Analytics Innovation, Issue 6

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ANALYTICS INNOVATION

JULY 2016 | #6

Diversifying Products With Analytics|6

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Does Machine Learning Mean The End Of The Data Scientist?

Machine learning is helping automate processes in all industries, with many losing their jobs as a result. But could this extend as far as the data sciences themselves? Olivia Timson investigates | 11

AI And The Future Of Art Art is considered the most human of things.

Tech giants like Google are, however, now looking at how machines can produce work equal to that of the great masters, but is it a good thing, or is it a slippery slope? | 22

Big Data & Analytics for Banking Summit July 12 & 13, 2016 | Melbourne Speakers Include

+61 (2) 8188 7345 vhernandez@theiegroup.com theinnovationenterprise.com

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ISSUE 6

EDITOR’S LETTER Welcome to the 6th Edition of the Analytics Innovation Magazine

When British politician Michael Gove declared recently that ‘people in this country have had enough of experts,’ he was effectively summing up the current political climate which has seen the rise of politicians at the extremes of the spectrum, where things like facts and expert opinion are eschewed as redundant and stupid,propaganda being used to diminish their own belligerent unfounded, stupid opinions masquerading as common sense.

It is difficult to see where data analytics fits into this narrative. Donald Trump’s own apparent dismissal of the power of data analytics with the firing of his head of data operations is evidence of this mindset. It will be interesting to see how this works out for him in the upcoming presidential election when he faces off against Hillary Clinton, who has invested significantly in her data operations.

In other areas of society, where the insanity seen in politics is not tolerated to quite the same degree, we are actually seeing advances in analytics

that could help us better understand human behavior. In this issue, we look at how marketers are using behavioral analytics, a subset of the data sciences which incorporates the field of psychology to gain a deep understanding of why we do the things we do.

render the very data scientists who created it redundant if left to find and analyze data itself.

As always, if you have any comment on the magazine or you want to submit an article, please contact me at jovenden@theiegroup.com

This push to use analytics also has tremendous implications for Artificial Intelligence (AI). We have seen tech giants increase investment in AI dramatically in the last few years, and there are now a number of startups operating in the sector that could push things forward. The technology is still in its infancy, but with analytics better understanding human behavior, it may not be long before machines recreate it to a level which makes them indistinguishable from humans.

Indeed, we are now moving past developing AI as a technology and thinking more about the practical real world applications. Later in the magazine, we look at some of these, including the banking industry and art.

Also, somewhat ironically, how it could

James Ovenden managing editor

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Big Data & Analytics Innovation Summit November 10 & 11, 2016 | London Speakers Include

+44 203-868-0516 acollis@theiegroup.com theinnovationenterprise.com

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contents 6 | DIVERSIFYING PRODUCT LINES WITH ANALYTICS Diversifying product ranges is one of the hardest things to do, and has been the downfall of many organizations.

Alex Lane looks at whether analytics can remove the element of risk from proceedings 8 | DATA ANALYTICS FOR WORLD PEACE We live in increasingly fraught times, in which it seems a new conflict rears its head every day. Maybe it’s simply that humans are a violent species, but there may be a solution to end all wars: data 11 | DOES MACHINE LEARNING MEAN THE END OF THE DATA SCIENTIST? Machine learning is helping automate processes in all industries, with many losing their jobs as a result. But could this extend as far as the data sciences themselves? Olivia Timson investigates 14 | CAN AI REVOLUTIONIZE THE BANKING INDUSTRY?

16 | HOW BEHAVIORAL ANALYTICS IS DRIVING MARKETING Understanding people’s behavior is core to marketing, but applying data algorithms to people is notoriously difficult. Meg Rimmer sets out how some in the industry are going about trying 18 | INTERVIEW WITH ADAM DATHI, SENIOR BUSINESS INTELLIGENCE CONSULTANT AT YIELDIFY Implementing a data-driven culture is one of the key challenges for firms trying to make the most of the information at their disposal. Adam Dathi, Senior Business Intelligence Consultant at Yieldify, gives his view 22 | AI AND THE FUTURE OF ART Art is considered the most human of things. Tech giants like Google are, however, now looking at how machines can produce work equal to that of the great masters, but is it a good thing, or is it a slippery slope?

