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By opening accounts with several sites, you can always get the best Big Brother odds when you want to bet on your favourite housemate. In the end, Memphis was the first in Big Brother history to get no votes in the jury vote. In a very similar format, Big Brother follows participants living together in a house fitted with dozens of high-definition cameras and that record their every move, 24 hours a day. Big Brother betting is available on licensed sites all over the internet. You can bet on Big Brother throughout the show.

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He runs a big fraud operation in New Orleans involving cash, identity theft, credit card spoofing, and more. Jesse is a young aspiring con woman who tries to rob him but fails miserably. She then asks Nicky to teach her the tricks of the trade, to which he agrees. It goes without saying that the two get romantically involved. You can watch the official Focus movie trailer via the link below: We would hate to spoil the movie for you, so you would have to watch it to know what happens next.

We will only mention a few things — at some point, Nicky and Jesse part ways and then the plot takes us to Buenos Aires, three years later. There Nicky Spurgeon would attempt one of his biggest hoaxes. Everything can easily go wrong, especially when there is a woman involved. Yes, Jesse shows up, and things get intense. Interested yet? You should be, and rightfully so! Gambling in the Focus Film Next, in our Focus movie review, we will discuss how gambling plays a significant part in the filmOne of the best gambling scenes in the movie is with Nicky and Jesse at the Super Bowl in New Orleans.

While watching the game, they start placing small bets just between the two of them for fun. Nicky and Tse continue their betting game, increasing the stakes significantly while doing so. Frustrated and clueless, Jesse refuses to participate but is left with no choice. It is when she takes the binoculars to try and guess the player Tse has chosen that she sees a familiar face on the field.

Jesse realises that this is yet another con, and they end up winning and doubling their money. There are clear indications that government interest in algorithmic auditing will continue to grow. There are numerous other groups that determine technical standards and specifications both broadly and narrowly, including open source communities that determine technical specifications for things such as programming languages, technical libraries and operating systems.

These efforts have been ongoing for several years, with implementation likely to take place over the next few years. In financial services, the international standards-setting organisation IOSCO set out non-legally binding guidance related to how regulators may best address conduct risks associated with the development, testing and deployment of artificial intelligence and machine learning. From our stakeholder conversations, it emerged that some companies are carrying out internal audits or governance initiatives as part of their due diligence.

For example, model risk management MRM , including model audits, are standard practice in areas of the economy that have traditionally used data and statistical methods, such as financial services. Current approaches to MRM, however, are designed for static models. These approaches do not take into account difficulties associated with data-driven machine learning models, particularly those that are updated following deployment.

Accordingly, it is likely that improvement will need to take place for current measures to be sufficient to meet any potential future regulatory requirements for audit. Externally, companies have begun offering algorithmic auditing services. Large traditional consulting companies have expanded their offering to include assessment and auditing services for algorithms.

For example, one consultancy states that they offer a range of services including expertise in algorithms, risk management and coding skills, and helping organisations to understand how they use algorithms and address governance and oversight. Another created a tool for clients to identify and mitigate unfair bias in AI systems, as part of its broader suite of AI testing services.

Recent years have seen the emergence of algorithm auditing start-ups and third-party small and medium-sized enterprises, which often offer services in relation to specific issues such as transparency or explainability of algorithmic processing systems. Audits conducted by start-ups can potentially target a wide variety of issues, and can also be used to perform due diligence on a system.

Further, the proposed European Commission AI regulation will require that organisations using high-risk systems should undertake ex-ante conformity assessments before placing a system on the market. A limited number of these systems will require external audits, which will drive the development of the sector.

However, there is currently a lack of agreed standards for such audits, at least across some sectors and areas of application. As will be discussed in this section , addressing this lack of conformity is a priority. Some researchers have also produced tools, such as the Aequitas bias and fairness toolkit, [footnote 74] and a method to detect discrimination in AI and machine learning systems created by researchers at Oxford that has been adopted by Amazon in its bias toolkit.

There is therefore a question to be raised about how much these tools support external accountability, where researchers can play an important role. Mechanisms to strengthen researcher access to data and algorithmic systems will need to be considered, to enable researcher participation in auditing and assessment efforts.

