One of the funnest games I’ve played this year. Awesome Music, graphics, and playability. I’ve enjoyed it a lot! 😀
One of the funnest games I’ve played this year. Awesome Music, graphics, and playability. I’ve enjoyed it a lot! 😀
Mongodb is extremely easy to start playing with, for free you can create a database cluster in the cloud with 512MB by registering in mongodb.com and getting a Mongodb atlas.
MongoDB Compass is a graphical client with which you can connect to your database and analyse your data. It analyses the data and gives you insights on the types of data, you can also filter just by selecting with your mouse the data you want to filter on in an intuitive way.
The CLI is the best way to power-query your data. below my cheat-sheet after completing the basic training course:
This month I returned to moneyconf, a yearly meeting of fintech enthusiasts. Apart from a pair of trendy LHoFT sunglasses I came back with a bunch of insights and inspiration to fuel me until next year.
Insights from moneyconf:
Every year late July I attend the Euskal encounter. A meeting of over 8,000 computer enthusiasts who gather to share their passion. For me this is an opportunity to catch up with old friends and discuss our vision of the state of the industry and the future to come. I have seen it evolve over 25 years from the early days when it was a meeting of amiga demoscene fans to today where we have gigabit access to the internet, drone races and training of 10 year old future engineers who are helped to work together to build electric karts and race them.
Every year I obtain a few wisdom pearls that I take back with me and fuel my inspiration. These are the highlights I take with me this time as exciting material for investigation:
This week I have started using my virtual reality headset and I have been totally blown away by it.
When you put on your headset you are transported to your virtual home, a spacious terrace on a mountaintop on a sunny day, with the birds chirping, the breeze blowing, and a beautiful mountainous landscape. In one wall of your virtual home you can manage your command center from which you can launch any VR application, and see which of your friends are online to play with. The sensation of immersion is perfect, after a few minutes you completely forget that you are not really in the virtual world, and after a few hours of VR, going back to the normal world takes a few seconds of adaptation. The realisation that you have been staring at a wall moving your arms around for hours is confusing.
Traditionally, videogames have always been limited by the screen size, the area of game was necessarily reduced to the size of the screen, hence your avatar in this world was small and so were the rest of creatures in it. This is no longer a limitation. Everything is life-size. You are transported inside the game’s world, for those of you who watched Tron, the movie, this is it. In “Star Trek Bridge Crew” you are captain of the Aegis space ship navigating through the galaxy. When you look out the window, the immense size of the planets, meteorites and stars is breath taking. In “TheBlu” you are in the bottom of the ocean swimming with medusas, tortoises and whales. The sensation is very real and awe inspiring. In“Richie’s Plank Experience” you have to walk a plank on the top of a skyscraper and you are dared to jump off it. Even if I knew I was in the safety of my room in the real world, I could not command my feet to move. The sensation of vertigo was stronger than my brain’s logic. This is amazing!
One drawback that VR experiences have is that when your body is moving in the VR world but not in the real world, for example if you are piloting a kart, or a spaceship doing barrel rolls, your brain is not capable of understanding why your inner ear is not feeling the pressure of the centrifugal force and you get motion sickness. The sensation is similar to getting seasick. I know first hand.
Imagine working on your computer, not from a chair in the office staring at a couple small screens, but on the top of the Everest on a sunny day, in the bottom of the ocean, or in the center of the galaxy. Imagine your screen is not limited by size, but can be as large as the sky, allowing you have a 360 degree screen where you can leave your different applications. This is what Virtual Desktop does. Currently it has some limitations since the VR headset only has reading-resolution where your head is oriented, not where your eyes are looking. Google is working on a headset with human eye resolution using a trick tracking your eyes to see where you are looking at to maximise resolution in this area.