The banking industry has undergone tremendous upheaval over the last decade, with FinTech, regulations, and scandals all driving change. And the upheaval isn’t over yet, with AI set to cause a stir WRITE FOR US

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managing editor james ovenden | assistant editor anastasia anokhina creative director oliver godwin-brown contributors alex lane, dave barton, olivia timson, alex collis, meg rimmer

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Diversifying Product Lines With Analytics Alex Lane, Deputy Head of Analytics

WHEN A BUSINESS IS FACED WITH disruption from insurgents utilizing more advanced technology, there are a number of strategies that can be employed to maintain market position.

Many close their eyes and simply choose to believe consumer habits working against them is a temporary blip and do nothing as a result.

Blockbuster could be taken as an example of such a company, having been caught seemingly unawares by consumer desire to stream movies online. It closed the doors on its last 300 retail stores in 2013.

While failure to keep up with online streaming is often cited as the reason Blockbusters went bust, according to Jonathan Salem Baskin, a former Blockbuster executive, the story is

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actually that of a more complex failure to diversify. Baskin wrote in Forbes that, when faced with falling traffic back in the 1990s, ‘the company elected to focus on upping the value of each transaction basket. This meant filling the stores with lots of candy, throw-away toys, and other impulse purchase items, displayed at little kid height so parents would be forced to buy it.’ Baskin argues that this missed the point of Blockbusters’ core reason for being - that its revenue relied on renting out box office hits and people did not necessarily enjoy the experience of going to the store. He argues instead that the solution should have been ‘to focus on consultative or advisory selling, and turn its store associates into de facto recommenders. It could have

implemented true social networks to rate and catalog movies, and used its customer data to develop a predictive engine that members could use to locate new titles.’

Blockbuster’s failure, ultimately, was the result of it not understanding its business model and what additional products its consumers would buy from it. History is filled with company’s failed attempts to diversify their product lines in order to drive growth.

Diversification is unpredictable and high-risk. There are many things to consider, such as understanding consumer desire for your new product, whether it fits in with your brand, and what kind of losses are acceptable.

While complicated, however, it is a necessity. The ‘product portfolio’ concept states that if a company is going to grow while also allocating resources wisely, it must mix established with new businesses. This is especially true in the digital age. 52% of the firms that made up the Fortune 500 at the turn of the millennium have now gone because they failed to make the digital shift.

Data analytics goes a long way to lowering the risk associated with diversification. It enables companies

to better gauge consumer demand for new product lines, applying sentiment analysis to unstructured data in social media and consumer surveys. It provides better analysis of the market, and what kind of losses are to be expected. It is usually assumed that expansion of any business will lead to losses, but the depth and length can make or break whether or not it is a worthwhile venture.

Starbucks is a recent example of a company that has used data to expand its product portfolio. It talked to its baristas about how customers ordered coffee, lattes, and tea in-store, and blended this information with several industry reports about at-home consumption. For example, data from consumer research firm Mintel found that 43% of the teadrinking customers don’t use sugar, while 25% of consumers don’t add milk to their iced coffee when drinking the beverage at home. To satisfy such demand, they created unsweetened and sweetened black iced coffee without milk or added flavors to sell in grocery stores, outside their core coffee shop business.

Ultimately, data analytics enables better decision making. There are few areas in business riskier and more complex than diversifying product lines, and to put in place the best strategy to do it, analytics is a vital tool.

