工智
能進行投注。
知名量化公司SIG (Susquehanna International Group)最近成立了新部門,利用人工智慧對包括籃球、橄欖球、足
球和網球等體育博彩項目進行投注。
新部門名叫Nellie Analytics,位於都柏林,主要娶焦地緣政治分析和體育分析。SIG官網顯示,該部門正在招聘對體育
博彩市場有所瞭解的Python軟體工程師。
SIG戰略規劃主管 David Pollard在接受美國主流媒體採訪時稱: SIG的做市商背景有助於進行體交易。儘管目前美國
運動專案在歐洲博彩交易所的
交易量並不大,但體育博彩是一個很好的領域,SIG將關注交易量是否會增長。
不走尋常路的量化基金
SIG從來都是一家不走尋常路的量化基金公司。
博弈論的盛行在SIG由來已久。SIG認為,對於人們做的每件事來說,博弈論都是真實的。交易高手對於競爭、策略和
風控的追求,與博弈高手如出一辙。
正因如此,SIG向來重視遊戲。他們
不但用撲克作為培訓工具,甚至用撲克比赛作為校園招聘的手段,來測試大學生的
數學水準和風險控制能力。
除了捷克以外,棒球、象棋、魔術也是SIG十分偏爱的運動和遊戲項目。
體育博彩基金的興起
事實上,SIG並非唯一一家對體育博彩有興趣的基金公司。
據英國金融時報,前高盛交易員Charles McGarraugh創立的Stratagem Technologies公司今年推出了體育博彩基金。
這家公司致力於借助人工智慧來分析足球、籃球和網球比賽,並利用演算法在比賽前和比賽期間進行下注。
McGarraugh認為,體育博彩市場是檢驗真公司AI策略的最佳場合:
體育博彩市場規模很大,但十分分散,而體育比賽則是高度結構化和重複化的。
除此之外,2016年3月,澳大利亞一家名為Priomha的投資公司成立了一隻體夸博彩基金,更早的時候,2010年,位於
倫敦的Centaur Corporate公司成立了押注足球、養馬和網球比賽的體育博彩基金,兩年後因虧損而關閉

