artificial intelligence

BIG TECH: YouTube, Facebook and NVIDIA powering hyper-realistic human avatars

The digitization of the human animal continues unopposed, with symptoms all over. Chinese firm Megvii, maker of software Face++ that has catalyzed 5,000 arrests since 2016 by the Ministry of Public Security, is looking for an $800 million IPO. The other champion of public/private surveillance, Facebook, is working a virtual reality angle. The company is improving the technology used to model rendered avatars of human faces, which can then be displayed across virtual environments. Using multi-camera rigs and hours of facial movement footage, Facebook is building neural networks that learn how to translate realistic facial muscle movement into models. The Wired article linked below is worth exploring for the videos alone, and the uncannily realistic motion these animation possess.

One of our recurring points is that frontier technologies -- AI, AR/VR, blockchain, and IoT -- appear disparate now, but are intricately connected. Take for example the new feature from Google called YouTube Stories. Similar to SnapChat and Instagram, video creators can apply 3D augmented reality overlays to their faces. While this technology looks like virtual reality rendering, it is primarily a machine vision (i.e., AI) problem to anchor rendered objects to a human face realistically. To do this Google provides a developer library called ARCore, not to be confused with Apple's ARkit. Human video avatars can be further extended and customized with code -- the twenty first century version of personal branding.

Another take on the same issue comes from generative adversarial neural networks (GANs). We've discussed before how hyper-realistic images and videos can be faked by a model where one algorithm creates images and another accepts or rejects them as sufficiently realistic, with repeated evolutionary turns at this problem. Highlighted below is a recent software release from NVIDIA, where a drawing of simple shapes and lines is rendered by a GAN into what appears to be a hyper-realistic photo of a landscape. We can imagine a similar approach being applied to the output generated by Facebook's avatars, which still border on creepy, to ground the outcome in reality. Little details, like a reflection of a cloud on water, are hallucinated by GANs automatically, based on massive underlying visual data. Expect these digital worlds to become increasingly indistinguishable from reality, and to spend way more time living in them for the years to come.

c0552a77-9b22-4f32-8c09-36f2ca71f5ac.jpg
water.PNG

Source: SCMP (Face++), Wired (Facebook Avatars), NVIDIA (GAN drawing)

BIG TECH: The macro-scams of Fyre and Theranos & the micro-scams of Google and Facebook

Spoiler alert: Fyre Festival ended up being a securities fraud that cost investors $27 million dollars, left hundreds of workers unpaid and emotionally ravaged, and negligently put attendees in dangerous conditions. Even Blink-182 cancelled their performance! Another spoiler: Theranos ended up being a securities fraud costing investors (including Betsy DeVos!) $700 million, leaving hundreds of workers unpaid and pushing at least one to suicide, negligently putting users of the product in dangerous medical circumstances. In both cases, the founders were young and narcissistic, optimizing the story-telling about their company over delivering on the promised expectations. Billy McFarland used Instagram supermodels to sell a false vision. Elizabeth Holmes leveraged the Steve Jobs black turtleneck and VC group think to do the same.

This stuff is so easy in retrospect -- to point fingers and throw the stone. Having spent a lot of time in the early stage ecosystem, we can tell you that all founders have these devils inside them. These are the devils that let you take the risk, tell the story and defend your tribe (e.g., see Elon Musk). The issue is that these particular people could not and did not execute -- and any reasonable person in their situation would know enough to stop marketing and selling lies. We can look at crypto ICOs to date and say the same thing. Surely the people who raised over $30 billion globally, and burned nearly all of it, sold us a falsehood. Some -- like John McAfee or Brock Pierce -- had to know what was up. Or did they, perhaps believing in a zeitgeist change tilting the axis of human industry? 

The issue is asymmetric information and intent to profit from that asymmetry. When someone sells us a broken car claiming it works great, they are selling a "lemon" -- something the US protects against with "lemon laws" that remedy damages from relying on false claims. Let's shift from these obvious macro lemons, to the invisible micro lemons sold by Facebook and Google. It was revealed that Facebook was -- in the worst case -- paying 13+ year olds $20 per month to install a research app that scraped all their activity (from messages to emails to  web) and provided root permissions to the phone, misusing Apple-issued enterprise certificates. Facebook should not have been able to create these apps for anyone other than its employees on internal apps (e.g., bug testing new versions). But it did, and got its access revoked by Apple immediately.

Google did a version of this too, exchanging gift cards for spying on web traffic. As yet another example, Google's employees refused to help the company build a war-drone AI for the US Department of Defense. So instead, Google outsourced the work to Figure Eight (a human-in-the-machine company), hiring gig economy workers for as little as $1 per hour for micro-tasks like identifying images (teaching drones to see). These workers had no clue what they were doing -- and we imagine that some would exhibit the same ethical concerns that Google employees did in refusing the work. In all these tech company examples, the lemon is the un-revealed total cost. Compared to Fyre and Theranos, where we pay billions, and get nothing in return, here we are given $1 an hour or $20 per month (i.e., nothing), but we lose our privacy, agency and humanity (i.e., everything). 

cbef13a2-5fb2-44be-a753-14836463401b[1].jpg
441ca0eb-3a96-4cec-ab2b-5f6ab911ddec[1].jpg
b83d1247-1bc8-4dd7-b1d3-1d0cfab382c9[1].png
3a1235ce-7bd7-4f7e-bc74-1fd2b1b46a11[1].jpg

Source: Wired (Project Maven), The Intercept (Google project maven), Gizmodo (Google micro-tasks), TechCrunch (Facebook, Google, Apple), Wikipedia (FyreTheranosLemon Law)

INSURANCE: Porsche and Mile Auto to cut premiums 40% using AI for pay-per-mile insurance.

