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Improving Diversity Through Recommendation Systems In Machine Learning and AI



recommendation system

Every day you are being influenced by machine learning and AI recommendation algorithms. What you consume on social media through Facebook, Twitter, Instagram, the personalization you experience when you search, listen, or watch Google, Spotify, YouTube, what you discover using Airbnb and UberEATS, all of these products are powered by machine learning and AI recommender systems.

Recommender systems influence our everyday lives

80% of all content consumed on Netflix and $98 billion of annual revenue on Amazon is driven by recommendation systems and these companies continue investing millions in building better versions of these algorithms.

There are two main types of recommender systems:

  1. Collaborative filtering: finding similar users to you and recommending you something based on what that similar user liked.
  2. Content-based filtering: taking your past history and behavior to make recommendations.

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There is also a hybrid-based recommender system, which mixes collaborative and content-based filtering. These machine learning and AI algorithms are what power the consumer products we use every day.

How recommender systems work. Image by Sanket.

The problem is these algorithms are fundamentally optimizing for the same thing: similarities

Recommendation algorithms make optimizations based on the key assumption that only similarities are good. If you like fantasy books, you will get recommended more fantasy books, if you like progressive politics, you will get recommended more progressive politics. Following these algorithms limit our world view and we fail to see new, interesting, and unique perspectives.

Recommender systems lead us down a one-track mind

Like a horse running with blinders, we fall into an echo chamber and the dangerous AI feedback loop where the algorithm’s outputs are reused to train new versions of the model. This narrows our thinking and reinforces biases. Recent events like the Facebook–Cambridge Analytica data breach demonstrate technology’s influence over human behavior and its impact on individuals and society.

Psychology and sociology agree: we fear what we do not know. When people become myopic, that is when the “us vs them” mentality is created and where prejudice is rooted. The civil unrest in the United States and around the world can be linked back to these concepts. Fortunately, research also demonstrates that diversity of perspectives creates understanding and connectedness.

This is also a business problem

The typical consumer has 3 to 5 preferences:

  • 3 to 5 favorite book or movie genres
  • 3 to 5 most listened-to musical categories
  • 3 to 5 different fashion styles
  • 3 to 5 preferred cuisines
Consumers are diverse

Why is this diverse consumer behavior not better reflected in our technology’s behavior? In fact, if a business is able to convert a customer into trying a new category, such as turning a running customer into a new road biking customer, that customer is likely to spend 5 to 10x more through onboarding and purchases in that new activity. For every diverse category a business is not recommending, that is lost sales and engagement.

The opportunity: how can we build a better recommender system that enables consumer diversity and increases customer lifetime value?

We can approach this problem through the customer lens. Let’s take Elon Musk as a model world citizen, who publicly stated he loved fantasy books growing up and Lord of the Rings having a large impact on him.

But if Elon continued to follow the recommendations of today’s most visible machine learning algorithm on Amazon, he would continue down the path of fantasy, fantasy, and more fantasy. Elon has also stated that business books shaped his world view, with Zero to One as his recommendation. Technology should be enabling, not limiting, more of these connections for everyone.

The status quo leads to more of the same, so how can we better match the customer’s interests? Images from Amazon.

How can we build a recommendation engine that would take an input book like Lord of the Rings and recommend an output book like Zero to One?

If we can solve this for an individual case like Elon’s, then we can start to see how a better recommender system can diverge from the similarity path to create more meaningful diversity.

Building a diversity recommender system

The data science process:
1. Define a goal
2. Gather, explore, and clean data
3. Transform data
4. Build machine learning recommendation engine
5. Build diversity recommendation engine proof of concept
6. Design mockups
7. Business value hypothesis and target launch

The data science process

1. Define goal

Books is the ideal industry to explore because there is a clear distinction between book categories and potential revenue, unlike music where the dollar value for listening to new genres is less clear. The goal is to build a recommender system where we input a book and have it output:

  1. Recommendations based on similarities, the status quo algorithm
  2. Recommendations based on diversity, the evolution of the status quo

The long term goal is to build a recommender system that can be applied across various industries, enabling customers to open doors to diverse discoveries and increasing customer lifetime value for the company.

2. Gather, explore, and clean data

Dealing with data

Goodreads provides a good dataset. Within here we need to determine what is useful, what can be removed, and which datasets to merge together.