分類
人工智慧

AI時代下人工智慧與線上市場的完美結合

「人工智慧」的概念廣泛應用於現代生活中各種領域。AI時代的來臨,全面改變了各產業的運作模式與方
案。近幾年開始有不少國家紛紛將AI應用於線上博奕,不僅大幅提昇遊戲技能、創造大量商機,也能有效
地協助玩家克服賭癮,博彩娛樂更能細水長流,為博奕市場上的業者及玩家帶來雙贏的進展!
Artificial Intelligence in Online Gambling
What is Al’s role in gambling?
The concept of “artificial intelligence” is extensively used in various spheres of modern life, especially those
related to computer and Internet technologies. What it is and how it can be applied to the gambling industry-
all these questions are answered in Slotegrator’s current review.
The academic notion of “artificial intelligence” (AI) stands for the process of creation and development of
intellectual computer software. A distinctive feature of such software is the ability to process and analyze
information, as well as to accomplish intellectual and creative tasks.
First AI was applied to gambling in the middle of the last century. American cybernetic scientist Arthur Samuel
was the first one to create a type of software that allowed mainframe computers to play checkers with humans.
During the gameplay, the system was learning by itself, improving its gaming skills based on previous
experience.
In 1962, the program managed to beat R. Neely, the best USA checker player of that time. Particularly this
incident triggered further observations and AI software evolution.
Today, there exist a lot of ideas regarding Al development. Broadly speaking, they can be divided into two
types: the first one is based on a semiotic approach, and the second – on the biological one.
As for the first type, the program attempts to copy the human mind, while the second one makes use of natural
evolutionary algorithms, functioning as biological neural nets, resulting in intellectual activities.
Currently, a remarkable progress has been reached in development and integration of programs of the second
type. Consequently, a technology of machine learning, also known as Deep Learning, is successfully used in
iGaming.
The AI based programs are used in overcoming gambling addictions, retaining regular players, as well as for
scientific and educational purposes.
Inventions on current market
of LTTE cating
Today there are several effective solutions on the market. Last year BtoBet successfully presented its innovative
AI platform for online casinos. In a very short time, the software was tested on the global gambling market and
proved to be quite effectual.
The main task of the new software platform is to track player’ s actions and react to them, identifying potential
needs of the user. It is one of the most important tools of customer retention technique.
The platform responds to the player’s behavior in various environments and systems: social networks, mobile
applications, etc. By doing so, it is getting easier to meet the needs of the most capricious online casino visitors
quickly and efficiently, subsequently increasing online gambling popularity.
The AI concept was also used in poker for cognitive and scientific purposes. This game has always attracted
attention of various researchers and scientists, a fact that contributes to the online gaming popularity in
general.
Poker is known as a game based on incomplete data, since it implies randomness, while the number of possible
gaming combinations is indefinite. Texas Hold’em is one of the most common types of poker with practically
no limitations to the number of combinations.
The first full-fledged program for poker was launched and tested in 2015. Several world leading poker players
battled with the AI supercomputer Claudico.
The next famous developments of the AI were introduced by the scientists from the University of Alberta,
University of Charles and the Czech Technical University. During an experiment a system called DeepStack AI,
played around 44 852 times against 33 world’s strongest professional players, actual members of the
International Federation of Poker. Consequently, throughout all the games, the AI system showed successful
response results of 492 mbb/g (milli-big-blinds per game). The final figures are thought to be quite high-
about 100 mbb/g among experienced players.
Current Libratus systems created at the University of Carnegie Mellon and based on AI are considered as the
most effective ones. This system was internationally recognized as the one with the maximum effectiveness
after its landslide victory in games against humans during a 20-day poker tournament. The game of Texas
Hold’em was carried out in one of Pennsylvania’ s casinos within participation of world’s four best poker
players.
In recent years, many countries have been seriously considering the gambling addiction problem. In order to
prevent ludomania, lawmakers have been continuously introducing different bans on gambling activities,
measures that turn out to be not very beneficial for the development of the said industry. As it has been shown
in recent studies, the AI systems can be very effective in combating gambling addiction and its consequences.
Consequently, recent studies carried out by the University of London have a significant value for the entire
gaming industry. According to the scientists, they managed to elaborate a unique system based on the AI
allowing to detect a pathological addiction to gambling, even before it transforms into a real addiction.
In order to create such a unique gaming platform, researchers have teamed up with a well-known software
developer BetBuddy.The new version of BetBuddy platform has become one of the most advanced solutions in
the gaming industry. It keeps track of user’ s gambling behavior in casinos through using AI technologies.
Player’s behavior model is identified through using inference mechanisms and neural networks, as well as
random forest algorithms, indicating and signalizing the exact time when players reach problematic levels. This
mechanism enables online casino operators to decide either to block problematic accounts or to apply some
limitations to them, etc.
Features of the platforms
These days the most interesting solutions of practical implementation of AI technologies in gambling are
offered by two developers – BetBuddy and BtoBet.
Sabrina Soldà, the Head of Marketing at BtoBet, commented on the idea of AI implementation in the online
casino industry: “Today, due to highly competitive environment, the gaming market requires special tools and
intelligent platforms in order to adjust to the needs and expectations of players worldwide. Over the past year,
BtoBet solutions have caught the eye of many operators from different countries. For this very reason, we have
decided to expand the global market coverage.
BetBuddy developers and scientists from the University of London observed the following facts regarding
practical use of the AI technologies: “We have presented an innovative system with artificial intelligence aimed
at prediction of problems based on player’s behavior, as well as at displaying of potential gambling
addictions. It is solely based on mathematical algorithms. Integration of the platform into online casino
websites will help operators to monitor potential problematic players, as well as to improve their customer
service.”
The future belongs to AI
John McCarthy, who was the first one to introduce the concept of “artificial intelligence” back in 1956, said:
“As soon as it starts functioning, we no longer call it artificial.”
The mankind has often used the AI in various fields of its activity, without even thinking about it. It’s just an
everyday reality. Currently, the concept of the AI is applied practically everywhere, particularly in the areas
connected with computer technologies. As it is frequently claimed by various researchers, the potential power
of the AI is limitless and often exceeds human capacities.
Slotegrator’ s experts do not consider surprising the fact that high-tech and iGaming industries are actively
developing gradually integrating AI systems into modern online casino platforms. Taking into consideration the
latest trends, the future is all about the superpowers of Artificial Intelligence.

 

 

 

 

 

 

 

 

 

 

 

 

分類
人工智慧

這五種AI應用,即將影響未來博弈行業!

