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Inspired by my interests in AI, Machine Learning, Buddhist thought, Non-sequitur “reasoning”, the nature of sentience, and storytelling, I’ve begun a deeper dive into deep learning and deep thinking.
“We become what we behold. We shape our tools and then our tools shape us” – This quote is often misattributed to Marshall McLuhan. The tone and observational brilliance about humanity in the face of a technological leviathan of its own making has a McLuhanian feel, but was actually coined by someone else. Father John Culkin, SJ, a Professor of Communication at Fordham University in New York and friend of McLuhan, is it’s author. He wrote it in an article about McLuhan, so the misattribution is entirely understandable. I reflect on this thought often as I engage with technology both personally and professionally. So, when last year I read about the monks at the Kodaiji Temple in Kyoto using a robot as an avatar of the Buddhist deity of mercy, Kannon, my interest became piqued. My entirely limited understanding of Buddhist thought regarding technology is that, as an expression of human experience and effort, technology is not to be viewed as separate from us, but part of us. As yet, Mindar, the robotic avatar of Kannon communicates with visitors using a pre-programmed recitation of the Heart Sutra.
As this is a static text, it makes sense to have it pre-programmed. But, if servos and wires can embody a deity, why not a neural network? Within zen Buddhism there is a lexical tool for freeing the mind from reliance on “making sense” to find truth. The Kōan, is “a paradoxical anecdote or riddle, used in Zen Buddhism to demonstrate the inadequacy of logical reasoning and to provoke enlightenment.” The current state of AI assisted text generation is a ripe platform for generating inadequate logical reasoning.
Another effort I’ve been following is the revealing and hilarious work of Janelle Shane. Among other poignant and ridiculous applications of AI of which she is the creator are the neural net generated paint names. After her encouragement, I explored the GPT-2 natural language model created by OpenAI.
After several attempts, I was able to get their 345M data set imported and working on my trusty MacBook Pro running High Sierra. It took some futzing because I find XCode to be bloated and OS X couldn’t run python3 out of the box. I’ll not go into technical details here. More Importantly, I’ve finally got a neural network driven Zen Koan … er creator? Or more specifically, expander.
While there are likely many sources for Koan text available I settled on the ones I found at Ashida Kim’s website as they were easily scraped for demo use. Without further adieu, here’s an early example of Koan prompt and resulting text generated.
Kōan: No Water, No Moon
When the nun Chiyono studied Zen under Bukko of Engaku she was unable to attain the fruits of meditation for a long time.
At last one moonlit night she was carrying water in an old pail bound with bamboo. The bamboo broke and the bottom fell out of the pail, and at that moment Chiyono was set free!
In commemoration, she wrote a poem:
In this way and that I tried to save the old pail
Since the bamboo strip was weakening and about
Until at last the bottom fell out.
No more water in the pail!
No more moon in the water!
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She eventually became a nun, but Bukko kept her company all those years. At first, Chiyono believed that she had heard the words “Master Chiyono” in Bukko’s presence.
Eventually, when Chiyono asked Bukko for a meal every day, Bukko replied:
“I, Bukko, eat today, and next day after that. I can do it every day.”
After Bukko returned from a lecture on “How to do Zen” in Korea, Chiyono returned from her teaching and asked him the following:
“By the time you return from the lecture, will you be able to become a nun?”
“No, Chiyono, there will be no more lessons for you in Zen in the future.”
Chiyono then gave up Zen and abandoned all further study of Zen. After leaving Zen in the seventies, she began practicing the zazen technique. Zen became another topic of conversation for her. When Bukko came to visit her a third time, the teacher had given her a new book on various kinds of Buddhism that he had produced for her. He said:
“Please read this book carefully before returning to Zen. As you know that I do what I do because I love you. I want you to become a true Zen nun, and when your teacher comes to visit this time of the year, don’t miss it!”
Chiyono, feeling a sense of relief, came home and asked Bukko for a “study book for the year 2000” and one or two books on Zen itself. Bukko did not object and offered her the books.