# Load book data from csv import pandas as pd books = pd.read_csv('../data/books.csv') books
# Explore features

There are 10,000 books in this dataset and we want “book tags” as a key feature because it has rich data about the books to help us with recommendations. That data lives in different datasets so we have to data wrangle and piece the data puzzle together.

# Load tags book_tags data from csv
book_tags = pd.read_csv('../data/book_tags.csv')
tags = pd.read_csv('../data/tags.csv') # Merge book_tags and tags tags_join = pd.merge(book_tags, tags, left_on='tag_id', right_on='tag_id', how='inner') # Merge tags_join and books
books_with_tags = pd.merge(books, tags_join, left_on='book_id', right_on='goodreads_book_id', how='inner') # Store tags into the same book id row
temp_df = books_with_tags.groupby('book_id')['tag_name'].apply(' '.join).reset_index()
temp_df.head(5) # Merge tag_names back into books
books = pd.merge(books, temp_df, left_on='book_id', right_on='book_id', how='inner')

We now have book tags all in one dataset.

3. Transform data

We have 10,000 books in the dataset each with 100 book tags. What do these book tags contain?

# Explore book tags
Example book tags for The Hunger Games and Harry Potter and the Philosopher’s Stone

We want to transform these texts into numerical values so we have data that the machine learning algorithm understands. TfidfVectorizer turns text into feature vectors.

# Transform text to feature vectors
from sklearn.feature_extraction.text import TfidfVectorizer
tf = TfidfVectorizer(analyzer='word',ngram_range=(1, 2),min_df=0, stop_words='english')
tfidf_matrix = tf.fit_transform(books['tag_name'])
This becomes a 10000×144268 matrix

TF-IDF (Term Frequency — Inverse Document Frequency) calculates how important words are in relation to the whole document. TF summarizes how often a given word appears within a document. IDF downscales words that appear frequently across documents. This allows TF-IDF to define the importance of words within a document based on the relationship and weighting factor.

4. Build machine learning recommendation engine

Now we build the recommender. We can use cosine similarity to calculate the numeric values that denote similarities between books.

# Use numeric values to find similarities
from sklearn.metrics.pairwise import linear_kernel
cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)

Cosine similarity measures the cosine of the angle between two vectors projected in a multi-dimensional space. The smaller the angle, the higher the cosine similarity. In other words, the closer these book tags are to each other, the more similar the book.

Next we write the machine learning algorithm.

# Get book recommendations based on the cosine similarity score of book tags Build a 1-dimensional array with book titles
titles = books['title']
tag_name = books['tag_name']
indices = pd.Series(books.index, index=books['title']) # Function that gets similarity scores
def tags_recommendations(title): idx = indices[title] sim_scores = list(enumerate(cosine_sim[idx])) sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True) sim_scores = sim_scores[1:11] # How many results to display book_indices = [i[0] for i in sim_scores] title_df = pd.DataFrame({'title': titles.iloc[book_indices].tolist(), 'similarity': [i[1] for i in sim_scores], 'tag_name': tag_name.iloc[book_indices].tolist()}, index=book_indices) return title_df

This is the foundational code we need for a recommendation engine. This is the building block for Amazon’s $98 billion revenue-generating algorithm and others like it. Almost seems too simple. We can stop here or we can expand our code to show more data insights.

# Get book tags, total tags, and percentage of common tags
def recommend_stats(target_book_title): # Get recommended books rec_df = tags_recommendations(target_book_title) # Get tags of the target book rec_book_tags = books_with_tags[books_with_tags['title'] == target_book_title]['tag_name'].to_list() # Create dictionary of tag lists by book title book_tag_dict = {} for title in rec_df['title'].tolist(): book_tag_dict[title] = books_with_tags[books_with_tags['title'] == title]['tag_name'].to_list() # Create dictionary of tag statistics by book title tags_stats = {} for book, tags in book_tag_dict.items(): tags_stats[book] = {} tags_stats[book]['total_tags'] = len(tags) same_tags = set(rec_book_tags).intersection(set(tags)) # Get tags in recommended book that are also in target book tags_stats[book]['%_common_tags'] = (len(same_tags) / len(tags)) * 100 # Convert dictionary to dataframe tags_stats_df = pd.DataFrame.from_dict(tags_stats, orient='index').reset_index().rename(columns={'index': 'title'}) # Merge tag statistics dataframe to recommended books dataframe all_stats_df = pd.merge(rec_df, tags_stats_df, on='title') return all_stats_df

Now we input Lord of the Rings into the recommendation engine and see the results.