人工智慧深深影響新一代各產業的運作模式,博彩業即是其中與AI密不可分的行業。AI為博奕行業帶來許
多前所未有的解決方案,無論是線上產業不可或缺的大數據整合、預測行業趨勢提供有效資訊;或是資金
與數據的安全保護問題,亦能利用AI來防止網路攻擊。且看以下五大將影響博奕產業的AI技術! !
As the development of artificial intelligence grows increasingly more sophisticated and begins to become
embedded in a number of solutions, the potential for its use in the gambling sector is becoming more
apparent. There are a number of ways in which AI can be harnessed, with it being employed to bet professional
poker and Go players making headlines recently. In this article, five ways that AI could potentially be of use to
the gambling sector will be reviewed, with some examples of companies that are working in these areas.
1. KYC and AML Compliance
Compliance to regulations is a vital part of any business, both to protect the company and its customers.
Rigorous standards to identify illegal or at-risk consumers need to be pursued to avoid damage to a company
through fraudulent activity, breaking the law or even simply bad publicity. In order to comply with Know Your
Customer and Anti-Money Laundering regulations, accurate and reliable methods of identifying customers and
monitoring behaviour need to be employed. This is often a time consuming and costly task which is only
needed to catch the small percentage of people who are engaged with illicit activity or are putting their
livelihoods at risk due to an addiction. AI can offer a solution here to identify and monitor customers, with the
ability to flag up potential dangers.
A couple of examples of companies working to achieve this are Onfido and Trulico. London-based Onfido
enables remote background checks on customers and utilises a number of different verification means. The AI
and machine learning enables Onfido to strengthen its fraud detection further as more data is added. Trulico
gives the capability for automatic identity verification on a global scale. One goal of Truiloo is to bring
identification services to areas of the world where people have no other record of existence or struggle to
prove identity. GlobalGateway, the company’ s instant electronic identity verification service is designed
explicitly to enable businesses to comply with cross-border Anti-Money Laundering and Know Your Customer
regulation.
2. Prediction
One of the key factors in a business that is going to successfully keep up with developments in technology and
consumer trends is the ability to monitor consumer behaviour for trends in activity and suggestions of future
behaviour. By doing this, companies can design effective new products, tailor existing offerings and plan the
next steps to take in development. Al can enable this through its capacity to analyse and learn from large
amounts of data to provide accurate and up-to-date reports to a degree that a human equivalent would not be
able to achieve.
An example of AI being put to use in this manner is in Seldon. Another London-based company, the Seldon
platform has the ability to Predict media and e-commerce customer future actions on web, tablet and mobile
devices. Seldon analyses behaviour, social information, data from first and third party sources and any
contextual information in order to enhance product and content recommendations. Another example, Opera
Solutions uses AI to enable companies to draw predictive intelligence and conclusions from big data. It
identifies patterns to assist researchers in understanding developments on an industrial or global scale to allow
these developments to be taken advantage of at the earliest opportunity. Na
3. Simulation
AI could also be used to provide new experiences in gaming, perhaps opening up a new area for the gambling
industry target as a wider audience is drawn in.
Improbable is a company that aims to enable the building of ‘simulated worlds’ by combining different
servers and games engines to combine into one massive multiplayer experience. It has been described as trying
to create The Matrix’ . Besides creating virtual worlds, the platform would also allow for numerous
simulations to be run, potentially assisting many different types of company that want accurate prediction
models. The British start-up is valued at over $1 billion.
4. Security
Naturally, security is an area that companies require to be as strong as possible in order to protect their own
funds and data, but also to ensure that customers feel secure in using their services – leaving their privacy and
finances in safe hands. If customers feel their funds or information are at risk, they will choose other options for
their gambling needs. The theft of money and documentation, as well as bad publicity, are threats that every
company faces on a daily basis.
Based in Cambridge in the UK, Darktrace uses AI and machine learning to identify patterns with the ability to
detect and stop a cyber-attack before it occurs. This early-warning solution is an effective method that is
already being employed by companies such as BT and Virgin Trains. By preventing the attack before it even
happens, the risk of failure is decreased.
5. Automation
Not being a living system, AI can avoid many of the downsides that human operators have. Al does not get
tired or hungry, doesn’t make mistakes and has no need of sleep. Implementing AI can improve the efficiency,
speed and accuracy of previously manual tasks, freeing up manpower to be used in more important areas. AI
can particularly excel at this in areas where large amounts of repetitive tasks are being carried out.
One example of a company putting AI to this use is Tractable, which is building deep-learning tools to perform
tasks previously performed by experts relying on visual methods. The Tractable platform uses AI to process and
understand thousands of images in seconds with pin-point accuracy-outperforming human counterparts.
These are just some applications of AI that could apply to the gambling industry and more uses and more
companies developing Al solutions exist. In the future, the potential of AI will expand even further so
investigating and investing in Al capabilities will enable companies to prepare for future challenges and
opportunities.

 

 

 

 

 

 

 

 

 

分類
人工智慧

線上博彩商BtoBet 在ICE 2017全力展現AI新技術!

線上博彩商BtoBet
大膽研發AI
技術,被今年ICE Totaly Gaming鲁為以AI平台為發展核心的成功企業,更
受邀參加ICE VOX會議。本次會議與知名博彩商Microgaming合作,除了探討AI的發展未來,也表示十分
關注非洲市場,並著手將零售業務轉移至移動市場。
2
BtoBet has hailed this year’s ICE Totally Gaming as a success with the company
showcasing AI platforms and participating in ICE VOX conferences.
co Torded the company sexpecta
The iGaming operator, BtoBet, claims that ICE 2017 exceeded the company’s expectations in terms of the
interest in their product launches on the showfloor.
BtoBet were also chosen to take part in events across ICE as experts in the industry, including at ICE VOX and
the exclusive Microgaming yacht-conferences.
Discussing the balance of the three-day ICE show, BtoBeť s CEO, Kostandina Zafirovska, said: “Enthusiastic
attendees experienced trial runs of the Augmented Reality potential and took part in BtoBet’ s Virtual Assistant
Simone demonstration, participating in her show, interacting, taking pictures and having fun with her,” she
added. “Operators had the opportunity to see how BtoBeť s sophisticated technology can provide the
perfect integration between the A.1. platform and the behaviour of the player, suggesting the best games and
events at the ideal time to each player – through the Recommendation engine – and catch the trend of
Augmented Reality to improve and speed up new marketing strategies, providing players with the most
advanced and exciting gaming experience on the market.”
Zafirovska was also the technology speaker at the panel The Future of Trading: innovation at the Door’,
during BetMarkets session of the ICE Vox Conference – sponsored by Sportradar.
Commenting on the panel, she highlighted: “We had the occasion to show the urgent need of innovation and
the intelligent platform in the betting industry to manage risk and trade in an effective way, monitoring player
behaviour and preventing fraud with immediacy.”
In addition to this, BtoBet’ s chairman Alessandro Fried was selected as an expert speaker for the exclusive
Microgaming yacht-conference on the Sunborn London at Excel. The session, organized in partnership with
Microgaming, focused on the African Market, and showed operators ready-to-use tools, technology and
opportunities to differentiate their brand and expand their business from retail to mobile.