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Data Analytics For World Peace

Dave Barton, Head of Analytics

ACHIEVING WORLD PEACE HAS BEEN A PRIMARY GOAL OF GOVERNMENTS AND BEAUTY PAGEANT CONTESTANTS since cavemen first started hitting each other with clubs. As democracy spreads and nations become relatively prosperous, we are seeing an end to war in the conventional sense.

Conflict is now less a release of pressure after a prolonged period of tension, and more a sudden outburst, flaring up suddenly with far less warning and opportunity to prevent it politically.

Technological advancement has, traditionally, been associated with war in a negative sense, helping to create bigger weapons to enable more killing. However, it could equally be used to bring peace. Kalev H. Leetaru, creator of the Global Database of Events, Language and Tone (GDELT) project, which describes itself as a comprehensive ‘database of human

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society’, has argued that big data has the potential to be a tremendous tool in the fight for peace, asking, ‘Can big data give us peace? I think the short answer is we’re starting to explore that. We’re at the very early stages, where there are shining examples of little things here and there. But we’re on that road.’

Big data can be used to identify patterns and signatures associated with growing instability and conflict. It can also pinpoint the exact causes. This enables governments to implement conflict prevention strategies and stop violence before it has a chance to escalate. There are now a number of initiatives focused on analyzing data from all kinds of sources in order to do this. Organizations such as the US Defense Department, the International Peace Institute, and the CIA have all launched programs in recent years that scrape public data from sources like social media, market data, world news, and so forth, and analyze it for indications of impending conflict.

The UN in particular has an unparalleled database of the world’s socio-economic and political history, and by blending it with contextual understanding - on-the-ground information collected by those actually in unstable communities. According to McKinsey, it is getting this on-theground information that is the primary challenge. Unstable communities need people that are credible, trustworthy, and unbiased - something that is always difficult to find in regions prone to instability. Without such people, however, big data is essentially useless.

Perhaps the most famous way that big data has been proven to predict wars is agricultural data. Researchers have, for example, managed to link the extreme drought in Syria between 2006 and 2009 with the unrest in the region that started in 2011, escalating the ‘Arab Spring’ movement across the region. In a report by Peter H. Gleik, he notes that,’Between 2006 and 2009, around 1.3 million inhabitants of eastern Syria were affected by

agricultural failures. An estimated 800 000 people lost their livelihoods and basic food supports… By late 2011, the UN estimated that between two million and three million people were affected, with a million driven into food insecurity’. This data, alongside historical evidence of droughts coinciding with conflict, suggests that areas where droughts are occurring should be a particular concern for forces like NATO looking to maintain stability.

The use of big data in conflict early warning systems undoubtedly improves the ability to predict when a conflict might flare up. However, they can only go so far. The most important thing is to formulate a response that deals with the issue, and factors such as lack of political will often stand in the way of anything being done. There is a real disconnect between formal early warning analysis and executive decision-making processes. In truth, it is unlikely that data-driven early warning systems will ever really eradicate violent conflict.

It will take a lot more from humans to do that. But, when paid attention to, at least it should help minimize the impact and keep them at a more manageable size.

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Sports Analytics Innovation Summit August 24 & 25, 2016 San Francisco

Speakers Include

+1 415 692 5514 sforeman@theiegroup.com theinnovationenterprise.com / 10

Does Machine Learning Spell The End Of The Data Scientist?

Olivia Timson Analytics Practitioner

THE DATA SCIENTIST ROLE HAS LONG BEEN CONSIDERED one of the most prestigious of the digital age, with wages averaging around $120,000. Despite this, there has always been a huge shortage of people qualified for the role. One McKinsey study projected that ‘by 2018, the US alone may face a 50% to 60% gap between supply and requisite demand of deep analytic talent.’

This problem is being dealt with in two ways, neither of which seem on the surface like they are particularly positive for those currently training in data science hoping for a big future in HBR’s ‘sexiest job of the 21st century’.