At any rate of advance in VR technologies, in the next 5-10 years it’s easy to foresee that VR graphics and sound will be indistinguishable from base reality. In this scenario, it would be possible for companies not to need to have physical offices for computer work. Workers could work remotely from their homes, teams would be distributed worldwide with a virtual office in the VR world. This office would be amazingly beautiful, spacious, sunny, with breathtaking landscapes. People would have avatars in this VR world which will allow them to have meetings, work together on projects, explain topics on a whiteboard…
Mining ethereum is an easy 5 step process:
Now for the bad part. I have two GTX 1080 TI and I am getting around 62 MH/s. This means my computer is capable of taking the Ethereum block we are trying to solve, add it a random string and calculate the hash of it to see if the first ten characters are zeroes and therefore win this block. 62 million times every second.
62 million times per second sounds like a lot! I should have good chances of winning a block once in a while. Taking into account a new block is generated every 15 seconds, I should have good chances..
The problem is that the Ethereum network has a hash rate capacity of around 60TH/s, this means that the number of hashes the network produces every second are one million times bigger than the hashes my computer produces. This gives me a chance of winning of one in one million every 15 seconds. Taking into account that the reward for winning is 5 ETH (approx 1,150 EUR), I have 1/1,000,000 chance of winning 1,150 EUR every 15 seconds if I leave my computer mining.. this does not sound too promising.
Usually when we need to understand our business’ numbers, we open Microsoft Excel and start applying filters, pivot tables and charts to try to gain insight on what story our data are telling us.
It’s 2017, Isn’t there a better way to do this?
I think it must have been around 2014 when I first heard about Tableau. I immediately liked the tool’s proposal: Easy tool to analyse data in real time without needing to be an expert in SQL, Olap cubes, or any technology. It could take as input for data any number of excel sheets, csv files, or database sources and would immediately allow the user to move the data around, establish relationships between sets of data and start visualising. I’m a visual person, I need to see things in graphical form to understand them, so I really like this concept. In the Gartner Magic Quadrant for Feb 2017, the 3 top players in the BI space by completeness of vision and capacity of execution are Tableau, Microsoft, and Qlik who offer similar solutions for front end analytics.
One of the weak points that I see in Tableau’s proposal is that it only addresses the front end, the data analysis, but leaves the back end, the data model, the origin of data, for you to deal with. In the past I’ve worked on a few data warehousing projects where the immense complexity was in creating a data model which made sense from a business perspective and which could grow with the business’ future needs. Designing the data model wrong was similar to shooting yourself in the foot, as future needs could not be adapted, changes would require refactoring of the whole data model and of the downstream systems which read from this data model and maintenance of this data model would be expensive and complex. In all cases I experienced in the past, creating a data model was similar to creating a monster which would grow in unforeseen and inelegant ways getting more rigid and patchy as time went by.
A good example of a world class level attempt at creating a data model for the capital markets world is ISDA’s (International Swaps and Derivatives Association) FPML (Financial Products Markup Language). The data model they have birthed is immensely complex, as could not be different when the goal is to be able to map any financial instrument. Any attempt at using FPML as inspiration for your data model promises a long and complex project.
Three weeks ago, I attended Moneyconf. There I saw a company called gooddata which offers an interesting model: They take care of your Business Intelligence needs as a service. You send them your data, they organise it and offer you a Tableau style front end tool to visualise it. This would take care of all data warehousing troubles.. however I have some concerns:
The BI world is very cool looking. It brings promise of granting better insights and fancy looking charts, but, is the price tag worth it when we can always just grab an excel and start applying filters, pivot tables and charts to understand the story our data are telling us?
In 2008, the first Distributed Ledger Technology (DLT) was created with Bitcoin. Immediately it became visible across industries that DLT technology could be very useful to transfer assets. As time passed, different implementations have come to light using the concept of the DLT which attempt to address different problems with different implementations of a similar idea.
One of these implementations is IBM’s Hyperledger. In late 2015, IBM partnered with 17 other companies:
The Hyperledger project’s governance is chaired by Blythe Masters (Ex-JP Morgan Executive, CEO of Digital Asset) under the Linux Software foundation. This open governance allows the open source community to contribute to the project, read the source code, download it for free and use it to create new software.
