As we continue to forge our path towards Artificial Intelligence becoming mainstream, it is important to reflect on the breakthrough discoveries along the way. Yesterday marks just that event for Gravity4 Data Science team – when AlphaGo, an AI, outmatched the Chinese Grandmaster Ke Jie in a chess match.
The event demonstrated the live capacity of a machine to collaborate with the other players for tactics and showcased its mastering of the abstract thought process. Furthermore, AlphaGo AI demonstrated its instinctively ability to play the game, and taught itself through a self-reinforcement learning process.
AlphaGo AI played and won Grandmaster Ke Jie in March of 2016 as well. However, yesterday’s match was significant as Ke Jie played the game using some of the tactic AlphaGo had used in one of the prior matches with other players. To Ke Jie’s nice surprise, the program went on to use brilliant new tactics.
“There was a cut that quite shocked me, because it was a move that would never happen in a human-to-human Go match. Last year, it was still quite human-like when it played. But this year, it became like a god of Go.” – Ke Jie
The fundamental idea behind AI is to imitate human brain’s decision-making process. This is where vast majority of our researchers have spent the past 50+ years to understand the mechanism and program it algorithmically inside a machine. Nonetheless, thanks to the cutting-edge digital innovation focused around big data, advances in computational mathematics and increasingly powerful computers, researchers are now constructing additional layers to further complete the simulated “neural network”.
We are super happy at Gravity4 that a machine algorithm won the game playing against the Grandmaster – proving machine learning’s efficiency to self-teach. This is exactly the type of ‘machine training’ our data scientists are incorporating within Mona Lisa!
Mobile programmatic advertising is well positioned to take over across the globe. With each day the growing internet connections, machine learning and big data has enabled the growth in the mobile programmatic advertising sector. Just earlier this year, mobile traffic surpassed the desktop in many countries. As consumer preferences continue to favor mobile devices, to deliver the best ‘consumer-to-brand connections are crucial, in providing the native brand messaging experience.
In March 2017, Gravity4 Asia showcased its strength in the mobile ecosystem. In an independent third party audit, Gravity4 HongKong secured 100% audience reach across their mobile consumers. This success is attributed to our strong brands and publisher relationships, secured by our Gravity4 Asia team. Layered on top of these relationships is the added infrastructure of the Machine Learning Artificial Intelligent platform, #MonaLisa.
“Mobile is a personal device that remains ON and is with always with the consumer. Gravity4 Asia is seeing a huge mobile ecommerce growth. We have pegged ourselves in a strong, growing market and now we have 100% market coverage. We are taking our geographical learnings and pushing forward to cover all of Asia. I am very proud of our stellar sales team. We connect brands with their consumers on-the-go. We have best of class branded media supply relationships that run rich media native mobile ad units. And, now we layer on a top notch technology using artificial intelligence – our AI engine – #MonaLisa.” – MD of Gravity4 Asia, Kevin Huang
Gravity4 has been utilizing Machine Learning to determine the optimal bid strategy for the rate, best placement as well as rich media creative to serve on ‘audience level’ across devices. #MonaLisa’s predictive machine learning algorithms utilize the cross-device data to formulate future prediction on user’s likelihood to engage with a brand.
“MonaLisa strives to optimize the media buying process with the target goal to enhance the customer-brand relationship. It’s machine learning algorithms are highly efficient in assessing the correlating attributes across the device impression level. This deeper data point helps it formulate effective predictors of brand ad engagements – even when the consumer switch between devices throughout their day.” – CEO of Gravity4, Gurbaksh Chahal
Our platform uses the predictive models of device behaviors and consumer preferences to formulate optimal audience clusters, which traditional media platforms have struggled with. The computing power of Gravity4’s platform enables the evaluation of various correlating data points simultaneously. Furthermore, the #MonaLisa’s algorithms improve the media bid decisions by enriching the audience level data on each impression call – taking in account not only the 1st party data, but also the 3rd party data streams.
Follow us and learn more on adtech and #AI. Or contact our sales teams, across the globe, to say “Hello to #MonaLisa.”
In a recent study by Yahoo and Ender Analysis, UK online ad spends will utilize Audience IDs in more than 2/3 of their campaign budgets by 2020. This budget increase is more than 28% than 2016. Furthermore, this projection comes despite the anticipated data regulation coming into full force next year by the GDPR.
Brands have expressed loss in trust by the recent concerns over Facebook’s measurability issues as well as YouTube’s brand safety concerns. The report highlights the benefits of cross-device audience buying as mechanism to authenticate the ad views and measure ad effectiveness.
Audience IDs are the online user profiles created from anonymous identifiers in order to recognize and match the same user across different channels, devices or both.
We, at Gravity4 UK, are happy to be part of this discussion as we have advocated the use of audience IDs for the past two years, to ensure accurate attribution value across the complex the cross-device marketplace. The use of the universal audience ID ensures relevant and custom targeted ad messaging, enabling platforms to connect the consumers with their brands across any device or media platform. In addition, Gravity4 has been taking active an advocacy role to ensure our brands and campaign measures are compliant with the proposed regulation of GDPR. As we continue to help our brands adapt to the ‘single view’ of their customer across platforms, our machine learning assistant #MonaLisa will garner clearer perspective of the correlation between ads seen & engaged as well as attribute the conversion to the correct media channel.
