分類
市場分析

Magos AI項目為區塊練領域帶來人工智能與神經網絡技術

據報導首款基於區塊鏈的預測軟體 Magos AI 在首次代幣發行(ICO) 籌集超過700,000美元資金。Magos
AI運用人工智慧,將神經網絡準確的預測能力與區塊鏈技術結合在一起。Magos AI以預測市場和體育博
彩為目標,在不斷發展的MAG生態系統,預測體育博彩產業將會迎接什麼樣的未來?
有史以來首款基於區塊鏈的預測軟體 Magos AI 在首次代幣發行(ICO) 中已經籌集超過700,000美元的資金。在這次眾
籌活動中,用戶可以購買全新發行的 MAG 權权,而該加密貨幣資產將使項目支持者能夠成為 MAG生態系統的一部
分,並且從不斷發展的成功中受益。
MAG 是一種基於乙太坊的權杖,使區塊鏈領域能夠獲得由 Magos 平臺產生的收益並對該項目的持續進展進行投票,
從而支持該平臺以真正的分散方式運行。那麼,為什麼投資者蜂擁參與 Magos AI 權杖發售呢?
利用共生關係下的不同技術創新成果,Magos AI 項目可以為區塊鏈領域帶來人工智慧和神經網路技術。 Magos 人工
AI 目可以是 領域帶來人工智慧和神经
智慧軟體目前以分散的預測市場和體育博彩為目標,可以從
不同來源中提取資訊,並提供基於人工智慧的篩檢程式
(核心預測機制)收集的資訊。

了。
由此產生的成果是一種非常高效的系統,能夠以較高準確度和較低價格預測未來活動的結果。通過植入協力廠商預測或
體育博彩平臺並從中提取資訊,Magos AI 可以通過各種參數組織與刪除資料。 Magos 藍皮書稱:
「核心預測機制包括先進的資料探、整理和分類,降噪,深層分析,權重分配和
自動調節。使用條波動評估這樣的專
業附件可以確保模型避免出極其不確定的事件,並且風險管理網路能夠實現基於模型性能的優化資金增長。」
儘管Magos AI 所推出的理念具有開創性,但持續權杖發售背後令人振奮的成功可能得益於該軟體將開放資料用於協力
廠商網站的初步測試所取得的成果。結果顯示投資回報率為28%,力壓所有競爭者。
Magos 目前關注於預測市場與體育博彩,然而該技術也可以被用於數位資產管理等其它市場。Magos AI 團隊表示:
「我們以常規的預測標誌和體育博彩作為開始,但我們的主要關注點一直都是分散的資產管理,以及像Augur、
Gnosis 、Melonport 和 Stox 這樣的預測市場平臺。我們希望這項技術到2018年能夠完全成熟,為我們的項目帶來更
多的透明性。這也將是邁向2019年開發 Oraculum 門戶網站所走出的重要一步一進而支持所有有意者將 Magos 作為預
測領域-從體彦和政治到商業和
金融一的首選。」

 

※本文章屬於TNZE天擇集團所有嚴禁轉載※

 

 

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
歐美訊息篇

非洲博彩運營商與AI數據商首次合作 實線東北博彩霸主之位 ?

AI將會如何顛覆全球博彩產業的未來? Stratagem是一家運用AI技術提供大數據整合的供應商,旨在以數
據分析達到高效準確的遊戲預測,並實現線上體育市場全自動化、最即時的定價模式。近日與博彩營運商
Worldstar Betting聯手合作,目標成為領先的體育博彩品牌,鞏固東非博彩市場。
Stratagem是一家新的行業資料供應商,旨在為體育博彩業帶來新的AI動態,近日宣佈與非洲運營商Worldstar Betting
達成首次合作。
Stratagem將成為Worldstar Betting旗下網球、足球和籃球市場實現領先的全自動化即時價格。
體育博彩的大資料專家Stratagem,提供專業的AI解決方案,並擁有優秀的客戶分析師,從歷史和遊戲資料中提供低成
本,高效和準確的運動遊戲預測,通過機器隨著時間的推移變得更加智慧化。
Worldstar Betting首席執行官理查·納什(Richard Nash)對此合作表示充滿期待,Worldstar Betting打算成為東非
地區領先的體育博彩品牌,而通過Stratagem的合作,利用他們高效的定價模式,完全自動化並以低成本交付,將促
使我們能夠在市場上建立穩固的業務。
Worldstar Betting作為Stratagem的第一家合作商,Stratagem對合作必定是全力以赴,對是長期前景更是充滿信心
的。

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分類
亞洲動態

狂賀 ! AI彩票入圍 2018 G2E [最佳彩票平台]

AI彩票在2018G2E Asia初試啼聲便一鳴驚人,自277個強力競爭對手脫穎而出,強勢入圍亞洲娛樂大獎
(G2E Asia Awards)「最佳彩票平台」項目,優異實力深獲官方認證肯定。
0
AI彩票是彩票行業巨擘 – 德勝集團投入超過500萬美金,以人工智能作為核心技術開發的彩票整合平台系統品牌。目
前,AI彩票已於菲律賓、馬來西亞和台灣組建超過180人專業團隊,成功將AI技術純熟應用在風險控制和
客戶服務兩大
領域,開創彩票包網行業新格局。
AI彩票表示,此次獲行業、2018G2E Asia評審小組和組委會成員青睞提名「最佳彩票平台」獎項,主要理由為AI彩票
成功將人工智能技術導入彩票包網行業,寫下行業新新里程碑。目前,AI彩票透過大數據分析與機器學習方式,即時分
析判斷注單風險數,審核注單過程邁向全自動化。此外,透過問題精準定位與答覆,建立正確率近100%的自動回覆客
服系統,有效降低人力成本、提高工作效率,重新定義經營彩票行業的必備優勢。
AI彩票獲「最佳彩票平台」提名,將進入最終評分/投票階段,由評審小組獨立進行公平、公正評分程序,整個過程由
安永會計師事務作為獨立監督機構,獲笑者和得獎名單被保存在保險箱中,直至2018年5月15日頒獎典禮才會公布。亞
洲娛樂大獎由亞洲領先行業盛會G2E Asia和行業權威出版物Inside Asian Gaming共同舉辦,12個獎項由來自行業各領
域55位專家組成的評審團進行票選。
2018年5月15日至17日,A彩票在亞洲國際娛樂長(G2E Asia)澳門威尼斯人3029展位提供產品展示、合作方烹、專
業諮詢、產業交流…等服務,歡迎各方嘉宾范臨指導。

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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.”