Insurance is the holy grail for Artificial Intelligence and the Internet of Things in finance, because it requires a messy interaction with the physical world, rather than living merely in a spreadsheet, database, or blockchain. To this end, we like the news of Porsche partnering with Mile Auto on pay-per-mile insurance. There is a reasonable demand-side argument: owners of Porsches don't drive the car as a primary automobile, and would prefer to only pay insurance for the time they are actually on the road. The second argument is even more fun -- owners of Porsches don't want to be tracked via GPS or a black-box by something like Cambridge Mobile Telematics ($500MM from Softbank) or Metromile ($90MM from VCs) because they are fancy and private people. No tracking please!

How does the thing work? You pay a cheap base rate to Mile Auto, and once in a while take a picture of the speedometer's reading in the app. The picture is translated to numbers via a machine vision algorithm, and your per-mile variable insurance rate is calculated on the spot. The company claims this will lead to a 40% reduction in premiums for the average user. For what it's worth, we hear that the growth of renter's insurer Lemonade is similarly fueled by people who are forced to get coverage (e.g., by the landlord) but are looking for the most discounted, easy to manage product. What does that mean? It means that the low risks self-select out of the insurance pool, driving up the price for unsophisticated non-techies that don't drive a Porsche.

Let's take the argument to an absurd extreme. On the developer website Programmable Web, there are 59 separate APIs that developers can use to build insurance apps and connect into underwriting engines and carrier capital. From Clearcover (affordable car insurance in your app!) to Haven Life (term life insurance on any website or application!) to Lemonade, OCBC Materntity, Qover and a plethora of others, developers have real choice in how to weave these more digital insurance products into the attention black holes in your phone. What happens when the tech-forward customer considers only these options, and the conservative customer considers only insurance sold by agents and direct mailing? Could there be a bifurcation of risk profiles that fundamentally injures the risk-pooling function of the industry? Perfect information about risk collapses the value of hedging. Half of us will know and live in a predicted future, while the other half will pay for the ignorance.

dc33b340-6e91-464e-ae0c-2e4cdb64883a[1].png
cb8107d5-26fd-4ee4-89d6-b66aac565087[1].png
5b9fd9a3-e19d-4096-bf5e-cea629568e49[1].png
a9eebfbd-05f1-4bc7-8e9e-29a0fea81f6e[1].png

Source: PR Newswire (Porsche), Company websites, Programmable Web (Insurance)

ARTIFICIAL INTELLIGENCE: "Financial Deadbeats" map is the worst things about Chinese Fintech

In our continued amazed gawking at the Chinese fintech landscape, we bring you the following. There is now a feature within WeChat, one of two channels for all mobile chat communication, to show a map of "financial deadbeats" around you. That's right -- a shaming visualization of people who are in financial trouble, like some sort of public sex offender list. We link to the article below, and assume that it is true despite how preposterous the whole thing seems. 

Offenses that could land you on the blacklist include serious ones like being the founder of a digital lender that collapsed with 12 million unpaid accounts, and trivial ones like being a single mother embroiled in a divorce proceeding. Once you are on the list, not only will your full name and financial information be public entertainment on this app, but access to credit, commerce and university admission could be revoked. To add insult to injury, a special ringback tone is added to the "discredited" person's mobile phone, alerting any potential caller about your poor financial management skills.

We add to this soup the idea of algorithmic bias exhibited by AI based on training data. We've covered this issue in the past, but point to Rep. Alexandria Ocasio-Cortez (D-NY) recently bringing it up into mainstream conversation. From propaganda bots to algo-racism, these arcane issues are starting to concern the broader Western polity. So when you combine historical training data reflecting past social and economic biases with social media enforcement systems, dystopia calls. One of the most important financial innovations in the West was bankruptcy, allowing entrepreneurs to fail and start over. This normalization of financial wipe-out led to an equilibrium with higher risk-taking and innovation. It is chilling to see technology being used, with potential for error and misuse, to stifle that spirit. Based on the US personal bankruptcy data below, you can see that 6 out of 1000 people would be guilty according to WeChat, skewed in large part to minority populations. No thanks. 

59ca575a-39a6-4f36-a60c-89c9ccfd9a01[1].jpg
f1102030-8e90-4366-8bcf-31e14f4b774b[1].gif
0f2b8c5a-2d85-479c-9ab1-239e47436d17[1].png

Source: Abacus News (deadbeat map), Independent (deadbeats), Vox (algo-racism), On bankruptcy normalization and bankruptcy zip codes

ARTIFICIAL INTELLIGENCE: Evolution of Creative AI and WeChat's Payment Score

One ongoing, false refrain is that machine learning does not generate creative outcomes. Increasingly, this is proven wrong by the technologists and artists playing with the technology. What started several years ago as "neural style transfer" (i.e., transferring Picasso's visual DNA to any photo) has moved on to BigGAN, which is a machine learning algorithm to manufacture images that appear realistic but are made from machine hallucination. Notably, artists are playing not just with the realistic versions of these hallucinations, which you can see below, but with the "latent space" in between. This mathematical term for interpolation is filled with abstract, surprising, and surreal outcomes. Our takeaway from these results is both (1) that machines will be far more precise in understanding and approximating humans than we assume, and (2) that machines will be far better at creativity that we assume.

Fitting a financial product to a ranked "perception" of a human being matters -- especially when it is done at a scale of a billion people. Tencent's WeChat is running a new initiative called "WeChat Pay Score", which is analogous to the Alipay's "Sesame Credit", both of which (we expect) flow to the Chinese government to make up the national social credit score. Sesame Credit looks at 5 dimensions: safety, wealth, social, compliance, and consumption from over 3,000 specific data points collected by the app. The WeChat version is collecting data on how users chat on the messenger, what they read and buy, where they travel, and how they run their life in general. These combined attributes grant access to perks, like waiving bank account minimums.

