The Black Bear Post

A new blockchain company called Coinfirm has announced partnership with one of Poland’s major banks – PKO BP. Coinfirm has developed a blockchain-based verification tool called Trudatum. The idea behind is to use blockchain’s permanently and immovably saved data. This news is quite big, as blockchain implications were estimated to have a wide variety of use but are actually only now starting to see real-world use (besides cryptocurrencies, of course).

Each single transaction will be represented by a permanent abbreviation or hash signed by the bank’s private key. Every client then can verify whether documents received by a partner or the bank itself are true or if a third party attempted any alterations. Coinfirm was founded by Pawel Kuskowski, Pawel Aleksander and Maciej Ziolkowski who already had some experience in dealing with cryptocurrency.

In Bulgaria, for example, banks are required by law to keep a paper trail on every single transaction that is done in the last several years. Trudatum is a digital solution, which creates “durable media” which can be permanently stored. It is not far-fetched to say that we expect banks to turn to blockchain very soon – mostly because it will save then efforts and additional costs in the long run. As for now, PKO BP is one of the first European banks to officially use blockchain technology for document administration. PKO BP’s top management is currently quite happy with the implemented technology and banking tests were “highly satisfying”, meaning the shift towards blockchain is imminent.

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Nowadays almost everyone is on social networks. With so many people providing and sharing content it is no surprise that a small portion of it consists of hate speech, extremism and sexual matter. On Twitter there is still targeted harassment and trolling, Facebook distances itself from fake news, but also does nothing to prevent them – it is a rather twisted understanding of freedom of speech. Nowadays politicians are undertaking a course to alter all of this.

First in this endeavor was Germany with its government proposing a €50m fine for social media companies whenever failing to delete abuse, slander, fake news or hate speech within 24 hours of its initial publishing. Interestingly, Europe is taking the driving seat in regulating social media – industry where all the big players are American.

While in the US freedom of speech is indefeasible, European law recognizes hate speech due to historical circumstances. German interior minister Heiko Maas recently stated that too little of inappropriate content in social media is deleted and even when this happens, it Is done rather slowly. Reporting practices are slow and ineffective. Germany is pressuring social media companies to battle fake news more rigorously. Strict sanctions will compel these organizations to delete apparent criminal content across the whole platform within 24 hours of its initial publishing. Other inappropriate data is to be removed within a week. Social networks are to publish reports on received complaints and how they were taken care of by their complaints team. When they do not meet the set requirements hefty fines are to be expected. All disputes are to be settled in German court in order to avoid differences in EU Members’ laws.

Maas mentioned in a speech that obvious lies are also protected under freedom of speech, but “freedom of expression ends where criminal law begins”. His argument is that social media companies are not simply mediums for information exchange, but responsible for what is conveyed through them.

According to different independent organizations, social networks delete between 40 and 80% of all illegal content in the first 24 hours, whereas German government’s initial target set will be at 70%.

German authorities are planning an EU-wide legislation, targeting abusive and criminal content in social networks and it will certainly be something we look forward to. The European Commission also informed Facebook, Google and Twitter to alter their terms and conditions in order to reflect EU consumer rules.

From a slightly different perspective, Germany might be worried the effect fake news could have on its federal elections this year given their correlation with the success of Trump’s campaign. There is also a large number of Refugees in the Federal Republic, being the object of increasing hate speech occurrences. The number of policy makers in the EU and US that want to see social network companies block harmful content is increasing, which will likely turn into a global initiative. On the negative side, this would give governments control over social media content. What constitutes hate speech exactly is also yet to be defined in criminal law in the United States.

One thing is certain – social media companies CAN battle fake news since they can do target marketing on us. The question is – will they do it? After all, it’s not nearly as profitable. Algorithms can distinguish hate speech content, but such a solution will make social network platforms slower, which in turn will hinder user traffic and ultimately revenue. Perhaps such an automated resolution can be implemented in the final stages of maturity and decline of product life cycles, which Facebook and Twitter have not yet reached.

