¨Love your job? Enjoy it while it lasts. There’s a 50% chance you’re going to be sacked and replaced by a robot.”

As stated by the Bank of England, there is a high possibility that machines, also known as robots, can take over 95 million jobs between American and British people over the next 10 to 20 years.

Andy Haldane, bank’s chief economist, affirms how sophisticated computers can become a big threat to humans, as their jobs are being put in risk of automation because these machines are totally qualified to substitute for human brains and hands too.

Even though this is not the first time this has happened, as it has already occurred during the Industrial Revolution, Haldane made a clear statement that this time it could probably be different, given that mid-skilled jobs are the ones being taken over, leaving low and high skilled jobs for humans.

Will robots eventually take control of work forces?  Is it in the near future?

For more information, read this article.

Picture: http://money.cnn.com/2015/11/13/news/economy/jobs-at-risks-robots/index.html

To some it’s scary and to others it’s exciting, but the scientific consensus is that artificial intelligence will have a drastic impact on humanity, probably within our lifetimes.

What exactly that will look like is a debated range of sci-fi scenarios. Stephen Hawking made waves when he claimed that the development of AI could end the human race. While real-life Tony Stark Elon Musk and Microsoft’s Bill Gates chimed in with concerns of their own, other experts believe the threat of artificial intelligence has been exaggerated. What everyone can agree on is that developing intelligence is something we should be very, very careful with.

When Google purchased DeepMind to the tune £400 million, the London-based AI startup had some pretty firm ground-rules regarding the two companies’ relationship. Demis Hassabis, theDeepMind CEO, said that a condition to their acquisition by Google was for Google to form an internal ethics committee. DeepMind also refuses to allow any of their technology to be used for weapons or military interests.

Hassabis has announced that he and many of the top minds currently working with AI research will be meeting in New York in early 2016 to discuss and debate ethical issues surrounding their work. Although no official list of participants has been released, big players such as Apple and Facebook will almost certainly have representatives present.

Since purchasing the AI company, Google has been using DeepMind’s technology in a wide array of implementations. Artificial intelligence has improved Google’s image recognition technology and is also helping services like Google Now anticipate user’s needs more accurately. Talks like the one expected to occur in New York will likely serve to create ethical frameworks that will guide the development of this and other technologies?

Given that we live in a world dominated by capitalistic desires, can humanity entrust the synthesis of such a pivotal base – the AI ethical framework, in the hands of Google, Facebook and Apple? Do these companies not have a vested business (profitable) interest in AI that may bias their respective inputs towards individual gain over that of humanity?

It maybe is time for humanity to be more involved in what will define the next evolutionary cycle. After all its seems that our position in the food chain is under threat!

Machine Learning to save distracted drivers

Self-driving vehicles will play a huge part in the automotive future but till they do, the top priority of the car companies is the safety of the drivers. Mitsubishi is using machine learning to analyze vehicle data (like speed and steering) and driver behavior(heart rate and the orientation of their head) to detect when the driver is distracted or feeling tired. The new technology compares normal driving to its algorithmic prediction using data generated and immediately alerts the driver of their potentially reckless actions.

Mitsubishi will show off its new technology at the Tokyo Motor Show and could be integrated in a new wave of driver-sensing units installed in new cars from 2019 and onwards.

Machine learning is already an incredibly powerful tool that can do a surprisingly good job of solving really hard classification problems and this is yet another interesting application of machine learning to the automotive industry.

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?