Listen, in a massive nation where a large swath of the population doesn't have traditional financial data or bank accounts, machine-learning based estimates of credit-worthiness are a life saver. Not every economy comes with a FICO score and legacy credit agencies (though the Equifax breach wasn't particularly kind to incumbents).  But they key question comes back to the two picture sets below. Do the machines see us like those perfectly generated, accurate pictures of people? Or like the surreal goo in abstraction? The former means distributed access to well-suited financial products, while the other is a Black Mirror nightmare.

8779262f-e84c-4f5f-b44d-760b434eb989[1].jpeg
7c2e985e-bd99-4c14-9acc-4524618f4782[1].png
17bad1d6-62d4-495d-ba05-3dfbed2985d7[1].jpg

Source: Medium (GANs), Joel Simon (GANreeder), TechCrunch (WeChat)

INSURTECH: Rage Against the Machine and $500MM telematics Softbank investment

Let's start off with the ridiculous, and get more ridiculous. SoftBank has a lot of money to invest in category killing fintech businesses, and one of the latest such players is Cambridge Mobile Telematics, which just received $500 million from the investor. What is it? A widget attached to a car windshield, and then used to collect data about the quality of a particular driver -- from speeding to breaking. This data is then tied to the purchasing of insurance, where "good" drivers have access to lower cost financial products. This is an interesting, and pioneeing, example of how edge computing will create orders of magnitudes more digital data that then feeds the manufacturing of finance. 

A sneaking suspicion in the back of our minds is that driving data is really good for training robots how to drive. Meaning, Google and the rest of the big tech companies are all running experiments with self-driving cars on the road to collect driving data. Something simple from a telematics device certainly is not equivalent to major machine vision and radar data. But it does paint a straight line towards how self-driving car insurance should be priced. Let's repeat that. If a widget in a car tells you insurance prices based on driving performance and you combine that with an AI car, you could compare humans and machines on an apples to apples basis.

The ridiculous part is the human response to tech-first transportation companies. In London, Chinese bike-sharing company Ofo is pulling out of the city because people steal and destroy their untethered bikes. In California, aspiring freedom fighters keep throwing scooters from Bird and Lime into oceans, lakes and rivers. Public service employees are straining to fish out these venture capital funded wonders out of the water. In Phoenix, self-driving Waymo cars are getting their tires slashed and assaulted by gun-wielding road-ragers (Mad Max style, we assume). All that to say that the human element in this story is allergic to being entirely prodded, measured, and automated away. Can politics catch up with SoftBank's Vision Fund, which could build Trump's wall 20 times over? We hope so.

d2233e7a-7e6d-4bc2-811d-f7328d39763e[1].jpg
d68567b1-b834-4a08-b05f-7ad055f5a3d6[1].jpeg

Source: DigIn (Softbank), Gizmodo (Ofo), Slate (Bird), Business Insider (Waymo)

2019 FINTECH PREDICTION: Government and Enterprise Platforming, led by AI and Mixed Reality

Source: Images from Pexels,     2019 Keystone Predictions Deck

Source: Images from Pexels, 2019 Keystone Predictions Deck

Over the last decade, consumer tech has undergone a cycle of platform building, user aggregation, data mining, and value extraction, resulting in GAFA monopolies. Exhaustion with Facebook and the adjacent issues of privacy and radicalization, in our view, will lead to problems building new splintered consumer attention platforms for AI, AR/VR and other new media ground up.  This implies that consumer platforms based on new technologies will be much more long-tail oriented, serving niche markets with very strong fit. Communities may be passionate, but smaller.

Enterprise tech lags retail adoption by, give or take, 5 years. Similar platforming has not fully penetrated on the enterprise side -- Salesforce is not yet the AI monopoly we should all fear, and Open Banking is barely a fizzle. Therefore, we expect increasing data transparency, aggregation and monetization to occur in enterprise underwritten by venture capital investors. As an example, augmented reality adoption and economics will be driven primarily by municipalities, utilities, large industrial manufacturers, and the military. Similarly, artificial intelligence at scale (and its meeker cousin Robotic Process Automation) are to be directed largely at the workflows and manufacturing processes of large corporates. Dont' get us wrong -- consumer AI is extremely important -- but within Financial Services, the scope for this in the corporate world is even larger.

The corollary is that the pricing pressure that started in consumer Fintech -- roboadvice (150 bps to 25 bps) or in remittance (600 bps to 10 bps) -- will spill over into B2B banking, money movement, insurance, treasury management and product manufacturing. An inevitable outcome is pressure on profit margins as prices equilibriate. For those companies that are able to re-design operations using a digital chassis, they will be able to compete on the margin with Fintech unicorns. Those that are not should exit, or retreat into more bespoke, relationship-driven business lines. 

ARTIFICIAL INTELLIGENCE: Morgan Stanley, Yext and Chinese AI-first Apps.

A point is not enough. It takes two points to make a trend-line, at least in a two dimensional space. One of the muscles we try to flex often is to connect points in different sectors and themes to see the limits of the possible. Let's contrast the following: (1) Morgan Stanley partnering with Yext for financial advisor business pages, and (2) Andreessen Horowitz' commentary on Chinese consumer artificial intelligence applications on a path to capture the hearts of teenagers everywhere. Disparate, funky, and painfully obvious.