On the one hand, requiring of social networks to self-manage inappropriate content will most certainly lead to “delete first ask later” policy in order to avoid serious fines. On the other hand, if western governments look over hate speech posts won’t this constitute posing limitations on freedom of speech?

Freedom of expression should have limitations if it leads to extremism. Also, developed western societies only lose to the emergence of fake news which seem like the tool for populist leaders to gain favor with the public. We have a responsibility to educate and advise less advanced users to stop using fake information portals.

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AI is already an inseparable part of our lives – it’s in our cars, our homes and our phones. Major tech companies have their own AI, implemented in a variety of their products – whether it’s Siri, Alexa or Google Assistant. These programs are becoming increasingly sufficient to answer our questions, so how does this translate in our educational system? They can help us find accurate information, so why are schools not jumping at this exciting new opportunity?

Our educational system is currently stuck in its well-tested “ancient” methodology where by default teachers are the one and only true source of information. Even though minimized in terms of exposure, personal beliefs of educators can still influence how information is conveyed. For example I had a teacher who loved providing religious examples, even though at a very young age I had atheistic beliefs. But this is still a small problem, compared to the job-related skills of tomorrow. The World Economic Forum estimated that 65% of primary school students today will have jobs that do not exist today. It turns out that while knowledge will always be valuable, flexibility and the ability to retrain will be even more important. Finland has abolished the passive learning paradigm and students work in groups, learning problem solving, under the guidance of a teacher. This step is definitely In the right direction in terms of personal development and acquiring a skill set that will serve well in the future.

We still have not reached singularity – the point, where algorithms will be able to learn by themselves. Today AI can provide you with information within seconds, whereas 30 years ago people had to go to the library. More often than not, however, personal assistants either cannot find the right answer or the one they provide is wrong. One still does not have to go to the library, but rather use search engines. Today we have enormous amounts of information and cheap computing power so it is no wonder that an ever-increasing number of companies are investing in creating their own AI – algorithms that “learn how to learn” in order to navigate this vast sea of information.

AI is far from what people would like it to be right now. Machines still need human guidance and are mostly used for people to improve their productivity. Since the dawn of time we have used tools to improve our work. However, now is the only time in history where the tools are being developed at warp-speed and one way or another we will reach singularity. So we have to tread carefully. Until this happens we have to train the only superintelligence we have – the human brain and its carriers – the people. This means radically reevaluating our educational system. It is estimated that creativity will become the most important job skill so lets minimize the risk of creating more future unemployment.

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Over the recent past there have been many mentions of machine learning, one might even think that something groundbreaking has been discovered. In reality it’s almost been around as long as we have computers and, no, nothing incredible has been discovered lately. Long ago Alan Turing asked whether machines can think and we have certainly come a long way in the pursuit of creating an artificial conscience, but still not quite there yet. This discovery might help us crack the mystery of our own mind and perhaps the eternal question “Why are we here?”. Apart from the philosophical implications, in this article we would like to shed some light on some aspects of Artificial Inteligence.

If my data is enormous, can Intelligence be created ?

Initial attempts at creating AI consisted of letting machines full of information run and hope for a positive outcome. Based on our limited knowledge of the universe and our place in it, this does not at all far-fetched. We are a result of entropy – given billions of years living matter emerged out of inanimate one. The concept is somewhat similar with two big restrictions – time is not unlimited and there is a memory capacity on these machines(as opposed to a seemingly endless universe). Google might be the pinnacle in this endeavor, but our search engines won’t evolve their own conscious.

In broader terms, machine learning consists of reasoning and generalizing, based on initial sets of information, applied to new data. Neural networks, deep learning and reinforcement learning all represent machine learning as they create systems, capable of analyzing new information.

Some 60 years ago, processing power was a fraction of what we have now, big-data was nonexistent and algorithms were primitive. In this setting, advancing in machine learning was nearly impossible, but people kept going. In recent decades we had neurology help advance neural networks. Machine learning patterns can be broken into classification or regression. Both methods work with previously provided data. The first class categorizes information, while the second develops trends that then help make prognosis for the future.