Firstly, companies are empowering all of their employees to analyze data themselves, a phenomenon known as

citizen data scientists. Secondly, by handing over control of the analysis to machine learning algorithms and AI, which could effectively render the data scientist obsolete.

Even data scientists don’t seem to hold out much hope. In a recent KDnuggets poll - which asked when most expert-level Predictive Analytics/ Data Science tasks currently done by human Data Scientists will be automated - 51% of respondents said that they expect this to happen within the next decade. Just a quarter said they expect this to happen in over 50 years or never.

Some of the brightest minds in both academia and industry have set their minds to automating data

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science. MIT researchers, for one, have designed the Data Science Machine, a system that both searches for patterns and designs the feature set. They enrolled the first prototype in three data science competitions, competing against human teams to find predictive patterns in unfamiliar data sets. Of the 906 teams participating across the competitions, it beat 615, and in two of the three competitions, the predictions it made were 94% and 96% as accurate as the winning submissions. In the third, it was 87%. Where it truly came out of top was in the time it took to achieve these. The machine’s human opponents typically spent months pouring over their prediction algorithms produce each of its entries. The Data Science Machine took hours.

Not only is data being processed automatically, though, it is also being visualized without human input so that it can be analyzed by business people with no interference. Visualizations, when done well, can reveal intricate structures in data that cannot be absorbed in any other way, and if these can provide business people with what they need to know about the data, it’s hard to know where the data scientist fits in.

In the short term, data scientists are unlikely to be replaced. Kevin Murphy, a Senior Research Scientist at Google notes that: ‘The first problem is that current Machine Learning methods still require considerable human expertise in devising appropriate features and models. The second problem is that the output of current methods, while accurate, is often hard to understand, which makes it hard to trust.’ Murphy cites the ‘automatic statistician’ project from Cambridge,

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which ‘aims to address both problems, by using Bayesian model selection strategies to automatically choose good models/ features, and to interpret the resulting fit in easy-to-understand ways, in terms of human readable, automatically generated reports.’ Their project won a $750,000 Google Focused Research Award, but it still has a number of challenges to overcome if it going to be a success.

What Murphy says initially still stands true, and Machine Learning methods require considerable expertise at the point of origination. Ultimately, what it probably means is that data scientists are going to become irrelevant at the front end, and the nature of the work will shift. Adoption of machine learning at scale will likely be slow at all but the largest of firms, and it is probably that machine learning will be an accompaniment, taking many of their more time consuming jobs. However, the nature of all work tends towards automation.

The volume of data that companies produce is now far beyond the capabilities of one data scientist to analyze, and it is inevitable that automation will consume the field entirely.

HR & Workforce Analytics Innovation October 19 & 20, 2016 | Chicago Previous Speakers Include +44 203-868-0516 acollis@theiegroup.com theinnovationenterprise.com / 13

CAN AI REVOLUTIONIZE THE BANKING INDUSTRY? Alex Collis, Analytics Pundit Taking risks is vital to success in banking, yet for a long time it has been out of control. One of the primary reasons for the 2008 financial crisis was that bankers were incentivized to take significant risks by the rich rewards if they paid off and the relatively light penalties if they failed.

The consequences of this were dire, costing the economy an estimated $22 trillion.

Given human tendencies towards greed and hubris, you would imagine that removing people from the equation would be a good thing.

However, as Artificial Intelligence (AI) and machine learning algorithms are adopted more readily by the major

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financial services companies, many are skeptical as to whether the benefits are worth the potential downsides, and whether banks are using AI to absolve themselves of blame.

Gartner identified machine learning as one of the top ten strategic technology trends of 2016, with advances coming thick and fast across all industries in all fields. In 2015 alone, tech giants Google, Facebook and Microsoft invested over $8.5 billion on AI research, acquisitions, and talent.

