Big Data and Artificial Intelligence are shaping the future of business with data insights, personalised content and improved user experiences and Halloween, one of the biggest consumer holidays, is no exception. The data is not only being used to inform companies on consumer habits and trends but to help generate costume ideas, design candy routes and most importantly, scare.

Halloween spending in the US peaked last year at an incredible $9 billion, with an average spend per person of around $86.79 on costumes, masks, candy, decorations and food. 30% of this spending starts in September, 40% within the first two weeks of October and the rest in the final run up to the 31st. People spend the most money on costumes, but not just for themselves, for their pets too…

The 5 most popular costumes amongst adults last year were, Witches, Vampires, Zombies, Pirates and, Avengers. Amongst children the costumes of choice were Princesses, Superheroes, Batman, Starwars characters and Witches. Finally, owners dressed their pets as Pumpkins, Hot Dogs, Bumble Bees, Devils and Cats. One of the biggest dilemmas every halloween is what to wear and how to be original in a sea of witches and ghosts. Well AI and Big Data can stop your anxiety about what to wear with costume generators.

What should I wear?

Janelle Shane, a research scientist and soon to be author shared how she used a bot to generate costume ideas. She built a unique neural network which can produce costume ideas, many of which had never been worn before. She fed the system 4,500 costume names and whilst it produced some crazy results at first, it soon learnt that a sexy lamp might not be all too sexy. Some of the most innovative ideas included a the following…

Machines that scare

Whilst personalised advertising finds the best way to capture our attention and compel us to buy a product, at Halloween the aim is to scare. A team of MIT media lab scientists designed a “hot or not” nightmare machine to create zombified versions of photographs. Each time someone rates a picture, the information helps the algorithm learn which factors in these images induce fear. The system slowly improves to ensure 100% scare rate.

The extra information in how humans perceive horror can be exploited to make even scarier faces

Pinar Yanardag, a postdoc at the MIT Media Lab.

A design agency is using AI to design scary Halloween masks. Using a general adversarial network (an algorithm implemented in machine learning systems) two systems work together, constantly improving to create the mask. Whilst one half generates Halloween masks, the other half judges how well it has performed. It continues until it produces the desired result. The Neural network was supplied with 5,000 images of Halloween mask examples on which it based its terrifying creations. Whilst each mask is based off the data it has been supplied, each result is unique. What if the system learnt to produce what each of us found the most terrifying. Do we need AI to make Halloween scarier or, is part of the fun being creative?

Trick or Treating with Data

So now we have our scary masks and unique outfits, how can Big Data perfect trick of treating. Abdul Rehman, a Data Scientist was asked by his 12-year-old son how he could get more candy than the previous year. As a Data Scientist he was keen to optimize his son’s route to ensure maximum time and sweet efficiency. From the 117 houses he visited, he collected his own data which he later complemented with public data. He was able to create a database with information on:

• Demographic of house members; name, age and political affiliation
• Gender of children in house
• Rent and socioeconomic status

There was a strong correlation between age and generosity:

• Wife aged 41 – 50 and Husband aged 51 – 60 = 100% candy rate.
• Wife > 51 = 25% candy rate.
• Either couple > 71 = Very low candy rate
• Parents with young children = High candy rate. (On average they spend $10-30 more on sweets than those without children)

There was no strong correlation between rent or birth location and their generosity, proving that purse strings do not govern the holiday spirit. Newcomers to the area were very generous so make sure you visit them to get the most sweets.

The data also provided insight into the type of sweets given away by each household. The houses with girls were likely to give away Hershey, Twix and Dove whereas houses with boys offered KitKats, Snickers and M&M’s. From the information he was able to design a route which favoured the sweets that his son like, avoiding those which he doesn’t.

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We hear of increasingly more applications of Artificial Intelligence ranging from improved medical diagnosis to autonomous vehicles to automatic translation to voice recognition. But when we look at the companies behind the development or use of those applications, we mostly see large companies, and a few technological start-ups. Most AI applications are currently developed or used by GAFAM[1], BAT[2] or the top 50 of national large enterprises listed on the international or national stock exchange. Think about the best facial recognition programs that are currently provided as a service by Amazon, Google and Microsoft. Most of those services are used by large organizations, both private and public. But what about the use of advanced AI technology for small and medium enterprises (SMEs), representing often the large majority of the economy, at least in terms of employees?

