January 30, 2020

Predicting the Future of the Economy with Machine Learning

By Prasad Pai, Technical Lead at YML | Jan 30

Presently, there is a major concern on the probability of the likelihood of having a global economic crisis.

In spite of the supposedly cautious mood adopted by few countries, nobody is willing to give a clear message on whether the next recession is just a week away or a year away. And then there are few others, who are giving indications that there is absolutely no economic slowdown at all.

Above all, the opinions of these financial gurus are changing daily from positive to negative outlook and even vice-versa. Everybody’s opinion is justifiable as predicting the future is not an easy task and everyone has his/her wealth of experience behind him/her.

Hence, we wanted to establish a quantifiable assessment to gauge and decide on what the world’s well known financial investors are thinking about the future of the economy.

We wanted to solve this problem through Machine Learning with the least available information/resources in the quickest possible way.

Data collection

To collect data for our problem, we cannot have a one-on-one discussion on a recurring (potentially daily) basis with these investors but we can scrape their interviews, discussions, speeches, etc from YouTube and their messages from Twitter.

To start with, we short-listed few financial behemoths and scraped the transcripts of YouTube videos through YouTube-Transcript-Api and Twitter feeds through Tweepy. We split each YouTube transcript into a duration of a minimum of 5 seconds each and then order them serially to preserve the time-series nature of data.

This is the summary of the data collected in our experiment:

Data summary

Let us focus on all the subsequent discussion in this article with Warren Buffet’s point of view.

Data Validation

As our data has been collected from YouTube and Twitter, we have to benchmark the authenticity and genuinity of the text data with the thoughts being as close to the financial world. This is necessary because we are going to train our models to predict the future of the economy and our text data transcripts have to be related to finance and economics.

While collecting the data we assumed that these financial investors are quite dedicated to their field and will mostly talk publicly every time related to finance and economics. But still, we have to validate our assumed heuristic.

We don’t wish to perform the recommended way of painstakingly filtering individual text statements in our dataset. Hence, we create a small sample of statements of what we believe talks about finance and economics and should represent the state of our dataset. Here is an example of one such sample set.

Custom_1: How is the economy doing in the United States of America?
Custom_2: The current state of affairs is not doing good.
Custom_3: Life will get difficult when inflation kicks in.
Custom_4: We are in a bull market.

a) Known language model embeddings

We generate the sentence embeddings of all the text transcripts in the dataset along with our artificially generated samples by making use of TensorFlow Hub’s Universal Sentence encoders embeddings.

You can experiment with other language model embeddings as well but we chose Universal Sentence encoder as it has been trained on a wide variety of topics. We plot these generated embeddings using TensorFlow’s embedding projector website. Upon performing T-SNE, we observe that most of the sentence embeddings quickly converge into one cluster along with our typically generated examples.

This is an indication that most of our text samples are related to the domain of finance and economics. Here is one of the example cluster what we observed in our experiments.

T-SNE convergence of dataset using Universal Sentence Encoders embeddings


b) Using custom-built language model embeddings

Another thing we have to validate and experiment is the coverage of our dataset. The dataset should extensively talk about as many concepts related to the finance and economics worlds. To check this aspect, we have to obtain a language model created out of general finance and economics.

We weren’t able to get any publicly available language model in this domain, so we ended up training our language model using free to use publicly available textbooks in finance and economics.

We generated the sentence embeddings for our dataset from the newly created language model specialized in finance and economics. We plotted the generated PCA components out of these sentence embeddings using embedding projector website and we were happy to observe that PCA components were wide-spread in all three dimensions.

This indicates that our dataset represents a wide range of subjects in our language model and is not restricted to one particular topic within our domain. Here is an example of PCA projections which we observed in our experiment.

PCA projections of the dataset using custom trained language model embeddings

We performed T-SNE on these sentence embeddings and we found that embeddings were converging into multiple dense clusters. Each cluster is an indication of a specific concept in our specialized domain of finance and economics and this proves the extensive coverage of various topics in our dataset.

On the whole, we are able to validate our heuristic that our financial gurus are speaking only of their area of interest. Here is an example of cluster projections using T-SNE.

T-SNE convergence of dataset using custom trained language model embeddings

Data Filtering

Though this particular dataset has been good enough for our experiment, we may always never encounter such good datasets. We may have a dataset that has text samples related to general discussion and not related to our desired subjects of finance and economics.

In such cases, we will have to filter out the samples whose sentence embeddings are located quite far from any of our artificially generated typical examples embeddings in a standard language model.

To achieve this, we make use of the NMSLIB library. We weed out all those text samples whose cosine similarity lies furthest from all of our custom-generated samples.

To attain a proper dataset in this crude but yet simple way, we may have to keep repeating this cycle of procedures described in data validation and data filtering section multiple times with several custom generated samples.

Sentiment analysis

Once we gather a good dataset of text samples, it is time to process them. Our problem statement is of arriving at a quantifiable measurement to forecast the economic outlook based on the public statements made by the financial investors.

Our dataset comprises only of finance and economic subjects and if we perform a simple sentiment analysis on these samples, we would be able to achieve a quantified metric to understand the underlying sentiments in the statements made by investors.

We make use of Google Cloud’s Sentiment Analysis from Natural Language APIs to perform sentiment analysis on each of the samples in our dataset. We get sentiment values ranging from -1.0 to 1.0 resembling bad to positive sentiment, thereby giving a sense of inner feelings of the person.

Training models

Now it is time to train the models. We have a univariate time series data comprising of sentiment values. Let’s train different types of models to solve our problem and compare them against each other. In each type of model, we split the initial 95 percent of data as training data and the trailing 5 percent as testing data.

a) LSTM model

We will start with a deep learning solution. We will make use of LSTMs in TensorFlow to train our model. After the training is over, we forecast the output one time-step at a time. The obtained result of predicted value vs ground-truth value is shown below. We are not plotting the confidence interval in our graphs as this is based on making predictions by using all the previous correct values after each time step as we proceed to predict the next timestep value.

Here are the graphs obtained in our experiments after training 10 and 25 epochs respectively.

LSTM test predictions at end of 10th and 25th epochs of training

b) ARIMA model

A deep learning solution doesn’t work well in scenarios where you have less amount of data and particularly when you are forecasting using a univariate dataset. We attempt to solve our problem using the statistical-based approach of ARIMA.

ARIMA will internally capture the trends inside the dataset but to do so, we have to transform the dataset to a stationary time series one. This method gives us a better result as we obtain a much smaller amount of test loss.