Major financial institutions - such as fund managers BlackRock, TwoSigma and Renaissance Technologies - have poached some of the best data scientists in the world, and are

spending big to keep up with Silicon Valley. And they’ve got the deep pockets to do so. Andrew Lo, Director of the Laboratory for Financial Engineering at the MIT Sloan School of Management notes how wide ranging the impact will be, saying, ‘I suspect that it’s going to transform all aspects of the financial industry because there are so many parts of it that can be automated using these kind of algorithms and access to large pools of data.’

In a survey by Synechron, Inc., a global consulting and technology innovator in the financial services industry, 71% of respondents said they believe AI will be hugely impor-

tant over the next decade. A 2015 report from McKinsey & Company also revealed that a dozen European banks had moved from traditional statistical analysis modeling to machine learning, with many citing increased new product sales of 10% and churn and capital expenditure down by 20% as a result.

The most dramatic changes brought about by AI and machine learning are expected to be in trading, financial analysis and risk assessment. The speed at which decisions need to be made in these areas make it vital, and they afford a more in-depth risk assessment to help the risk analyst or underwriter to find information that may have been hidden, deliberately or otherwise, to ensure investments are the right ones. The amount of information available to analysts already far exceeds their ability to make sense of it, and only AI is fast and cheap enough to cope.

The temptation for bankers to rush to adopt the technology is clear. However, a good rule of thumb in banking is that if you’re tempted to do something that will bring in easy money, don’t do it. And AI has trouble written all over it. In 2013, AI taught itself to play Tetris. Eventually, it learned that the easiest way to avoid losing was simply to pause the game. The potential for AI to game the financial system and destroy the economy is always there. There is also a great risk of malfunctioning algorithms. In 2012 US market maker Knight Capital lost over $400m (£261m) in 30 minutes because of a computer glitch. And when this happens, who gets the blame? In the recent Jodie Foster film Money Monster, Dominic West’s antagonist blames a glitch in the algorithm for the loss of $800 million.

When there’s no-one to blame, who takes responsibility when things go wrong? The person who created the

algorithm? The CEO? The traders? It is extremely doubtful that regulators will be able to respond appropriately to the changes. Few enough people on Wall Street go to jail as it is, AI will likely see that number fall even lower.

According to McKinsey, ’We see financial regulation continuing to broaden and deepen as public sentiment becomes ever less tolerant of preventable errors and inappropriate practices, or of bank failures’. AI can help banks abide by existing regulations, but equally a whole new framework is needed to rein it in. At the moment, a McKinsey survey found that just 3.5% have actively deployed AI. This number is going to shoot up rapidly, and regulators need to be on the mark to ensure the technology does not run out of control.

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How Behavioral Analytics Is Driving Marketing Meg Rimmer, Big Data Commentator

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IN A RECENT MEDIA RELEASE FROM UNIVERSITI TEKNOLOGI MALAYSIA, research scholar Dr Ikusan R. Adeyemi said, ’Our research suggests a person’s personality traits can be deduced by their general internet usage,’ and it could do so using machine learning algorithms by analyzing just half an hour of web browsing.

This idea that personality traits can be determined by analyzing online behavior is nothing new, but it is only recently that is has started to really be applied by marketers. The potential impact it could have is profound.

Personality tests such as MyersBriggs have been around since the 1920s. The idea that you can gain such insight based simply on someone’s behavior online may seem imprecise, but a wide body of research suggests to the contrary. In a research paper released by the university, it was noted that a ‘study presented in Golbeck et al. (2011) showed that humans reveal their personality trait in online communication through self-description and online statistical

updates on social networking sites through which the FFM (someone who displays personality traits such as openness to new experience, conscientiousness, extraversion, agreeableness, and neuroticism) can provide a well-rounded measure of the human–computer relationship. The study observed that the personality trait of users can be estimated (in social media) to a

degree of =11% accuracy for each factor based on the mean square error of observed online statistics.

This implies that personality trait prediction can be achieved within 1/10 of its actual value.’