AI in Spanish SMEs

In this post, I describe AI in Spanish SMEs, dealing with questions such as:

• Are SMEs aware of AI?
• Do they know what it is useful for?
• Are they aware of the risks?
• Do they have access to the right skills?
• What about usage in different sectors?

I have the honour to teach an AI class to more than 250 Spanish SMEs in the context of the DigitalXBorder program, an ambitious program to help SMEs in their digital transformation with participation of experts from Google, eBay, Amazon, Telefonica, Salesforce, Microsoft, etc, etc. AI is one of the around 25 topics of the course. The course takes place in more than 25 cities in Spain with about 30 SMEs participating in each city over the course of three years.

The AI course consists of a bit of history and definitions of AI along with emblematic examples (General Problem Solver, ELIZA, the Turing Test), enterprise AI examples, ethical challenges, and what this all means or can mean for SMEs, who usually have less data, less budget and little access to the right skills. The course ends with practical examples of Machine Learning as a Service, allowing any SME to do Machine Learning on their own data sets within minutes or hours. If you are interested in the full course, here you can access its four videos (in Spanish).

At the start of the program, I designed a simple questionnaire to get a better understanding of the state of play of AI in SMEs. Now that the first year of the course is over, I analyse the initial findings covering 7 cities and more than 50 SMEs. This is of course a small sample, so the interpretation is more qualitative than quantitative.

Questionnaire results

Following are some of the results. Except for one, all SMEs have heard of AI. Asked about what SMEs do understand by AI, the answers are not surprising. Machine Learning and intelligent software are at the top, followed by “thinking machines” and robots. A significant part also considers that AI can refer to all of the above (see Figure 1).

About 80% of the participants know about some AI use cases. And 75% is aware of the risks associated with this technology (bias, undesired discrimination, explainability, future of work). The large majority, however, believes that the opportunities significantly outweigh the risks (Figure 2).

Most SMEs think that AI is already here, but that much more is to come. No one thinks that AI is only something of the future (Figure 3).

However, the main challenges that SMEs face is to have access to the required technical skills such as data engineering, analytics and machine learning. More than 75% of the SMEs do not have access to the right knowledge (Figure 4). Obviously, that hinders the uptake of AI.

Asking whether SMEs are actually using AI, about 30% state that they are. Almost 60% has plans to use it, and only a bit more than 10% has currently no plans to use it (Figure 5).

When asked what problems they solve or plan to solve with AI applications, usual applications were reported like improving sales, market understanding, predictive maintenance, machine design, etc.

Given the large variety of sectors where SMEs are active, a broad categorization has been used including services, technology, industry, consumer, food & agriculture, but almost 50% is from the industrial sector.

Conclusions

In summary, we can see that there is high awareness of Artificial Intelligence in SMEs in Spain; several uses cases are known; and over 70% are implementing AI or planning to do so. But we also learned that the main challenge is to have access to the required AI skills such as Machine Learning, or even simpler “analytics” skills.  This does not mean, however, that SMEs cannot start using AI. There are increasingly more tools available that offer Machine Learning as a Service through intuitive graphical interfaces, allowing non-data scientist to apply, evaluate and execute Machine Learning models directly on excels with data from the SMEs.

As with many new technologies, first applications are pioneered by innovative early adopter enterprises. But when solutions are becoming more mature and automated, they come within reach of smaller organizations. This is the “normal” democratization process of new technology, and it will be no different with AI, which is reaching increasingly more SMEs.

If you prefer to read this post in Spanish, you can find it here:

https://empresas.blogthinkbig.com/la-inteligencia-artificial-en-pequenas-y-medianas-empresas/

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[1] Google, Amazon, Facebook, Apple, Microsoft

[2] Baidu, Alibaba, Tencent