Frank Rosenblatt’s perceptron is an example of linear classifier – it’s predictions are based on a linear prediction function that splits data into multiple parts. The perceptron takes objective features (length, weight, color etc.) and gives them a value. It then works with those values until an accepted output is achieved – one, fitting into predefined boundaries.

Even people working in this field find it confusing

Neural networks are many perceptrons that work together, creating similar structure to the neurons in our brains. In more recent years scientists tried to create AI by mimicking how our conscience works – or at least as far as we know.

Deep learning has been the next big thing in AI development. These are neural networks with more layers, adding more levels of abstraction. It is important to remember that a computer does not consider the traits that human would between two or more objects – machines need abstraction in order to fulfill their task. This difference in perception is perhaps the final frontier to developing an AI, capable of passing Turing’s test.

Despite our solid progress, there is a long way to go. The black box of machine learning is an example of issue that we still can’t quite figure out. We can say the exact same thing about the human mind. The good news is that scientists are working on both problems and not knowing something has never stopped us into digging deeper and ultimately finding the answers we are looking for.

As far as 15 years ago we had slow connectivity and expensive storage. Whenever thinking about the future of computing two major trends were feasible – either storage would become so cheap so every device could store gigantic amounts of data or connectivity would become fast and widely spread so information is stored remotely.

This concept could easily apply to one very new and attractive market – will self-driving cars make decisions based on traffic conditions and optimal routes or send data to the cloud and get feedback? Same question applies to more advanced robots and what will be the benchmark for complex Artificial Intelligence.

Today we observe a trend which includes both big storage capabilities and fast connectivity, but with scales tipping towards cloud-based solutions. Naturally, Smartphones are becoming more advanced but the speed of hardware improvement is not incredible and most of the advancements are software-related.

But beside complex multi-system machines today we also have incredibly simple tools like Google Home which consist of microphones and speakers, combined with connection to the cloud – the place where all the work is done.

We should alter the way we look at electronics in terms of software and hardware to new systems of devices, operating via programs and most importantly – partnerships. In order to clarify this statement we will examine the way mass technology changed in four main topics – analog, digital, smart development and personalized digital systems.

Analog

Before digital media we had devices, capable of doing only one thing – a TV, Walkman or later Discman. Almost every year there was one new amazing device improving on the experience provided by its predecessors. One interesting fact is that at that time record stores had to adapt by selling several physical formats of the same album just to keep up with technological advancements while still reaching wide customer base.

Digital

Digitalization really changed what was established in the previous period and made life much simpler. One mp3 player replaced the Walkman and Discman and later Smartphones virtually became a computer in your pocket. Yes, some “dinosaurs” expressed nostalgia, but they either disappeared or quickly embraced the benefits of one device having more than a single functionality.

Smart Development

With the smartphone becoming an inseparable part of everyday life, capable of serving from a flashlight to a small inch TV, any other non-smart device needed improvement in order to justify its existence for the consumer. ChromeCast improving TVs or the emerging of Smartwatches are just examples for companies adjusting to a complex environment. It is only logical to focus on partnerships between these devices, solely based on their wide functionalities.

Personal digital systems

Tesla and Echo are companies revolutionizing our view of consumer products. Thanks to machine learning, one can buy a product that gets better by itself over time. These are items developed with software and hardware by companies that came to the conclusion that products are more appealing when they provide access to a system. It also adds to customer loyalty as this system is part of the same corporate family.

Digital money will work only if retailers are ready to accept it. Smartwatches and Smartphones are the kind of devices that should be embraced as personal access tools to one’s online boarding pass for example. An important question for the future is not what the divice will be capable to do, but to what extend of his personal information will the owner allow to have this digital “key”.

One thing is certain – the future holds more interconnectedness than we could ever imagine and it is up to us to make sure we do It in a way that cannot be exploited by third parties.

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