The ability to predict someone’s personality presents a clear opportunity for targeting advertising, enabling marketers to segment audiences according to personality type, test campaigns that will most appeal to that kind of person, and send them the marketing materials most appropriate based on this. The benefits of personalization are well proven. In a 2015 Harris Poll study, 95% of respondents say they’d be more likely to respond to personalized outreach, yet around a third say that they don’t get them. The Aberdeen Group also found that agencies best at personalization companies achieved up to a 36% higher conversion average and a 21% stronger lead acceptance rate.

analytics and realized this, a fashion retailer can customize their messages and campaign accordingly.

Total digital ad spending in 2017 is estimated to reach $77.37 billion in the US alone. With ad blocking software

growing rapidly - up 48% in 2015 - it is becoming increasingly important to make sure you are presenting people with things they want to see.

Analyzing people’s data to gauge their personality type is not easy, and it raises justified concerns around online privacy. However, anonymity should be guaranteed simply by removing any identifiers, such as a name or phone number, before the data is passed on to the marketer. Assuming this is always the case, it is potentially game changing.

While many marketers already personalize their campaign material based on basic metrics such as age and sex, segmenting by personality is likely to provide far greater returns, as people’s wants and needs often vary wildly within specific age groups and genders.

Take, for example, two middle-aged women from the same area who are both interested in buying a new dress.

One is an extrovert and wants people to notice her, while the other is an introvert and would prefer to fly under the radar. Having applied behavioral

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Interview with Adam Dathi, Senior Business Intelligence Consultant at Yieldify

Anastasia Anokhina, Assistant Editor

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IN RECENT YEARS, A DATA-DRIVEN CULTURE WITHIN STARTUPS has become synonymous with success.

Championed early on by only the most data literate or strategically savvy, the use of data in companies large and small is now spreading to every team and individual. Used to evidence decision-making, generate insights or test hypotheses, giving all employees access to data is proving to be a significant competitive advantage.

Yieldify, a British-based startup backed

by the likes of GV and Softbank, is testament to the philosophy’s success.

Its phenomenal growth in the past two years has been in no small part down to its use of data, and its democratization of data to every nook and cranny of the business. Data is core to both Yieldify’s operations and its product. Its software, used by some of the biggest e-commerce vendors globally, uses browsing history and onsite behaviour to increase conversion rates.

We spoke with Adam Dathi, a Senior Business Intelligence Consultant at the startup, to shed some light on the successes Yieldify has had in democratizing data around the company.

1. How important is it to democratize access to data?

It’s extremely important. Democratizing data means giving all stakeholders access to the data in the company and nurturing a culture that understands how, when, and why to use it. Yieldify’s key values are openness and transparency; we need to show our employees the key metrics that illustrate how the business is performing and where there is potential for improvement. To achieve this, it is imperative to build accessible data: a single source of truth that is democratized and understandable. For example, say you’ve got a team that needs to increase revenues by 5%, each member needs to contribute to reaching this target. By democratizing the data, every person is able to see their individual impact and can drill into the underlying problems themselves to better understand what they need to do. There are other less obvious advantages. All data has latent value that may not have been realized, with insights and uses waiting to be discovered. In the ‘old world’, a company may have been reliant on a BI team (or equivalent) to act as gatekeepers. This naturally leads to a bottleneck in terms of accessibility and value-generation. Through the use of Looker (our BI tool), there are now a lot more eyes interrogating the data, more minds attempting to use it in new ways. Insights and innovation are now delivered much faster than before and by a far wider-array of teams.

2. What challenges have you faced in your attempts to build a more data-driven culture?

There were three main problems

that we encountered: accessibility of data, the understandability of data and ‘inertia’. Let’s start with accessibility.

When I joined the company, we had far less data available and what was available was laborious to get.