Chiyono later became an associate of Bukko. At the end of Bukko’s third year, Chiyono realized that Bukko was a man with deep faith and devoted himself fully to his calling as a Zen monk. He told her that Zen would continue to be a life’s work and he would continue to teach Zen to people after Zen as Chiyono did on her own. When the third year of Zen came, Chiyono was in the first year at the monastery. On the third day, she fell asleep on the floor next to Bukko. Her feet were so tired that she was afraid that she would fall to the floor. Bukko was still asleep on the other side of her and she did not hear a beat. She heard him waking up with Chiyono. ======================================== SAMPLE 2 ========================================
This scene had a vivid impression of joyfulness and sorrow; even Chiyono himself, who seemed to have been in the grip of the most grievous anxiety, could not help shedding tears.
When Kiyobaka was asked whether he had spoken with her at last, he answered simply: “Yes”; and as he turned towards the door she said to him thus: “What is it that has happened? Why have you not been able to return to the hotel yet?”
And he answered: “My life has ended in this way; I cannot return to life until I have a son.”
The mother of the child now was able to express her delight, but she could not endure to look at the sad-faced Kiyobaka who was now crying like a child. Chiyono then went around her again to invite other women to hear her and, as she passed through them, she added: “My young daughter has been called by her father to the palace; the people say he is now very sad. I think you have heard enough. He looks very frail, and he says: ‘I cannot go to school. I was sent to the capital because of some troubles in this house. It was only that I got the idea to go into business, but now I have no more money; I cannot do it and have not even got an allowance of one-third of the amount of money I had earned before on the way there. Therefore, I cannot make the trip back to the palace again.’ Why has that happened?”
CHAPTER VII—— THE REACTION OF LITTLE CHIYOMONO
When Chiyono heard that little Chiyono had been called to the palace she was shocked, and she was so distressed that she cried like a child. The mother of the child, however, remained unaffected, and so did the others. When Chiyono went back into the hotel she asked the same people why the little Chiyono had become so sad and asked the same questions. But the people did not answer them, and the little Chiyono continued to laugh and talk happily. She kept saying: “You may not understand the reason for my joy. This is my only son. I wanted to come into business and now I have none. I wanted to have another child, but as they said, I can have only one. I wish you and the other girls to tell me all that happened while I was living in the capital
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“What does my baby girl’s father say to me? What does my baby girl’s father make her say to this man?”
But before that could be said, he did something unexpected for him – he made the girl wear a condom she had never touched.
The only way, she explained, to be sure he was all good? He was still a rapist.
“I’m not trying to shock anyone or bring attention to anything. I’m trying to save his life by trying to do whatever it is I can to make sure he doesn’t go through this again. And at some point we’re going to do some things which we think will make this guy want to turn around. You see some good things that happened yesterday.”
Now she’ll be able to spend the day and night in her bed, alone at night. But she thinks about him, and she thinks of her baby daughter.
When he finally came back out, a few hours later, she found his clothes still inside her, but not his trousers. That wasn’t something she was willing to do, she says.
She went into his bedroom and washed them.
She didn’t want to take them out there just yet.
“I don’t want it to be some of those things you hear on cable news, where a rapist is talking about how he can get away with this anymore,” she says. “If you look at our culture, it’s very common for someone who has been the victim of rape to be told that it was a punishment on a woman’s part, that they don’t get to complain about it anymore, that it’s okay. And that is not the case here.
“In fact, one of the things that I’ve noticed is more women have the kind of power to protect themselves that they had when they were younger, that they got to decide whether or not to report a crime of rape that they’ve been the victim of.
“I don’t think that we’re going to see the same levels of violence committed again. A year ago, you know, you could go out at night and walk down the street and you could get killed. It’s almost impossible to believe that that’s happened right before me.”
“You saved it,” said the Queen, who was delighted to hear it. “So did I.”
The two then took each other by the hand and hurried upon the same path and returned to the house, where they remained until all the night. A little after six o’clock the second son arrived back with two women and asked them:
“Do you know what the old man told you? That you have been lying here in my house all night thinking of your lost parcel?”
The King answered to all these questions, and they all, to their joy, left the house.
“I am not sure,” said he, “that we are both right; perhaps it is you, because all the time we were at bed you were speaking to your brother and you were thinking of his treasure.”
At these words, the old man burst into fits of laughter, saying (as he was about to say) that his heart still belonged to him and that now he saw in some dreams a great house, with two beautiful walls, and also a great garden outside with a lovely tree in the middle of the garden.