# Find book recommendations
lor_recs = recommend_stats('The Fellowship of the Ring (The Lord of the Rings, #1)')
Top 10 recommended books based on book tag similarity score

We get a list of the top 10 most similar books to Lord of the Rings based on book tags. The recommendations look nearly identical to Amazon’s website: as of August 2020

Success! Producing a similarity recommendation was part one. Part two is producing diversity.

5. Build diversity recommendation engine proof of concept

This next part is where evolution happens. The diversity recommendation algorithm does not currently exist (publicly) so there will be some art to this science. In lieu of spending months in the research and mathematics lab, how can we build a proof of concept that can either validate or invalidate the feasibility of producing a diversity recommendation? Let’s explore the data.

Since we are reverse engineering through the Elon Musk customer lens and wanting the recommender to output Zero to One, let’s find where this book is positioned in relation to Lord of the Rings.

# Find Zero to One book
lor_recs[lor_recs.title == 'Zero to One: Notes on Startups, or How to Build the Future']

In relation to Lord of the Rings, Zero to One is rank 8,592 out of 10,000 books based on similarities. Pretty low. According to the algorithm, these two books are on opposite ends of the spectrum and not similar at all. This book is statistically in the lowest quartile which means neither you nor Elon would be recommended this diversity of thought.

# Calculate statistical data

Using a boxplot, we can better visualize this positioning:

# Boxplot of similarity score
import matplotlib.pyplot as plt
lor_recs.boxplot(column=['similarity']) # Boxplot of percentage of common tags
Positioning based on cosine similarity and common book tags compared to all 10,000 books

Based on your own knowledge, would you say these two books are so extremely different?

We can explore the data further and find the most common book tags using NLTK (Natural Language Toolkit). First, we clean up words such as removing hyphens, tokenize the words, and then remove all the stop words. After the text is clean, we can calculate the top 10 frequent words that appear in the Lord of the Rings book tags.

# Store book tags into new dataframe
lor_tags = pd.DataFrame(books_with_tags[books_with_tags['title']=='The Fellowship of the Ring (The Lord of the Rings, #1)']['tag_name']) # Find most frequent word used in book tags
import matplotlib
import nltk top_N = 10
txt = lor_tags.tag_name.str.lower().str.replace(r'-', ' ')' ') # Remove hyphens
words = nltk.tokenize.word_tokenize(txt)
word_dist = nltk.FreqDist(words) stopwords = nltk.corpus.stopwords.words('english')
words_except_stop_dist = nltk.FreqDist(w for w in words if w not in stopwords) print('All frequencies, including STOPWORDS:')
print('=' * 60)
lor_rslt = pd.DataFrame(word_dist.most_common(top_N), columns=['Word', 'Frequency'])
print('=' * 60)
lor_rslt = pd.DataFrame(words_except_stop_dist.most_common(top_N), columns=['Word', 'Frequency']).set_index('Word')'ggplot')
Most frequent words in the Lord of the Rings book tags

Since we want diversity and variety, we can take the most frequent words “fantasy” and “fiction” and filter by unlike or different words in the context of book genres. These might be words like non-fiction, economics, or entrepreneurial.

# Filter by unlike words
lor_recs_filter = lor_recs[(lor_recs['tag_name'].str.contains('non-fiction')) & (lor_recs['tag_name'].str.contains('economics')) & (lor_recs['tag_name'].str.contains('entrepreneurial'))]

This narrows down the list and only include books that contain “non-fiction”, “economics”, or “entrepreneurial” in the book tags. To ensure our reader is recommended a good book, we merge ‘average_rating’ back into the dataset and sort the results by the highest average book rating.

# Merge recommendations with ratings
lor_recs_filter_merge = pd.merge(books[['title', 'average_rating']], lor_recs_filter, left_on='title', right_on='title', how='inner') # Sort by highest average rating
lor_recs_filter_merge = lor_recs_filter_merge.sort_values(by=['average_rating'], ascending=False)

What appears at the top of the list — Zero to One. We engineered our way into recommending diversity.