The root cause was that the data was ‘siloed’, which basically means that we had a lot of disparate data sources that didn’t connect. In order to solve this, our engineers built a data warehouse (using Amazon Redshift), where we now keep all the data together. The BI team then worked with our Operations team to develop a number of processes that enforced the mapping of these databases at the point of creation. This ensured that the data was accessible for processing and analysis. Next, we had an issue with the understandability of the data. The data collected in the BI Warehouse were raw and difficult to use. We circumvented this through the use of custom tables created in Looker. These tables were summarized, enriched and the fields renamed before being presented to the user in comprehensible language.

This created useful, understandable data that no longer required an engineering or data background to grasp. Finally, we had inertia. This is a common problem whenever one attempts to implement change to a business. We had the reports and dashboards, we knew that they had value, but we needed to break everyone out of their routine and ensure that they actually began to use it every day. To achieve this we firstly convinced the managers of the

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value of what we were doing and made data the focus of meetings and performance reviews. Once people understand that they’re being judged by certain metrics by their managers, they become far more engaged in monitoring and understanding them for themselves. We also selected and worked closely with ‘champions’ from within the teams. Champions were our advocates, consultants, and BI/Looker mentors. They would encourage usage from the ground up, advise us on what reports and data points were useful and ran the training sessions. It’s taken a while to break through this inertia, but we’re finally at the point where data is a necessity for our business to function, rather than a nicety.

3. How much ground do you think you lost as a result of siloed data?

Operationally, we’re much stronger than before, but it’s hard to say exactly simply because we don’t have the data from before to compare it too.

By having the data in one place, the main two advancements have been our ability to automate and provide visibility. Automation has cut down the number of hours it takes us to conduct analyses and reporting across teams. Thanks to Looker, we are now a lot more efficient, which has enabled us to spend more time on client meetings, campaign creation and less time on pulling data. Visibility around how teams are performing towards goals was previously extremely limited, but it’s now easy to see whether

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people are meeting targets, their levels of performance, and the best practices that are driving improvements.

4. How has Looker helped you?

Before the whole process started, there was data everywhere. We had to provide the right people with access when they needed it. In order to do this, it was key to get it all into one interface. Looker acts as that interface, providing all the teams with access to everything they need, whenever they need it, and in a manner that is curated and understandable.

The ability to use LookML (Looker’s language that structures and presents our SQL queries) to define our own tables and iterate quickly has bypassed our engineering bottleneck.

Now the engineers can focus on creating products for our customers whilst BI worry about internal data. At the moment, Looker is the go-to tool for our Services, Technical Solutions and Finance teams. They use it for performance reporting, investigation or analysis. We have started the process of on-boarding our Sales team and are rolling-out a suite of dashboards that present a clear overview of individuals’ and teams’ pipelines and revenue generation against targets. Our goal is to get every team on-boarded by the end-of- the-year and using data in every business decision.

Big Data & Analytics Innovation Summit September 14 & 15, 2016 | Sydney

Speakers Include

+61 (2) 8188 7345 vhernandez@theiegroup.com theinnovationenterprise.com / 21

AI And The Future Of Art James Ovenden, Managing Editor

Microsoft rembrant

The Next Rembrandt // news.microsoft.com

JOHN GIANNANDREA, GOOGLE’S HEAD OF MACHINE LEARNING, recently told a Google I/O panel at the company’s developer’s conference that we are currently ‘kind of in an AI spring.’

Giannandrea evidenced this progress by citing speech recognition and image understanding. Google is investing heavily in machine intelligence - more so than it is any other technology - and has more than 100 projects currently in development internally. Among these are the more well publicized practical applications, such as driverless cars, as well as a number of more novel applications, among which is art and poetry.

The race for artistically creative AI has been going on for a while now, as tech giants move to imbue the technology with more human characteristics. Microsoft recently applied machine learning algorithms to 346 of Dutch master Rembrandt’s paintings. Its analysis found that the Dutch master mostly painted men between the ages of 30-40 that looked to the right. It analyzed his use of proportions, lighting, clothes, and multiple other metrics, to extract the key features that defined his style. Using this

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Google’s Deep Dream knowledge, it was able to produce a completely original Rembrandt of its own - or at least some kind of aggregate of his work.

