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The latest Super MILLION$ final table promises some terrific action, featuring as it does some of the best poker players on the planet.

The action will kick off with New Zealand’s David Yan in the lead, sitting as he does with 5.3 million chips, a good chunk more than his closest challengers Yuri Dzivielvski (3 million) and Niklas Astedt (2.6 million). Of those two, it is perhaps the Swede Astedt who will fancy their chances more than anyone, desperate to seal the deal having accumulated almost $2 million in winnings in the tournament series but never having closed one out to take the outright win.

Elsewhere, there are some superstars of the game who are taking their places lined up behind the leaders in pursuit of poker profit. Canadian poker legend Sam Greenwood comes into the final a little short-stack with 1.9 million chips, but he’s immediately followed by another luminary of the game in Mikita Badziakowski. The Belarussian only has 1.4 million but will be doing everything he can to get over the line. The final table betting prices are slightly different to the order in which the players find themselves in the chipcounts:

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The partnership also plans to analyse operational data, develop pilot programmes and construct a model for national deployment.

A systemic policy cooperation mechanism is expected, targeting youth gambling prevention and student safeguarding. Rather than implementing short-term campaigns, the focus will be on embedding gambling prevention into everyday school routines. 

It will include combining restrictions on smartphone use with engaging RAS activities to effectively block and fill the time students might otherwise spend being exposed to gambling content.

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The charter also made a series of other recommendations. It suggested that companies should designate a senior member of staff responsible for governance of the technology. It additionally recommended an AI oversight or ethics committee.

The framework also urged companies to implement rollback mechanisms and “kill-switch” controls for higher-impact systems.

For AI-powered customer support and chat, operators are encouraged to set clear parameters for when conversations should be moved to a human member of staff. These include repeated low-confidence responses, unresolved queries, indications of distress, or a customer simply asking to speak to a human.