Hi @cripto.piotr I have read your interesting post. Well-written, very well-documented.
Nearly a hundred and thirty years ago, Charles Darwin published his initial work, "The Origin of the Species" and a dozen years later a second work, "The Descent of Man".
Today, Darwin's ideas along with the views of some of his predecessors and contemporaries form a significant part of the framework of the Theory of Evolution. The Theory essentially views man as a genetic accident--a product of chance in time and space.
Interestingly, we face a very similar situation in the scientific community today with the advent and maturing of the body of knowledge called artificial intelligence (AI).
There is currently an unprecedented interest in AI; researchers are making wonderful claims for this newfound technology while government agencies and business and industrial organizations are shelling out millions of dollars to acquire a piece of the action. Don't misunderstand, there are many, many potential benefits to be gained from AI; in medical diagnostics, in manufacturing, in mineral exploration, in communications, in space exploration--in practically every arena of human endeavor.
Both physicist Stephen Hawking and business magnate Elon Musk had publicly voiced the opinion that superhuman artificial intelligence could provide incalculable benefits, but could also end the human race if deployed incautiously. Hawking and Musk both sit on the scientific advisory board for the Future of Life Institute, an organization working to "mitigate existential risks facing humanity". The institute drafted an open letter directed to the broader AI research community, and circulated it to the attendees of its first conference in Puerto Rico during the first weekend of 2015.
Embedding Human Intelligence into a machine via machine learning languages, which enables the device to see, think, act & learn is termed as Artificial Intelligence. To clarify more on this, let’s see what Ben Thompson, Stratechery has proposed
“First, there are two types of artificial intelligence: Artificial General Intelligence (a.k.a. weak AI), that is, a computer capable of doing anything a human can. That is in contrast to Artificial Narrow Intelligence (a.k.a. strong AI), in which a computer does what a human can do, but only within narrow bounds. ”
What can be done to ensure that reason and truth prevail in the unfolding inquiry into the relationship between human intelligence and machine intelligence? How can we assure that our society and our world will be shaped by true truth and not speculation?.
Let the debate continue!....
Thank you for your kindness, dear
I wish you good health, happiness and prosperity.
Have a nice night
A big hug
What an amazing comment @euriguiwi2018
Thank you for sharing your thoughts :)
I feel flattered :) Thank you for your kind complement.
I like that you mentioned Darwin and his theories. The thing is that I strongly believe, that universe is huge enough and time is endless so it create perfect environment for such a "accidents".
For that reason I believe that humanity is not the only "accident" within time and space that is surrounding us. Does it even make sense?
Very well said.
Some people mentioned that we need to develop solutions that will allow us to distinguish fake audio/video from real one.
That's a great business opportunity for those who can provide such a solutions. Dont you think?
Big thx for taking the time to reply,
Yours
Piotr
Thank you very much for all your comments. Of course @crypto.piotr I'm agreeing with you, that's a great business opportunity for those who can provide such a solutions.
You may have noticed that Facebook and other apps now recognize many of your friends’ faces in posted photos and prompt you to tag them with their names. An app running on your smartphone will recognize virtually any bird in the wild.
Machines have already beaten the finest (human) players of poker and Go, achievements that experts had predicted would take at least another decade. Google’s DeepMind team has used ML systems to improve the cooling efficiency at data centers by more than 15%, even after they were optimized by human experts. Intelligent agents are being used by the cybersecurity company Deep Instinct to detect malware, and by PayPal to prevent money laundering. A system using IBM technology automates the claims process at an insurance company in Singapore, and a system from Lumidatum, a data science platform firm, offers timely advice to improve customer support.
Also, dozens of companies are using ML to decide which trades to execute on Wall Street, and more and more credit decisions are made with its help. Amazon employs ML to optimize inventory and improve product recommendations to customers. Infinite Analytics developed one ML system to predict whether a user would click on a particular ad, improving online ad placement for a global consumer packaged goods company, and another to improve customers’ search and discovery process at a Brazilian online retailer. The first system increased advertising ROI threefold, and the second resulted in a $125 million increase in annual revenue.
Recently I read in a magazine that the biggest advances have been in two broad areas: perception and cognition. In the former category some of the most practical advances have been made in relation to speech. Voice recognition is still far from perfect, but millions of people are now using it, think Siri, Alexa, and Google Assistant. The text you are now reading was originally dictated to a computer and transcribed with sufficient accuracy to make it faster than typing. A study by the Stanford computer scientist James Landay and colleagues found that speech recognition is now about three times as fast, on average, as typing on a cell phone. The error rate, once 8.5%, has dropped to 4.9%. What’s striking is that this substantial improvement has come not over the past 10 years but just since the summer of 2016.