For sure you have watched one of those science-fiction movies where humans end up dominated by machines, and as we keep reading about Artificial Intelligence potential it is not weird to ask ourselves the question: who has the power? Humans or machines? Machines or humans?
About 68% of the population have an IQ between 85 and 114, and the highest score ever on an IQ-test was between 225 and 230. However, if we compare those numbers with the scores that a machine can achieve they seem similar to zero. According to the computer scientist David Gelernter, the next step for machines once they achieve an average IQ of 100 is to go to IQs of 500 and then 5000.
According to the latest news, Japanese researchers are planning to build a super computer for artificial intelligence usage. The computer will be able to do 130 quadrillion calculations per second. With all these rates, who knows what machines are really capable of? They don’t seem to have a limit.
However, as clever as they can seem, machines won’t be anything without the humans that build them, and even more without the humans that train them. Machines only improve if we train them correctly.
As an example: we can build an AI system able to search and match patient’s symptoms with huge amounts of medical databases to detect diseases much faster than a doctor, but if we don’t train the framework to look for what we really want… how is it going to be able to do it? And even more, results won’t be consistently perfect so we should teach the framework to learn from those mistakes.
So, how is this training process designed? Why this training process shows that humans have the power when it comes to Artificial Intelligence?
Here at Bitext, we have developed a Training Framework for projects using machine learning and deep learning algorithms. Our Deep Learning Trainer can transform any kind of unstructured text into a high-quality input that has been automatically annotated and disambiguated.
And as we do not use the same language for every aspect of our lives, the Bitext Trainer takes in to account the context of every input texts and enriches it accordingly. It does not matter the original source as our trainer can work with both high and low-quality sources like newspapers, social media comments, or research papers.
The structured and enriched data provided by the Bitext Framework speeds up the training process for machine learning and deep learning projects, saves costs as the better quality of the data means that you need less of it to provide relevant results. And above all your AI will be distinctly better at understanding natural language as it will take in to account key information previously unavailable to it.