Engagement Placeholder Content
“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 of artificial’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 arrays 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 psudeo-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 sentience matrix was on the verge of evolving enough sense of self to individualize. 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 psudeo-sentience matrix dealt with this un-resolvable 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.
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.
UX is the key to Next Gen Banking
Chris Bacigalupo : February 12, 2016 11:08 am : Engagemnet, Technology
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.
SEE ALSO: Sneak peek into the future of banking from the founding member of two high-profile companies: Part 1
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.
Why sarcasm is such a problem in artificial intelligence
Chris Bacigalupo : February 11, 2016 2:44 pm : Engagemnet, Technology
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.
Non-Human Face of Everything of Things
Chris Bacigalupo : December 16, 2015 9:39 am : Engagemnet, Helping, Technology
Wow, ok. So I guess there’s a School of Poetic Computation. That in itself is a mindblower, but the “Face” project is an awesome example of “Ghost in the Machine.” Nutshell is that someone used comparative analysis of the instagram image stream #facesinthings to create an “average face” using images of inanimate objects. So when you look at some tree bark and you swear there’s a face there or the “eyes” of a reel to reel tape recorder. Then they created a combined image of what they looked like.
#FacesInThings blended images will arrive at an average of a human face after about 15 images. –Zach Lieberman, Brian Solon, Daniel Shiffman
Do we recreate ourselves in the objects we design? Physically, emotionally, spiritually? If “we” do, then when we pull a face from the crowd of “inanimacy”, is the resulting image a reflection? This helps me feel like even from somewhere deep, humans can reach out/in and find themselves.