Why your next data hire should be a neural network

Neural networks are becoming more complex and powerful, creating a revolution in data analysis and making them indispensable to the travel and tourism industry according to EyeforTravel’s Does Deep Learning Hold the Answer? report.

Although Artificial intelligences (AI) are a long way from truly emulating the human brain and replacing your data analysts, they are taking their first steps and should definitely be a part of your data team. This is the conclusion of EyeforTravel’s new report into deep learning, which is free to download now.

The case for using neural network-powered deep learning techniques lies in the potential return on investment that they can provide. Not only can neural networks undertake complex analysis but they can also reduce workloads, freeing up data professionals to work on more demanding tasks. For example, the report notes that Stena Line’s deep learning program to understand price competition for its onboard products saves weeks of analyst labour, increases accuracy dramatically and all for a cost of roughly EUR15,000.  

Not only can they help with some of the more mundane tasks and working through very large data sets but it is constantly growing in complexity and will soon be able to take more tasks on. “Instead of building very complex models to understand the parameters that influence revenue, you just feed the data into a system and let the system – thanks to a deep learning algorithm – learn what works,” says Marion Mesnage, Head of Innovation and Research at Amadeus IT. “There is no assumption on the model whatsoever. That’s very disruptive…but we believe it could equal or outperform what humans can achieve.”

It's not just data teams that deep learning can help, marketing teams also stand to benefit substantially from deep learning. Neural nets can learn huge amounts about what makes customers tick, such as optimal pricing points and the best creative as well as where to deploy this. The report notes that The Travel Corporation is using deep learning to track online sentiment and automatically adjust advertising to appropriate formats and destinations. 

To find out more about deep learning download the free report now and see:

• What brands such as Amadeus, Expedia, Stena Line, and The Travel Corporation are doing to harness deep learning.
• How neural nets have been developed and how they power deep learning.
• Where deep learning will transform the industry.
• How deep learning can save time and reduce costs.
• What the limits are to deep learning and how regulation might affect it.

The report is part two of EyeforTravel’s How Will Artificial Intelligence Transform Travel? report series. You can find the first report, which studies chatbots in travel, by clicking here.

How travel brands are using deep learning to get ahead

Artificial intelligences running deep learning programs are already helping travel brands understand pricing, improve the customer experience, reduce workloads and make marketing smarter according to EyeforTravel’s new Does Deep Learning Hold the Answer? report.

The report, which is free to download now, examines the role that deep learning can play in travel and finds that far from being a futuristic concept, the machine learning technique is already creating real-world return on investment for travel brands.

One of the most obvious areas where deep learning is being deployed is in the area of predictive pricing. The report notes several different brands that are using deep learning in the field of pricing but in different ways. Both technology company Amadeus and metasearch firm Amadeus are deploying deep learning to understand airline pricing and model it into the future. Amadeus aims to maximise prices and revenues from airline tickets, whereas Aviasales is approaching the challenge from the consumer perspective. They claim that they can predict air fares with a 5% error margin and are applying this to make recommendations to customers about when and with which airline to book.

Stena Line on the other hand has combined deep learning’s ability to recognise objects and its pricing strategy. Their challenge was to make sure they were offering the cheapest prices on board compared to what consumers could buy on land but not to undercut to such a degree they were losing revenue. To do this manually would have been exhausting and expensive as there are tens of thousands of products to monitor. Through machine learning, neural networks and image recognition software, deep learning can recognize products and their prices and present findings back to the team with a more than 90% accuracy rating. 

To find out more about deep learning download the free report now and see:

• What brands such as Amadeus, Expedia, Stena Line, and The Travel Corporation are doing to harness deep learning.

• How neural nets have been developed and how they power deep learning.

• Where deep learning will transform the industry.

• How deep learning can save time and reduce costs.

• What the limits are to deep learning and how regulation might affect it.

The report is part two of EyeforTravel’s How Will Artificial Intelligence Transform Travel? report series. You can find the first report, which studies chatbots in travel, by clicking here.

Can deep learning change travel and tourism?

As data becomes the difference between a growing brand and one in decline, EyeforTravel’s new Does Deep Learning Hold the Answers? report finds that deep learning can and is changing the travel landscape.

Travel could be one of the main beneficiaries of rapid advances in Artificial Intelligence (AI) learning techniques as struggles to utilise huge amounts of data in order to understand complex human behaviours. Key to this is deep learning according to the report, which is free to download now. This a method where neural networks organised in hierarchical layers evaluate data sets. Deep learning deliberately aims to emulate the way that human and animal brains interpret information and consequently is making huge strides.

Currently, the technology still is at an early stage and the neural nets that power deep learning are far simpler than biological counterparts, usually using layers of nodes that passes a decision onto the next layer of nodes once a value has been reached. Therefore, what it truly excels at is focusing on a single task, which is typically finding relationships and patterns in very large quantities of data.

“What we are doing now is artificial narrow intelligence, AI that’s specific to a certain task,” says Amer Mohammed, Head of Digital Innovation at Stena Line. “We need to come up with mathematical models that can actually understand the world, not just fake understand it.” In the meantime some of these tasks that deep learning is already being used for in travel include pricing, language processing, image recognition, consumer analysis, and market modelling.

In the future, AIs will be able to tackle multiple tasks and come closer to human abilities as rapid advancements are being constantly made at the bleeding-edge of machine learning. Already Google’s DeepMind division has been able to build a multi-tasking AI and the rate of advance is staggering.

However, there a major issues and bottlenecks still to conquer if neural networks genuinely want to get close to the capabilities of the human brain. One key issue is the data and power that deep learning requires. For a neural network to effectively learn it most often required and often a guiding hand when initially tackling the task. They also a greedy when it comes to IT requirements. Whereas the human brain runs on the equivalent of around 20 watts, the AI that beat the top Go player in the world required 50,000 times that. Russian metasearch company Aviasales notes in the report that “System resource is the only limit. Even our test library consumes a lot. Hence, we could be more productive by achieving [a] new level of computer performance.”

“The opportunities are enormous and already unveiling themselves,” says Alex Hadwick, Head of Research at EyeforTravel. “Almost everything in travel has a huge number of variables as trip itineraries are complex with multiple decision points, making deep learning especially suited to drawing conclusions from the masses of data. The other thing about deep learning is that we are training it to get better and better every time we add information, so in theory this could be a really powerful tool for personalization. There is the potential to conduct far larger scale and more variated testing and then to combine this information and refine at each stage through dep learning.”

“It’s definitely going to come with ethical challenges, however,” believes Hadwick. Neural networks can end up being black boxes due to their complexity and multiple layers of decision making. You can find yourself with an answer but not knowing how the AI arrived at it. This will run counter to European data regulations in the case of customer-facing decisions and data for starters. We can also end up programming in our own biases and ignore potential mistakes, so we need to understand and master this technology and ask where it best deployed. If we do though, the possibilities are vast.”

To find out more about deep learning download the free report now and see:

• What brands such as Amadeus, Expedia, Stena Line, and The Travel Corporation are doing to harness deep learning.
• How neural nets have been developed and how they power deep learning.
• Where deep learning will transform the industry.
• How deep learning can save time and reduce costs.
• What the limits are to deep learning and how regulation might affect it.

The report is part two of EyeforTravel’s How Will Artificial Intelligence Transform Travel? report series. You can find the first report, which studies chatbots in travel, by clicking here.