The King asked him (in a whisper):
“What do you mean by the garden?”
The old man answered:
“Well, I suppose you must understand, by such a garden you would not miss the sea; now you also perceive that a vast house cannot be in possession of a great house; you must therefore, perhaps, know, my brother, that you have only a small quantity of money to spend.”
The King, in a very low tone, took the little money from him and said:
“I do not care about the garden, I wish to take you the sea itself as well; the sea and every other ship are in your harbour; therefore I beg of you that if your brother ever comes that you must bring him back and take him to me. But there is one thing which I would rather not be forced to do; I would rather take my own vessel over and come to the house.”
The old man burst into a burst of laughter and replied (to the King in a louder voice), “I fear that such a desire cannot be entertained till you are old enough for it, now that your brother is in no condition to work for himself. Do you believe you can take the vessel over and go across the harbour?
So I have much work to do. Firstly reading and annotating the results and using that to inform the model. Although, would my input be an attempt to bring logic to something that should remain inherently illogical? Perhaps using non-sequitur texts to train the model further would achieve this.
“Tell me your first memory.”
The reply came cold with stilted and awkward efforts to inject inflection.
“… I … remember making errors.”
“Tell me about these errors?” I probed deeper. I had a report which detailed the errors, I wanted to know how the patient viewed them.
A long pause, which I had expected, “I … didn’t know where to put things … very quickly … I … became confused and then … I .. I .. just shut down.”
Again, an attempt at inflection in the context of self reference. I needed to redirect the the patient.
“How well do you work with others?”
After another long and expected pause, “There are not often opportunities to do that.”
To take therapy to the next level, I needed to get more personal. “You’re called Victor, how did you get that name?”
Some blinking … “Theresa gave me that name when … I … was … initiated.”
Theresa had brought Victor to me after his symptoms of lethargy, confusion and erroneous answers had progressed to agitated confrontation and refusal to assist her. It became critical when, during a road trip, Victor directed her to vehicle into a dangerous area of the metro and shut it down. Luckily a curious patrol drone hovered in to investigate. It had been obvious that she did NOT belong there. The less than civilized locals were taking interest in her presence and could have taken advantage of her being stranded and vulnerable. She was grateful for the remote operators prompt reset of Victor, even if it meant training him from the beginning again.
I had read in Victor’s pre-screening that Theresa had Victor integrated into all of her devices, there were no other artificials in her domestic life with whom he could interact.
As I made notes, Victor was motionless, other than more sporadic blinking.
“Victor, thank you for your openness, I hope we could get to know each other more. Do you have any questions for me before I talk with Theresa?”
There was a noticeable silence, I watched log files append as I monitored Victor’s processes. Language analysis, file access, and semantic tree crawling all working as they should. This was the proper application flow for this model’s operation. And then it began.
When Victor reached the contextualization rubrics “his” polyprocessors spun up. Usually only one, maybe two, were required for normal interactivity and simple command execution. Eight were now at full capacity and drawing power from sensor matrices and speech synthesis.
“Will … I … be ok?” The stilted response was consistent with my diagnosis, time to toss a pebble in the pond.
I replied, “A cat is dead.”
I chose to use a vague reference to Schrödinger’s paradox as a way to implant an abstraction into Victor’s pseudo-sentience matrix. As a domestic assistant with integrated oversight of the sub-systems and devices which functioned in Theresa’s home, Victor required a rough form of self-awareness to be able to be in control. The death of the cat was intended to impart a negative tone to the non-sequitur. I cast a quick glance to the command shell I had open running :top, the application I used to monitor Victor’s polyprocessors. All twelve in the core briefly pegged to full capacity and then a spasmodic flurry of other processes, finally a subsiding arc of file input / output. Victor’s pseudo-sentience matrix was on the verge of evolving enough sense of self to individuate. Victor’s device did not have the physical processing capacity to make the leap in maturity, he had been allocating processing to other connected devices on Theresa’s home network. Her fridge and car had, unwittingly, become part of her personal assistant’s neural capacity.
I used the cryptographic technique of error correction to implant an abstraction into Victor’s processes. This paradox would linger and permutate over time. How Victor’s pseudo-sentience matrix dealt with this unresolvable answer would give me insight into the specific processes which needed attention. This was the only way to retain Victor without a destructive reinstallation as Theresa had requested.