About ten years ago, "hyper-local" became a venture catchphrase. News would go from being general to local, video would go from main-stream to niche, and so on, contextualized by the GPS in our pockets. Yext is a company that won one of the battles for hyper-local content by building the retail knowledge graph that gets printed on Google Maps. Simply, if you see a business listing for a laundromat on your Maps app, likely the app provider is licensing local data from Yext. This data then scales up into pre-made business websites, analytics, and customer funnel conversion. Morgan Stanley inked a partnership with this scale content manager to give their 15,000 financial advisors a digital presence. Controlling and printing out that content at scale, with embedded compliance and into every Google/Apple phone, is hard and smart. And perhaps physical presence is the main value of a human advisor.

Now for Chinese AI. Unlike Americans, with their hand-wringing about privacy, choice, and human agency, Chinese apps don't care. The next generation version of Instagram and Snapchat is called TikTok, and the storied venture firm Andreessen celebrates them for taking away any human choice in what content a user would see. The algorithm is not a search support tool, it is the only and ultimate arbiter of where your attention goes. And it tends to make kids happy (unlike Youtube, which generally makes them into Twitter trolls). 

So let's mesh these things together. A financial services version of TikTok with a Yext overlay would be an app that is tied to the physical world, perhaps through Augmented Reality or just simple Maps, that would decide for you which financial provider to find. It would know that you still want to talk to a person for that emotional connection, and would find one that's closest geographically and a best-fit emotionally -- a two factor optimization problem for an AI. Yext financial advisor reviews, combined with a Morgan Stanley risk/behavioral client questionnaire could do this. Thus the TikTok aspect kicks in, with the human in the loop simply being a form of physical content marketing, gaming the algorithm with a meatspace presence. 

98edf5ec-17a0-419f-95db-a4e668a4d7e5[1].jpg
3c004f7a-4647-426c-9c4d-95eb75713c85[1].png

Source: Finextra (Yext), Andreessen Horowitz (AI apps), FactorDaily (App downloads), 

ARTIFICIAL INTELLIGENCE: Apple trying to catch up in conversational interfaces through privacy

Apple acquired Silk Labs, an AI startup with significant tech pedigree, whose tagline is to "embed instant cognition into your next product". We have to respect the science fiction marketing, of course. But we also respect that the machine learning solutions from this company allow machine vision, sound recognition and natural language processing to be done locally on a particular device. That means that a specific device that you use for conversational interface interaction will be locally better at understanding you -- rather than some giant squid-like monster AI hosted on Amazon Web Services. And of all the tech companies, Apple is the most credible in its claim to protect your privacy on the iPhone, with such an acquisition potentially powering other edge-computing / Internet of Things products.

Edge computing is the concept that there are lots of unique distributed smart devices scattered throughout our physical world, each needing to communicate with other humans and devices. Two layers of this are very familiar to us: (1) the phone and (2) the home. Apple has become a laggard in artificial intelligence -- behind Google on the phone, and behind Amazon and Google at home -- over the last several years. Further, when looking at core machine learning research, Facebook and Google lead the way. Google's assistant is the smartest and most adaptable, leveraging the company's expertise in search intent to divine meaning. Amazon's Alexa has a lead in physical presence, and thus customer development, as well as its attachment to voice commerce. Facebook is expert in vision and speech, owning the content channels for both (e.g., Instagram, Messenger). We also see (3) the car as developing warzone for tech company gadgets.

Looking back at financial services, it's hard to find a large financial technology provider -- save for maybe IBM -- that can compete for human attention or precision of conversation with the big tech firms (not to mention the Chinese techs). We do see many interesting symptoms, previously covered in our Augmented Finance analysis, like AllianceBernstein building an AI-based virtual assistant for bond traders, but barely any compete for a relationship with a human being in their regular life. The US is fertile ground for this stuff, because a regulated moat protects financial data from the tech companies. Is there room for a physical hardware financial assistant in your home? How much of your financial life would you delegate to some*thing* that decides how you should live it?

368895aa-acb4-4856-87e2-119bef79f727[3].png
073ac488-611b-44c4-86ff-67641d64844a[1].png
29e979c0-e59a-44a5-b7b0-318bc1c10945[1].jpg

Source: Silk Labs, Bloomberg (Bond Bot), TechCrunch (Google to be nicer if you say please), Autonomous NEXT (Augmented Finance), USA Today (car AI), Voicebot (Install Base)

ARTIFICIAL INTELLIGENCE: Facebook's Lasso clones Asia's TikTok to grab 500 million users

ByteDance is a $75 billion AI-powered Chinese attention gathering machine. Their marquee application TikTok -- a frankenstein formed from the combination of Vine-like videos and the acquisition of Musical.ly -- boasts 500 million users, and is currently ranked the #6 free app in China and #7 in the US. That position is ahead of Facebook (surely angering comic book supervillain Mark Zuckerberg), Snapchat and Messenger, having achieved this result in mere months since launch in the US market this past July.

TikTok engages teenagers with personalized content driven by ByteDance’s proprietary machine learning algorithms, emoji video commentary features, Snapchat-like augmented reality renders, and glitchy filters. Creators on the platform have the chance to make viral content, which is distributed at scale and mass-targeted at consumers by a machine. Using AI this way is a growing strength for Chinese companies. It is also a strength of recently beleaguered Facebook, which is fighting back by launching a clone called Lasso. The app features nearly identical gesture features, structures, endless content feeds, and hashtag groupings for browsing. The main differences lie in (1) video creation, where TikTok offers up to 60 second videos compared to Lasso’s 15 seconds, and (2) TikTok’s ability to customize content using filters, music, and lenses, which far outweigh the limited selection of Facebook's Lasso.
 