 

 

 

 

 

 

 

 

 

 

 

 

分類
人工智慧

機器自學 ! 青出於藍的AI人工智慧 !

近年來,AI人工智慧在各項益智遊戲中擊敗人類早已不是新聞。加拿大阿爾伯塔大學的科學家邁克爾·鮑靈
與他的AI撲克團隊,不斷地透過新的演算法及深度機器學習突破機器的規律性,成功促使其AI技術
DeepStack得以透過自學的方式,模仿人類大腦與習性,屢次在撲克遊戲中青出於藍,贏過人腦!
n
Two artificial intelligence (AI) programs have finally proven they “know when to hold’em, and when to fold
em,” recently beating human professional card players for the first time at the popular poker game of Texas
Hold’em. And this week the team behind one of those Als, known as DeepStack, has divulged some of the
secrets to its success-a triumph that could one day lead to Als that perform tasks ranging from from beefing
up airline security to simplifying business negotiations.
tyear one conquered Go
Als have long dominated games such as chess, and last year one conquered Go, but they have made relatively
lousy poker players. In DeepStack researchers have broken their poker losing streak by combining new
algorithms and deep machine learning, a form of computer science that in some ways mimics the human brain,
allowing machines to teach themselves.
“It’s a… a scalable approach to dealing with complex information) that could quickly make a very good
decision even better than people,” says Murray Campbell, a senior researcher at IBM in Armonk, New York,
and one of the creators of the chess-besting AI, Deep Blue.
Chess and Go have one important thing in common that let Als beat them first: They’re perfect information
games. That means both sides know exactly what the other is working with—a huge assist when designing an
AI player. Texas Hold’em is a different animal. In this version of poker, two or more players are randomly dealt
two face-down cards. At the introduction of each new set of public cards, players are asked to bet, hold, or
abandon the money at stake on the table. Because of the random nature of the game and two initial private
cards, players’ bets are predicated on guessing what their opponent might do. Unlike chess, where a winning
strategy can be deduced from the state of the board and all the opponent’ s potential moves, Hold ’em
requires what we commonly call intuition.
The aim of traditional game-playing Als is to calculate the possible results of a game as far as possible and then
rank the strategy options using a formula that searches data from other winning games. The downside to this
method is that in order to compress the available data, algorithms sometimes group together strategies that
don’t actually work, says Michael Bowling, a computer scientist at the University of Alberta in Edmonton,
Canada.
His team’ s poker AI, DeepStack, avoids abstracting data by only calculating ahead a few steps rather than an
entire game. The program continuously recalculates its algorithms as new information is acquired. When the AI
needs to act before the opponent makes a bet or holds and does not receive new information, deep learning
steps in. Neural networks, the systems that enact the knowledge acquired by deep learning, can help limit the
potential situations factored by the algorithms because they have been trained on the behavior in the game.
This makes the Al’ s reaction both faster and more accurate, Bowling says. In order to train DeepStack’s
neural networks, researchers required the program to solve more than 10 million randomly generated poker
game situations.
To test DeepStack, the researchers pitted it last year against a pool of 33 professional poker players selected by
the International Federation of Poker. Over the course of 4 weeks, the players challenged the program to
44,852 games of heads-up no-limit Texas Hold’em, a two-player version of the game in which participants
can bet as much money as they have. After using a formula to eliminate instances where luck, not strategy,
caused a win, researchers found that DeepStack’s final win rate was 486 milli-big-blinds per game. A milli-
big-blind is one-thousandth of the bet required to win a game. That’ s nearly 10 times that of what
professional poker players consider a sizable margin, the team reports this week in Science.
The team’s findings coincide with the very public success several weeks ago of Libratus, a poker AI designed
by researchers at Carnegie Mellon University in Pittsburgh, Pennsylvania. In a 20-day poker competition held in
Pittsburgh, Libratus bested four of the top-ranked human Texas Hold’ em players in the world over the course
of 120,000 hands. Both teams say their system’s superiority over humans is backed by statistically significant
findings. The main difference is that, because of its lack of deep learning, Libratus requires more computing
power for its algorithms and initially needs to solve to the end of the every time to create a strategy, Bowling
says. DeepStack can run on a laptop.
Though there’ s no clear consensus on which AI is the true poker champ—and no match between the two has
been arranged so far—both systems have are already being adapted to solve more complex real-world
problems in areas like security and negotiations. Bowling’ s team has studied how AI could more successfully
randomize ticket checks for honor-system public transit.
Researchers are also interested in the business implications of the technology. For example, an AI that can
understand imperfect information scenarios could help determine what the final sale price of a house would be
for a buyer before knowing the other bids, allowing that buyer to better plan on a mortgage. A system like
AlphaGo, the perfect information game-playing Aſ that defeated a Go world champion last year, couldn’t do
this because of the lack of limitations on the possible size and number of other bids.
ULTTa GarminG
Still, DeepStack is a few years away from truly being able to mimic complex human decision making, Bowling
says. The machine still has to learn how to more accurately handle scenarios where the rules of the game are
not known in advance, like versions of Texas Hold ’em that its neural networks haven’t been trained for, he
says.
Campbell agrees. “While poker is a step more complex than perfect information games,” he says, “it’s still
a long way to go to get to the messiness of the real world.”