ARIMA test predictions

c) TensorFlow Probability model

TensorFlow has launched a new framework of TensorFlow Probability which can be used to leverage domain knowledge of various probabilistic models with deep learning. Like how we had employed simple models previously, we create an elementary model using TensorFlow Probability and fit our univariate dataset into it.

TensorFlow Probability can be trained to capture local, seasonal and many other trends inside the dataset which was either absent or little difficult to be explicitly instructed to do so in earlier models.

TensorFlow Probability test predictions

Comparison of different models

This is the average test loss we obtained in our experiments. Note however that these results are local to our dataset and need not necessarily conclude anything.

Loss summary

Understandably, we observe that the ARIMA model is giving the least test loss as our dataset was small and univariate in nature.

Forecasting economic outlook

Finally, we feed the entire dataset and we make use of our best model to predict the future economic outlook. This is the result we obtain in our experiment.

Forecasted Output: 0.100

We will however not emphasize this result as our experiment had several shortcomings which we are listing next and the quality of the result can be improved when we solve them.

Drawbacks in our experiment

  1. First and foremost is the data. We need data to be as recent as possible. As we had a limited amount of data, we had to scrape quite old videos and tweets from YouTube and Twitter respectively.
  2. Data has to be obtained periodically. We had completely ignored this aspect in our experiment and if it is not possible to obtain regularly spaced data, we have to interpolate the missing values.
  3. We evaluated sentiments of our dataset using a generally trained sentiment analysis tool. It would have been better had we created our own sentiment analysis tool which was specifically trained in finance and economics statements.
  4. We factored only sentiments of the statements made by the investor as the training attribute to our model. Though the sentiment is a major factor, yet there may be other minor factors worth exploring like assessing in what mood was the statement made, was it an interview or discussion, etc.
  5. We didn’t concentrate much on hyperparameter tuning as the motivation was to just prove our concept and we employed only simple models.

Future work

Apart from the above-listed problems, there are few other good things worth looking into in our experiment.

  1. The public statements made by investors keep coming every day and the dataset keeps evolving continuously. Online learning methods have to be integrated into our work and the best way to do this is to fit our entire pipeline into TensorFlow Extended flow.
  2. All three models used in our experiment may individually be good in certain cases and it is in the best interest to apply boosting techniques to improve the results.
  3. Club the individual investor’s economic outlook forecast to form a single score.

If you would like to take a look into code used in this experiment, you can look into my GitHub repository.

Y Media Labs is closely working with Google in improving the experience of TensorFlow to all its users across the world and is a part of one of our case studies of our work.

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About the author

Prasad is a Machine Learning Engineer at Y Media Labs. He is currently responsible for developing prototypes showcasing machine learning capabilities to prospective clients and the development of full-fledged projects which involves experimentation with neural network architectures.

January 29, 2020

Getting to Know Debasish Bhadra, Program Manager at YML

January 29, 2020

Who are you, and what do you do?

Hello! I am Debasish, an ardent technophile and one among the passionate ‘Dreamers and Doers’ of YML 💪

As a program manager, I lead and drive program objectives, determine deliverables, make milestones, and pursue the critical path to help achieve strategic and business goals of YML and it’s clientele. 

Fresenius Medical Care, ADAA, Orig3n, FS and EMC are a few of my most recent completed programs at YML. 

Where are you from?

I was born and grew up in the suburb city of Joy, Kolkata, India. 

Coming from a city with a soul which carries 330+ years of rich culture, as a kid I was curious to explore creative, abstract art, sports, technology, and education. The pursuit of knowledge and the warmth of being together with family helped me to build my 'never back down' attitude towards life. 

I moved to Bangalore, India, to study information science and engineering. With ample freedom to flourish and nurture my passion that converge — skills, value, quality, and yes, the cult of the great minds around — I’ve discovered what it takes to deliver a lasting impact on everything I do. 

Tell us a little about your background.

I have had a strong passion for technology since my childhood. It fascinates and pushes me to find a simple tech solution for a complex problem. 

When I received my first computer, I was dazzled to experience the symphony of MS-DOS, Windows 95, CPU system and a printer orchestrating together. I was happy to be hooked to the computer all day long.

Fast forward 15 years, I carry the identical enthusiasm every moment with strong tech management in mobile platform, AI/ML, data science, enterprise and human aspects of software engineering. 

I joined the Vimeo Livestream team as a fresh grad and over the years contributed significantly to deliver world class quality products and projects across multiple Fortune 500 clients later on including Facebook, Dell, HP, and Disney before joining YML.

Why did you choose to come to Y Media Labs?

I believe in the vision and mission of YML's founders and leadership team.

We have some of the brilliant people in the industry working here. 'Work hard, play hard' is our culture. YML means business and value with a lasting impact.  

Apart from work, I love the engaging events of YML. YML hack day is organized for 'YML'ers who want to showcase their unique ideas and coding skills. We regularly participate in corporate sports events such as marathons, duathlon, and corporate relay races. 

Above all, I love the energy, the vibe, and passion of YML. Thats why I choose to come to this world-class mobile development studio. 

What about this industry are you most passionate about?

Technology, software, people, innovation and value converges to motivate me. Faster access to internet enables ubiquitous access to new innovation and new opportunities at our finger tips. Clearly, the world is evolving at a rapid speed.

My conscious involvement with the rapid transformation in the tech industry helps me with effective visualization and successful execution. It feels great to be a part of this industry! And, It's a feeling that will never go away. 

What are some other companies you admire?

Microsoft, Google, Apple, Amazon, and Samsung need no introduction and I admire them for the contributions they have made so far to the technology industry.  Lesser known start-ups and good business models I admire are:

  • CureFit - A digital and offline platform offers a healthy lifestyle and holistic cure across fitness, food, and mental well-being through its three products: cult.fit, eat.fit and mind.fit. 
  • Byju’s - Ed-tech startup that offers highly-adaptive, engaging and effective learning programs for students from classes 4-12 and competitive exams.
  • Practo - Digital healthcare platform that enables users to find medical services & solutions. 

What are your favorite spots to eat?

My favorite spot is my residence because I am in control of taste, portion and quality of the food. We like to experiment with fresh quality ingredients and we absolutely love what we cook! Here is a list of nearby favorite places in Bangalore, India, we enjoy dining out:

My favorite spot to eat outside of India is Epicure - Le Bristol Paris and Hibou Deli in Chamonix, France because of their heavenly dishes and flavors of the world they offer. 

How do you spend your spare time?

My family is surrounded with strength of love and with every union in my spare time, our bond grows stronger.

