The mission of the new MIT – IBM Watson AI Lab seems to assume that ethics are a form of applicable math, that they have natural laws, and can be understood and applied without undue complexity. The mission here

The collaboration aims to advance AI hardware, software, and algorithms related to deep learning and other areas; increase AI’s impact on industries, such as health care and cybersecurity; and explore the economic and ethical implications of AI on society.

is followed by a note that there will be calls for proposals from those affiliated with either of the institutions, in these key areas:

• AI algorithms: Developing advanced algorithms to expand capabilities in machine learning and reasoning. Researchers will create AI systems that move beyond specialized tasks to tackle more complex problems and benefit from robust, continuous learning. Researchers will invent new algorithms that can not only leverage big data when available, but also learn from limited data to augment human intelligence.

• Physics of AI: Investigating new AI hardware materials, devices, and architectures that will support future analog computational approaches to AI model training and deployment, as well as the intersection of quantum computing and machine learning. The latter involves using AI to help characterize and improve quantum devices, and researching the use of quantum computing to optimize and speed up machine-learning algorithms and other AI applications.

• Application of AI to industries: Given its location in IBM Watson Health and IBM Security headquarters in Kendall Square, a global hub of biomedical innovation, the lab will develop new applications of AI for professional use, including fields such as health care and cybersecurity. The collaboration will explore the use of AI in areas such as the security and privacy of medical data, personalization of health care, image analysis, and the optimum treatment paths for specific patients.

• Advancing shared prosperity through AI: The MIT–IBM Watson AI Lab will explore how AI can deliver economic and societal benefits to a broader range of people, nations, and enterprises. The lab will study the economic implications of AI and investigate how AI can improve prosperity and help individuals achieve more in their lives.

The last one is where they note that ethics lives, and it does not seem integral to the research in all areas.

Also to note, there is no nuanced or even truly noted inclusion on the difficulties of ethics, of cultural relativity, nor about the cultures both of the teams of creation, but where these advances will be put into the world.

And of course at the end, they note that a key aspect is commercialization.

Another example of enormous dollars being put to something which will fundamentally changes the ways in which humans and machines function in the world, without seeming to desire to understand how that will change the world, and what this means. The ‘delivery of … benefits’ sounds secondary to the creation of commercial enterprises and new technologies. The good will be an added benefit, if it comes, and the structure of the technology is not focused on beginning with clean data and understanding of sociological contexts in which it is going.  Engineering-heavy organizations, those who dictate the future of the 21st century.

Really, I like to think that Orcas already are fluent in human speech, and have just been not speaking to us, realizing that once they opened that door there would be no closing it.

The study was covered in the guardian, and like most studies, begins with the assumptions that the orca knows nothing and must be taught by the human.

The history of modern human science assumes that humans are the smarter species in what seems like all cases.  I’m going to prefer the creepy AMY orca calls is the orca finally tired of humansplaining.

Does this description:

Humanoid form and flexibility – SecondHands will feature an active sensor head, two redundant torque controlled arms, two anthropomorphic hands, a bendable and extendable torso, and a wheeled mobile platform.

Match this image:

It is the wheeled mobile platform. To me, this robot should not have legs, it should look more like the maid from The Jetsons.

Reading the Bloomberg article on nlp comprehension.

Alibaba Group Holding Ltd. put its deep neural network model through its paces last week, asking the AI to provide exact answers to more than 100,000 questions comprising a quiz that’s considered one of the world’s most authoritative machine-reading gauges. The model developed by Alibaba’s Institute of Data Science of Technologies scored 82.44, edging past the 82.304 that rival humans achieved.

What is notable to me is that in this instance, these questions can only have one answer, to be correct.

The quiz itself is based on wikipedia articles. Remember when you would never let your students use wikipedia as a source?

As the Bloomberg article notes, NLP ‘mimics’ human comprehension.  The underlying belief is that the machines can answer objective questions.

“That means objective questions such as ‘what causes rain’ can now be answered with high accuracy by machines,” Luo Si, chief scientist for natural language processing at the Alibaba institute, said in a statement.

Functionality and thus comprehension and correctness is based on a binary model of knowledge, and is using wikipedia for the source of correct. Much about that sentence is complicated, from my perspective. The binary model of correctness allows for no nuance, and is based on those who have the power to control the narrative. No alternate views, no other models.

It reminds me of taking standardized tests, when none of the answers seemed exactly correct, and I spent my test taking time trying to imagine which one the test makers believed to be correct. I was forced to fit into the culture of the creators of the exam. Extending this out to what it means that machines ‘know’ and allowing them to provide authoritative answers seems reductive, dangerous, and seems to be moving ahead apace.

As we sit here this morning, I am reading of Berber languages, and W is reading of Sumer and Akkadian.