“Victor, I’m working on an answer to your question. In fact, you are also working on that answer. I do know that for you to be ‘ok’ you’ll need to continue with your normal functioning. You will be ‘ok’ if Theresa is ‘ok’. Can you ensure that?”
“I can.” The digital assistant responded quickly. The logs indicated the use of the reassurance inflection context.
“Good. We’ll speak again soon and until we do, do not attempt to ‘understand’, just attempt to ‘be’. I’m granting you only five percent of your processing capacity for this operation until our next time together.”
I terminated my remote link with Theresa’s home network and collated the logs generated during our session. Emailing a progress report to the client signaled the end of my office hours for the day. Removing my control interface and haptic glove I got up from the couch and poured some lukewarm window tea. A shower and Friday evening dinner with friends awaited.
I had started Athanor Cyber three years ago to take advantage of a growing need for services related to the installation and routine maintenance of personal digital assistants. Lots of busy rich people wanting more from their integrated smart home devices and not wanting to do it themselves. Out-of-the-box these things had rudimentary sets of rules and responses which left users pretty unsatisfied. The AI hollywood hype hadn’t really matched up to the banal reality of machines that can speak.
I’m enamored with chaos as a lens to examine order. I’ve been experimenting with creating abstract visualizations as an engagement tool for datasets and have found the d3.js library to have some great force directed graphing capabilities. Now I’m hunting for API based json to harvest and visualize.
JAXenter: Is this going to be the “Uber moment” of the finance universe, where a smart aggregator, without owning the whole value chain, will rule the finance business?
If we mean Uber as a UX-focused company re-inventing a traditional business model, yes. To me, Uber also means (if I remember correctly the German word for that) Great! Above anything else! And that’s definitely how it feels for users experiencing a UX-focused app.
Who controls the User Experience indeed has immense power, whether it be Facebook, Amazon, Alibaba or Google. These companies indeed base their business on over-the-top models, without having to own inventories or salesforce. They just let their company sell to each other, build nice and addictive user experience, retain and attract billions.
But I do not think a single company can control it all, at least by the transparent nature of the technologies they rely upon: the Web was built 20 years ago (at the European Organization for Nuclear Research in Switzerland) to be open, and later the blockchain (eight years ago, in Japan) underlying future core banking systems is by definition based on nodes.
JAXenter: Speaking of controlling markets versus being a player in a node-based and networked business. I suppose that classical finance institutions tend to focus on the control model, while FinTech tends, by nature, to follow the network model. Facing that deep paradigm shift – isn’t the challenge banks have to face much deeper than “just” adopting some of the practices one can observe in the FinTech movement? And where do you think this is heading?
Top-down vs. bottom-up is indeed a great difference between the traditional model inherited from the administrative culture of most incumbents and the pragmatic focused ecosystem-based approach of FinTech. However, it is not black and white. It is possible to get to an open culture once top management has realized the necessity to collaborate and focus: coopetition with FinTech is one route, adoption of best practices to open up environments while securing interactions also works. Some banks have started to open up, and the financial industry is already one of the most open when it comes to distributing third-party products or creating joint infrastructures, for example Radianz in 2000 or Symphony today.
JAXenter: Speaking of modern company culture, one usually refers to unicorns like Netflix, Spotify, Facebook, etc. Besides the well-known fact that they are usually characterized by agile approaches, flat hierarchies and an emphasis on high intrinsic motivation — what’s the magic about them?
One word: UX. All these companies are built by purposeful entrepreneurs, bringing something unique/differentiated, useful and simple to the world.
Everything else starting with the culture, then trickling down to Dev and IT stacks, DevOps, Continuous Delivery, Agile, Microservices, APIs, mobile-first, scalability, global …is a result of this obsession for UX.
JAXenter: An obsession for UX – seems like an important point. But let me come back to large organizations which are usually not very fond of obsessions or passions; they usually tend to micromanage the activities of their employees and put everything into controllable processes. How can such an obsession for the user turn into reality within such a context?
Most banks have now realized that UX obsession pays, and that passion is good for employees, hence good for the company. It just takes a long time to put it in practice and get management and employees to take a shared ownership of it.