I enjoy reading, boxing, football, yoga, and activities for physical & mental fitness. I also love adventure sports and building prototypes. Expressing myself through fine art painting is another treasured passion of mine. I feel accomplished and get the sense of fulfillment when I create a wonderful masterpiece because art works as a springboard for exploration of my inner self and peace ✌🏼.

October 29, 2019

How Data Might Blow Up Your Project Plan, and Why That’s Actually a Good Thing

By James MacAvoy, October 29, 2019

Data. It’s a word that strikes fear and excitement in the hearts of all project managers, scrum masters, and project teams alike. 

We know we want it, but we’re not 100% sure what to do once we get it. 

“Now what.”

We request, remind, chase down, test for, and eventually receive this precious data - only to have these familiar questions raised:

  • Where do we fit this into our project life-cycle?
  • How do I make this data actionable?
  • Who ate my clearly labeled chicken salad sandwich in the office refrigerator?  (I know it was you, Jeff)

Although answering these questions is an important step, at the core we have to dig into why we have to ask these questions in the first place. 

1 / Fear of Data

The primary issue we have to deal with when it pertains to data is fear. 

At its root the inherent nature of data can force us to rethink our direction, disprove our hypothesis, or cause us to realize that we’re trying to solve the wrong problem. 

Any of these results can force a major shift in your project direction. For project managers in particular, who typically hate seeing their project plans flushed down the toilet, at first glance data can feel like a problem.

Data does present a problem also familiar to project management regarding the implications of data in the project and how do we mitigate potential issues.  The reality is, those questions are much easier to answer than potentially developing a product that is completely useless to the user.

As Shayna Stewart asks in her articleDoes the consumer find value in my product?”, data — no matter how scary it might be — allows us to answer that question before our product potentially falls flat with that consumer.  

2 / Project Management Life-Cycle

The standard project management life-cycle typically consists of:

Initiation, Planning, Execution, Performance Monitoring, and Closure. 

In a typical digital project, if we incorporate data at all then it is usually within the planning phase. Then, often to a lesser extent, the performance monitoring phase and even worse, usually with a brand new team with no historical knowledge.

To effectively deliver a consumer-centric product that adds value to our users we need to incorporate the use of data throughout the project life cycle.

This means that we need to continuously be reviewing our direction against any learned insights as well as continue testing to validate our hypothesis and the decisions we are making through the project.

Additionally, the considerations we make while running a project will need to be reconsidered. 

As Project Managers, it is ingrained in us to deliver a project that meets all scope requirements, on-time, and at/under budget. 

We’ve all seen the project management triangle of constraints - and likely seen the illustrations of how when one of those constraints is affected the overall quality of that project is in jeopardy. 

3 / Value Delivered

What is typically not considered in the triangle of constraints is an incomplete picture of project quality: in addition to these constraints we should be considering value.

We have all delivered a project over budget, or later than planned. All of those situations are never fun, but the far worse situation is delivering a product that the consumer finds no value in. If we do that, then it really doesn’t matter if it's over budget or late because it’s already a failure.  

A reasonable argument might be that the value is already factored into quality, which in a sense is true. But all too often the project lead’s focus on quality is based on requirements or at the very least a project brief. Without the necessary data those requirements could be wrong. 

In this scenario how we calculate quality is just one part of what we need to factor. When we consider the overarching value to the customer, our definition of quality could actively change, as it should.

But There Is Hope…

Much of what we have discussed above revolves around being comfortable with fear and uncertainty.

We have to know and understand that the more information that data provides, the more that it could change our best laid plans. 

Additionally, the more we incorporate data into the traditional project management methodology and process the more likely we are to see those fears come to fruition.  

However, as project leads there are ways that we can avoid the potential pitfalls described above.  If we incorporate data into every phase of the project management life-cycle, and plan for the potential disruption that this new information may cause, we are far less likely to be surprised when this disruption happens.  

“What do you mean we need to revisit the problem statement?”

We know there will always be changes to a project, but as long as we do not ignore all the information we could have, no matter how scary, we can get in front of that risk and minimize what causes this fear in the first place. 

Training ourselves to understand that change is good, disruption is good, and ultimately adding value to our consumer’s lives is best.

September 23, 2019

Getting to Know Edward Cessna, Senior Director of Engineering at YML

Published on September 23, 2019

Who are you, and what do you do?

Hi, I’m Edward, an introverted minimalist who loves taming complexity and solving problems.

As a Senior Director of Engineering at YML, I lead and mentor engineering teams who thrive on solving problems and creating software solutions.

Semi-officially, I have been bestowed with the title of Chief Cheesecake Officer. This honor is solely due to a devoted following by YML’s staff and some clients for my White Chocolate Cheesecake.

Where are you from?

I grew up in Aiea, a small town on the island of Oahu.

As a child growing up in Hawaii, I took for granted its rich and unique cultural diversity. From the Pidgin English language spoken amongst my friends (“Eh, pau hana! Going go home?”) to hitting the manapua trucks after school, or enjoying the tropical outdoors, it was uniquely Hawaii, and it was home. Now, as an adult, I appreciate and treasure the cultural diversity of my upbringing; I am a better person because of this diversity.

Tell us a little about your background.

I caught the programming bug in high school when my physics teacher taught us FORTRAN at the local sugar mill using an IBM minicomputer and punchcards. Yes, this was before the Internet, the introduction of the IBM PC, and hitting up Stackoverflow for answers to programming challenges. It was a fantastic time to begin a career in the software industry.

Since college, I’ve reinvented myself several times as technologies changed and my interest matured. One constant throughout most of my career was the systems I worked were large, complex, and mission or life-critical systems. These systems ranged from realtime flight-control software to embedded cryptographic software. Software that had to work correctly or people could get hurt; this work taught me the definition of quality and the value of a software development process.

I joined the first wave of mobile developers when Apple released the first iOS SDK in March 2008. This platform allowed me to lead and participate in a team that developed the first clinical-research mobile applications that have impacted thousands of people. The effort also allowed me to become a first-time author with the publication of the first book on ResearchKit.

Why did you choose to come to Y Media Labs?

I’ve been an early adopter of technologies since college.

When I first interviewed at YML, I discovered the founders were also early technology adopters and that we were aligned with my goals. I’ve stayed at YML for over five years because of the people. I have a great team that I like and respect.

An added benefit: the crazy ones make the job more enjoyable and rewarding.

What about this industry are you most passionate about?

I am passionate about software, teams, and what it takes to produce high-quality and secure software systems.

Successfully engineering and delivering a software solution while satisfying programmatic constraints requires a team that has a wide range of technical skills as well as a refined set of soft skills. Developing teams with this set of skills have been and continues to be very rewarding.