Journalists, civil society and whistleblowers Journalists have played an important role in surfacing algorithmic practices. In the Wall Street Journal alleged that Amazon had changed its search algorithm to more prominently feature listings that are more profitable for Amazon.

The Markup is a nonprofit newsroom employing quantitative journalists to collect and analyse evidence on how technology is being used. Their investigations have found several instances of detrimental impacts of algorithms. Some are bringing legal cases before the courts to challenges harmful practices by companies using algorithms. Foxglove, a digital rights non-profit, pursues legal challenges such as the cases brought against the Home Office and government over the use of a visa algorithm and an A-Level grading algorithm, [footnote 82] respectively.

In addition, the Irish Council for Civil Liberties has taken legal action against adtech companies over violations of the GDPR, [footnote 83] while 2 not-for-profit organisations in the Netherlands have brought privacy litigation against Facebook. This is a non-profit group that has created a downloadable browser extension to build a crowdsourced global database of political adverts placed on social media.

The aim is to tackle a lack of transparency around personalisation in political advertising. One of the most recent examples of this is the role Frances Haugen, a former employee at Facebook, played in exposing alleged inaction on harms caused by its recommender systems. To identify issues with the current landscape and potential solutions, we spoke to stakeholders with expertise in the use of the algorithmic processing systems across academia, industry, the public sector, and civil society.

We also hosted a workshop in partnership with the Ada Lovelace Institute, which brought together a broader group of representatives from academia and civil society. We held a further workshop for industry representatives to explore our initial ideas in more detail. Stakeholders were chosen based on their experiences and perspectives on auditing algorithms and knowledge of the landscape.

Key takeaway: Where a market for algorithm auditing services exists, it is at an early stage of development. Efforts to proactively surface and identify new issues in algorithmic systems through auditing have been particularly slow to emerge. This is where there may be a role for the public sector in incentivising an ex-ante approach.

The following sections summarise specific issues raised by stakeholders we spoke to. Lack of effective governance in the ecosystem Stakeholders in the AI auditing industry were concerned about the quality of audits in what they perceived as a currently largely unregulated market for auditing. Indeed, some investigative journalistic publications such as The Markup have established themselves because of a perceived lack of accountability and regulation around algorithms.

Several stakeholders noted that regulators are unlikely to have the capacity to investigate every instance of alleged harm, and that journalists, academics and other third parties play an important part in scrutinising algorithmic systems. In our engagement with stakeholders we also observed that there is a lack of clarity about standards that auditors should be auditing against. As mentioned before, regulators such as the ICO have been developing toolkits to provide guidance.

This lack of clarity around what good auditing and outcomes look like could act as a disincentive for organisations to pursue internal or external algorithmic auditing. We found that the development of sectoral or domain-specific principles and testable criteria could help to address this lack of clarity.

For example, such developments are happening in the health sector. As such, members of the DRCF may wish to undertake further work to explore which types of regulatory measures are appropriate in which contexts. Standards bodies are likely to fill some of the gaps left by regulators, yet there are concerns around a lack of transparency over how they are developed and by whom.

Insufficient access to systems From speaking to stakeholders from academia, civil society and industry we observed that the quality of audit may be compromised if it is not possible for appropriate auditors to obtain sufficient access to algorithmic systems. In some cases, extensive transparency and granular explanations should only be shared with those that need to understand them i. Such access for academics would, however, need to be balanced with any legitimate privacy and commercial concerns relating to the organisations being audited.

A possible technological solution could rely on a third-party sandbox in which algorithms can be shared by their owners and analysed in a privacy-preserving manner by appropriate external parties. There are technologies in active development that could further support such privacy-preserving analysis. Furthermore, academics and other auditors will often need access to domain experts who can explain the context in which algorithms have been developed and how they function.

Academics and others also noted that a significant barrier to their success in finding new and unintended harms was their perceived lack of legal protection or certainty for their investigative methods. Some participants noted that they can face legal action based on what they saw as an unclear legal stance on web scraping and risks around creating fake users of systems to understand how they operate and uncover harms.

