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An expert warned that if mankind unlocks the key to a secret master algorithm to create self-replicating robots, we may be "asking for trouble."
Professor Pedro Domingos, the author of "The Master Algorithm", told C2C that we are being propelled very rapidly into a brave new world of Artificial Intelligence – whether we like it or not.
"Computers are getting more powerful," he told George. "Everything in the world now is spewing data in every conceivable way and these learning algorithms turn that data into knowledge - into predictions - into decisions - and actions."
"The master algorithm is what we need to do this. The master algorithm is the source of all knowledge. If you have that algorithm than it is able to discover everything there is to be discovered."
"In theory, the master algorithm can discover anything."
Lots of plot lines have been built around sentient computers that go awry or take over the world or do harm. Is this something to worry about, or are there other potential dangers?
PD: “The Terminator” scenario of an evil AI deciding to take over the world and exterminate humanity is not really something to take seriously. It’s based on confusing being intelligent with being human, when in fact the two are very different things. The robots in the movies are always humans in disguise, but real robots aren’t. Computers could be infinitely intelligent and not pose any danger to us, provided we set the goals and all they do is figure out how to achieve them — like curing cancer.
On the other hand, computers can easily make serious mistakes by not understanding what we asked them to do or by not knowing enough about the real world, like the proverbial sorcerer’s apprentice. The cure for that is to make them more intelligent. People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.
Pedro Domingos is Professor at University of Washington. He is a leading researcher in machine learning and known for markov logic network enabling uncertain inference.
Domingos received an undergraduate degree and M.S. from Technical University of Lisbon. And then at University of California, Irvine, he received an M.S. and Ph.D. After spending two years as an assistant professor at IST, he joined University of Washington in 1999 and now is a professor.
originally posted by: neoholographic
a reply to: liteonit6969
There can also be an anomaly for the Master Algorithm if consciousness in non computable like Penrose says it is. It could create programs that can learn and do anything except when it comes to human consciousness.
originally posted by: FamCore
But I will rest easy at night, even if I become a slave for the robots ... knowing that I have a soul, and a robot... never will.
originally posted by: Cygnis
Personally, A.I. would be welcome in my world. I would be rather keen to have a Jarvis type companion.. If nothing more, the conversation would be fascinating, as it sure would beat talking to myself all the time.
originally posted by: Blue Shift
originally posted by: Cygnis
Personally, A.I. would be welcome in my world. I would be rather keen to have a Jarvis type companion.. If nothing more, the conversation would be fascinating, as it sure would beat talking to myself all the time.
Hopefully you would be able to hold up your end of the conversation, so the machine wouldn't be too bored to talk with you.
This was a great interview but this is such an important point that I have been saying. Machine intelligence will not look like human intelligence. We make the mistake of thinking that machine intelligence has to be like and look like human intelligence.
originally posted by: Cauliflower
We keep reading about these super intelligent AI algorithms but if they exist where are they?
Is there even a simple image compare interface available on the internet that can search for similar graphics?
originally posted by: Cauliflower
We keep reading about these super intelligent AI algorithms but if they exist where are they?
Is there even a simple image compare interface available on the internet that can search for similar graphics?
Some examples of M usage that Facebook saw include:
Negotiating with cable companies to cancel service or obtain refunds or discounts without the user having to call the company.
Handling toll violations for a rental car by getting M to work with the car company so they waive late fees and pay for the ticket.
Getting a car towed and booking a last-minute hotel room after a user loses their keys.
Receiving travel assistance and retrieving a lost item thanks to M communicating with locals who only speak a certain language.
Planning a wedding by arranging vendors for makeup, flowers, programs and manicures.
Booking a vacation by setting up flights, creating itineraries and planning activities.
Assisting new parents with research on child care providers and completing random tasks.
Because computers are able to look at a large data set and use machine learning algorithms to classify images, it’s relatively easy to write an algorithm that can recognize characteristics in a group of images and categorize them appropriately.
For example, it takes four highly trained medical pathologists to review a breast cancer scan, decide what they’re seeing, and then make a decision about a diagnosis. Now, an algorithm has been written that can detect the cancer more accurately than the best pathologists, freeing the doctors up to make the treatment decisions more quickly and accurately.
In one project, a computer was taught to write photo captions describing the pictures. In its first iteration, human readers thought the computer generated description was better than the human generated words one out of four times.
This has broad implications for all kinds of data entry and classification tasks that previously required human intervention. If a computer can recognize something — an image, a document, a file, etc. — and describe it accurately, there could be many uses for such automation.
Another example I have covered before is how during the 2015 Wimbledon tennis championships machine learning algorithms were used to automatically turn match statistics and sensor data collected during each game into automated news stories which read as if they were written by sports journalists.