What are some other companies you admire?

I admire companies who put their corporate reputation behind issues of humanity and challenge established norms.

One of the first company that comes to mind is Virta Health. They are successfully challenging the conventional wisdom of diabetes prevention and achieving incredible reversal/remission results for a disease that is pandemic.

Apple is another company that I admire. Their belief that privacy is a fundamental human right resonates with me, given that I have a software-security background. Even though Apple is far from perfect, their privacy stances keep me as a customer.

What are your favorite spots to eat?

My favorite spot to eat a meal is my home. Not because the food is spectacularly good but because I can control the quality of the ingredients that go into my meals. Frequently, my best meals are simply those comprising a few quality ingredients with minimum effort. Some of the best examples of this are Caprese salad and Affogato. Both are dead simple to assemble and delicious if the ingredients are fresh and high quality.

When I’m lazy (a little too often) and want a break from cooking, I tend to go to restaurants close to my home. Here are some of my favorite:

My all-time favorite place, but not in the bay area: Helena’s Hawaiian Food. Helena’s is frequently my first stop after landing at Honolulu International Airport.

How do you spend your spare time?

I thoroughly enjoy spending time with my family, cooking, reading, and learning about new technology and health information.

My preferences for cooking or baking a dish is to make is anything and everything from Stella Parks.

My interest in health, however, creates an internal conflict that I wrestle with more often than I care to admit. Occasionally, I lose the dessert-health match, and I surprise my family or coworkers with a little treat. (I, of course, eat none of the treat. Nod, nod, wink, wink.)

August 27, 2019

Getting to Know Weston Hanners, YML Engineering Manager in Indianapolis

Published on August 27, 2019

Who are you, and what do you do?

My first name is Lionel, but I prefer to be called by my middle name — Weston. I am an Engineering Manager for our excellent Indianapolis Team.

My speciality is in iOS development, but in my free time occasionally dabble in web technologies, server administration and when I am feeling it, I blog at Alloc-init.

Where are you from?

Born and raised in Southern Indiana, I grew up in a mostly rural town called Bedford, its claim to fame is that the limestone for the Empire State Building came from a nearby quarry.

I recently moved near Indianapolis to be closer to the office. Aside from a year I lived in Georgia when I was a kid, I have lived in Indiana my whole life.

Tell us a little about your background.

I started learning programming in high school, mostly Visual Basic. I was a member of our BPA (Business Professionals of America) chapter and even won a couple of awards in regional software engineering competitions.

After high school, I did a couple semesters at IVY Tech, but ended up dropping out due to money troubles. After Apple released the iPhone SDK in 2008, I decided to try to get back into programming so I saved up to buy a MacBook Pro, an iPod Touch and a programming book. I spent the next couple years self-teaching myself iOS development while I worked days in tech support at a local ISP, and in 2012 I entered the professional software engineering world.

I came to YML in 2014.

Why did you choose to come to Y Media Labs?

Before I worked at YML, the place I worked was very corporate and I wasn’t a fan of the project variety or the corporate politics. When it came to YML, I won’t deny that the idea of working for a Silicon Valley company with an impressive portfolio wasn’t also a factor.

I felt like YML also opened up my growth options.

What about this industry are you most passionate about?

When I am not programming, I do most of my computing on my iPad Pro. I think it is amazing that this 11” device can do so much and I am very interested in finding ways to do more and more on it. My friends actually think I am a bit nuts that I try to do things that most people would think of as a “computer” only task on it.

iPadOS is coming in September and it will even further expand what it can do, I am so excited! (Fun Fact: I actually am writing this on my iPad)

What are some other companies you admire?

I suppose Apple is the obvious choice here. I don’t think they are perfect, but I typically align with their decisions (especially when it comes to privacy and security).

Nintendo is another one, I love how they refuse to follow the typical Triple-A gaming pattern. Their games might not have the “best graphics,” but when it comes to pure fun they easily win against any other major game developer.

What are you favorite spots to eat in Indianapolis?

My favorite things to eat are burgers. And I have two favorite places in Indy: Between the Bun and Punch Burger. I love places that add unusual toppings to their burgers.

How do you spend your spare time?

I am an input nerd. Ever since I taught myself iOS development, I have been binging on educational YouTube videos. My favorite topics are Engineering and Design.

While I cannot draw to save my life, I have gotten pretty good at recognizing good design and this has really helped the work I do at YML. I also want to give a quick shoutout to the amazing channel Crash Course, where I’ve found a new passion in world history.

I also play a ton of video games. My current favorite gaming system is the Nintendo Switch and the rapid growth of my game library has probably annoyed my wife a bit. (If she is reading this, sorry honey!)

July 17, 2019

We Are People: What it Means to Have a People-First Approach

By Shayna Stewart / July 17, 2019

A people-first approach is neither easy to create or quick to implement. But it is the secret sauce at the core of the biggest and best brands in the world today.

Customer experience is a strategy that all digital insiders know has to be a focus if they want to have a lasting impact in their industry. However, the execution of customer experience isn’t as easy as just coming up with a plan to leverage emerging technology and building digital products. It’s as much about igniting cultural change within a company as it is about planning for the evolution of the experience.

At YML, we’ve designed a dynamic and thorough people-first strategy built to cultivate cultural change.

That people-first approach is what is missing from the majority of CX initiatives — and it shows. 

  • Executives who have made a push for a CX strategy have not seen a tangible business improvement.  20% of companies scored 9-10 for seeing a Return on Investment, with 14% of companies scoring 0-2 (Confirmit, 2018). 
  • The public doesn’t believe they have reaped much benefit from CX initiatives. 
    • 54% of U.S. consumers say customer experience at most companies needs improvement (PWC, 2018).  
  • Culture and legacy technology systems have been major reasons for people not seeing the consumer benefit of CX initiatives.
    • 54% of organizations cite culture as the primary challenge to becoming more agile, followed by the inflexibility of legacy technologies (Confirmit, 2018).
  • The companies who are reaping the rewards of CX initiatives, whom are mainly located in Silicon Valley, are the ones who have unequivocally added benefit to people’s lives.  
    • The S&P Index is largely a Technology Index as of 2018, including companies such as Alphabet/Google, Facebook, Apple, Microsoft, Amazon (Seeking Alpha, 2018).

What differentiates the Silicon Valley behemoths and startups is the people-first approach. 

