AI News, Artificial Intelligence: Taking the Good With the Bad

Artificial Intelligence: Taking the Good With the Bad

Over a year ago, I started researching artificial intelligence (AI).

As a marketer for a company with an advanced digital solution that uses AI-type technology, I thought it was important for me to know the competition and learn about solutions that could help me currently and in the future.

Throughout my research, I found both pros and cons to AI, and it inspired me towrite an article in which I argued the benefits of AI outweigh the risks.

lot has transpired since I wrote the piece in February 2017, but my argument that tech adoption is slow and therefore it will be years before AI reaches its full potential is still valid.

Even though we are seeing incremental progress in the role AI plays in our day-to-day lives, the change has been so gradual that, without a bit of research, it’s hard to tell the difference between today and a year ago.

I’m going to look at AI from two points of view: How the technology affects us in our day-to-day lives, and how it is making inroads toward real change in professions such as medical science.

Because companies have to try harder and harder to rise above the noise, they are more and more willing to invest in marketing technology —

I spend half my day deleting spam with pitches for the latest and greatest personalization methods from vendors that should try some sort of personalization before marketing to me —

There are some extraordinarily intelligent people at work making excellent technology, but often that technology is expensive, which is why AI is tailored for companies that can afford it or companies in the business of gathering data.

While those scenarios seems like the scary futuristic stuff of science fiction, in my opinion it is far less scary than the way AI is used in data collection and analysis.

Both doctors and patients benefit from advanced technologies that help medical professionals do a better job of keeping people healthy.

When machine learning is applied in medicine, a machine can become a superdoctor, spouting out case histories and suggesting possible treatments that could take an entire medical team weeks to think of.

How Technology Will Continue to Refine People-Based Marketing

Ever since “people-based marketing” was introduced at Advertising Week in 2014, it has become a strategic imperative for almost every brand.

Fortune 500 companies are investing in and adopting the tech, agency media buyers are mandating it in insertion orders, and DSPs, DMPs and measurement providers are strategizing around how they can implement this into their offerings.

This hierarchical approach draws a better map of the real world, has broader applications in marketing and user experiences and is more future-proof as the basis for enterprise customer relationship management.

If grocery chains adopt multi-resolution identity, they can tie their consumer-based information to other people in the household as well as to the devices owned by those family members to drive higher-performing and more measurable marketing programs.

Today we live and breathe in cookies and device IDs, but it’s easy to imagine email, postal address, phone number, conversions and online reach data being applied to records.

In the world of people-based marketing, there are known knowns—the data marketers know they know, such as customer records—and there are also known unknowns—the data marketers know they do not know, like the other devices those customers own or people in the same household.

An independent provider of people-based identity at multiple resolutions helps brands fill in the data in a manner that maps to the real world, thus solving the known unknowns.

15 examples of artificial intelligence in marketing

Artificial intelligence (see the Wikipedia definition), specifically machine learning, is an increasingly integral part of many industries, including marketing.

If you're interested in marketing applications of AI, Econsultancy's Supercharged conference takes place in London on May 1, 2018 and is chocked full of case studies and advice on how to build out your data science capability.

Speakers come from Ikea, Danske Bank, Channel 4, Just Eat, Age UK, RBS and more) Predictive analytics allows Netflix to surface and finesse recommendations. This kind of clustering algorithm is continually improving suggestions, allowing users to make the most of their subscription.

32-year-old woman who is training for a 5km race could use the app to create a personalized training and meal plan based on her size, goals, lifestyle.

RankBrain should mean better natural language processing (NLP) to help find relevance in content and queries, as well as better interpretation of voice search and user context (e.g.

For example, in a review, someone might say, 'This place has the best chips and salsa anywhere that doesn't cost a fortune.' That sentence will help now with someone searching for something like, 'I'm on a budget, where's a good restaurant with awesome chips and salsa?' Machine learning is, of course, nothing new at Google, already used in search, advertising and YouTube recommendations.

Retailers, for example, have been subject to high profile data breaches (e.g. Neiman Marcus) as a result of a system based solely on usernames and passwords (without any stronger type of authentication).

However, deep learning (a term used often to refer to machine learning on large datasets - a neural network recognising abstract patterns) has plenty to get its teeth into on social.

Sentiment analysis, product recommendations, image and voice recognition - there are many areas where AI has the potential to allow social networks to improve at scale.

Wired magazine covers a particularly novel use (outside of Facebook's social network) - the tech giant analysing overhead images of topography to find evidence of human life.