 

 

 

 

 

 

 

 

分類
人工智慧

撲克玩家的地獄 ? AI強勢來襲 !

人工智慧當道,已成為市場顯學。舉凡任何與網路相關的產業,無不積極投入與開發AI的技術,其中博奕
遊戲產業也深受影響。匹茲堡超級計算中心的研究團隊以AI與四位世界級職業撲克選手展開人腦與電腦的
對決,並在競賽中赢得了170萬美元!本文為您詳細解析,A是如何擊敗四位世界頂尖撲克選手!
slavina Trocco hands of heads-ub. no-lim
“That was anticlimactic,” Jason Les said with a smirk, getting up from his seat. Unlike nearly everyone else in
Pittsburgh’s Rivers Casino, Les had just played his last few hands against an artificially intelligent opponent on
a computer screen. After his fellow players – Daniel McAulay next to him and Jimmy Chou and Dong Kim in an
office upstairs — eventually did the same, they started to commiserate. The consensus: That AI was one hell of a
player.
The four of them had spent the last 20 days playing 120,000 hands of heads-up, no-limit Texas Hold’em
against an artificial intelligence called Libratus created by researchers at Carnegie Mellon University. At stake: a
total pot of $200,000 and, on some level, the pride of the human race. A similar scene had unfolded two years
prior when Les, Kim and two other players decisively laid the smackdown on another Al called Claudico. The
players hoped to put on a repeat performance, finish up the event January 30th, and ride the rush of
endorphins until they got home and resumed their usual games of online poker.
The fight wasn’t even close. All told, Libratus won by more than 1.7 million (virtual) dollars, and — just like that
— the second Brains vs. Al competition came to a close. To understand what these players were up against and
what makes Libratus work, let’s go back to a time before all hope of victory was lost.
Men vs. machine
For the four men playing against Libratus, victory didn’t always seem impossible. The AI was in the lead from
the get-go, building an impressive streak of wins for the first three days. Then came the counter-attack. Day
four saw the gap narrow $40,000, and a string of successes on day six brought the humans to within $50,000 of
the lead.
“In the start here, we lost the first day,” Les explained. “Whatever — not a big deal. And then we were losing,
but then we fought back up to nearly equal. We were feeling really confident! We know how to play, we’re
going to be able to win.”
On the night after the sixth day of competition, the humans did what they did every other night: sift through
the Libratus hand data provided to them by CMU in hopes of devising a winning strategy. With spirits high after
a big day, they decided on a seemingly crazy strategy: three-betting on every hand that came along.
Three-betting, for the uninitiated, is poker slang for reraising on a hand. When you decide to play a hand in a
situation like this, paying the blinds is the first bet. If you’re confident in your cards, you raise — that’s the
second bet. Generally, when you reraise — the third bet-you’re pretty sure you’ve got the exchange in the
bag. Based on their understanding of Libratus’ play style, the humans thought they could knock if off balance
by playing this aggressively for a while. It backfired.
“We applied this crazy strategy we would never do online,” Kim explained. “Basically, we reraised all of our
hands. All of us went in, like, ‘Let’ s just try this, let’ s go crazy.”
“We had a reason to believe that specific size-three-bet was going to work well against the AI,” Les added.
“We just fired off all day doing that.”
Les and Kim concede that they just got unlucky, too, but either way: Libratus was unfazed by their plan and
started demolishing them. “It just kept improving every single day, and we started going backwards and
backwards,” Les said. In fairness, the humans weren’t playing with their usual setups. The four competitors
are almost exclusively online poker pros, and when duking it out at virtual tables at home, they always have
their HUDs handy. These heads-up displays are filled with stats and probabilities that help online players make
the best moves. Their absence here in Pittsburgh was noticeable.
“Without the HUD, without the numbers, you don’t know if you’re being paranoid or not,” Daniel McAulay
said, leaning back in his chair after winning a hand. “Is it folding less? We were never sure. We would always
say the same thing to each other. Just play it out until we get home and we’d see the sample of hands and
then we’ll change the plan. But that cost us a lot of money. A lot of money.”
Those losses would only continue to mount.
Building the beast
One of the men responsible for the players’ anguish can usually be found in his ninth-floor office, overlooking
Carnegie Mellon University’s snow-flecked quad. Professor Tuomas Sandholm might live a second life as a
startup entrepreneur, but he has spent years trying to perfect the algorithms that make Libratus such a potent
player. It wasn’t out of any particular love for the game — Sandholm admits he’s no poker pro — but he was
fascinated by the thought of complex computer systems that make decisions better than we can. That fixation
led him to co-create Claudico (the earlier AI that the humans trounced) with PHD student Noam Brown, and it
led the two of them to try again with Libratus.
To think of Libratus as just a poker-playing champ is to sorely underestimate it. Instead, Sandholm says, it’ sa
more general set of algorithms meant to tackle any information-imperfect situation. Confused? Don’t be.
Broadly speaking, the term just describes any situation in which two or more parties don’t have the same
information. Something unlike, say, chess, where the entirety of the game’ s world is splayed out on the board
in front of players. Those players can figure out exactly what’s going on and, assuming they have decent
memories, draw on their understanding of the events that led them there. This is a perfect information game.
No-limit Texas Hold’em is different. You don’t know which cards your opponent has, your opponent
doesn’t know which cards you have, and those minutes playing a hand to its conclusion are spent trying to
make the smartest moves possible with a shortage of intel. And unlike the limit variant, where there’s a cap on
how big your bets can be, no-limit gives you the freedom to bet whatever you want. There’s so much
information a person — or an AI — can infer about an opponent’ s strategy based on their bets that it sno
wonder researchers have been trying to crack the game.
“Heads-up, no-limit Texas Hold’ em poker has emerged as the leading benchmark for measuring the quality
of these general purpose algorithms in the AI community,” Sandholm told me.
With that in mind, Sandholm and Brown jointly built Libratus from three major components. The first is an
algorithm that devises overall strategies based on Nash equilibria. In other words, Libratus spent a total of 15
million computing hours chewing on the rules of the game before the competition, finding rational ways to act
when both players are making the best possible moves with the information available. Thanks to a new logic
model developed by the two researchers to minimize Libratus’ “regret,” the AI could solve larger
abstractions of the game faster and with higher accuracy than before.
The second is what Sandholm calls the end-game solver. This is the part that players actually faced during their
20 days of combat. Unsurprisingly, too, this is where Sandholm says most innovative breakthroughs have
happened. Essentially, this allowed Libratus to cook up an approach based on the first two cards it was dealt,
and modify that approach based on its opponent’ s actions and the river and flop that are dealt. Sandholm
says Libratus was also designed to keep tabs on how safe its options are. Let’s say a human player screws up
and loses $372. That money is viewed as a gift of sorts, so the AI can freely lose up to $372 and still remain
ahead.
ULTTE Garming
“That gives us more flexibility for optimizing our strategies while still being safe,” Sandholm explained.