Organisations may be more willing to provide access to systems to auditors where the audit is not made public and for the benefit of the organisation only; for external audit that may be provided to the regulator or the public, they may offer only limited access. Nevertheless, where enforcement action is taken against an organisation, regulators have full access to systems. Some clients will use open source code downloaded from the internet, add their data to it and fail to effectively document where the source code comes from.

This could prove to be problematic if the code has been developed in a different jurisdiction such as the US, as it will not have been developed for the UK regulatory context, complicating auditing activity. It can provide them with evidence that they could use to seek redress.

However, there is an apparent lack of clear mechanisms for the public or civil society to challenge outputs or decisions made with algorithms or to seek redress. Regulatory cooperation will be important to ensure that people can seek redress without having to navigate separate regulatory systems themselves. We also noted that stakeholders considered it important for regulators to commit to actions in response to audits that surface problems.

They have also recommended the creation of a mechanism for public interest organisations to lodge a complaint with national supervisory authorities. These include the right to: access a subject access request , rectify, erase, restrict or object. Where individuals are subject to automated decision-making with legal or similarly significant effects, they have additional rights, which include the right to obtain meaningful information about the logic involved and to contest the decision.

However, some have argued that data protection law only provides remedies for unfair outcomes for individuals and lacks them for groups or communities. This makes it harder to detect the systemic-level impacts arising from automated systems. In some cases, they thought that it will be necessary to develop statistical tests for technical audits to evaluate the concerns that we as regulators and society have with algorithmic systems.

Regulators may also signal the categories of harms or aspects of AI they find particularly relevant to their remits. For example, in a blog, the US Federal Trade Commission stressed the importance of being transparent, explaining decisions to consumers, ensuring that inputs are accurate, and that inputs, processes, and outcomes are fair. There is no agreed upon definition of what constitutes fairness, [footnote ] with this differing depending on sector, application and context of use. Typically, fairness falls into 2 categories: procedural fairness that focuses on fair treatment of individuals; and outcome fairness which is concerned with what decisions are made.

However, it has been reported that the parties had agreed that the audit focus only on a specific use case, and it therefore did not examine assessments using facial analysis. Pymetrics, a company offering a candidate screening service, conducted a joint audit with researchers at Northeastern University in the United States.

The examples also demonstrate that there may be a need for bespoke approaches to auditing algorithmic systems depending on the context in which they are deployed. Other issues Another concern we heard among industry stakeholders related to the relationship between the auditor and any potential rivals of the audited organisation, and whether there are any existing commercial relationships. Strong legal protections around the use of data in the context of an audit would be required to address this.

Finally, the costs of audit and any other related regulatory activity must be carefully considered. Audits may have high financial costs associated with them, which may mean that larger organisations are better able to adapt and absorb the additional costs than smaller organisations. Any perceived burden of regulation or audit could also impact the incentives on firms to innovate. We are highly mindful of these potential consequences, and we strive to ensure our regulatory activities are proportionate and encourage innovation for both small and large organisations.

As the DRCF we are exploring topics of common interest across our combined remits, but as individual regulators our regulatory responses and approaches to auditing may necessarily differ in line with the different contexts we oversee and our overarching regulatory approaches.

Some regulators may favour a hands-on approach to auditing companies or introducing specific requirements, while others may choose to rely more heavily on industry self-governance. For instance, the FCA is technology-neutral and holds firms accountable for fair consumer outcomes irrespective of the technologies and processes they deploy.

Where they are required in the future, the type and depth of audits will likely vary depending on the nature and size of the potential harms, the ability to specify clear ex-ante rules, and the costs of auditing. For instance, there may be stronger requirements around effective assurance of algorithmic systems where they are involved in sensitive applications and the stakes are particularly high for people, such as in healthcare, recruitment or education. In some contexts, regulators could consider requiring higher risk systems to undergo a third-party audit.

In other contexts, regulators may wish to require the results of internal or external audit be made available to the regulator before the system is put on the market, as occurs in the regulation of some medical devices. This could be the case in areas judged to be lower risk or where regulatory requirements and expectations are already established and strong.