Google has gone one step further, attempting to create entirely unique art. This started with DeepDream, which uses artificial neural networks to learn how to recognize shapes in pictures. It fed images into the neural network and asked it to emphasize features it recognized - in this case, animals - to create a sort-of unique set of images that it unveiled last year.

While both of these could be said to create new images in some sense of the word, Google is now trying to take things one further and have AI create entirely new images without any assistance. It unveiled its ‘Magenta’ last weekend at Moogest in Durham, North Carolina, with the full launch expected on June 1. The project comes from Google’s Brain AI group - which is responsible for many uses of AI in Google products - and will use their machine learning open-source library TensorFlow to train computers to create art. Although details are currently hazy, the demonstration seemed to suggest that the software would analyze other pictures, in the same way that Microsoft did with the Rembrandt’s although on a larger scale that creates a more unique piece - or at least as unique a work as a human would produce.

Reception for current AI-produced artwork has been roundly negative.

Writing in the Guardian, Jonathan Jones said of Microsoft’s Rembrandt painting : ‘What a horrible, tasteless, insensitive and soulless travesty of all that is creative in human nature. What a vile product of our strange time when the best brains dedicate themselves to the stupidest

challenges, when technology is used for things it should never be used for and everybody feels obliged to applaud the heartless results because we so revere everything digital.’

Criticism of Google’s efforts has been equally forthcoming, with many commentators comparing it to the kind of psychedelia you find on the door of a frat house toilet.

Looking at the art, it’s hard to disagree with these appraisals, and you would be forgiven for being skeptical about Magenta’s ability to produce anything better. There is also the question of whether or not you could consider it art at all. Of course, this depends on how you define art. Is it that it inspires a reaction in the viewer? They inspired quite a visceral reaction in Jonathan Jones, but if inspiring a reaction is all you’re looking for, you could equally consider stamping on someone’s foot to be art.

Tolstoy defined the activity of art as ‘based on the fact that a man, receiving through his sense of hearing or sight another man’s expression of feeling, is capable of experiencing the emotion which moved the man who expressed it. Every work of art causes the receiver to enter into a certain kind of relationship both with him who produced, or is producing, the art, and with all those who, simultaneously, previously, or subsequently, receive the same artistic impression.’ In this sense, it is impossible to see how it could be art.

The relationship Tolstoy refers too is a kind of transcendental relationship, one which is impossible to have with a machine - no matter how much films like Short Circuit and Ex-Machina try and convince you otherwise.

However, the paintings do raise a number of questions. Such as what it means to be human, and what it is we

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really want from artificial intelligence.

Are we looking to entirely render ourselves redundant as a species?

Do we want artificial intelligence, or artificial super humans?

The question of what it means to be human is complex. George Orwell once wrote that the essence of being human is that one does not seek perfection. The primary objective of machines is to be perfect. To try and imbue machines with humanity would therefore seem doomed to failure. Art is the essence of humanity, and the best of it shows our flaws - something machines will never be able to understand by their very nature.

Machines will only ever really be able to replicate and mimic. Much of what they produce will likely be nice to look at, but endeavors to have them produce art are doomed to failure.

Perhaps the more pressing question is why exactly we even want to? AI

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when developed as a tool for humans to live, is one thing, and whatever the ramifications it is still ostensibly providing a benefit. There is a clear benefit to driverless cars, for example.

But attempts to use AI to produce art veers too close to AI as a technology that entirely replaces humans.

Imagination is the most human of qualities, and art the manifestation of that. Attempts to give machines imagination is to render us almost completely useless, and even if it won’t produce a real work of art, is a step too far down a path that we probably shouldn’t be walking down.

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