We’ II get to the last key component a little later. In any case, the sheer number of complex calculations meant
Libratus couldn’t run on the desktop in Sandholm’ s office. If nothing else, the human players can take solace
in the fact that it took a supercomputer and millions of computing hours to beat them. If you thought Gowas
tough to wrap your head around, consider the complexity of no-limit Texas Hold’em: When you’re dealt into
a game, the hands you’re dealt and the communal cards that appear are one possibility of 10^160.
“That’s one followed by 160 zeroes,” said Sandholm. “That’s more than the number of atoms in the
universe. You cannot just brute-force your way through it.” Still, it takes some degree of brute force to build as
close to optimal a strategy as possible. That’s where “Bridges” comes in.
If Libratus is the brain of the operation, Bridges — a supercomputer made of hundreds of nodes in the
basement of the Pittsburgh Supercomputing Center – is most definitely the brawn.
“Libratus is running on about 600 nodes at Bridges, out of 846 total compute nodes,” said Nick Nystrom,
senior director of research at the Pittsburgh Supercomputing Center. Most of those 800+ nodes have two
CPUs, each with 28 computing cores and 128GB of RAM. Forty-eight of those nodes have two state-of-the-art
GPUs, and still others were loaded with even more power: NVIDIA’ s Tesla-series K80 and P100 GPUs.
༧.༠༠
There’s more: 42 of those nodes have 3TB of RAM each, and a very special four nodes have a whopping 12TB
of RAM. That’s some serious firepower, but all those nodes were ingeniously woven together to maximize
data bandwidth and minimize latency. It’s just as well, considering the amount of data involved: Libratus was
using up to 2.6 petabytes of storage during the competition.
When not being used to best humans at card games, Bridges was being used for around 650 projects by more
than 2,500 people. Think of Bridges as a supercomputer for hire: Researchers from around the country are using
it to gain insight into arcane subjects like genomics, genome sequence assemblies and other kinds of machine-
learning.
The beauty of Bridges, according to Nystrom, is that those researchers don’t need to be supercomputer buffs.
“It’ sa very cloud-like model letting people who are not programmers, not computer scientists, not
supercomputer users make use of a supercomputer without necessarily even knowing it.” That’ s what
happened with Libratus, and everything seemed to be working perfectly.
ULTS 2 Garmin
GATING
Game theory
After the humans’ gutsy attack plan failed, Libratus spent the rest of the competition inflating its virtual
winnings. When the game lurched into its third week, the AI was up by a cool $750,000. Victory was assured, but
the humans were feeling worn out. When I chatted with Kim and Les in their hotel bar after the penultimate
day’s play, the mood was understandably somber.
“Yesterday, I think, I played really bad,” Kim said, rubbing his eyes. “I was pretty upset, and I made a lot of
big mistakes. I was pretty frustrated. Today, I cut that deficit in half, but it’s still probably unlike for me to
win.” At this point, with so little time left and such a large gap to close, their plan was to blitz through the
remaining hands and complete the task in front of them.
For these world-class players, beating Libratus had gone from being a real possibility to a pipe dream in just a
matter of days. It was obvious that the AI was getting better at the game over time, sometimes by leaps and
bounds that left Les, Kim, McAulay and Chou flummoxed. It wasn’t long before the pet theories began to
surface. Some thought Libratus might have been playing completely differently against each of them, and
others suspected the AI was adapting to their play styles while they were playing. They were wrong.
As it turned out, they weren’t the only ones looking back at the past day’ s events to concoct a game plan for
the days to come. Every night, after the players had retreated to their hotel rooms to strategize, the basement
of the Supercomputing Center continued to thrum. Libratus was busy. Many of us watching the events unfold
assumed the AI was spending its compute cycles figuring out ways to counter the players’ individual play
styles and fight back, but Professor Sandholm was quick to rebut that idea. Libratus isn’t designed to find
better ways to attack its opponents; it’ s designed to constantly fortify its defenses. Remember those major
Libratus components I mentioned? This is the last, and perhaps most important, one.
“All the time in the background, the algorithm looks at what holes the opponents have found in our strategy
and how often they have played those,” Sandholm told me. “It will prioritize the holes and then compute
better strategies for those parts, and we have a way of automatically gluing those fixes into the base strategy.”
If the humans leaned on a particular strategy — like their constant three-bets — Libratus could theoretically
take some big losses. The reason those attacks never ended in sustained victory is because Libratus was quietly
patching those holes by using the supercomputer in the background. The Great Wall of Libratus was only one
reason the AI managed to pull so far ahead. Sandholm refers to Libratus as a “balanced” player that uses
randomized actions to remain inscrutable to human competitors. More interesting, though, is how good
Libratus was at finding rare edge cases in which seemingly bad moves were actually excellent ones.
“It plays these weird bet sizes that are typically considered really bad moves,” Sandholm explained. These
include tiny underbets, like 10 percent of the pot, or huge overbets, like 20 times the pot. Donk betting, limping
— all sorts of strategies that are, according to the poker books and folk wisdom, bad strategies.” To the
players’ shock and dismay, those “bad strategies” worked all too well.
Poker and beyond
On the afternoon of January 30th, Libratus officially won the second Brains vs AI competition. The final margin
of victory: $1,766,250. Each of the players divvied up their $200,000 spoils (Dong kim lost the least amount of
money to Libratus, earning about $75,000 for his efforts), fielded questions from reporters and eventually left to
decompress. Not much had gone their way over the past 20 days, but they just might have contributed to a
more thoughtful, Al-driven future without even realizing it.
Through Libratus, Sandholm had proved algorithms could make better, more-nuanced decisions than humans
in one specific realm. But remember: Libratus and systems like it are general-purpose intelligences, and
Sandholm sees plenty of potential applications. As an entrepreneur and negotiation buff, he’ s enthusiastic
about algorithms like Libratus being used for bargaining and auctions.
“When the FCC auctions spectrum licenses, they sell tens of billions of dollars of spectrum per auction, yet
nobody knows even one rational way of bidding,” he said. “Wouldn’t it be nice if you had some AI
support?”
But there are bigger problems to tackle – ones that could affect all of us more directly. Sandholm pointed to
developments in cybersecurity, military settings and finance. And, of course, there’s medicine.
“In a new project, we’re steering evolution and biological adaptation to battle viral and bacterial infections,”
he said. “Think of the infection as the opponent and you’re taking sequential actions and measurements just
like in a game.” Sandholm also pointed out that such algorithms could even be used to more helpfully
manage diseases like cancer, both by optimizing the use of existing treatment methods and maybe even
developing new ones.
Jason, Dong, Daniel and Jimmy might have lost this prolonged poker showdown, but what Sandholm, Brown
and their contemporaries have learned in the process could lead to some big wins for humanity.