Two conclusions of note. First, Facebook has defended their turf before, and succeeded. For photos, it outright bought Instagram. For video stories, it failed at buying Snap but succeeded at building the feature into Instagram. For messaging, it bought Whatsapp and built Messenger. We wouldn't count it out in this case either. Second, these attention companies exist to deliver advertising and form consumer preference functions. In China, data about customer preferences already informs access to financial services, such as credit, payments and investing. In the US, increasingly Facebook is seen as a conduit for opinion manufacturing to the highest advertising bidder, with such data still a step away from being included in a financial underwriting decision. Yet as tech solutions and norms are exported between global jurisdictions, we expect that line to increasingly bend.

bc46086e-b155-4367-8e38-38e966cd99f6[1].jpg
4d8a5b9e-11da-4112-97fd-cb84ec0a8f6a[1].jpg

Source: TikTok, Lasso, Slate on Facebook's Recent Allegations

ARTIFICIAL INTELLIGENCE: Self-driving cars and self-speaking news anchors inch us closer to dystopia.

Financial services regulators have been so hard on crypto currencies, roboadvisors, digital lenders and payments companies. It's as if that money is a life or death situation! But getting a permit to drive a robot car on a public road without a human being holding the wheel -- not a problem in California. Waymo, which is the Google car spinout, has been given the green light to put 40 autonomous cars on the road. This is already happening in Arizona, with 400 users that can get into a robot car via an app around Phoenix.

3dc3f300-8696-4d51-ae39-a4581f08f17d[1].jpg

We don't want to be alarmist, of course. Statistically, these machines are likely much better than humans at driving -- they are just more likely to make mistakes that humans would think are preventable. The same process took place in regards to machine vision, with early prototypes making classification mistakes between cats and dogs; now, such algorithms can tell apart the difference between hundreds of breeds. So we hope to see similar progress as driving and visual data is incorporated into autonomous car systems. We'd be remiss not to mention our white paper on the topic, which models out how the insurance industry may lose its lunch when cars don't crash. On the other hand, we note that the DMV required a $5 million bond to put a self-driving car on the road, so the risk is still wildly unknown.

fd5430d6-f02d-4862-8d0e-321cf43a30af[1].png

In a more sinister move, China's state-owned news agency recently launched "composite anchors", which is a machine vision version of a news anchor that can be manipulated with text. Here's how it works. You shoot dozens of hours of video of a person speaking, and then spin up neural networks that can (1) manufacture sounds similar to the target's speech and (2) manufacture video resembling the human making that speech. Presto -- just type in whatever into a command box, and your generated anchor will say it, in any language you would like. Given the recent video editing experiments that the White House supported in relation to denouncing a journalist, we are acutely terrified of how this can impact the attention economy. Not to mention the implications for selling a human likeness for endless manipulation. 

ad628a3f-c9da-4f6b-8136-24ca748f5c44[1].png

Source: SF Chronical (Waymo), South China Morning Post (AI Anchor)

ARTIFICIAL INTELLIGENCE: From BMO's Chatbot to a full virtual avatar

Let's paint the progression. Finn.ai, a chatbot company that integrates banking services into Facebook Messenger and other chat channels, launches Bolt for the Bank of Montreal. The project took 10 months to private label and deploy to BMO's 12 million customers, covering 250 questions at launch. As the app gathers more information about what customers ask, its usefulness grows and it becomes an increasingly relevant channel for customers to ask their financial institution informational questions.

bd55987e-762a-40c2-97ba-066f2fd58f5f[1].png

By the end of 2017, 40 million smart speakers were installed worldwide, with 2018 projected to land at a 100 million install base. People are getting more than one instantiated smart assistant -- littering their home with several Echo dots. And, reportedly, Alexa lives in 3,000 others types of smart-home devices -- giving this bot army 45,000 skills, from Spotify to financial conversations. Google and Apple are working to catch up to these numbers, releasing eerily realistic robo-conversationalists like Google Duplex that can answer telemarketing calls and book hair salon appointments. And maybe cancel your financial subscriptions. What's perhaps most important is that an answer to a voice query is 40-80% fulfilled by the "featured snippet" at the top of a search engine's list, according to Martech. That means no more long tail of any kind, full stop.

c12bc0cc-7c56-4e78-93a0-656a2d02b04d[1].png

Eventually, it is no longer enough for our avatars to be disembodied functions powered by a retail product recommendation engine. From virtual worlds and into augmented reality, agents will take on hyper-realistic rendered physical bodies. A recent Intel whitepaper describes how machine learning is now being combined with a modeling of animal and human bodies under a physics simulator to quickly and realistically build CGI for games and movies. Instead of an artist using intuition to draw the perfect frame, machines build skeletons with physical properties, connect bones with digital ligaments, fire up virtual muscles, and package the final version in the species of choice. Machine learning algorithms add realistic, generalized movement to the equation. They can also add speech, appearance and function. And maybe even help the UBS chief economist look a little bit more lifelike!

f0b0778c-0de0-4ee2-8466-25dc54c01e18[1].png

Source: Finn.ai BMO case study, Fast Company (Amazon AI), The Atlantic (Smart Speakers), Martech Today (40% answers from featured snippet), Intel (Rendering CGI), Financial Review (UBS CIO)

ARTIFICIAL INTELLIGENCE: Explaining Black Box Algorithms to Avoid Discrimination

Speaking of Amazon, news broke that the company had built out an AI-based recruiting tool that was supposed to help it rank candidates at scale. They certainly are not the only ones -- the tech startup space is littered with applicant management and analysis software, especially given that employees have many more jobs on average than in prior decades. What this AI did, however, was systematically discriminate against women, down-weighting resumes that included the phrase "women's" in descriptions or candidates that came from all female colleges. This result came unintentionally from the underlying data. If you correlate the language in thousands of employee resumes, you will get the status quo, which is that on average the Amazon employee, or any tech employee, is more likely to be male. Another artifact that mattered is the way candidates used language itself, which can be gendered in output.