There was only one leap of faith, but quite a large one, in linking the relationship between Lord of the Rings and Zero to One. The next step to moving this forward would be in programmatically identifying the relationship between these two books and other books so it becomes reproducible. What is the math and logic that might drive this? The majority of current machine learning and AI recommendation algorithms are based on finding similarities. How can we find diversity instead?

Intuitively we can understand that someone interested in fantasy books can also benefit from learning about entrepreneurship. However, our algorithms currently do not provide this nudge. If we can solve this problem and build a better algorithm, not only will this significantly help the customer but also increase a company’s revenue through more sales and satisfied customers. First, we bridge the gap through domain knowledge and expertise for the book industry. Then we move towards applying this across other industries. If you have thoughts on this, let’s chat. This may be a game-changer.

5.1 An alternate diversity recommendation engine

Here’s an alternative solution. Instead of recommendations based on book tags of the individual book, we can make recommendations based on categories. The rule: each recommendation can still have similarities to the original book, but it must be a unique category.

For example, Amazon has 5 results to display book recommendations. as of August 2020

Instead of 5 books with similar book tags to Lord of the Rings, the result should be 5 books with similar book tags, but with a different category to Fantasy, such as Science Fiction. Subsequent results would follow the same logic. This would “diversify” the categories of books the user sees, while maintaining a chain of relevancy. It would display: Fantasy → Science Fiction → Technology → Entrepreneurship → Biographies.

Within these categories, individual books can be ranked and sorted based on similarities to the initial book or by user ratings. Each slot presents the book within that category with most relevancy to the original book.

Slot 1: Fantasy
Slot 2: Science Fiction
Slot 3: Technology
Slot 4: Entrepreneurship
Slot 5: Biographies

This categorization becomes a sort within a sort to ensure a quality, relevant, and diverse read. This concept could more systematically connect books like Lord of the Rings and Zero to One together, eventually scaling to different product types or industries, such as music.

6. Design mockups

How might we design this? We could deploy our algorithm to allocate a certain percentage to the exploration of diversity recommendations, such as a split of 70% similarity and 30% diversity recommendations.

One potential user gave feedback that they would like to see a “diversity switch”. Let’s mockup this potential design.

Design mockup of with a “diversity recommendation” switch

Once the customer switches it on, we can keep 3 books as the usual similarity recommendations and the next 2 as our diversity recommendations.

Switching on diversity

In our product metrics, we would track the number and percentage of times users interact with this switch, increase/decrease in the number of product pages visited, other subsequent user flow behaviors, and conversion rate of purchasing the recommended books or other products. As a potential customer, what are your thoughts on this?

7. Business value hypothesis and target launch

What is the potential business value behind this idea? We can start by narrowing down the target customer launch to USA customers that have in the past 12 months: purchased a book, searched for fantasy books, and bought from multiple book categories. Conservative assumptions:

USA customers: 112 million
x book buying customers: 25%
x searches for fantasy: 25%
x buys multiple categories: 25%
= roll out to 1.75 million customers x conversion rate: 10%
= 175,000 customers convert x increase in average annual spend $40
= $7 million additional annual revenue

In 2019, the average Amazon customer spend was about $600 per year and Amazon’s annual revenue was $280 billion. This estimate is light in comparison which is good as an initial rollout test. If we increase the scope of the launch we will get a larger potential value. Let’s expand our reach and roll this out to all USA Amazon customer that has purchased a book, with a conservative assumption of 25%:

USA customers: 112 million
x book buying customers: 25%
= roll out to 28 million customers x conversion rate: 10%
= 2.8 million customers convert x increase in average annual spend $40
= $112 million additional annual revenue

Finally, if we are more aggressive and assume half of Amazon customers can be book buyers, increase the conversion rate, increase the average annual spend uptick, we get into the billions of additional value:

USA customers: 112 million
x book buying customers: 50%
= roll out to 56 million customers x conversion rate: 20%
= 28 million customers convert x increase in average annual spend $90
= $1 billion additional annual revenue

The potential pitfall is that this new recommender system negatively impacts the customer experience and decreases the conversion rate, which becomes a revenue loss. This is why initial customer validation and smaller launch and test plans are a good starting point.

The business value upside is significant seeing as how 35% ($98 billion) of Amazon’s revenues were generated through recommendation systems. Even a small percentage improvement in the algorithm would amount to millions and billions of additional revenue.

The end to end data science process of building a recommender system

What’s Next?