It will be important for us to ensure that compliance costs are proportionate, and implementation is as straightforward as possible, so that the introduction of new auditing requirements can feasibly be met by organisations. This would help facilitate the trustworthy use of algorithms and provide the regulatory clarity needed to stimulate innovation.

Likewise, it will be important that regulators coordinate and where possible minimise additional burdens to industry beyond those necessary to ensure good outcomes for society. This is not to say that regulators will be the only important actor within the future audit landscape. Our stakeholder engagement suggested that industry-led initiatives, as well as regulator-industry collaboration, could be important for the development of an effective algorithmic auditing ecosystem.

The remainder of this section considers the potential role that regulators and industry could play in the future auditing landscape. Role for regulators Regulators will likely play an important role in the future audit landscape to ensure that the application of algorithmic processing systems is trustworthy and legally compliant. However, some of the stakeholders we engaged with suggested that regulators would not have the capacity to assess or audit algorithms themselves at the scale required to address harms of various degrees across sectors.

Instead, in their view possible roles for regulators included: stating when audits should happen to ensure that parties are more likely to comply with the law; establishing standards and best practices to reflect our views on how audits may encourage legal compliance; acting as an enabler for better audits; ensuring action is taken to address harms identified in an audit where appropriate; and identifying and tackling misleading claims and practices.

Stating when audits should happen Depending on the potential risk of harm of an algorithmic system, regulators could issue guidance on when an audit should take place. Audit requirements could be internal to an organisation, undertaken by a regulator, or provided through an external organisation — a topic that will be specifically addressed in this section. There are likely to be 3 primary ways regulators could state an audit should happen as appropriate : Before or as algorithmic systems go live.

At regular intervals: given the dynamic and changing nature of algorithmic systems, it may be necessary to monitor them through regular audits on an ongoing basis. Google has made commitments to address these concerns, including a monitoring arrangement to ensure ongoing compliance with these commitments. Establishing standards and best practice Our engagement with stakeholders suggested a need for clear standards and best practices on which audits could be based, and clear criteria against which algorithms could be tested.

Further regulatory guidance could make auditing more accessible through creating clear criteria and providing guidance on documentation and the level of required transparency. This could make it easier for companies, regulators, and external auditors to inspect algorithmic systems. Such guidance could be created by regulatory bodies, either by themselves or in collaboration with other public-sector groups and standard-setting bodies.

Guidance could also be general or could exist only for specific use cases where high-risk outcomes have been identified. Finally, guidance could be prescriptive where appropriate, for example in relation to a specific sector, or outcome-focused, with the private market left to create the tests to demonstrate that systems are producing those outcomes in the latter approach. There are advantages and disadvantages to each approach. For instance, the private market could be more agile than regulators in innovating the tests that could demonstrate that the processes and outcomes desired by regulators have been achieved.

On the other hand, the tests developed by the private market may not adequately assess for the outcomes desired by regulators. Further, outcome-based regulation may fail to provide the clarity needed to industry, potentially impacting innovation. It is likely that any regulatory measures introduced will be a hybrid of the approaches described above. As an example, broader outcome-based guidance could be complemented by context and use-case specific guidance that is more prescriptive where higher levels of risk are perceived.

In deciding which measures are appropriate, digital regulators could also draw on other sector regulation for inspiration. For instance, where the impacts of harms to individuals and society are potentially very high, financial regulation offers examples, including the notion of embedding compliance staff within large companies. This model could be applied for digital organisations. Act as an enabler for better audits At present, there may be obstacles to conducting governance and technical audits effectively, as this typically requires the full cooperation and agreement of the organisation being audited.

Some experts we spoke to from academia and industry stressed that regulators could facilitate better audits through introducing specific algorithmic access obligations; whether these access obligations can be implemented will vary across regulators and depend on their respective remits. Some regulators may wish to explore avenues for enabling scrutiny of algorithmic systems without unduly burdening industry.

One possibility might be to only provide access to certain elements of an algorithmic system. Organisations that are the target of audits may be concerned about their intellectual property. Likewise, access to the algorithmic system itself may not be required if the auditor is undertaking a governance audit focused more on the organisational measures in place around the algorithmic system.