Source: Conformit, 2018

A people-first approach comes with a shift in mindset that is drastically different from the historical business executive mindset. You suddenly are talking about the broad spectrum of all people, internally and externally,  instead of just customers, and ultimately revenue. You are talking about emotions as opposed to products. Instead of technology solutions, you are building conversational tools. Lastly, whereas a business-centric mindset is one that optimizes based on minimizing risk and maximizing revenues, a people-first mindset is one that optimizes for transparency and intrinsic value.

In the short term, when first making this cultural shift, these optimization goals can constrain each other. In the long run, a people-first approach will maximize revenue, reduce risk, build loyalty with your team, and, quite frankly, keep your business relevant. 

But this is a very difficult story to tell when in a boardroom meeting. Often times a savvy executive can make the initial case for investing in CX, but isn’t able to clarify the full scope of that CX investment, which includes a gradual and tangible, cultural change to people-first. What ends up happening is that the first part of the project may go well and the customer may come first, but then the returns on revenue and reduced risk are not immediately recognized and therefore the mindset shifts back to business as usual.  

The trick is to trust the strategy. Trust consistency of message and approach. 

Here are some examples of companies optimizing for people-first.

  • Netflix created an easy to cancel monthly subscription experience along with reminders to cancel after the trial period so that customers never felt like they were overcharged or cheated in someway. However, this people-first change, optimizing towards transparency, had an estimated loss of $50M in subscription revenue. At the time, that was still a small percentage of overall revenue and in making the change towards transparency it built long term trust. As a result of improved brand perception, they continue to increase their monthly subscription base, hitting their highest level of subscribers in Q1 2019. 
  • In 2016 McDonald’s invested in elevating the interior environment of their stores to feel more premium, along with adding in self-ordering digital kiosks and table service. Investing in improved interiors is a table stakes strategy. Let’s face it — they needed to make this people-first investment just to stay relevant. It is table stakes because the outcome will get you to a net-neutral spot; it’s not going to increase customer base, it’s just going to make sure you don’t lose customers at a faster rate than if you did not implement that update. A clean, premium eating environment is the expectation. But the digital kiosk paired with the improved interior is what took the strategy to a level that would actually increase sales.
Image result for mcdonalds self service

  • The digital kiosk solved a customer pain point of waiting in lines in a way that was hard for competitors to copy right away.  Their strategy was to ensure their experience met standards and then improved the standards of the industry. This investment didn’t start to see a return until 2018 for stores within the test. McDonald’s has many other competitive pressures, such as new restaurants with the perception of better quality food and convenience offered through delivery overriding in-store speediness. But refreshed strategy may not be enough to overcome these new customer expectations. Changing expectations raises the importance of adopting a people-centric approach that will allow you to rethink the entirety of the business and how it can pivot from an existing model to a new one.

In both of these instances with Netflix and McDonalds, the immediate impact on the business metrics (revenue, profit) went down. In the long run, these CX strategies resulted in heightened retention over time. Brand perception and revenue drastically improved. They illustrated how creating a people-first culture will help mitigate the initial shock of investment and reduce risk over the long run because the investments made are directly informed by people’s emotions. 

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According to Forrester, a one point gain in CX index results in a $5M-$185 million return on the business (depending on industry). Netflix has been ahead of the curve when it comes to CX and a people-first approach.

At YML we have created a step by step hierarchy to help you understand what actually goes into creating a people-first cultural mindset. Breaking it down into steps can help your teams understand where they are in maturity. The plan is also a tool to understand what steps were missed in the past. The key to this model is that it implies a high level of collaboration from stakeholders from historically siloed teams at every step.   

Levels to Creating a People-First Culture: 

  1. Feel What People Feel 
    • Extensive marketing research that looks beyond your customers, your competitors’ customers and the points of interaction with you and your competitors
    • Employees from each team pretending to be your own customer
    • Employees from each team pretending to be a service rep that interacts with the customer
  2. Empathize to Solve Problems
    • Build your strategy around the crucial moments of emotions in step 1
    • Identify what part of the strategy is table stakes vs. what will move customer expectations
    • Projects that only have table stakes will fail because that only postpones the inevitable of customers churning, it will not promote long term engagement
    • Ideas that will move customer expectations should be prioritized despite being harder to develop (See how to prioritize innovation with Innovation Index
  3. Igniting Cultural Change
    • All team members should be aware of the new people-first research and strategy 
    • The people-first strategy should be outlined in terms of how every person and team can help implement this new strategy and what is expected of them
    • New rules of engagement defined, highlighted by a culture of not being afraid to fail, must be adopted.  This about making a transition from fear of change to perceiving of smart risk-taking as admired 
  4. Talk the Way People Talk
    • Your backend systems and content need to reflect the nomenclature of the way people talk, as opposed to the way an industry insider speaks.
    • The backend systems must be able to support people’s desired navigation 
    • This sometimes can be a significant change to legacy data architecture. 
  5. Build The Experience
    • Design, develop and deploy
  6. Continuous Optimizing of The Experience
    • Must have the ability to move quickly and make quick decisions.
    • Much of this is about empowering mid-level employees with the ability to have more decision making power.  
  7. Creating New Customer Expectations 
    • Continuous pulse on changing expectations and creating new solutions to meet those new expectations
    • Creating new technology
    • Taking a new technology to solve an unsolved problem 

Each step is crucial, and completion of a step without completing the one before it will invalidate all steps. In addition, the investment in each level is additive and represents a cost that is continuously incurred. This means the investment does not go away once a team has leveled up. The result for each step will be unique to every brand and even the approach to all steps is not a one-size fits all. Even if you meet the requirements in each step there are still some cultural habits that will undermine this entire investment. 

Habits to Avoid in Order to Preserve a People-First Mindset

  • Don’t forget to create advocates across all teams. Be sure to allow lines of communication for input and collaboration from all teams. This is a high-collaboration sport. 
  • Don’t say the investment will end with a specific project. Your teams should be continuously optimizing the project and there is no end to the investment. Remember, the CX leaders are actively investing billions every year in creating new expectations (i.e., Steps #1-#7 never go away).  
  • Don’t a business case around just Step #5. Steps #1-#4 are crucial to making sure the investment incurred in Step #5 is not wasted. 
  • Leveraging emerging technology without contextualizing why and how people would use it creates costly mistakes. That can only come once you have hit step #7 and shouldn’t come sooner. 
  • Not investing in robust people-first research. This seems simple enough, but most companies think they have the right research based on satisfaction scores from customers. This is too narrowly focused for a people-first approach.  
  • Not properly communicating the people-first research and initiatives built from it to all teams in the entire company. Teams need to understand what this shift means for them and how they can support it. 
  • Not allowing for employees to feel comfortable about outcomes that weren’t positive. Not everyone will get it right the first time, but they should learn from why it didn’t work. That insight will get teams to the next big thing.  
  • Not expecting team structure shifts in order to become more agile. 
  • Not expecting major changes to database warehousing teams. Usually CX initiatives are considering just what it takes to build a website or app, but fail to consider that the systems that they may read from are not set up to comply with the new people-first strategy.  