Artificial intelligence, machine learning, deep learning, big data are the buzz words and often are used interchangeably or incorrectly in the advertising ecosystem. Artificial intelligence is used where machine learning should be and machine learning is often confused with data mining. Machine learning is a method of developing algorithms for recognizing patterns within data. Data mining uses techniques developed in machine learning, i.e. machine-learning algorithms, and statistics.
In this blog, I will help provide some clarifications pertaining to advertising platform. Perhaps, view part of the blog as “Artificial Intelligence, Machine Learning and Big Data – 101”. In the future blogs, I hope to introduce you to #MonaLisa, Gravity4’s Intelligent Cloud Marketing Platform.
Understanding the data
With the ever growing libraries of the big data, data mining has been an ongoing task. Platforms need to have the computational power to form meaningful advertising correlation, otherwise, much of this data is of no use. Predominant dimensions of “big data” are characterized in four sectors:
Volume of “big” data (while important) is not the sole principle variable; rather, it is the method in which the platforms organize this data – to create better decisions & strategies for the marketing campaigns.
Velocity is the measure of the data inflow capacity. It is often high, especially for platforms like Gravity4, where the inflow stream is ingesting social, mobile, and sensor based data. Aside from the efficient inflow of this data stream, some variables require real-time processing (ie: geo-location from mobile), which means data stream must be handled in timely manner.
Variety is further of importance to decipher the data from structured and unstructured formats.
Variability of the data, adds interesting complexity as it is routinely inconsistent. A lot of social data depends on the trending news in the media. Hence, makes the variability dimension unpredictable.
In the past few years, machine learning has made a significant breakthrough. Many big organizations now offer low-cost options for storing data, which previously couldn’t be supported by bootstrapped start-ups. We now also have faster processors, high connectivity, high throughputs, virtualization, cloud computing, large grid platforms and clustering, which few years ago were the main restrictions to do advance computations. In additions, we now have distributed big data platforms, like Hadoop, that are open source and affordable.
Importance of AI in marketing
The environment of advertising tech is increasingly complex. We now have more data than to know what to do with it. The role media analysts were able to play in ad-tech, a few years ago, is no longer scalable. Machine Learning provides depth of insights that data analysts simply can’t. The ongoing challenge is to comprehend the analysis on large volume of data streams, as the potential correlations and relationships between disparate data sources are too large for any human analyst to compute.
For effective audience segmentations, it is no longer analyzing the different websites and influencers, rather the consumers are now constantly switching between devices. This additional layer of complexity adds depth to the data inflow. For this machine learning or ‘machine-assist’ is effective than ‘human-only’ analysis. Machine learning is able to test reasonable hypotheses and derive meaningful insights buried in the libraries much faster than humans. For technology platforms using the advanced computation methods, you have to be able to process this information in real-time and draw statistically relevant ad to engage the consumer.
Effects on media buying or RTB
Programmatic media buying is a mechanism through which brands buy audiences. It is a marketplace and RTB is a transaction that occurs on that platform. Think of machine learning as a catalyst to that marketplace. It is now able to match the brands with their current and potential consumers in real-time, across devices, and offer various statically relevant correlations – thereby adding an enhanced degree of certainty for a suggested action.
Machine Learning is NOT a rule-based system that requires an analyst to hard-code domain knowledge into a system. Rather, Machine Learning learns to make decisions based on the inflow of data and experience. As we continue to train the algorithm, we continue to advance the Artificial Intelligence.
So, what does this mean for marketers? Well, brands do not have to worry and figure out the cumbersome process of Machine Learning on their own. They can partner with technology platforms such as Gravity4 and utilize #MonaLisa’s Machine Learning capabilities to stay connected with their consumers.
The method of analyzing the data varies in each company. Some platforms take in all the data, but don’t know what to do with it. Some will determine subset of variables to look at before the analysis of the data and throw away (or not use) the rest of the data. Either of the processes (and perhaps everything in between) are ways in which companies are forming their confidence in their decision making predictions. At Gravity4, we test various variables. It allows us to have a strategy in place for specific vertical with abundant variables to assess correlation.
Aside from the ad-tech, there are many industries using machine learning. So, the journey to that beautiful world of Artificial Intelligence isn’t a lonely one. You may very well already be interfacing with some technologies.
Retail: exploring AI to best market their customers, up-sell products and enhance customer win-back
Healthcare: exploring to find ways to improve delivery of patient care
Finance: exploring ways to identify ways to minimize fraud
Industrial: aiming to reduce waste and increase efficiency
As we continue to refine the algorithms and advance our understanding of recurrent neural networks and deep learning, we are essentially moving towards Artificial Intelligence.
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Want to learn more about Gravity4’s #MonaLisa, contact your local sales team. We are serving over 20 countries.
In 2017, over 300 million smartphones will have on-board neural network machine-learning capability, according to a study by Deloitte. Machine Learning will power applications for “indoor navigation, image classification, augmented reality, speech recognition and language translation even where there is little or no cellular or Wi-Fi connectivity, such as in remote areas, underground or on an airplane. Where there is connectivity, on-board machine learning may allow tasks to be done better and faster, or with more privacy.”
Contact to get a demo on how Gravity4 can power your digital mobile media strategy. We have offices in over 20 countries and are leading mobile efforts across many countries. Let us #MakeYourBrandLimitless.