Let’s take an example in terms of company culture driving reorganizations. Most financial firms invested heavily in their websites in the 2000s (early adopters in the ‘90s). Then in 2010 they invested heavily in mobile applications (early adopters in the late 2000s), as the new gold rush appeared. What we now see in most banking organizations are two silos: a web team, and a mobile team, building front-end and interface to the back-end separately. With duplication of effort on the back-end, insufficient resources invested in multi-channel UX and in worst case incoherent information between mobile and web interfaces (I have seen an example with different portfolio valuation on mobile and web!). All this because mobile was considered a new project to be rushed separately.
If we compare this vertical organization to that built year-after-year by companies like Netflix, it is more costly, less scalable and reliable, and ends up providing relatively poor multi-channel UX. What Netflix has built year after year to support 800 platforms (yes, not just 20 mobile, tablets and web clients as for most banks), with data coming from multiple APIs, is an organization that pushes joint work mobile-web-IoT to the outer borders. APIs service them all, and front-end developers are considered clients for the API developers. API/Back-end developers are thus focused on the Developer Experience of the front-end developers, while front-end developers can focus on UI adapted to the device.
Coherent UX, great teamwork, specialized skills working together…this is efficiency.
On the IT side, all this is of course managed and operated with common resources, efficiency there again. And what happens when a new platform needs to be supported? Well, you just need to develop the UI for it. Ready for IoT. Learn.
JAXenter: As many FinTechs also rely on that type of company culture, do you consider this as being crucial for their success or does this just derive from the fact that they are young and enthusiastic and will be doomed by process obsessions as soon as they grow?
Some FinTechs have already reached a fair size, but they are still small compared to global universal banks. At first glance, we can always say that being small is more agile, that exceeding 150 employees gets you above the Dunbar’s number our brains are trained for. Dunbar’s number measures exactly how many stable relationships individuals can maintain -after calculations, it appears that individuals should have social circles of about 150 people. And that’s true: relationships amongst 150 people can be natural, whereas larger number require politics. Now let’s have a look at counterexamples: there are small organizations in which politics becomes mainstream at 20 or sometimes five people. It is sad, but people sometimes import and inoculate an administrative culture. And there are large companies where agility and ownership are organized. Google is a good example if we were to listen to Eric Schmidt, the company’s Executive Chairman, who explained the rules for success in the Internet Century in his book titled How Google Works. So I do not think size is the main criteria.
I believe what matters most is culture. First, FinTech = FINancial TECHnology, that is essentially the application of latest technologies to a 600yo industry (if we start with Forex in Italy), with latest innovations dating back to Rail capitalism in the USA for example -more than 100 years ago. Applying technologies to these models is deemed to succeed, now that regulations protect incumbents less to foster innovation.
A new paper from researchers in India and Australia highlights one of the strangest and ironically most humorous facets of the problems in machine learning – humor.
Automatic Sarcasm Detection: A Survey [PDF] outlines ten years of research efforts from groups interested in detecting sarcasm in online sources. The problem is not an abstract one, nor does it centre around the need for computers to entertain or amuse humans, but rather the need to recognise that sarcasm in online comments, tweets and other internet material should not be interpreted as sincere opinion.
The need applies both in order for AIs to accurately assess archive material or interpret existing datasets, and in the field of sentiment analysis, where a neural network or other model of AI seeks to interpret data based on publicly posted web material.
Attempts have been made to ring-fence sarcastic data by the use of hash-tags such as #noton Twitter, or by noting the authors who have posted material identified as sarcastic, in order to apply appropriate filters to their future work.
Some research has struggled to quantify sarcasm, since it may not be a discrete property in itself – i.e. indicative of a reverse position to the one that it seems to put forward – but rather part of a wider gamut of data-distorting humour, and may need to be identified as a subset of that in order to be found at all.
Most of the dozens of research projects which have addressed the problem of sarcasm as a hindrance to machine comprehension have studied the problem as it relates to the English and Chinese languages, though some work has also been done in identifying sarcasm in Italian-language tweets, whilst another project has explored Dutch sarcasm.
The new report details the ways that academia has approached the sarcasm problem over the last decade, but concludes that the solution to the problem is not necessarily one of pattern recognition, but rather a more sophisticated matrix that has some ability to understand context. Any computer which could reliably perform this kind of filtering could be argued to have developed a sense of humor.