A people-first mindset should permeate the underlying thinking of all teams. It should be an iterative process that produces long term business results.

It should unite and empower all employees to stand up for what’s right for the customer.

Employee thinking should be able to shift seamlessly between their executive persona and people persona. And most importantly, it should allow employees to feel like people feel because, at the end of the day, all of us are just people.

July 8, 2019

Speaking the Same Language: How UX and Data Strategy Can Work Together to Design for Voice-Based AI

By Shayna Stewart & Amit Garg | July 8, 2019

To say the stakes with voice interactions are high would be an understatement. This is the moment for voice technology.

Voice has the power to capture attention like never before because it hooks directly into the mechanism for how people think. It removes the friction of reading, clicking and translating like with other technologies. 

However, voice-based AI is highly constrained in terms of what it can and cannot do:

  1. Voice can only perform tasks that it was programmed to do, which can result in inaccuracies
  2. The user is not aware of all the potential tasks that can be completed
  3. Obviously, voice is not suitable for tasks that require sight 

These constraints make the design of voice one with little room for error.

At YML, we think about building products (including voice projects) in the form of an infinity loop, a repeated steps of moments that are continuously optimized as you learn more.

Below we outline how UX and Data Strategists can partner in each moment for a voice-based AI project to reduce risk of voice AI going terribly wrong.

1. DEFINE - Align tone and personality of the conversation

Having a clear, distinct vision is essential for voice.

The utility of the conversation is the most important part of the vision. At this point in time, the voice-based AI has not mastered the art of casual conversation where it can react to what the person has said and feed them what they are anticipating to hear through compliments and relatability.

AI is programmed to learn in smaller verbal tasks and take the learning of the smaller tasks and associate learnings to other tasks (though progress is being made there).

UX should define the utility, personality and context of the conversation. Why is this interaction important to have? At what point should the conversation happen, particularly if the conversation is prompted by another interaction? What is the intended outcome of the conversation? What qualities of our brand will this voice represent?

We must provide evidence for why voice is the right channel to design for in a given interaction, especially by understanding its context. For example, a bedroom voice interface that reduces volume to 25% at late night and is less wordy understands we don’t want loud robotic voices at midnight. Practically, a feature like that could be documented in a user flow.

Understanding the tone and the personality is not something a data person typically creates (or even understands in the real world), however, in voice this is a critical element because the personality is actually a data requirement. For example the answers to the following questions are data requirements:

  • How human does the voice need to sound?
  • How should the AI respond?

They should also start identifying any current or potential datasets that may be relevant to help train the AI in the subsequent product phases. They will need to work with a variety of teams to collect and get it into a format that can be easily used during training.

2. DESIGN - Examining vocal vs graphic UIs

Fundamentally, the way we think about the design of sound is different from sight. 

Of course there’s overlap, but it’s interesting to take a closer look. For example, a designer strives for visual consistency. Repetition and visual hierarchy help us stay organized when looking at an interface. 

But with speech, that kind of repetition gets rather annoying. Therefore, we should think of the journey as a carefully crafted conversation full of familiar variety.

In an app or website, the way people interact is relatively constant. GUI interaction occurs in a fairly regular rhythm of cognitive load. Mentally navigating the interface, reading text, and executing tasks requires a sustained level of attention through out. 

It’s a very different situation for voice. 

People make the first move, unprompted, and the system responds immediately. And, due to the transient quality of sound, people need to give their full attention to process the response. The luxury of closing an alert dialog without reading it on a GUI is not afforded by voice, nor is the action of reading and re-reading information. Instead, our full attention is required during voice interaction, and absolutely no attention when not interacting. 

Therefore, voice experiences should feel like a conversation - an interaction that we want to give more of our attention to when it matters - in order to have the highest likelihood of re-engagement.

UX should define a framework for the desired flow of the conversation.

Similar to designing for GUIs, the overall flow of the interaction needs to be designed, as well as defining the user intent the system should be recognizing.

UX should be asking questions like:

How can we remove friction in the process? Is this how someone would actually think about this interaction? Is the system doing everything possible to pick up on the nuances of speech and trying to move the conversation forward?

Even in the case that the engineering team leverages machine learning techniques to let the AI learn the conversation flow on its own, this framework will help the team identify if it is producing the intended results. 

For example, at YML we recently worked with a Fortune 500 insurance company to reimagine their self-service digital strategy, which involved a concept for using in-home voice assistants to handle basic transactions like paying a bill.

Along each step of the conversation, we outlined how the system should move the conversation forward by capturing user intent, setting the variables of intent, and the next action to be taken - all packaged into a helpful and professional voice that emanates confidence and security.

The data strategy team should partner with UX to understand the basic conversation framework and then work with the engineering team to understand their methodology for building the dialog model.

The data strategy team member will need to be able to translate the constraints of each methodology whether its’ rule-based or machine learning-based.

For example, Amazon Alexa skills experts recommend that the conversation has no hierarchy.

This makes sense when designing skills - which typically are a one use product. This is because it prevents questions to have to go through a menu-like conversation (think pretty much any credit card company call centers first line of defense, having to answer a multitude of yes’ or nos before just getting directed to someone).

Though the implications of having no hierarchy mean that:

  • The skill does not have to go through a rule-based system to answer the question (positive) 
  • However, the conversation can get repetitive leading to an outdated dataset in which the AI is making conversation from, reducing engagement overtime (negative) 

This synthesis of the UX framework and engineering approach is important in this step because it will provide input on how to evaluate the success, the learning methodology and optimization strategies post-launch.

3. DEVELOP - Bringing the vision to life and defining metrics

This step is owned by the engineering teams, but this step should entail having regular meetings with both UX and data strategy to ensure that the assumptions they are making are in line with the overall vision from UX and data strategy.

This is also where the AI starts to learn from the team.

Part of defining conversation flows requires defining trigger words in order to move forward in the task. These are documented in the user flow, and are launching points for a task.

During development, UX can conduct usability testing. The classic task-based metrics (effectiveness, efficiency, and satisfaction) are still relevant here, in addition to qual research (in-home ethnography, surveys, interviews, etc.) to learn how customers respond to the design in context.