分類
人工智慧

全球區塊鍊高峰會 AI彩票共襄盛舉

全球區塊鏈高峰會於2018年6月24日在菲律賓馬尼拉盛大舉行,由全球區塊鏈應用研究基金會和世界一帶
一路基金會主辦。AI彩票亦受邀出席,密切關注區塊鏈技術脈動。
此次全球區塊鏈高峰匯瑟來自全球業界的專家和重量級權威,一同探索區塊鏈和虛擬貨幣技術的發展與應用。關於區塊
鏈技術具備的安全、透明、防竄改、去中心等特色,包括可應用在物聯網、身分驗證、資產交易、電子商務、社交通
訊、檔案儲存等領域的巨大能量,領先業界的彩票巨擘德勝集團注意到了。
2010年成立的德勝集團,在2017年獨步亞洲導入人工智能技術,推出尖端彩票系統平台品牌 – AI彩票。始終將洞悉科
技趨勢和突破創新奉為圭臬的AI彩票,領先業界推出數位貨幣交易功能服務。對科技趨勢的高度敏銳,及對區塊鏈技術
的應用掌握,和全球區塊鏈高峰會的推廣宗旨不謀而合。此外,AI彩票早已鎖定當前熱門排行的數位貨幣進行儲值、提
領和下單等,幣種包含當前全球前五大數位貨幣中的比特幣(Bitcoin)、乙太幣(Ethereum)、萊特幣(Litecoin)
等,種類持續增加中。
事實上,AI彩票早已意識現今線上數位環境快速發展,數位貨幣的高效率和安全性也日趨成熟。有越來越多民眾,開始
認識此種貨幣單位是如何以加密資料,透過區塊鏈技術經點對點網路監控和管理。所以當德勝集團推出AI彩票時,即
毫不猶豫地將數位貨幣交易功能納入其中,看中的正是區塊鏈技術對全球虛擬世界產生的巨大影響。
AI彩票指出,區塊鏈技術會是帶動第四次工業革命的重要引擎之一,其產生的影響涵蓋各行各業。 AI彩票不斷檢視今日
趨勢科技,除已成功應用AI人工智能技術至後台功能,立下彩票包網行業的
全新里程碑。AI彩票從未停下關注趨勢科技
的腳步,不放過任何能給彩票行業創造機會的可能性,持續為彩票行業更具競爭力的未來努力不懈。