This proof of concept illustrates the potential for diversity recommender systems to improve customer experiences, address societal problems, add significant business value, as well as outlines a feasible data science process to improve our technology.

Through better machine learning and AI recommender systems, technology can enable more diversity of thought. Society’s current day ethos says that similarities are good, whereas being diverse can be mixed. Perhaps that is why the majority of recommendation systems research and development today has only focused on finding similarities. If we do not implement change within our algorithms, the status quo will give us more of the same.

Technology to enable a diversity of thought

Imagine the world where diversity of thought is enabled across all the products that influence us every day. How might that change the world for you, your friends, and people who think differently to you? Machine learning and AI recommendation algorithms can be a powerful change agent. If we continue pursuing this path of diversity, we can positively impact the people and the world around us.

This article was originally published on Towards AI and re-published to TOPBOTS with permission from the author.

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Artificial Intelligence

Global Economic Impact of AI: Facts and Figures



Sharmistha Chatterjee Hacker Noon profile picture

@sharmi1206Sharmistha Chatterjee

Summarization of Research Insights from Emerj, Harvard Business Review, MIT Sloan, and Mckinsey

Wall Street, venture capitalists, technology executives, data scientists — all have important reasons to understand the growth and opportunity in the artificial intelligence market to access business growth and opportunities. This gives them insights on funds invested in AI and analytics as well potential revenue growth and turnover. Indeed, the growth of AI, continuing research, development of easier open source libraries and applications in small to large scale industries are sure to revolutionize the industry the next two decades and the impact is getting felt in almost all the countries worldwide.

To dive deep into the growth of AI and future trends, an insight into the type and size of the market is essential along with (a) AI-related industry market research forecasts and (b) data from reputable research sources for insight into AI valuation and forecasting.

The blog is structured as follows :

  • To provide a short consensus on well-researched projections of AI’s growth and market value in the coming decade.
  • To understand the per capita income and GDP of each country from businesses driven by AI and analytics.

Impact of AI is so widespread, touching and vivid that:

IBM’s CEO claims a potential $2 trillion dollar market for “cognitive computing”).

Google co-founder Larry Page states that “Artificial intelligence would be the ultimate version of Google. The ultimate search engine is capable of understanding everything on the web. It will become so much AI driven that in near future ,it would understand exactly what you wanted and it would give you the right thing. We’re nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we’re working on”.

Different sectors exhibit dynamics in terms of adopting and absorbing AI, leading to different levels of economic impact.


On comparing different industry-sectors we see from the figure above:

In high-tech industries like Telecom and media has already adopted AI relatively rapidly and looking for transformations in all possible avenues. They are then followed by Consumer, Financial Services and Professional Services.

Healthcare and Industrial Sector are adopting AI slowly. Energy and Public Sector are the slowest adaptors to this transition.

Further, the economic impact in the telecom and high-tech sector could be more than double that of healthcare in 2030. If the national average of macroeconomic impact is 100, healthcare might experience 40 percent lower impact (i.e. 60). The fast and rapid adopters like the telecom and high-tech sector are highly influenced by AI and could experience 40 percent higher impact (i.e. 140) than the national average.

Several internal and external factors specific to a country or a state, have been known to affect AI-driven productivity growth, including labor automation, innovation, and new competition. In addition, certain micro factors, such as the pace of adoption of AI, and macro factors such as a country’s global connectedness and labor-market structure also plays a certain factor to the size of the impact.

The end result is to grow the AI value chain and boost the ICT sector, making an important economic contribution to an economy.

Production channels: Direct economic impact of AI aims to automate production and save cost. It primarily considers three production dimensions. Firstly it includes calling labor and capital “augmentation”, where new AI capacity is developed, deployed, and operated by new engineers and big data analysts. Second, investment in AI technologies saves labor as machines take over tasks that humans currently perform. Thirdly, better AI-driven innovation saves overall cost (including infrastructure), enabling firms to produce the same output with the same or lower inputs.

Augmentation: Relates to increased use of productive AI-driven labor and capital.

Substitution: AI-driven technologies offer better results in the field like automation, where it has been found to be more cost-effective. It has also discovered ways and means to substitute other factors of production. Advanced economies could gain about 10 to 15 percent of the impact from labor substitution, compared with an impact of 5 to 10 percent in developing economies.

