Thought Network Patented Technology Brings Equality to the AI and Data Industries Thought Follow Mar 16, 2018 · 7 min read

Artificial Intelligence is everywhere

Have you ever wondered how one day you searched “Best Smartphone of 2018” on Google and the next day you were bombarded with ads from smartphone companies? Or how watching a video on the ‘World’s Best Beaches’ on YouTube inevitably leads to plethora of holiday ads being shown on sites you are currently viewing?

As you might already understand, there aren’t millions of employees working at YouTube who personally follow you and suggest to you the most exciting videos or people monitoring your searches on Google to show you the most relevant advertisements. It is all done by computer coding and algorithms, or in other terms, by Artificial Intelligence, or AI.

General vs. Narrow Intelligence

Now, when many people hear about AI, they automatically imagine:

a) intelligent robots that could make our lives easier, by helping us automate many tedious tasks and being our personal assistants;

b) robots that at some point become too smart for us to control them, finally turn upon humans and start taking over the world (thoughts that are most likely inspired by “Terminator”).

These AI have what is called general intelligence. This means that, just like humans, they are proficient at many different and unique tasks — they can solve complex mathematical problems, identify animals, analyze the weather, speak, and more. The possibilities are endless.

But our current AI is far from being the intelligent machines depicted in Sci-Fi movies. The AI we have today have narrow intelligence, meaning that they are exceptionally proficient in once specific task, but can’t really do anything else.

AI in our lives

Some examples of artificial intelligence in our everyday lives include:

· Google’s search engine, which is incredibly efficient in showing the most related results to your search input;

· Email platforms that are incredibly effective at detecting spam email and automatically moving them to the “Spam” category;

· Amazon knows exactly that when you buy a new pot; then you might also be interested in a new cookbook, an apron and a set of cutlery; and so on.

As you might see, AI is already a part of our everyday lives. But how do these platforms know all of these things? How do they know your preferences or what to show you? How did AI become so effective?

AI training

Artificial Intelligence starts off quite similar to humans — not very efficient at doing anything. To make it work like we want to, AI has to be trained. It has to learn, make certain connection, recognize patterns, and start making decisions based on the information gathered.

For that, humans have to feed enormous amounts of training data to the AI, so it could start learning and making connections.

For example, if you’d want to create an AI that would be incredibly efficient in recognizing bananas on pictures, then you’d have to give the AI millions of pictures of bananas. The algorithm starts to analyze these pictures and makes certain connections. After seeing characteristics like yellow, curvy, eaten by primates, and so on, repeating the patterns over and over, it comes to a conclusion that every banana is like that. When it has to identify a banana on a random picture it immediately starts looking for a yellow curvy object.

The baseline is that, to make artificial intelligence efficient, it needs vast amounts of training data. Gigantic companies like Google, Facebook, and Amazon have extensive amounts of information to work with, which is exactly why these companies’ platforms are so efficient.

Problems with AI training data

But problems appear when it comes to smaller companies and organizations. While Facebook boasts over 1.4 billion daily active users, and Google processes almost 2.5 million searches every minute, smaller organizations and businesses rarely have any data to be used for training AI. This means that the only organizations capable of developing artificial intelligence in this way are large multi-billion dollar corporations, big data companies, and government-backed research facilities.

Just finding enough data to be able to train AI is a strenuous task on its own, not to even mention finding the exact right data for your AI, especially when you don’t have millions of dollars laying around or a user base of a few million.

AI and data industry is monopolistic

Another problem comes with the fact that these organizations that do have the sufficient amounts of data to train AI are not particularly keen on sharing information about their algorithms and what their intentions are behind developing their algorithms. Leaving the common user in the dark about how their personal information is used, how these complex algorithms operate and what is done with the results.

These large internet companies know more about you than you might think, and quite frankly, more than you might like. You have no way of knowing exactly what Facebook does with your personal information, or how Google utilizes the things you have searched for in their engine.

Although the amount of data created every year grows exponentially, allocation of it is hugely disproportionate — a few large organizations have access to vast quantities of data, while the majority has almost nothing to work with.

Currently, the advancements in AI technology are not limited by our ability to create smart and functional algorithms, but by the availability of useful data to train these algorithms.

Most, if not all, world-changing innovations have started off small, but with artificial intelligence finding a way to realize an idea might prove to be almost impossible, without having access to vast amounts of information.

Maybe someone comes up with a brand new way to create self-driving cars, that would eliminate the problems we currently face in this industry, but without having a way to gather billions of miles worth of driving data, making such an invention a reality is impossible.

Thought Network is changing the current unequal paradigm

Thought is an AI and blockchain company changing the current, unreasonable situation with the AI and data market by creating a transparent and decentralized platform. It creates a rich ecosystem where the community is in charge of the algorithms and the data. Users can purchase and sell information, develop algorithms and sell the access to other users, creating a place where even the smaller companies have access to data that they can use to innovate and develop the field of artificial intelligence further.

“In the current paradigm, every piece of data is routed through servers, stored in data centers, and compiled and sifted through big data analytics and AI algorithms.” explains the CEO of Thought, Professor Andrew Hacker. “Thought introduces a new paradigm — a platform that combines data analytics and artificial intelligence to change the way the world creates, processes, interprets, and disposes of the near-limitless amounts of information being created.”

Thought is backed by Harrisburg University of Science and Technology and holds a patent for their innovative way of utilizing data. Thought is embedding every bit of data with artificial intelligence, making traditionally ‘dumb’ information, that only become valuable in the context of applications, smart and able to act on their own.

Professor Hacker continues, “In the Thought paradigm there is no difference between the data and the application layer; they are one and the same. In Thought, the data is smart and takes action as soon as it is created.”

Thought’s Smart Data patent applies to creating data containers called Nuances that store the information as well as a piece of application code that makes it able to function on its own. Nuances can communicate with each other and work together to realize their goal. No longer does data have to go through an extensive process to be analyzed. Instead, it can analyze and categorize itself.

“Thought has built a foundational, information transformation network with data as the commodity. The ecosystem hosts data-hungry applications for researchers in AI and cognitive computing, in diverse industries such as healthcare, transportation, government, media, utilities, and finance. The ecosystem is fuelled by monetization of sensor-based data and analytically rich data sets”, explains Professor Hacker.

Conclusion

Right now, only the largest organizations have access to sufficient amounts of valuable data to truly develop artificial intelligence, while the smaller guys are left out of the game, leaving many potential ground-breaking ideas undeveloped.

But by making AI and data more accessible to regular people who don’t have the luxury of having billions of dollars laying around, Thought can open up the field of AI to the smaller teams who may have incredible ideas. It encourages further development and provides the means to make these ideas happen.

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