Data strategy should be listening in to how the AI is progressing over time. If it is not producing the anticipated results, the data strategy expert will need to evaluate why. It may be because the dataset is biased, it may be due to the training dataset not being reflective of the task at hand.

Once the issue is identified, the data strategy expert can make recommendations as to how the dataset should be modified.

Also in this phase the data strategy expert should be outlining a measurement strategy for how the AI will be evaluated based on its current progress. Datasets that will evaluate the performance also need to be built into the development phase. This measurement strategy should include workflows and resources needed to update the AI as it encounters new phrases, as this can be a manual process post-mvp launch.

4. DEPLOY - Monitor, measure, and understand

Voice-based AI is a product that needs constant optimization to not only ensure that it continues to work as anticipated, but also to keep audiences engaged.

If the experience starts to lose it’s initial utility or becomes repetitive, the usage of the product will plummet. Teams should be monitoring and optimizing based on the workflows outlined in the data strategy to ensure the sustained quality of the product.

In addition to refining the design, UX can provide insight into why any failures may be happening.

Was there an insight missing from the define period that changed the perspective of the utility of the conversation? Or is there a technical failure happening? Is the developed conversation mismatched from what was designed? Maybe the personality feels off.

All of this needs to be caught as soon as possible and translated into any new requirements for refinement.

To get deeper insight, UX should review transcripts from all conversations had, as these will provide rich qualitative data to help understand how the product is performing.

The data teams should be analyzing the number of failed conversations, understanding why they failed and making recommendations on how to teach the AI based on these conversations.

This is when the measurement strategy workflow outlined in “Develop” is working.

The data strategy team member will likely need to ensure that the work flows are increasing in efficiency overtime through monitoring the AI KPIs. This is what will lead to continuous optimization of the product infinity loop.

In Conclusion

The qualities of voice-based AI defined in this process result in the underlying identity of a brand.

It’s a high impact touchpoint, that when it goes wrong, goes really wrong.

Though, it also has the potential to reach people in new ways. It is the personification of a brand and has the potential for businesses to create new relationships with their customers.

To protect your brand from a potentially high-risk situation, partner your UX and Data Strategy teams together.

Sources

June 25, 2019

Don’t Overthink It: Design is a Tool For Making Businesses Better

By Stephen Clements — June 25, 2019

Talking about good customer experience is easy. It’s just doing it that is hard.

You’d think it’d be easy for me to talk about it because I have spent the last 15 years talking about it to people who do it every day — clients and designers.

And I have often wondered why is it that all these hours get spent — often on conference calls or in long meetings — by people trying to do the same thing, but in constant disagreement about how it should be done.


A colleague of mine, his wife works as a United flight attendant. That's not her below 🙂

His wife says that on every trip, without fail, at least one customer complains. And they are so bitter, so vitriolic, and they get so angry that they say things like:

“United is the worst.”
“You have just lost yourselves a customer.”
“I am never going to fly United again.”

But she smiles professionally, all the while thinking:Of course you will. You will go on Kayak and you will pick the cheapest option—you will consider your milage plan—and I will see you next time.”

In short, improving the customer experience sounds cool, but it will no doubt raise the cost of a United flight. And this might actually turn off more customers than it’s worth.

I was recently having dinner with my wife, and she asked me, “What are some of the questions you have been asked a lot by clients recently?” (A fun date night, right?)

Well, I thought, something I have been asked a lot is,Yeah, yeah, yeah. This customer experience thing sounds cool. But how will it move the needle?” I have been asked this question in many different ways. By many different clients. At many different organizations.

And, look, I get it. No one wants to invest in all this trendy customer experience goodness, but not see any actual results. Or worse still, see negative results, and they get fired. 

For example, I was at a meeting with the head of e-commerce from a major airline, and he said to me that their most vocal customers — the ones who complain the most bitterly — are in fact the ones that are most profitable.

Funny right?

Probably because they have to pay extra for all the things they didn’t plan for—extra bags, extra legroom seats, extra food, etc. Which got me thinking.

First-of-all, is all revenue good revenue? And is it worth putting short-term profits ahead of long-term customer lifetime value?

It has taken me many years as a designer to reach this state of business-minded enlightenment. And finally I have come to the conclusion: Clients don’t actually hire us to make their customers happy. They hire us to make their businesses better.

*And sometimes (sometimes) it’s the same thing.

Now, I realize this might strike you as a little odd. I mean, is this a creative person giving us a business lecture? Just please stick with me.

You see, in the beginning — when I was a young and idealistic designer — I wanted to make things that were beautiful. I wanted them to be just drop dead gorgeous. And I didn’t care about much else.

I call this my aesthetic design phase.

All I wanted to do was create porn. Not real porn. Visual, aesthetic, product porn — and I was lucky to get to do this for brands like Nike and Xbox.

Then, as my career matured, I began to develop more empathy for people. I wanted to create experiences that helped people, were loved, and talked about. Experiences that tackled really high-value life moments, like shopping for a car — like the work I did with Audi, or buying a house — like work I led with Trulia.

I call this my experience design phase.

And then, when I matured again, I developed a deeper understanding for the clients I served. I began to appreciate and better understand their decision making process, and how they are optimizing to make more money.

I wanted to create experiences that solved real business problems — which we proudly did here at YML by doubling The Home Depot’s mobile revenue in one year.

I call this my business design phase.

I think all great designers must go through this evolution. It is the holy trinity of design. 

First it was aesthetic problems.
Then, experience problems.
And lastly, now it’s business problems.

Designers that don’t graduate along this path… well usually, they fall short of their promise, and their careers are stunted. They aren’t opening their minds to this business imperative.

Some years ago, I was given some advice to buy my clients’ stocks on Robinhood — a beautiful, designer friendly app, if you don’t know it. I bought just a hundred bucks worth, here or there. I bought Nike. I bought Activision. I bought NVIDIA (that one went gangbusters). I’m definitely not a power-investor, by any measure, but it gave me valuable insight into my clients’ world — the business of business.

At YML we did work for First Data on their Clover point-of-sales system, and a year ago I bought some of their stock. It’s been quite a ride, but I have nearly doubled my money.

Now, I am no expert in macro-economics, but that trough on the right of the graph pretty much happened all across the stock market — because of the China tariffs, the fed raising interest rates, reports of economic slowdown, and maybe the Mueller investigation, too. 

Right at the bottom of that trough we had a meeting with the head of the Clover team. It didn’t go too well. She was obviously distracted and when she was engaged, she was asking very tough, business-minded questions. Maybe it was bad timing, but it was also a reminder of how significant business realities are, each and every day.