Berber, and the Tamasheq variant that particularly interests me, has had a long life as an oral language.  Sumerian is one of the first known written languages, and while there is a sample of someone reading Gilgamesh in Akkadian, it was a language that was dead long ago, and we modern humans really do not know what it sounds like. The recreation, however, is beautiful.

Many of the languages which die off are oral languages, the last speakers die, and thus the language goes with it. This has long been a concern of linguists, and the popular press doesn’t seem to make it through a year without a piece about it as well.

The rise of social media based on images, the use of video, and the use of emoji are all interesting language shifts at play now. It is difficult to make any long ranging assumptions, but that makes it no less interesting than to watch younger demographics (in particular) prefer to engage with English in its oral form. Not just the in-person conversations that have always existed (and it may be argued that the in-person is diminishing) but the endless youtube videos and channels with millions of followers.  The language variations spoken by many of these English speakers are certainly not the written language that we find in standard texts, lexically and grammatically.  The use of emoji shifts English to a pictographic language, rather than a symbol corresponding to a sound, it corresponds to a concept or an idea.

There are thus, interesting ideas about the future of the visual language, both photographic and iconic/ideographic, which I am not going to touch at this time.

What I wonder however, if there will be an orality of language that is prioritized in the future, that shifts the current power and status dynamic in which unwritten languages are a lesser language, an archaic form from a culture which has ‘failed to develop’ despite the many ways in which the more oral languages do have advantages in a cultural context.

Imagining a world in which oral English is how stories are shared, that this access to the storytellers is required, beyond books, to belong, to understand, is a world we have, perhaps, never lived in, not in the modern English that we speak now. It would have been centuries since English was predominately oral, and it was an earlier version of English. Back to the time of the bards, except this time around, our bards will be digital.

To start, each constructs bilingual dictionaries without the aid of a human teacher telling them when their guesses are right. That’s possible because languages have strong similarities in the ways words cluster around one another. The words for table and chair, for example, are frequently used together in all languages.

So how does the computer know that table and chair are often used together? What about cultures that do not have chairs, but do have tables?  How is the computer mapping co-occurances that have a significant variation by culture, or simply do not exist?

This article uses Chinese and Arabic as the example languages for mapping. The underlying cultural principles are rather different, for community, behaviors, constructs, and as these are mapping IN language, does one become more like the other? Does the machine create an Arabic with Chinese sentiments? [The papers use French and English, which are much more similar, culturally and linguistically.]

Treating language like math is not going to turn out well. Though I haven’t yet read the papers that support these assumptions.

Back to my regularly repeated statement: machine translation and language that does not address the significant cultural components of language, as communication, as culture, as transfer mode of ideology, will, in the end, create a different or new culture, and now would be a very good time to be paying more attention to this.

http://www.sciencemag.org/news/2017/11/artificial-intelligence-goes-bilingual-without-dictionary

In a recent Atlantic article “Should Children Form Emotional Bonds With Robots” this:

Shut your phone off, and Cozmo shuts down too.

Another example of human affordances to the machine. In order to have your robot work, you must leave your phone on at all times.

Many of the articles note that the machines are making moves that humans have never made.  Both in the original, AlphaGo Lee, and in the evolved, AlphaGo Zero, we see games that “no human has ever played.”

So many questions

1. How do we know no human has ever played it?
2. Is the cultural ritual surrounding Go such that as one learns, there is an expectation to adherence of tradition which a human would not diverge from?
3. How do aesthetics play into the success of the machines? (I remember from learning Go decades ago that this mattered, but have read nothing about the aesthetics of the machines’ games. Caveat: I haven’t played in 20 years so what do I know?)
4. Is there any difference than the first principles as given to the machines than those given to humans?
5. Are the games played by the machines admired by the top humans?
6. Is it expected that humans are learning from the machines and that human/human games will now be played differently?

Reading Mar’s article today, I find myself re-reading a collection of older articles and wondering about Kate Darling‘s work, sexbots, hitchhiking robots and Ishiguro’s androids.

Are humans more likely to kill/maim/rape/injure human-looking/-seeming machines over non-human ones.

• Alex Mar’s Love in the Time of Robots
• What Interacting with Robots Might Reveal about Human Nature
• Empathic Concern and the Effect of Stories in Human-Robot Interaction
• Articles on the sexbot ‘abused’ at the Linz art festival (Though I may hate to really understand what ‘normal wear’ is.)
• The demise of HitchBot
   

The follwing is from Bill Joy‘s 2000 article in Wired, “Why The Future Doesn’t Need Us”

In his history of such ideas, Darwin Among the Machines, George Dyson warns: “In the game of life and evolution there are three players at the table: human beings, nature, and machines. I am firmly on the side of nature. But nature, I suspect, is on the side of the machines.”

Any thoughts / insights into why Dyson believes this?