The core goals of artificial intelligence (AI) in computing are problem solving and task completion. These capabilities are intended to replace, or complement human function. The application of AI in the emerging new cognitive technology era is driven by big data analytics, cloud computing and abundant Internet access.
The confluence of these combined forces have enabled cloud-based Machine Learning concepts to be applied at a much lower cost than was previously possible. Moreover, the productivity advances made possible by cognitive computing are going to be truly remarkable.
One of the most promising new use cases is within the media, advertising and smart programmatic marketing-related arena.
Machine Learning Market Development
Juniper Research has found that machine learning algorithms used to enable more efficient ad bids over real-time bidding (RTB) networks will generate an estimated $42 billion in annual advertising spend by 2021 -- that's up from an estimated $3.5 billion in 2016.
Machine learning, a subset of AI, has arrived at a point where it's both accessible as well as affordable to a wide range of stakeholders. Juniper anticipates that the technology will eventually permeate into nearly all industries in the next five years.
In the case of the media industry, machine learning is being used to develop so-called bots and digital assistants, as well as maximize returns on digital advertising. In the case of the former, companies such as Facebook and Google are leading the drive, with companies like Rocket Fuel and Datacratic developing innovative solutions for the latter use case.
The latest Juniper market study has found that today's RTB bidding mechanisms are based on simplistic segmentation, as opposed to the individual, while rules for determining bid amounts are often rudimentary.
In contrast, machine learning can transform this segment of the digital advertising market, because algorithms will predict the successful outcome of an advert impression, and thus adjust bid amounts dynamically.
For example, when a consumer has recently been exposed to a digital advert, it's unlikely they'll respond positively again to another ad impression that's identical. Meanwhile, behavioral and contextual attributes are being used to predict how successful an advert impression may be, on a personal level.
Market Outlook for Machine Learning Apps
"Typical RTB allows the advertiser to target demographics or various population subsets," said Steffen Sorrell, senior analyst at Juniper Research. "Adding machine learning into the mix effectively allows RTB networks to target the individual. This is a much more powerful tool."
In addition, the study found that machine learning is likely to lead to an era of fully-personalized advertising delivery. This concept was demonstrated last year by M&C Saatchi via their adaptive digital poster in London, England.
Meanwhile, advances in machine learning will enable cloud-based computer systems to understand both the images and text delivered to web pages, offering an opportunity for unique advertising campaigns.
The confluence of these combined forces have enabled cloud-based Machine Learning concepts to be applied at a much lower cost than was previously possible. Moreover, the productivity advances made possible by cognitive computing are going to be truly remarkable.
One of the most promising new use cases is within the media, advertising and smart programmatic marketing-related arena.
Machine Learning Market Development
Juniper Research has found that machine learning algorithms used to enable more efficient ad bids over real-time bidding (RTB) networks will generate an estimated $42 billion in annual advertising spend by 2021 -- that's up from an estimated $3.5 billion in 2016.
Machine learning, a subset of AI, has arrived at a point where it's both accessible as well as affordable to a wide range of stakeholders. Juniper anticipates that the technology will eventually permeate into nearly all industries in the next five years.
In the case of the media industry, machine learning is being used to develop so-called bots and digital assistants, as well as maximize returns on digital advertising. In the case of the former, companies such as Facebook and Google are leading the drive, with companies like Rocket Fuel and Datacratic developing innovative solutions for the latter use case.
The latest Juniper market study has found that today's RTB bidding mechanisms are based on simplistic segmentation, as opposed to the individual, while rules for determining bid amounts are often rudimentary.
In contrast, machine learning can transform this segment of the digital advertising market, because algorithms will predict the successful outcome of an advert impression, and thus adjust bid amounts dynamically.
For example, when a consumer has recently been exposed to a digital advert, it's unlikely they'll respond positively again to another ad impression that's identical. Meanwhile, behavioral and contextual attributes are being used to predict how successful an advert impression may be, on a personal level.
Market Outlook for Machine Learning Apps
"Typical RTB allows the advertiser to target demographics or various population subsets," said Steffen Sorrell, senior analyst at Juniper Research. "Adding machine learning into the mix effectively allows RTB networks to target the individual. This is a much more powerful tool."
In addition, the study found that machine learning is likely to lead to an era of fully-personalized advertising delivery. This concept was demonstrated last year by M&C Saatchi via their adaptive digital poster in London, England.
Meanwhile, advances in machine learning will enable cloud-based computer systems to understand both the images and text delivered to web pages, offering an opportunity for unique advertising campaigns.