 

 

 

 

 

分類
人工智慧

人工智慧與區塊鍊技術的結合將會成為未來的趨勢

人工智慧與區塊鏈為近年最熱門的話題,若將兩者的技術結合在一起,將如何顛覆我們的未來?
人工智慧與區塊鏈為近年最熱門的話題,前者自形成以來,不論是在理論上還是應用上均以日益成熟,其應用之領域也
不斷擴大,後者具有去中心化的特質,以信任的方式集體維護一個可靠資料庫。若將兩者的技術結合在一起,将如何顛
覆我們未來?以下為幾種已將兩者技術結合運用的領域。
分辨真偽
美國每年因假貨所造成公司損失的成本超過6000億美元,分辨真偽的技術不斷推陳出新。日前,IBM研究部門發表了
一款以人工智慧為基礎的膺品檢測器,名為Crypto Anchor Verifier。這款檢測器的特色是結合手機的相機配合區塊鏈
技術來驗證物品的真偽。
操作的方式其實很簡單,使用者只需拿出手機並打開應用程序對要檢測的物品拍照,即可完成物品鑑定。據悉,操作的
原理是採用影像處理技術,將物體與區塊鏈中的分散式賬本中的資料庫進行交叉比對。目前這項檢測的技術已確定可用
於分辨鑽石、紙幣及紅酒的真偽。
醫療分析
Google DeepMind透過區塊鏈技術開發出一套醫療資料審計系統,而透過人工智慧的運用,醫務人員可從病人的簡介
中獲取相關的資料進行醫療分析,得出更精確的判斷。
數位版權
對於創作合約管理而言,區塊鏈技術為藝術家和創作者提供即時支付的方式。人工智慧則運用在學習規則以識別那些這
反國際版權法的人。此外,Verizon和Ujo Music有意使用區塊鏈來稽核許可證與經銷產品,如Ujo Music已透過以太坊
區塊鏈平臺來播放歌曲。

分類
人工智慧

線上博彩商BtoBet 在ICE 2017全力展現AI新技術!

線上博彩商BtoBet
大膽研發AI
技術,被今年ICE Totaly Gaming鲁為以AI平台為發展核心的成功企業,更
受邀參加ICE VOX會議。本次會議與知名博彩商Microgaming合作,除了探討AI的發展未來,也表示十分
關注非洲市場,並著手將零售業務轉移至移動市場。
2
BtoBet has hailed this year’s ICE Totally Gaming as a success with the company
showcasing AI platforms and participating in ICE VOX conferences.
co Torded the company sexpecta
The iGaming operator, BtoBet, claims that ICE 2017 exceeded the company’s expectations in terms of the
interest in their product launches on the showfloor.
BtoBet were also chosen to take part in events across ICE as experts in the industry, including at ICE VOX and
the exclusive Microgaming yacht-conferences.
Discussing the balance of the three-day ICE show, BtoBeť s CEO, Kostandina Zafirovska, said: “Enthusiastic
attendees experienced trial runs of the Augmented Reality potential and took part in BtoBet’ s Virtual Assistant
Simone demonstration, participating in her show, interacting, taking pictures and having fun with her,” she
added. “Operators had the opportunity to see how BtoBeť s sophisticated technology can provide the
perfect integration between the A.1. platform and the behaviour of the player, suggesting the best games and
events at the ideal time to each player – through the Recommendation engine – and catch the trend of
Augmented Reality to improve and speed up new marketing strategies, providing players with the most
advanced and exciting gaming experience on the market.”
Zafirovska was also the technology speaker at the panel The Future of Trading: innovation at the Door’,
during BetMarkets session of the ICE Vox Conference – sponsored by Sportradar.
Commenting on the panel, she highlighted: “We had the occasion to show the urgent need of innovation and
the intelligent platform in the betting industry to manage risk and trade in an effective way, monitoring player
behaviour and preventing fraud with immediacy.”
In addition to this, BtoBet’ s chairman Alessandro Fried was selected as an expert speaker for the exclusive
Microgaming yacht-conference on the Sunborn London at Excel. The session, organized in partnership with
Microgaming, focused on the African Market, and showed operators ready-to-use tools, technology and
opportunities to differentiate their brand and expand their business from retail to mobile.