Another possibility might be to control who has access to different elements of the algorithmic system. Access could be given to different extents to different parties, for example where access is required, an auditor certified by an appropriate body could undertake the audit. The DRCF could consider precedents from other audited sectors in developing its governance mechanisms. However, where parts of an algorithmic system are built on elements from several different providers, identifying where in the supply chain the auditing should take place could be challenging.

This challenge could be addressed through contracts between organisations in the supply chain that clarify at which stage auditing takes place, and who the responsible parties are for ensuring the timely completion of such audits. The feasibility of this solution for open source code will require further consideration.

Alternatively, some regulators may want to expand the use of regulatory sandbox environments in the future, to test algorithmic systems and check for harms in a controlled environment. Regulators could also collect data from organisations, for example on the safety and bias of algorithmic systems. In some cases, they may wish to analyse the data themselves to determine whether they are satisfied that the systems are sufficiently safe and free from bias.

If appropriate, regulators could share sufficiently anonymised data with select third parties such as researchers to enable further investigation and reporting. Another possible step for some regulators could be to issue case studies and guidance on how existing regulatory principles or rules apply where algorithms are deployed. Ensure action is taken to address harms identified in an audit When a significant problem or breach of the law is identified through an audit, regulators could, subject to their powers, prohibit organisations from using the system until the organisation has addressed and mitigated the harm.

This approach would vary widely depending on the nature of the harm and legal breach identified, and the nature of the impact on consumers and citizens of disrupting the use of the algorithmic system. For instance, regulators could work together to establish certain red lines where algorithmic systems cannot be used based on their perceived risk to the public, building on the right to restrict processing of personal data under the UK GDPR.

Regulators could also share insights gained from audits on how algorithmic systems can create harm, and how this can be mitigated. This can help inform algorithmic design from the outset, or allow companies to gain a better understanding of how they should audit their own algorithmic systems.

Some stakeholders we spoke to from civil society warned that any remedies created by regulators or the parties involved following an audit need to be carefully considered. If they are badly designed, they could fail to address the underlying problem and result in negative unintended consequences. In order to incentivise organisations to come forward to the regulator when they identify problems in their algorithmic systems, regulators could choose to adopt a form of leniency system. Such a system would provide incentives for organisations to disclose harmful outcomes to regulators, for example less strict enforcement or reduced fines.

The public may also benefit from a way of reporting suspected harms from algorithmic systems, alongside the journalists, academics and civil society actors that already make their concerns known. This reporting could include an incident reporting database that would allow regulators to prioritise audits.

That being said, without parallel transparency requirements on firms across regulators, it may be difficult to evidence differing treatment or other algorithmic harms. This could lead to regulators being provided with poor quality information that they cannot act on effectively. It may also be beneficial to include the public in the design of algorithmic systems themselves, rather than only being involved once the system has been deployed.

We heard that some consumers want much more control over the algorithmic systems they are subject to, in part to be able to enforce their rights. Identifying and tackling misleading claims Regulators have an important role in receiving, understanding, and responding to complaints surrounding misleading claims made about and practices involving algorithmic systems.

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Bitcoin cash trezor Strong legal protections around the use of data in the context of an audit would be required to address this. We then finish the article with some conclusions for gambling research, policy, and practice oriented towards harm reduction. To achieve this, we suggest that research methods such as visual ethnography can be helpful. This model allows us to understand the influence of click characteristic on the four possible gambling choices from a model that jointly estimates the probabilities of each alternative gambling choice. Data were collected in the context of the COVID pandemic and research has demonstrated an increase in sports betting and a decrease in other types of gambling e.
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To keep this title and to prove themselves, they are asked to dive into the ocean of fire, which is a miles race across the merciless Najd desert. The conditions are harsh, and the competition is tough, but Hopkins rallies from behind to win the race, despite nearly dying of dehydration and contemplating killing Hidalgo to end his misery.