Intelligent image recognition and cropping, algorithmic pallette and typography selection - The Grid is using AI in certain areas to effectively automate web design.

Dynamic price optimisation using machine learning can help in this regard - correlating pricing trends with sales trends by using an algorithm, then aligning with other factors such as category management and inventory levels.

Serving ads is basically running a recommendation engine, which deep learning does well.”  Optimising bids for advertisers, algorithms will achieve the best cost per acquisition (CPA) from the available inventory.

However, language recognition may be increasingly used by brands to digest unstructured information from customers and prospects.  WayBlazer is a so-called 'cognitive travel platform', a B2B service using IBM's Watson AI to power consumer applications from third parties such as hotel chains and airlines.

As Techcrunch points out, Facebook's platform, previewed at F8, could conservatively soon lead to chatbots replacing '1-800 numbers, offering more comfortable customer support experiences without the hassle of synchronous phone conversations, hold times and annoying phone trees.' It's worth pointing out that AI and machine learning still need people, such as Google's raters, to improve their accuracy and to train algorithms properly.

How Machine Learning Will Be Used For Marketing In 2017

In my 25 years of working with large datasets, from developing early machine learning algorithms for multimedia systems in the 1990s to optimizing the email marketing infrastructure at GSI Commerce in the 2000s and now applying machine learning to big data to find actionable insights in real time, I'veseen the convergence of machine learning and marketing firsthand.

Machine learning techniques are being used to solve many diverseproblems, andwe standto benefit as we move towards a world of hyper-converged data, channels, content, and context -- having the right conversation at the right time with the right person in the right way.

Prognosticators in 2016picked up on the trends around natural language interfaces like IBM Watson, data growth acceleration with new sources like the internet of things (IoT), and maturation of technologies thatmake ML techniques efficient such as Kafka and Spark.I don't see any of these trends slowing down, as they represent the infrastructure that supports what we'll see in 2017 and beyond.

Asimple example: A hypothetical A/B test can show that after a testing period for a campaign, the audience interacts best when “Welcome” is used instead of “Greetings.” Machine learning algorithms can be applied to the data fromall campaigns to deduce the best textual introduction for emails sent to an audience, or even to an individual.

What we're really talking about is the ability to modify a solution that is already in place by introducing new data rather than having to stop using the current solution before building a new modelfrom scratch.An example would be the addition of an extra lane on a highway, instead ofclosing the whole road and building a new road with more lanes right next to the old one.Construction processes and technologies have modernized to accommodate the need to add lanes to existing roads.

AI will turn PR people into superheroes within one year

But with billions being poured into artificial intelligence (AI) and machine learning over the next 30 years, the tech PR industry is next in line for a serious AI upgrade.

Here are the big takeaways: According to Jeff Hardison, VP at Lytics, a customer data platform and a major player in analytics, “Machine learning is already helping marketers make more efficient use of customer data, and complementing what they’ve had for centuries: intuition and experience.”

Soon vendors will be able to combine hundreds of different factors and billions of social posts to make predictions with an incredible degree of accuracy.

Once your data tells you that a crisis is going to be massive, your agency will be able to confidently put the brakes on pre-scheduled posts and respond more appropriately to the situation, unlike what happened with #deleteuber and #boycottunited.

During a recent client crisis, Shift was able to crunch more than 15,000 content-rich blogs for a medical client in just 1.5 seconds to identify insights, trends, and keywords in hopes to identify the root cause of a situation.

BuzzSumo, the powerful tool that allows any user to find out what content is popular, uses machine learning in a number of ways across its various products.

“Our primary use [of machine learning] is classification of articles into topics and extracting phrases from questions to help classify the question,”

More generally, we are seeing the benefits of machine learning using large data sets such as in translations, image recognition, and spam detection.

For example, we crawl hundreds of thousands of forums to identify questions being asked on any topic and we extract phrases to group these questions into sub-topics,”

As a leading platform for PR media measurement and attribution analytics, AirPR crawls, processes, and analyzes billions of data points per day and uses natural language processing (NLP) and deep learning techniques to “teach”

“We use AI and machine learning to improve filtering of the data our customers have access to, removing the majority of spam and non-relevant URLs that distort business impact reporting,”

AirPR’s own content marketing and PR teams leverage the machine learning capabilities of the AirPR Analyst platform to research which topics and story arcs are trending for its target audience.

The platform provides deeper insight into the articles, authors, influencers, and messages that drive actual engagement with a customer’s brand.

“With machine learning, Trendkite is able to provide insights and recommendations based on reviewing more data points than humans could possibly reason over.

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