Product and service innovation and extension: Motivation for investment in AI beyond labor substitution can produce additional economic output by expanding firms’ portfolios, increasing channels for products and services (for e.g. AI-based recommendations), developing new business models, or combination of the three.

Externality channels: It serves as one of the external channels where the application of AI tools and techniques can contribute to economic global flows (for e.g. chatbots, news aggregation engines). Such flow happens inter-country (states and geographical boundaries) and even between countries that facilitate more efficient cross-border commerce. It is found that countries that are more connected and participate more in global flows would clearly benefit more from AI. Further AI could boost supply chain efficiency, reduce complexities associated with global contracts, classification, and trade compliance.

Wealth creation and reinvestment: AI is contributing to higher productivity of economies, efficiency gains. Further innovations result in an increase in wages for workers, entrepreneurs, and firms in the form of profits, higher consumption, and more productive investment.

Transition and implementation costs: Several costs incurred while executing the transition to AI like organization restructuring costs, adoption to new solutions, integration costs, and associated project and consulting fees are known to affect the transition in a negative way. Businesses should do a trade-off between cost and benefit analysis and correctly strategize their roadmap.

Negative externalities: AI could induce major negative distributional externalities affecting workers by depressing the labor share of income and potential economic growth.

The following figure illustrates the detailed overall economic impact sustained due to the wider adoption of AI techniques and strategies by businesses.


AI-driven businesses have led to a positive impact on the growth of revenue over consecutive years. More so, the statements made by renowned founders, CEOs, entrepreneurs and visionary leaders is evident from the figure below as it shows the impact of AI on global GDP, the maximum being obtained from venture-backed startups.


“Tractica forecasts that the revenue generated from the direct and indirect application of AI software is estimated to grow from $643.7 million in 2016 to $36.8 billion by 2025. This represents a significant growth curve for the 9-year period with a compound annual growth rate (CAGR) of 56.8%.”

Tractica has taken a conservative adoption of AI in the hedge fund and investment community, with an assumption that roughly 50% of the hedge fund assets traded by 2025 will be AI-driven. Under this estimate, the algorithmic trading use case remains the top use case among the 191 use cases identified by Tractica.

Further as per reports from Tractica, the market for enterprise AI systems will increase from $202.5 million in 2015 to $11.1 billion by 2024, as depicted in the following figure.

View of Worldwide growth of AI revenue, Source — Tractica

The growth forecasts over the next decade clearly show China’s dominance over the AI market yielding a significant increase in GDP, followed by USA, Nothern Europe, and other nations.

In China, AI is projected to give the economy a 26% boost over the next 13 years, measuring an equivalent of an extra $7 trillion in GDP, helping China to rise to the top. As North America’s companies are widely using AI, the adaptation is at an accelerating phase that it can expect a 14.5% increase in GDP, worth $3.7 trillion.

As the GDP growth varies across continents and nations, the level of AI absorption also varies significantly between the country groups with the most and the least absorption. The below figure demonstrates statistics of economies with higher readiness to benefit from AI. Such countries achieve absorption levels about 11 percentage points higher than those of slow adopters by 2023, and this gap looks set to widen to about 23 percentage points by 2030. This further gives an indication of the digital divide created from AI, between advanced and developing economies.

Source: Mckinsey

The resulting gap in net economic impact between the country groups with the highest economic gains and those with the least is likely to become larger, for e.g. a large gap in economic impact between the leading and the lagging — between Sweden and Zambia. The gap could widen from three percentage points in 2025 to 19 percentage points in 2030 in terms of net GDP impact.

AI is internationally recognized as the main driver of future growth and productivity, innovation, competitiveness and job creation for the 21st century. However, there remain certain technical challenges, that need to be overcome to take it to the next step. The key challenges include

  • Labeled training data
  • Obtaining sufficiently large data sets. 
  • Difficulty explaining results
  • Difficulty generalizingScaling challengesRisk of bias

Apart from the common technical challenges, risks, and barriers faced by organisations implementing AI are evident.

It is now the responsibility of policymakers and business leaders to take measurable actions to address the challenges, support researchers, data scientists, business analysts, and all included in the AI ecosystem to drive the economy with huge momentum.

As rightly quoted by Stephen Hawking, Famous Theoretical Physicist, Cosmologist, and Author:

“Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks.”


