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.
, 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.