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.
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.