In the past, I have heard promising designers, sometimes Creative Directors even, saying things like:

"Money is evil."

"Business is for other people."

Clients...they just don't think like us." (But, of course, they all still want a raise, come review time.)

And it is true, most clients don’t think — or talk — like us. Or real people, for that matter. Imagine if they did.

I mean, I don’t know about you, but as a real person, I often find myself wishing there was more info.

Or, looking for a solution that is more scalable. 

And, who isn’t absolutely thrilled when they fall into the correct user segment?

But designers aren’t any better.
They are just different.
I mean, that must be an awesome Kleenex site!

What I have learned, over the years, is that clients and designers are often talking about the same thing. But they come at it from different angles.

It's like they are speaking different languages. It leads to a lot of disconnects, frustration, lengthy meetings, and rather cantankerous conference calls.

Let me tell you a true story. Many years ago, I was on an hours long conference call to discuss a website with a client. And we were talking round in circles when this happened.

"I want you to uplift the branding quotient," the client said.

"Do you mean make the logo bigger?" one of our designers asked.

"Yes" the client said.

And that is a true story.

It’s a perfect example of where we are talking about the same thing in different ways. And it’s this sort of disconnect that makes it a lot harder for anyone to actually buy or sell work. Therefore, I find my day job is partly to play the role of translator. 

As translator, I have spent years helping clients understand designers, and I help designers understand clients. And I have discovered it works like this. 

Designers love to create simple, human, 1:1 experiences. They obsess over all the small details, crafting quality experiences that connect with people on a personal, emotional level.

And clients, well, they love scalable, enterprise-ready solutions that are 1 to many. They, quite rightly obsess over how it will make them a gajillion dollars.

But, of course, these things aren’t at odds. They are 2 sides of the same coin, and they have mutual benefit. Each one makes the other better.

It was this realization that has led me to not only be a better consultant to my clients — because I learned to speak their language — but it also made me a better designer — because I learned to understand their business.

The business of business.

It might seem obvious, but design is a tool for making businesses better.

We are not artists. Plain and simple.

At Y Media Labs — where I am the Chief Creative Officer — we have this way of thinking built into our DNA. And every day, we strive to use our superpowers of strategy, design and technology to make a lasting impact.

A lasting impact on the people that use our experiences. And on our clients’ businesses, too.

1 to 1. And 1 to many. 

Formerly co-founder of Junior: the Rapid Invention Company, a product design accelerator for big brands, and before that, Executive Creative Director for AKQA, San Francisco, Stephen has over 15 years industry experience working at the top of the game. An accomplished product design and innovation leader, he created breakthrough work for brands such as Activision, Anheuser-Busch, Audi, eBay, Jordan, Levi’s, NVIDIA, Verizon, Visa, Xbox, and YouTube to name a few.

June 3, 2019

Y Media Labs Partners with Google To Define Future of Machine Learning with TensorFlow and Tensorflow Lite

June 3, 2019 — By Will Leivenberg

After months of working together informally, Y Media Labs is proud to announce it's partnership with Google focusing on their TensorflowLite technology. YML is helping the worldwide developer community learn and adopt TFLite technology — the core open source library to help developers of all levels understand and train machine learning models.

Google worked specifically with YML's Innovation Lab team. The T-Lite team challenged YML to translate a complex topic like machine learning into something approachable and easy to navigate. Since then YML has become a leader in the application of Tensorflow for developers worldwide.

"The YML team produced thoughtful and thorough documentation concentrating on how best to on-board developers from various backgrounds, ranging from machine learning to mobile and web," says Sumit Mehra, YML Founder and President. "The work ultimately laid the foundation for how developers across the spectrum can create world changing applications, not to mention a collaboration yielding four applications that make the framework as seamless as possible," Mehra added.

The partnership is ongoing.

May 29, 2019

YML Teams Up With Damjanski to Build First of Its Kind, MoMA-inspired AI

May 29, 2019 — By Will Leivenberg

Listening to the dialogue between Stephen Clements, YML's CCO, and Damjanski is like overhearing two mischievous kids hatching their next scheme. It's whimsical, suspenseful, and kind of terrifying.

Only difference is it's happening over Zoom, and each of these guys are sporting a bit of gray hair.

The two have a storied past of creative endeavors and recently found their way back together about one year ago around an unexpected project. Damjanski had become fascinated with AI, to the point that he'd began considering if there was a way to actually collaborate with a program.

"Was there a way to integrate it into my thinking process?" said Damjanski about his original idea. "Could I create original work that demonstrated this concept, was ultimately the idea."

Stephen saw Damjanski's vision, and knew just the team to bring it to fruition — YML's Innovation Lab, the scrappy team of six based in YML's Bangalore office. What ensued was a plethora of emails, Zoom calls, sketches, math equations and countless cups of coffee over more than six months. Led by Innovation Labs Director Darshan Sonde and Kinar Ravishankar, YML helped not just conceptualize, but ultimately build the AI for Damjanski's project.

The final work would come to life as "Damjanski: Natural Selection," a collaboration between Damjanski, his longtime collaborator, Vasco, and YML that debuted May 1st at ONCANAL in New York City.

From left to right: Damjanski, Darshan Sonde, Kinar Ravishankar, Vasco.

As the exhibition website reads, Natural Selection "investigates ideas of collaboration with an AI and its integration into the artist’s practice."  Damjanski specifically wanted the AI to build around all the archived exhibition statements of the MoMA, in New York.

"The challenge was to create more lifelike speech for AI," said Darshan Sonde. "We had created lots of models, but in the end the model released by OpenAI worked the best. This wasn't the work we were used to doing, and it was especially different because this is for an art show, but that also gave us more liberty in what the text could generate."

The exhibition comprises a headset where people can interact with the AI to create new exhibition statements that will be delivered by a printer. Each statement is a new source of information that will inform the artist’s thinking process. Damjanski compares this process to the biological evolution of genes, which is driven by reproduction and survival in order to procreate or grow.

"The unique challenge was training the AI on data," Sonde said. "Data has to be large, so we had to write custom scripts to scrape the data from the MoMA website and cleanup and tweak it to generate good results."

"The YML team was an outstanding partner and I'm proud of the work," said Damjanski, who's work at the exhibition is live through the end of May, and open 7 days a week (11am–7pm) located at 322A Canal Street, New York City.

---

Damjanski is an artist living in a browser. He is a co-founder and member of the incubation collective, Do Something Good, and also the co-founder of the MoMAR gallery within New York’s Museum of Modern Art. More info: http://damjanski.com

For more information contact 

@d.a.m.j.a.n.s.k.i                  #oncanal

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