We should be going for … radically new ideas.

WIRED: The recent boom of interest and investment in AI and machine learning means there’s more funding for research than ever. Does the rapid growth of the field also bring new challenges?

GH: One big challenge the community faces is that if you want to get a paper published in machine learning now it’s got to have a table in it, with all these different data sets across the top, and all these different methods along the side, and your method has to look like the best one. If it doesn’t look like that, it’s hard to get published. I don’t think that’s encouraging people to think about radically new ideas.

Now if you send in a paper that has a radically new idea, there’s no chance in hell it will get accepted, because it’s going to get some junior reviewer who doesn’t understand it. Or it’s going to get a senior reviewer who’s trying to review too many papers and doesn’t understand it first time round and assumes it must be nonsense.

Anything that makes the brain hurt is not going to get accepted. And I think that’s really bad.

What we should be going for, particularly in the basic science conferences, is radically new ideas. Because we know a radically new idea in the long run is going to be much more influential than a tiny improvement. That’s I think the main downside of the fact that we’ve got this inversion now, where you’ve got a few senior guys and a gazillion young guys.

 

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How Artificial Intelligence is different from human reasoning

Human decision tree to decide to talk “Bob”

You see a man walking toward you on the street. He reminds you of someone from long ago. Such as a high school classmate, who belonged to the football team? Wasn’t a great player but you were fond of him then. You don’t recall him attending fifth, 10th and 20th reunions. He must have moved away and established his life there and cut off his ties to his friends here.

You look at his face and you really can’t tell if it’s Bob for sure. You had forgotten many of his key features and this man seems to have gained some weight.

The distance between the two of you is quickly closing and your mind is running at full speed trying to decide if it is Bob.

At this moment, you have a few choices. A decision tree will emerge and you will need to choose one of the available options.

In the logic diagram I show, there are some question that is influenced by the emotion. B2) “Nah, let’s forget it” and C) and D) are results of emotional decisions and have little to do with fact this may be Bob or not.

The human decision-making process is often influenced by emotion, which is often independent of fact.

You decision to drop the idea of meeting Bob after so many years is caused by shyness, laziness and/or avoiding some embarrassment in case this man is not Bob. The more you think about this decision-making process, less sure you’d become. After all, if you and Bob hadn’t spoken for 20 years, maybe we should leave the whole thing alone.

Thus, this is clearly the result of human intelligence working.

If this were artificial intelligence, chances are decisions B2, C and D wouldn’t happen. Machines today at their infantile stage of development do not know such emotional feeling as “too much trouble,” hesitation due to fear of failing (Bob says he isn’t Bob), or laziness and or “too complicated.” In some distant time, these complex feelings and deeds driven by the emotion would be realized, I hope. But, not now.

At this point of the state of art of AI, a machine would not hesitate once it makes a decision. That’s because it cannot hesitate. Hesitation is a complex emotional decision that a machine simply cannot perform.

There you see a huge crevice between the human intelligence and AI.

In fact, animals (remember we are also an animal) display complex emotional decisions daily. Now, are you getting some feeling about human intelligence and AI?

Source: Fosters.com

Shintaro “Sam” Asano was named by the Massachusetts Institute of Technology in 2011 as one of the 10 most influential inventors of the 20th century who improved our lives. He is a businessman and inventor in the field of electronics and mechanical systems who is credited as the inventor of the portable fax machine.



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Siri as a therapist, Apple is seeking engineers who understand psychology

PL – Looks like Siri needs more help to understand.

Apple Job Opening Ad

“People have serious conversations with Siri. People talk to Siri about all kinds of things, including when they’re having a stressful day or have something serious on their mind. They turn to Siri in emergencies or when they want guidance on living a healthier life. Does improving Siri in these areas pique your interest?

Come work as part of the Siri Domains team and make a difference.

We are looking for people passionate about the power of data and have the skills to transform data to intelligent sources that will take Siri to next level. Someone with a combination of strong programming skills and a true team player who can collaborate with engineers in several technical areas. You will thrive in a fast-paced environment with rapidly changing priorities.”

The challenge as explained by Ephrat Livni on Quartz

The position requires a unique skill set. Basically, the company is looking for a computer scientist who knows algorithms and can write complex code, but also understands human interaction, has compassion, and communicates ably, preferably in more than one language. The role also promises a singular thrill: to “play a part in the next revolution in human-computer interaction.”

The job at Apple has been up since April, so maybe it’s turned out to be a tall order to fill. Still, it shouldn’t be impossible to find people who are interested in making machines more understanding. If it is, we should probably stop asking Siri such serious questions.

Computer scientists developing artificial intelligence have long debated what it means to be human and how to make machines more compassionate. Apart from the technical difficulties, the endeavor raises ethical dilemmas, as noted in the 2012 MIT Press book Robot Ethics: The Ethical and Social Implications of Robotics.

Even if machines could be made to feel for people, it’s not clear what feelings are the right ones to make a great and kind advisor and in what combinations. A sad machine is no good, perhaps, but a real happy machine is problematic, too.

In a chapter on creating compassionate artificial intelligence (pdf), sociologist, bioethicist, and Buddhist monk James Hughes writes:

Programming too high a level of positive emotion in an artificial mind, locking it into a heavenly state of self-gratification, would also deny it the capacity for empathy with other beings’ suffering, and the nagging awareness that there is a better state of mind.

Source: Quartz

 

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The Growing #AI Emotion Reading Tech Challenge

PL – The challenge of an AI using Emotion Reading Tech just got dramatically more difficult.

A new study identifies 27 categories of emotion and shows how they blend together in our everyday experience.

Psychology once assumed that most human emotions fall within the universal categories of happiness, sadness, anger, surprise, fear, and disgust. But a new study from Greater Good Science Center faculty director Dacher Keltner suggests that there are at least 27 distinct emotions—and they are intimately connected with each other.

“We found that 27 distinct dimensions, not six, were necessary to account for the way hundreds of people reliably reported feeling in response to each video”

Moreover, in contrast to the notion that each emotional state is an island, the study found that “there are smooth gradients of emotion between, say, awe and peacefulness, horror and sadness, and amusement and adoration,” Keltner said.

“We don’t get finite clusters of emotions in the map because everything is interconnected,” said study lead author Alan Cowen, a doctoral student in neuroscience at UC Berkeley.

“Emotional experiences are so much richer and more nuanced than previously thought.”

Source: Mindful

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