Other examples of unethical AI are plenty. For example, image recognition algorithms make an error of 3% for white male faces but 30% on black females faces. Or, when used in automating sentencing criminals in the US, algorithms punish minorities more harshly. Or, when underwriting credit, AI disfavors historically disadvantaged protected classes using Zipcode. But the math isn't wrong -- it is in fact painfully correct. These outcomes are a mirror to how things are, not a solution for how we want things to be. Yet AI will be used regardless. Just this week, Lloyds adopted speech to text passwords for telephone banking, replacing pins with the sound of a customer's voice. Will this service work better for majorities and not minorities? Further, such security can be gamed using pre-recordings, or generated voices. Similarly, image recognition can be gamed with photos or by twins.

This is why we are excited to see two initiatives make the news. The first comes from the MIT Lincoln Lab, focused on machine vision. The software builds a visualization based on how a neural network sees an object, highlighting which parts and features of the object drive a particular decision. The picture below shows how the computer detects "large metal cylinders", first looking for size, then for materials and finally for shape -- each  highlighted by importance-ranking heatmaps. The second comes from IBM, called the Trust and Transparency service. In an example around insurance claims automation, the company shows an explanatory overlay on the AI that points out the probabilistic weightings for different drivers of an approval/rejection decision. A human analyst can then understand why the machine made its judgment. We think such tools will be required for any serious AI company.

b2f8d655-c7ea-442c-90df-5bfc871ee4a7[1].png
ff92aa6a-9ef6-401b-9be8-0c109374cb03[1].png
f3a0f7f4-6446-4a01-93b4-5747f9d21af8[1].png

Source: Reuters (Amazon), IBMMIT Media Lab, Business Insider (Amazon), FS Tech (Lloyds)

ARTIFICIAL INTELLIGENCE: Paying by Smile with Alibaba or by Blinking with Ping An

c8cfca7d-6522-4213-90ee-01c0c27fcb3d[1].png

Chinese commerce is very digital already, far outpacing the US in both nominal and percentage terms. Since almost no mobile payments in 2011, China now sees almost 100 trillion yuan, or $14 trillion USD, in mobile payment transaction volume. This compares to less than $100 billion in the United States -- a 10x difference in adoption of using phones, rather than cards or cash, to pay for things. Further, unlike in the West, the vector of payments intersects much more closely with social identity and networking, which is the platform globally for developing artificial intelligence. Just check your Facebook Newsfeed.

So we give to you implementations of AI for payments in the East. The first is from Alibaba. If the customer has Alipay's app and has enabled facial recognition, a smart vending machine is able to scan your face and associate it with the payment account. We would guess that there is a geolocation element involved as well for two factor authentication, or perhaps just a phone or pin verification. The second example is the newly launched Ping An partnership with Danyang Rural Commercial Bank. The plan is to use facial recognition combined with "blink detection" to authorize a payment. The Bank claims to target 1,000 merchants for the initial pilot of the program. Reminder -- Ping An has built out machine vision capabilities to cut down on time processing insurance claims, and here it is trying to rent it out as a cloud service to other providers.

We end with a few questions. First, if Ping An was able to stand up real machine vision capabilities within a couple of years, what's stopping Visa or Mastercard or JP Morgan from building the same? Why have American finance firms failed to own the AI technology layer and its associated cloud? We think the answer has to do with the role of enterprise tech firms and implementation consultants in the US, which make the default option to out-source rather than in-source such capability. Why build, when you get this from Google for free as part of a cloud deployment? And second, we observe that massive data processing and hosting infrastructure is needed to accurately process image recognition on video for millions of people in real time. Likely, you also need high definition images to pick up blinks and smiles. So let's refresh that 5G network!

cd0fc4bd-4bb9-4a36-a86b-106b613549e3[1].png
5d254a84-b240-4a28-a8ef-dce0451d336a[1].jpg

Source: Walk the Chat (Charts), Fung Global Retail & Tech (Chart), TechCrunch (Alibaba), MPayPass via CrowdFundInsider (Ping An)

ARTIFICIAL INTELLIGENCE: AI Struggles in Enterprise, Because of Human Frustration.

08121e34-8e99-4962-8a8f-e1cb0e6514b1[1].jpg

The American and Chinese tech giants are racing to patent machine learning algorithms. While so far, much of the work has ended up in academic communities and has been open sourced, as larger revenues start to become associated with AI deployments, the fangs of the FAANGs will come out. Further, when we look at Asia vs. American patent deployments, which we did in our Augmented Finance analysis, Asian patents start to outpace those of the West. So is it all worth it? When the tech hits enterprise deployments however, we hit a snag. See below two reports on the topic.

In the first, Bain points to slow adoption of AI and robotic process automation in corporate finance departments, due to very human factors -- there are too many tools, they tools are not integrated, they make too many errors, and user interfaces are hard to understand. This is in part due to how AI is sold and deployed to local environments through a bespoke consulting model. A better approach is an API integration into workflows behind the scenes as a service.

bc78dae9-a3ba-41fe-ac1d-88e1a498c051[1].gif

The second report, from O'Reilly, shows the state of adoption in enterprise of machine learning (ML) and associated headcount. We highlight the chart breaking out adoption by stage and geography: (a) companies exploring the technology having implemented anything yet but at least thinking about it; (b)  early adopters have been working with a system for at least 2 years; and (c) sophisticated users have had something in place for 5 years. Though the sample size of 10,000+ is quite large, we are surprised to see Western countries lead Asia in enterprise ML adoption. Perhaps the difference lies in whether the software faces into the corporation to make it more cost effective, or whether the software faces the public and acts as the interface. We need to look no further than a dystopian NYTimes article describing the current state of machine vision monitoring of Chinese citizens by various Skynet companies to see that Western society is quite far behind. Perhaps thankfully!