  1. Valuing the Artificial Intelligence Market, Graphs and Predictions:
  3. USA-China-EU plans for AI: where do we stand:


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This AI Prevents Bad Hair Days



Louis Bouchard Hacker Noon profile picture

@whatsaiLouis Bouchard

I explain Artificial Intelligence terms and news to non-experts.

Could this be the technological innovation that hairstylists have been dying for? I’m sure a majority of us have had a bad haircut or two. But hopefully, with this AI, you’ll never have to guess what a new haircut will look like ever again.

This AI can transfer a new hairstyle and/or color to a portrait to see how it would look like before committing to the change. Learn more about it below!

Watch the video


►The full article:

►Peihao Zhu et al., (2021), Barbershop,

►Project link:


Video Transcript


This article is not about a new technology in itself.


Instead, it is about a new and exciting application of GANs.


Indeed, you saw the title, and it wasn’t clickbait.


This AI can transfer your hair to see how it would look like before committing to the




We all know that it may be hard to change your hairstyle even if you’d like to.


Well, at least for myself, I’m used to the same haircut for years, telling my hairdresser


“same as last time” every 3 or 4 months even if I’d like a change.


I just can’t commit, afraid it would look weird and unusual.


Of course, this all in our head as we are the only ones caring about our haircut, but


this tool could be a real game-changer for some of us, helping us to decide whether or


not to commit to such a change having great insights on how it will look on us.


Nonetheless, these moments where you can see in the future before taking a guess are rare.


Even if it’s not totally accurate, it’s still pretty cool to have such an excellent approximation


of how something like a new haircut could look like, relieving us of some of the stress


of trying something new while keeping the exciting part.


Of course, haircuts are very superficial compared to more useful applications.


Still, it is a step forward towards “seeing in the future” using AI, which is pretty cool.


Indeed, this new technique sort of enables us to predict the future, even if it’s just


the future of our haircut.


But before diving into how it works, I am curious to know what you think about this.


In any other field: What other application(s) would you like to see using AI to “see into


the future”?


It can change not only the style of your hair but also the color from multiple image examples.


You can basically give three things to the algorithm:


a picture of yourself a picture of someone with the hairstyle you


would like to have and another picture (or the same one) of the hair


color you would like to tryand it merges everything on yourself realistically.


The results are seriously impressive.


If you do not trust my judgment, as I would completely understand based on my artistic


skill level, they also conducted a user study on 396 participants.


Their solution was preferred 95 percent of the time!


Of course, you can find more details about this study in the references below if this


seems too hard to believe.


As you may suspect, we are playing with faces here, so it is using a very similar process


as the past papers I covered, changing the face into cartoons or other styles that are


all using GANs.


Since it is extremely similar, I’ll let you watch my other videos where I explained how


GANs work in-depth, and I’ll focus on what is new with this method here and why it works


so well.


A GAN architecture can learn to transpose specific features or styles of an image onto




The problem is that they often look unrealistic because of the lighting differences, occlusions


it may have, or even simply the position of the head that are different in both pictures.


All of these small details make this problem very challenging, causing artifacts in the


generated image.


Here’s a simple example to better visualize this problem, if you take the hair of someone


from a picture taken in a dark room and try to put it on yourself outside in daylight,


even if it is transposed perfectly on your head, it will still look weird.


Typically, these other techniques using GANs try to encode the pictures’ information and


explicitly identify the region associated with the hair attributes in this encoding


to switch them.


It works well when the two pictures are taken in similar conditions, but it won’t look real


most of the time for the reasons I just mentioned.


Then, they had to use another network to fix the relighting, holes, and other weird artifacts


caused by the merging.


So the goal here was to transpose the hairstyle and color of a specific picture onto your


own picture while changing the results to follow the lighting and property of your picture


to make it convincing and realistic all at once, reducing the steps and sources of errors.


If this last paragraph was unclear, I strongly recommend watching the video at the end of


this article as there are more visual examples to help to understand.


To achieve that, Peihao Zhu et al. added a missing but essential alignment step to GANs.


Indeed, instead of simply encoding the images and merge them, it slightly alters the encoding


following a different segmentation mask to make the latent code from the two images more




As I mentioned, they can both edit the structure and the style or appearance of the hair.


Here, the structure is, of course, the geometry of the hair, telling us if it’s curly, wavy,


or straight.


If you’ve seen my other videos, you already know that GANs encode the information using




This means it uses kernels to downscale the information at each layer and makes it smaller


and smaller, thus iteratively removing spatial details while giving more and more value to


general information to the resulting output.


