Recently, Google AI has announced that the use of a machine learning system for rapid training to game-playing agents. This framework can be used by game developers to release human game-playing agents, who can efficiently focus on other priority duties to boost productivity. Google AI will provide an open-source library to show the techniques used in this practice. Google AI wants to harness machine learning algorithms for designers to balance their games, artists to generate assets of top-notch quality within a short period of time. The models can also be used to train challenging opponents to compete at the highest level of any game efficiently.

Traditionally, game developers leverage machine learning algorithms for direct access to the source, the uniquely interactive nature of video games, and many more functionalities. But, in the current scenario, Google AI has launched a machine learning system that can be used for rapid training to game-playing agents and seeking serious bugs instantly. The modern solution can work on some popular game genres and generate game actions from a game state within one hour on a single game. Google AI is determined to produce a machine learning system that can only play the game for the game developers while detecting and fixing bugs automatically.

This updated machine learning system can allow game developers to train multiple game-playing agents instead of one super-effective agent with a single end-to-end machine learning model. Game developers experienced a most fundamental barrier in implementing machine learning to computer games- bridging the gap between the simulation-centric world of games and the data-centric world of machine learning. The current machine learning system provides efficient as well as game-developer-friendly APIs with a Dagger-inspired interactive training flow to develop user-friendly video games by describing what a player perceives and the semantic actions related to it like joysticks, 3D objects, 3D locations, buttons, and many more.

Google AI is ready to provide an open-source library for game developers with no prior knowledge of machine learning while the training of game-playing agents can be finished within an hour on a single developer machine. The reason is the effective performance of imitation learning that teaches machine learning models by observing the behaviors of professional players in the games. The imitation model is inspired by the DAgger algorithm that allows taking an advantage of the interactivity of video games.

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AI in fraud investigation makes the whole process efficient by generating relevant data

Right from deploying machines to get the work done to the robots assisting the doctors in surgeries, we’ve come a long way – thanks to Artificial Intelligence, truly a remarkable innovation! Today, the business models that we get to see extensive use of technology. Also, the new and complex challenges behind managing the fraud investigation are a strenuous task in itself. Well, it doesn’t end there. Cross-border probe adds to the already existing complexity. Such an investigation could highlight bribery, corruption, data breach, conflict of interest, fraud in financial reporting and IP theft, to name a few.

There are a lot of factors that need to be accounted in case of a cross-border probe. Some of them are –

• Local rules, laws and regulations
• Cultural attributes
• Language barriers
• Different standardization levels, etc.

No wonder why such a probe is complex and full of challenges. With these complexities stepping in, deploying the right tools with a well-laid investigation methodology standardisation is the need of the hour.

The procedure followed by such a probe is as stated –

• Needless to say, the first step has to be getting in as much information as possible. Relying on both – external as well as internal sources for the same yields fruitful results. External sources include media, open source information, etc. whereas internal sources obviously revolves around employees, vendors, business operations, etc. A key point to note is that the information collected should be on a timely basis and also from as many sources as possible.
• Next up, try identifying the relationship between key entities and individuals
• Many tend to ignore this but is equally important – a sound knowledge on the event chronology.
• Transactional data holds a lot of crucial information. Being able to understand what goes in and how to draw necessary insights is the key here. The probe is incomplete when transactional data is not being addressed.

Now that you have a pile of data to analyse, sitting to scan every bit of this makes no sense. Identifying relevant content is the key here. It is here that Artificial Intelligence comes into the picture. Embedded artificial intelligence helps in filtering down the content and classifying it as required. The feature of semantic search is no less than a blessing here for it automatically identifies related concepts and documents.

Artificial intelligence in fraud investigation makes the whole process efficient by generating relevant data and leaves us in a position to draw meaningful insights.

Data extraction is a tedious task and when sensitive data like the data pertaining to banks, financial institutions, hospitals, etc. is involved, one cannot afford being negligent here. With advanced computer vision algorithms in place, it is possible to extract information from bank statements, and various other documents. Natural Language Processing (NLP) techniques help in extracting information and also aid in performing automated verification using digital channels. Artificial intelligence is no less than a saviour for financial institutions as it caters to verification of the critical details and documents.

Analytics also plays a pivotal role in investigation. Two types of analytics, namely advanced analytics and process analytics have a lot to offer to ease the investigation process. Talking about advanced analytics, it performs the following –

1. Network analysis: This is where a relationship between individuals and entities is established.

2. Sentiment analysis: When documents are to be differentiated on the basis of tone, subject, etc., analytics comes into play. Also, any suspicious review, article, conversation, etc. can be effectively identified.

3. Detecting anomalies in the transactional data is easier than ever.

4. Using AI, it is also possible to identify undisclosed entities.

Process analytics: What can get better than getting to know how your processes are performing in addition to what needs to be done to perform better? This is exactly what process analytics has in store for you!

Though these tools and technologies can help in fraud investigation and management, what needs to be understood here is that the process is complex and comes with their own challenges. Sound knowledge about these tools and techniques might help. With frauds and crimes in the world of technology continue to rise, it is high time that the investigators have access to the right AI tools and technologies to tackle these situations.

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