03f05d9e-114d-4418-9f2e-bbbb7bdffaff[1].png
88198abc-c9a3-4c57-8b28-bd2597119345[1].jpg

ARTIFICIAL INTELLIGENCE: UBS Chief Investment Officer now a Video Game AI

File this one under -- "They'll never automate my job, oh wait". We've got three delicious data points for you. The first is Google's voice generation platform called Google Duplex. We're sure you've seen the demos by now (if not see the source below), so we'll merely place this into context. Google's virtual assistant has an experimental new feature that can be your agent by calling restaurants and other small business and booking appointments. Google has the map of all SME data, their hours and phone numbers, can generate and route call, and now makes a robot that sounds eerily human as well. The virtual agent comes with "ehmms", "umms" and lip smacking in its voice generation algorithm, to the point where the clerk really has no idea they are speaking with a machine that's doing busy work. Neural networks are getting really really realistic with speech.

Second, remember Alibaba, the Chinese version of Amazon plus eBay plus all of Facebook and JP Morgan in one, give or take. One of the requirements of the platform is to enable merchants to advertise and sell goods to consumers. But the scale of the selling is beyond human management -- with some days seeing $25 billion in revenue. So the firm has launched an AI written copy generator which can manufacture description of products based on the millions of data points the firm already has on prior commerce. Yep, just casually writing 20,000 lines of proposed description, in styles ranging from "promotional, functional, fun, poetic or heartwarming.” The company claims this tool is now used on average 1,000,000 times a day. 

Last data point, which picks up nicely from several observations we made prior about HSBC using Pepper robots in branches and other physical/digital interactions. UBS is launching something fresh in Switzerland. The first is a cute virtual assistant animated object that will be able to help people do basic account actions in physical branches. It looks to us like a Siri or Cortana attempt, but for finance troubleshooting. The second is an animated 3D rendering of the firm's Chief Investment Officer, to be displayed on a screen while visiting a private banker. This AI CIO will be able to answer more complex questions in real time about markets and investing, and has been developed by IBM and FaceMe. We'll let you connect the dots.

e354076c-ecd2-473e-abae-b776886780af[1].jpg
4cd53b5d-b6bf-4f2e-a0dc-4e95e405b941[3].png
26f4431d-76f8-43cd-ae70-c338593bfa57[1].png

Source: Finextra (UBS), Alizila (Alibaba AI writer), Ars Technica (Google), Autonomous NEXT (Alibaba $25BHSBC)

ARTIFICIAL INTELLIGENCE: HSBC's branch robot and BBVA's facial recognition payments

We are always searching for what's exciting about Fintech, what's at the edge of the wave. It's encouraging to see, for example, JP Morgan launching its digital bank Finn out of St. Louis into the world, Goldman's Marcus lending to lots of subprime risks, and Venmo (i.e., PayPal) putting out a debit card to fight over the neobank consumer. But it's also sort of obvious. This is the innovation of 5 years ago, deployed at scale. Of course large finance firms have no choice but to innovate and copy startups, of course startups will diversify products from payments and banking to investments and insurance. We know the direction of travel, there is only one way to go.

The edge in consumer banking, apart from crypto, is figuring out the role of the human in the context of artificial intelligence. About four years ago, we started seeing chatbot companies like Finn.ai and Kasisto building out natural language interfaces into financial data, and virtual agent companies like Digit and Trim perform account actions like savings and planning. Now, robots are spilling out into the physical world to do the emotional labor of human employees. Take a look at HSBC, parking Softbank's Pepper robots into its physical branches. And while yes, this is a gimmick like Saudi Arabia giving citizenship to the Sophia robot, it is another step towards embodied digital agents.

e8e2efaf-a1e2-4169-ac6e-7c5bf8a3012c[1].jpg

The robot is currently used only for generic queries, like a chat window on a website but in a physical location. Its cartoonish form moves it out of the uncanny valley, to which both Sophia and something like Soul Machines still succumb. It is not integrated into a user's account or actual financial situation, but we can see a future where such automated interfaces are a bridge to bring in new customers -- both those that are not tech savvy and require the emotional labor, and those that want to geek out over a new user interface. And if you want to understand how something like this could become a window to your financial soul, just check out BBVA's new payment system. A camera placed at the checkout counter reads your face, identifies it using machine learning, and charges your account. All it takes is one camera, and one robot.

88f3d7be-3b1c-4c57-910c-3223ce9b779c[1].jpg

Source: Reuters (JP Morgan's Finn), Business Insider (Goldman subprime MarcusSoftbank Pepper), Techcrunch (Venmo debit), Independent (Sophia citizenship), FSTech (BBVA facial recognition payments)
 

PAYMENTS: Alibaba's New Retail digitizes physical retail stores

Under our augmented commerce theme, we believe that mixed reality, AI and other digital enhancements will leave the browser and become anchored in the physical world through hardware and software pushed by companies like Apple and Amazon. Such a transition will be as transformative to retail commerce as the web and the browser were to e-commerce. The United States is showing glimmers of this through incremental symptoms. For example, Walmart and its competitors are implementing an IBM blockchain solution called Food Trust, which acts as a ledger for each food item across the entire supply chain, and cuts down identification time from 6 days to 2.2 seconds. Or look at DHL employing augmented reality headsets to help employees identify required packages in a warehouse.

