Over the course of the last few decades, the gaming industry has seen great strides. Beginning with simple games like Pong* and Pac-Man* which offered players a short escape from reality and growing into such involved games like World of Warcraft* and Call of Duty 4* which are serious hobbies to those that play them. Today's gamers, who according to the Entertainment Software Association (ESA) have an average of 13 years of gaming under their belt, have grown accustomed to seeing each new game become increasingly complex, engaging, and intelligent. For developers, the challenge becomes pushing the envelope to create games that are increasingly compelling. Computer-controlled Artificial Intelligence (AI) has evolved in many forms to meet the test.

We typically think of artificial intelligence (AI) within our industry in terms of processes and calculations. Media buying, for example, is ripe for intervention by sophisticated algorithms and machine learning systems. The commonly-held assumption is that developments such as these will free up humans to spend more time on creative tasks, like campaign strategy and content production. But as we move from rule-based automation to true AI, should we believe that creativity will remain a singularly human pursuit? How close is artificial intelligence to being able to carry out the role of a content marketer?

Instances of reported cybercrime are growing astronomically – and yet many successful attacks are still not reported, or even detected. In response to the escalating threat, detection capabilities are constantly being refined, improved and almost fully re-imagined. As new threats arise, so do technologies that offer a control against these threats. Automating the process without compromising the accuracy or effectiveness of the measures helps to augment the role of a human in security operations. The automation wave is the progression of technology and machine learning into intelligent software that can act to both identify and remediate incidents, leaving security professionals to tackle more complex and relevant issues.

AI experts take much less punishment than NFL quarterbacks do, and some earn just as much, apparently. Forget all the hype around artificial intelligence (AI), if you've really got the skills that Silicon Valley and Wall Street need, you could haul in millions of dollars a year. "Right now, AI is an elitist sport – there are very few people who know how to practice it," said Tom Eck, the CTO of industry platforms at IBM and a software developer who has been involved in AI as far back as the early '90s. "The top-tier AI researchers are getting paid the salaries of NFL quarterbacks, which tells you the demand and the perceived value." While Eck, speaking at the Markets Media's Summer Trading event in New York, didn't specify whether he meant starting quarterbacks or their backups, he did say that the best AI researchers are earning as much as the highest paid position in the National Football League.

Despite investing enormous amounts in people resources to prevent money laundering and terrorist financing, and comply with regulations, banks have paid approximately $320 billion in fines over the last ten years alone. Cue artificial intelligence (AI) and machine learning, the latest technologies promising financial institutions a way to outflank criminals in the world of digital finance. While people versus machines arguments grab headlines, the most successful banks will use a combination of humans and AI to prevent fraud. I listened to some interesting fintech scenarios during the launch of the SAP Next-Gen Innovation Community for Financial Services at the SAP Leonardo Center in New York City, and one of the most impressive was from Surendra Reddy, Founder and CEO of Quantiply. The California-based startup is infusing AI into its software solutions to help banks address financial crime, risk and compliance.

Telstra is set to inject artificial intelligence and machine learning capabilities into both customer and agent facing systems. The telco is in the process of appointing internal product owners to oversee "applied" artificial intelligence and machine learning projects respectively. It is intended that the new "delivery-focused" central resources will sit with project teams out in the business "throughout the entire project lifecycle from idea through development and delivery into production". They will then have responsibility for expanding the AI or machine learning capability of that system, creating and "grooming" a new feature "backlog" to prioritise features that can deliver the most value. The telco did not go into specifics for how and where it intends to extract value using AI and machine learning algorithms first. However, it stated an interested in pursuing projects that involved some form of natural language processing, rule-based systems, and "automated inference and decisioning" technology, which could help it – for example – predictively react to customer preferences.

Despite investing enormous amounts in people resources to prevent money laundering and terrorist financing and comply with regulations, banks have paid approximately $320 billion in fines over the last ten years alone. Cue artificial intelligence (AI) and machine learning, the latest technologies promising financial institutions a way to outflank criminals in the world of digital finance. I listened to some interesting fintech scenarios during the launch of the SAP Next-Gen Innovation Community for Financial Services at the SAP Leonardo Center in New York City, and one of the most impressive was from Surendra Reddy, Founder and CEO of Quantiply. The California-based startup is infusing AI into its software to help banks address financial crime, risk and compliance. "Working with SAP, can we bring machine learning and AI to augment investigators so they can proactively stop activities before anything happens. Unlike humans, machines can see patterns in minutes not hours, and they never sleep," said Reddy.

One thing that makes ISIS so hard to fight is that the terrorist network is diffuse and scattered, with small cells of operatives all over the world. Not only does this make it hard for law enforcement to predict where the group might strike next; it makes it incredibly complicated to track activity on the network--activity like banking transactions. Small sums of money flow from foreign fighter to foreign fighter, yet banks struggle to identify it within their systems. Banks have long used anti-money laundering systems to flag suspicious activity, and in the aftermath of September 11th, they have turned to those same legacy tools to catch terror-related transactions, too. But these legacy tools are not up to the job.

This whole topic – where (and how) should ML and rule-based verification meet – has been on my mind for a while, but I still don't have good answers. I do think it deserves significant attention from researchers and practitioners. The next three chapters will discuss why I expect ML to keep growing in dynamic verification, why there will always be an unavoidable, irreducible non-ML part, and some ideas about connecting the two. Finally, the last chapter will talk about rules in ML-based systems, explainable AI and all that. If you are not into verification, just go directly there. Please take a quick look at my Dynamic verification in one picture post.

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