The vision for Hyperledger is a tool for confidential asset transfers in an enterprise environment. These assets can be of any nature: Securities, gold, legal contracts, wills, patents, medical information… Key differences with other DLT solutions such as Ethereum, are that there is no cryptocurrency involved and that access to transaction information is restricted with access rights. Whereas in Ethereum every miner node has a copy of the whole blockchain and can access all the information of any transaction, in Hyperledger all information is encrypted, and only thanks to having the right access rights can you decrypt and access certain information. In a corporate environment this would be a mandatory requirement as you may want to give one client a 10% discount without all your clients knowing about this.
Some interesting projects going on in the corporate world using Hyperledger:
Currently there are hundreds of players in the DLT world, in my view the top 4 are R3 Corda, Ethereum, Bitcoin, and Hyperledger. It is hard to tell who will come out ahead in the end, but Hyperledger is certainly worth keeping an eye on.
Imagine you are driving your car at 90kmh, and you realize the brakes are not working. 20 meters ahead of you, crossing the street, are five people who will inexorably die if you run them over. Looking to your right you see one person which is sitting in a terrace eating an ice cream. Your steering wheel is working well, so you could steer your car towards the terrace killing one person, but sparing the five.
What would you do? Most people would choose to kill the one person to spare the five. Sacrificing one life in order to save five seems the right thing to do.
Now imagine you are standing on a bridge overlooking the road. Down the road comes the brakeless car, and at the end of the road are five people who are about to die run over by this car.
You feel helpless to avert this disaster, until you notice, standing next to you on the bridge, a very heavy man. You could push him off the bridge, on to the track, into the path of the car. He would die, but the five people crossing the road would be saved. To make your job even more similar to the previous example, this man is standing on top of a trapdoor which you could open by turning a car-sized steering wheel to the right.
Would you push the unsuspecting fat man? Most people would find it terribly wrong to push the man onto the track. But this raises a moral puzzle: Why does the principle that seems right in the first case – sacrifice one live to save many – seem wrong in the second?
This example, taken from a beautifully taught Harvard online free course goes to explain how the consequentialist moral reasoning of Jeremy Bentham’s utilitarianism (it is the greatest happiness of the greatest number that is the measure of right and wrong) does not hold under the scrutiny of Immanuel Kant’s Categorical moral reasoning (Act only according to that maxim whereby you can, at the same time, will that it should become a universal law).
After 11th May 1997,when IBM’s Deep Blue beat human champion Garry Kasparov, no human will ever again, beat AI at chess.
After 27th May 2017, when Google’s Alphago beat human champion Ke Jie, no human will ever again beat AI at Go.
Once AI driven cars are mainstream, no human will ever again be allowed to drive.
While a human driver has visibility of 120 degrees, an AI has 360 vision from the car, through connectivity could simultaneously have 360 vision from other neighbouring cars, from traffic cameras, or from satellites. Other AI senses could come into play which the human counterpart does not have, for example an AI car could have sonar, similar to bats echoing a signal to detect proximity. An AI could have real time knowledge of where every other car in a radius of 10 kilometres is, in what direction it’s headed, and if it has any technical issue.
Human drivers get road rage, get impatient when sitting in a traffic jam, try to stick their car’s nose in front to gain one inch if possible… all these human behaviours cause traffic jams, accidents and inefficiencies. A perfect AI driver will have none of these traits, and thanks to this, traffic jams will be greatly, if not totally, reduced.
Having a human drive in the future will be as rare as having a human clean the clothes today. I could do it, but why woud I? I have a washing machine for that.
One human problem, is what to tell the AI to do when faced with the example at the beginning of this story. Should an AI follow Jeremy Bentham’s utilitarian principles? Should it choose to kill one to save five? This is particularly dangerous if we will also want the AI to perform surgery, as in order to save 5 patients who needed a heart, a lung, a kidney, a liver, and a stomach, it could choose to kill and butcher a perfectly healthy patient which had just gone to have a regular check-up in order to get the needed organs.
In the brink of a new era, these classic, unresolved questions are more important than ever.