1300b67e-544e-4b58-8dc5-822bc8fdccdd[1].jpg

But Chinese tech companies are many steps ahead. A great Axios article focused our attention on an Alibaba initiative called "New Retail". A mom and pop store can pay $6,000 for a digital renovation, and a $620 annual membership fee which locks the store into the Alibaba ecosystem. The digital tools include a heat sensor to track foot traffic, an AI-backed app and the Alipay payments system, and the entire Alibaba delivery and fulfillment infrastructure. The store becomes a physical endpoint for Alibaba's e-commerce platform. JD and Tencent are doing the same.

703c5f82-cc03-43b9-be9d-c436dc743ee2[1].png

A chain called RT-Mart updated 400 of its stores in this manner. Here are the bells and whistles: (1) one-hour arrival on e-commerce orders in a 3km radius to the physical store location, (2) orders through a branded app are fulfilled by inventory from local physical stores and carried by conveyor belts to packing/delivery areas, or all the way to a customer's home, (3) in-store physical e-commerce kiosks with payments using QR codes, and (4) red envelope coupons that are gifted within Alipay can be redeemed for physical goods. Will customers adopt these solutions? Well, 500 million already have the needed app on their phone and are trained in  digital payments behavior. Maybe China will save the American mall! And as we've been saying about Amazon, technology and finance are mere enabling features of the commerce happening in the system. 

346217b8-5f29-472c-8d6f-130e91e56026[1].png

Source: Alibaba (AxiosAlizila), WSJ (Walmart and Blockchain), UploadVR (AR for business)

ARTIFICIAL INTELLIGENCE: 10,000 People at Citi Fertile for Machine Processing

http_%2F%2Fcom.ft.imagepublish.upp-prod-eu.s3.amazonaws.png

Nothing quite makes human people like us perk up more than being described as "most fertile for machine processing". And yet, that's exactly what Jamie Forese, the president of Citigroup told the FT about the investment bank's personnel. Out of 20,000 operating roles, he sees 10,000 potentially going away over the next five years. Now that 50% is a pretty big number, and not everyone agrees. HSBC, for example, see only 5-10% more automation potential over the same time period. So let's cut the pizza at 25%, and still gawk in disbelief.

Yet, this live data point is exactly in line with our analysis of what artificial intelligence will do across the financial services sector. In Augmented Finance, we identified $1 trillion of cost at play across banking, investment management and insurance. Behind that cost are those real human people -- 2.5 million of which are in the United States, and about 160,000 of which sit in the middle office of investment and banking organizations. Looking at the last 10 years only, the FT found 60,000 jobs cut from the top investment banks. The way things are headed, sounds like it's time to take programming courses at General Assembly.

None of us should be glib about the potential impact on the lives of employees of these companies. One of the ethical questions with which we struggle is -- whose responsibility is the welfare of these employees? Does Google and free machine learning software share a responsbility? Do CEOs of too-big-to-fail finance firms share a responsbility? Do investors looking for cost cutting share a responsibility? Does the consumer, wanting to pay nothing for banking, share a responsibility? And if yes to all, how do we come together to make a world where we celebrate not the cost cutting potential, but instead the potential human productivity growth? Is technology a shield or a sword?

cf08ce01-ffd1-4b3d-8b99-55d5a19698fc[1].png
a1c9084b-68fb-47a0-bfed-18dcdb8dbb6e[1].jpg

Source: Autonomous NEXT (Augmented Finance), FT (Citi), Look and Learn (Lamp Lighter),

SOCIAL MEDIA: For $7.5 Billion, GitHub is now Microsoft, and what that means for Fintech.

0e0d8dd3-7693-43ff-88d4-33b313f054ad[1].png

Remember when Microsoft got left in the dust by Apple's iPhone? Or when Bing tried to beat Google? If you're a tech firm, missing a platform shift like search, mobile and social is profoundly painful. Well, no more. The enterprise tech giant is in cloud, blockchain, and augmented reality. And it's in enterpise social media -- big time. Microsoft is putting up $7.5 billion to purchase GitHub, which has 28 million developers in the community, and over 60 million code repositories. Think of that as shared documents on a massive cloud drive, but those documents are executable and the people doing the sharing are engineers with razor sharp skillsets.

This is an interesting turn for Fintech and Crypto. To be honest, we always thought of GitHub like a community resource, similar to Wikipedia, and not a corporate entity with founders that want to get monetized. But like LinkedIn, this asset found its way to Redmond. While Facebook is still cleaning off the hangover from Cambridge Analytica and selling customer data, Microsoft has positioned itself as a developer- and open source-friendly ecosystem catalyst. Which is ironic, given its history as a monopolist, open source antagonist, and start-up crusher. 

GitHub has tendrils into every startup team in the world. If you write code, you use GitHub for version control, collaboration, and sharing. Where do you think all the crypto code sits? If you're Ethereum or Bitcoin or anyone else in the world, code commits to GitHub are a fundamental indicator that crypto funds use to evaluate the health of your project. Or, if you're trying to work out crypto governance and decide which proposals to include or exclude in the main codebase, again, you are using GitHub to manage decisions. Or if you're a teenager wunderkind interested in using open source Artificial Intelligence frameworks to build your machine -- e.g., Tensorflow or PyToch -- you use GitHub to learn and get started. Welcome you tired, poor, huddled masses yearning to breathe free; welcome to Microsoft.

bfb172b6-8b2f-4593-bb6b-2fb2781ddb6f[1].png
70f3fab4-3c97-484f-af96-3aa2c0255f81[1].png

Source: Techcrunch ($7.5 billion), Wikipedia (infographic), Deloitte, Azeem Azhar (AI frameworks)