I have a confession to make: I’m addicted to trip planning.

I blame my first-World problem on two major reasons: curiosity and dynamic pricing. While curiosity might fuel my craving to explore new places, dynamic pricing has taken this craving to a whole new level.

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Long gone are the days I would walk into a travel agency and accept what was given. Today, I spend days monitoring flight prices online – always in the outlook of a good deal.

Early 2013, travel search engine Kayak introduced a fare-forecasting tool that allows travelers to assess whether the search prices will rise or fall within the next week. This was the first time I began to wonder how the travel industry could forecast demand and optimize its prices. What lies behind those flight purchase recommendations?

I soon learned that Kayak uses large sets of historical data from prior search queries and complex mathematical models to develop its forecasting algorithm. I imagined that some of the input variables include the current number of unsold seats, the date and time of the booking (specially the number of days left until departure) and the current competition on the same route. But what if Kayak also learned more about my personal preferences based on my past behavior? If there were only one window seat left on that flight, would it change its purchase recommendation from wait to buy? Would it be able to recommend me a trip, knowing I prefer nature over cityscapes, am most likely to buy on Tuesdays and usually travel over the weekend?

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A lot has been done on dynamic pricing since 2013, of course. One of the latest companies to join the trend is AirBnB. Its hottest new feature, Price Tips, helps hosts to easily price their listings dynamically by using the company’s new open source Aersosolve machine learning tools. Their use of machine learning isn’t just limited to the standard dynamic pricing, though: their models automatically generate local neighbourhoods, rank images and learn about AirBnB hosts’ preferences for accommodation requests based on their past behaviour.

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What does this mean for trip planning addicts?

Well, I'm personally looking forward to the time when choosing my next travel destination is made simpler. I picture personalised travel discovery and contextual insights - the kind that take into account both, your conscious and unconscious preferences. The Texas-based startup, WayBlazer, already is tipping into this by employing IBM Watson cognitive computing technology to listen and understand its customers and present considered, tailored travel suggestions.

What could be next?

¨We are using artificialintelligence so people can remain in their homes for as long as possible¨  

Lead scientist Alex Mihailidis

Several years have passed by since the moment smart homes started to be created for people with Alzheimer´s disease or other cognitive impairments. Due to the amount of time people with Alzheimer require from their caregivers, they move in to institutions were 24 hour service is provided to them by professionals. In the case of the majority of patients, they would prefer to stay home during a longer period, however, this results impossible in many families were none of the members is available all day, every day, to take care of them.

Due to this reality, artificial intelligence was used to create smart homes, allowing people with Alzheimer and other cognitive impairments to live longer in their house and at the same time providing them a solution that would help them live a more independent life. 

Picture: www.huffingtonpost.com

In order to capture specific activities and movements happening inside the house, these smart homes include technological devices (sensors). These sensors, for example, can be cameras in the ceiling of the house which are linked to computers and are able to detect whether a person has fallen down. Additionally, prompts are also used in order to assist them with any necessity when needed and give them ¨hints¨ in order to accomplish a task. There are various types of prompts used: auditory, pictorial, video and light. Auditory prompts can be divided in three categories: verbal (instructions), sound (alerts) or music. Pictorial prompts can be either photographic (pictures) or textual (keywords). Video prompts are pictorial or modelling (someone performing what should be done). For instance, bathrooms include a computer screen with a video showing them how to wash their hands. Finally, the light prompts are changes of different colors of a light bulb or laser pen to reflect an action. It order for these prompts to be effective it is essential to take into account that each patient is very different: different prompts work to different people.

Smart homes give a sense of hope not only for people with Alzheimer disease, but to their caregivers as well, since they feel these technologies will allow them to remain with their loved ones for a longer period of time at home, rather than sending them to an institution.  

In the future which other diseases will artificial intelligence be able to cure?

On one hand, Machine Learning has been developing for the last couple of decades, and during the past years has been on the spotlight for several industries. On the other hand, legal profession is one of the most ancient fields in the world. So, how can we relate machine learning to the application of the law in these days?

About being a lawyer

Many people may think that being a lawyer is like being Harvey Specter or John Milton: Closing big deals, wearing glamorous suits in fundraisers and dazzling jurors with emotive speeches about reasonable doubt and guiltiness. That may be true for 1% of the lawyers around the globe, but most legal work includes long research hours, gathering and analyzing data for clients, and comparing probabilities when preparing for a case.

So, even if some lawyers obtain the spotlight in big cases matters, there is a team of hard-working paralegals and junior associates that work 14-16 hours a day to get the proper data to their superiors in order to prepare for big litigation cases or closing deals (every lawyer has gone through that process).

What can Machine Learning do for the lawyers?

Picture the scenario: 11:00 p.m. in the office and a Junior Partner comes to your paralegal cubicle and asks for a thorough research on jurisprudence and court precedents for - you name it -: discrimination of gender, merger control in the pharmaceutical business or [insert long and tedious topic here]. Of course, the deadline is 09:00 a.m. the next morning. The typical form of doing this is looking for a keyword of the case and then start reading all this (long) related cases until you figure, hopefully, a track of action before 09:00 am in the morning. Well, apparently, there is another way! A group of young guys graduated from a Stanford Law with a digital and more technological approach has developed a tool to make this job easier: Ravel Law. By using data visualization, analytics and machine learning, Ravel Law enables lawyers to find, contextualize, and interpret information that turns legal data into legal insights.

By this type of tool, based in machine learning, lawyers can save several hours preparing for cases and doing research. Ravel’s data base is huge and contains information of more than 5 decades of cases. What’s more, the filters that can be applied are really varied. As an example, two of the functions that this tool provides are (i) "Relevance", which aligns cases vertically, with the most relevant cases moving to the top of the screen, while (ii) “Ravel” organizes cases with a gravity model, so cases that are heavily cited by other cases in your search results will be the center of gravity and pull other cases towards them.

The use of this type of services definitely saves Firms a lot of (usually) non-billable hours, which can be addressed then working in another cases. Time is of the essence, especially when you work in a Law Firm, and machine learning is making the time management more effective for lawyers. As we can see, machine learning and the law are getting closer, and it should not surprise lawyers to have more and more of these tools later on in the future.

What does Suits have to do with this?

Not a lot actually. Besides that I really like the show, I just wanted to point out that seeing the likes of Harvey Specter in the big and small screen might be misleading to people not related to law practice; and thus, the reality is that not everybody that goes to Law School, can automatically become Harvey Specter (especially without researching (a lot!) first).

Picture: The black tie blog