The movie is about the horse, Secretariat and Disney took the initiative to tells its story. The horse is a great breed and in it won the triple crown ending a drought of over 25 years. Secretariat recounts the story of Penny, a woman navigating the world of horse racing, which is dominated by males. She is also balancing family duties while attempting to keep her family stable. Penny is a cunning woman, with great control over her nerves. The horse is her best friend and she thinks that he is going to be with her forever.

She trained her with the help of Lucien and it turned out to be a success. They always sensed something special about Secretariat. The movie is available on YouTube if you want to watch it. The movie is about Nazis who are in working on their evil plan in Austria and they have these stallions who they save in the war. The movie was made by Disney and thus it is not very widespread, unlike some other classic movies that they have produced, and for obvious reason, winks!

The movie has elements of being a war film and it shows a real-life accident of Col. Podhajsky played by Robert Taylor. He is in charge of the famous Spanish Riding School somewhere in Vienna. When the film opens, the Nazis are losing the war, and Podhajsky is struggling to safeguard this extremely rare breed of horses…an Austrian national treasure in many respects. To make matters worse, the higher-ups will not allow him to evacuate the horses to the countryside because they do not want to admit how awful the conflict is.

This film is about his attempts to save the creatures and maintain this tradition and, thankfully, the real-life Podajsky was present to ensure the tale was accurate. Despite a sluggish start, this movie is truly vintage Disney and well worth your time.

The tale of the Lipizzaner stallions and Col. Podhajsky, the man who sacrificed all to rescue them, should be known to everyone. Jim Wilson has done a great job with casting and the movie shows that. The movie has everything in it, laughter, singing, and sorrow. The movie follows what happened in the Kentucky Derby where an absolute nobody won the derby and that nobody was the horse, Mind That Bird. The journey is of its jockey who never gave up on it. In my opinion, this is an excellent film for the entire family!

A great movie to watch while relaxing on the couch. An exciting film with an outstanding cast and team that will leave you speechless. It transports you to another place and, most of all, it makes you feel wonderful. I have watched a lot of horse movies, but this one takes the cake. I was very affected by this genuine story! I also enjoyed the music!! Most of the time.

I highly suggest this fantastic film to everyone. It is a must-see film!! So settle in, unwind, and enjoy the trip! Let It Ride Let It Ride Let it ride is a movie about gambling on horse racing and it has an element of comedy in it, in fact, it is a comedy movie. The characters in the movie have done a great job and it is clearly inspired by true stories.

Jay has a gambling addiction and swears to his wife that he will stop. That appears to do It anyway until he runs across his pal Looney, a cab driver like Jay. At a pre-race party, Nicky runs into Jess, who is now Garriga's girlfriend. After faking heavy drinking upon seeing Jess, Nicky has a convincing fight with Garriga in public and after being thrown out, is recruited by McEwen to provide the component.

Nicky begins pursuing Jess again, and they eventually rekindle their relationship. The head of Garriga's security entourage, Owens Gerald McRaney , is suspicious and narrowly misses catching the two together. Nicky not only delivers the real component to McEwen for three million euros but also sells it to the other teams for similar amounts.

Nicky and Jess attempt to return to the US together. However, they are caught by Garriga's men and taken to his garage. Jess is tied up and her mouth is taped shut whilst Nicky is given a beating. Garriga is convinced that Jess had something to do with Nicky gaining access to EXR and begins to suffocate the gagged Jess.

To save her, Nicky explains that he gained access to EXR by tricking her into believing he still had feelings for her and that the necklace he had given her was equipped to secretly record Garriga's password and login information. He explains that Jess was conned and knew nothing about this. However, Jess then reveals that she was only trying to seduce Garriga in order to steal his valuable watch and to make Nicky jealous.

Nicky promises to come clean in order to spare Jess's life but Owens shoots him in the chest, causing a horrified Garriga to leave. Owens then reveals himself to be Nicky's father, Bucky, and assures Jess that he avoided any major arteries. He simply employed the "Toledo Panic Button". Bucky then tapes up Nicky's wounds and draws excess blood out of his son's chest with a plunger so that he can breathe. They flee the garage in Garriga's vehicle. Bucky drives Nicky and Jess to the hospital to treat Nicky's punctured lung.

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After she proves her worth, she is allowed on the team. Jess meets Nicky's friend and associate Farhad Adrian Martinez. He is involved in more complicated schemes, as he is seen removing a fake ATM that is used to nab private information. Jess takes a liking to Farhad despite his often crass behavior. Over time, Nicky and Jess develop a mutual attraction. After sex, Jess asks Nicky why some people refer to him as "Mellow".

He says his father used to call him that because he told him he was soft like a marshmallow. Nicky believes that there's no room for heart in this game because it can get you killed. Nicky and Jess go to a football game. They make small bets with each other such as if one fan will catch a hot dog, and if another is too drunk to get up for the wave. They see a woman in short shorts and bet how many men will look at her ass as she walks by. Jess bets 8 while Nicky bets 3. A curious man named Mr.

Liuyan BD Wong joins the game and bets 5. Seven men check out the woman as she walks by, making Jess the closest. Liuyan makes more bets with Nicky over the game, practically begging him to play when Nicky tries to back out.

The bets escalate as the two men double the bets, even as Jess tells Nicky they should just leave. Liuyan pulls the card with a higher number, winning all their money. Nicky chooses not to walk away and decides to double the bet if Liuyan picks any player on the field and he makes Jess pick the one that Liuyan spotted. Liuyan gives them a chance to back out, but Nicky makes Jess pick. Jess grabs a pair of binoculars and looks carefully through the field before spotting Farhad wearing a jersey with 55 on it.

She picks him, and to everyone's surprise, that's the correct answer. On the cab ride home, Nicky, now with a LOT of money on him, explains to Jess that this was set up from the beginning of the day. He knew that Liyuan is a notorious gambler, so he had it set up so that Liuyan would see the number 55 throughout the day, thereby subconsciously getting him to pick that number when the moment was right.

Nicky then has the cab pull over while another car pulls up behind them. He tells the cabbie to take Jess to the airport. He leaves her with her share of money and departs without another word, leaving Jess confused and heartbroken. They plan to sell a software system developed by Garriga to an Australian investor named McEwen Robert Taylor , though it's obvious Nicky is really planning to swindle Garriga. Nicky goes to a party at Garriga's mansion where he sees Jess for the first time since he left her, descending the staircase in a stunning red dress.

She kisses Garriga, to Nicky's disdain. He approaches Jess afterward, where she assures him that she's doing just fine on her own. Nicky spends a lot of his time trying to get closer to Jess, even though she's done with him. This eventually leads to them going back to Nicky's hotel room and having sex. The next morning, Owens goes to Nicky's room while Jess stays out of sight. Owens complains about his feelings toward the younger generation and his lack of progress in the job.

That was another con! Thanks to this little trick, Nicky and Jess manage to get their money back, even some extra on top. A lot of people are not very pleased with the Focus gambling scene. This strategy does not guarantee an immediate win, but you can easily trick your opponent.

Sports betting is definitely a game of luck. Unless you have planned your moves in advance like in the movie. Slick move, Nicky! You can either rent or buy the movie using Amazon Prime. It offers a wonderful cast, an interesting plot and a little bit of gambling. We recommend you watch it and see for yourself.

You can watch the movie online or, if you are old-school, you can easily find the title on DVD and Blue Ray. There are a lot of different reviews and ratings on the Will Smith gambling movie. It is all up to you, but we will say that we really liked the film.

The cast is on point, and the storyline has its twists and turns, plus, there is a hint of gambling that you might find interesting. There is use of bad language, sexual content, and brief violence. Nicky and Jess start a romantic relationship. He is not happy.

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Mar 29,  · As promised, we’re going to have a look at the gambling presented in the Will Smith betting movie. There are a few gambling scenes, including a poker game. The main . Jun 12,  · The first part of the betting scene from the movie Focus released in featuring Will Smith and Margot Robbie. Sep 28,  · The Cast of Will Smith’s Gambling Movie, Focus. The cast of the movie, Focus is as follows: Will Smith as Nicky Spurgeon; Rodrigo Santoro as Rafael Garriga; Margot Robbie .