This structural information is obtained, as always, from the early layers of the GAN,


so before the encoding becomes too general and, well, too encoded to represent spatial




Appearance refers to the deeply encoded information, including hair color, texture, and lighting.


You know where the information is taken from the different images, but now, how do they


merge this information and make it look more realistic than previous approaches?


This is done using segmentation maps from the images.


And more precisely, generating this wanted new image based on an aligned version of our


target and reference image.


The reference image is our own image, and the target image the hairstyle we want to




These segmentation maps tell us what the image contains and where it is, hair, skin, eyes,


nose, etc.


Using this information from the different images, they can align the heads following


the target image structure before sending the images to the network for encoding using


a modified StyleGAN2-based architecture.


One that I already covered numerous times.


This alignment makes the encoded information much more easily comparable and reconstructable.


Then, for the appearance and illumination problem, they find an appropriate mixture


ratio of these appearances encodings from the target and reference images for the same


segmented regions making it look as real as possible.


Here’s what the results look like without the alignment on the left column and their


approach on the right.


Of course, this process is a bit more complicated, and all the details can be found in the paper


linked in the references.


Note that just like most GANs implementations, their architecture needed to be trained.


Here, they used a StyleGAN2-base network trained on the FFHQ dataset.


Then, since they made many modifications, as we just discussed, they trained a second


time their modified StleGAN2 network using 198 pairs of images as hairstyle transfer


examples to optimize the model’s decision for both the appearance mixture ratio and


the structural encodings.


Also, as you may expect, there are still some imperfections like these ones where their


approach fails to align the segmentation masks or to reconstruct the face.Still, the results


are extremely impressive and it is great that they are openly sharing the limitations.


As they state in the paper, the source code for their method will be made public after


an eventual publication of the paper.


The link to the official GitHub repo is in the references below, hoping that it will


be released soon.


Thank you for watching!


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Artificial Intelligence

The rising importance of Fintech innovation in the new age



The rising importance of Fintech innovation in the new age

The rise of fintech has opened an array of opportunities for smart cities to develop and thrive. Its importance has actually increased in the age of the pandemic that calls for social distancing or contactless transactions.

The leading global payment solutions provider Visa recently indicated the increasing role of digital payments. Thanks to the expanding role of fintech, digital payments are expected to enter different smart city sectors.

Reportedly, fintech application is going to be instrumental in the transportation sector. It will come to people in different forms of contactless payments. It will also ease the process of paying for parking or hiring bikes and scooters.

More than that, whether it’s about loans, money transfer, investment, accounting and bookkeeping, airtime or fundraising. Smart cities and businesses are going to hugely rely on fintech in the coming future. 

Going ahead, we are delving into understanding the fintech situation in three smart cities. All three are important fintech hubs that the entire world looks upon.


In the smart city culture, London has the reputation of being the ‘fintech capital’ of the world. The number of fintech giants in the city is valued at more than $1 billion.

However, the pandemic has caused a number of businesses to shut down. At the same time, it has also catalysed the shift to digital and contactless. Businesses are now adopting new ways to support their customers.

Even in this time of crisis, London is at the foremost position of producing the next generation of fintech leaders. This is as per the Ed Lane, VP of Sales for the EMEA region at nCino, a US-based cloud banking provider. 

Remote work is becoming a necessity due to COVID-19. Hence, investments in different technologies and solutions in financial organisations and service providers are “more important than ever”. And so Lane claims that this has increased the adoption of cloud-based banking software developed by his firm. 

The UK recently introduced the Bounce Back Loan Scheme and the Coronavirus Business Interruption Loan Scheme (CBILS). This is helping Lane’s company nCino and others. They are offering a Bank Operating System to aid SMEs with effective processing of loan applications. 

Fintech companies are surviving and tapping into benefits in the COVID-19 age due to their disruptive mindset. The crash of 2001 and the financial crash of 2008 are drivers that lead them to become proactive.

Innovatively, fintech companies started offering mobile banking, online money management tools and other personalised solutions. Today, the same is enabling them to prevail during this pandemic. Besides all, partnerships have proven to be key strategies in achieving even the impossible, as experts say. 


Singapore is showcasing a pioneering move in the fintech industry. Fintech is at the core of Singapore’s vision to become a ‘Smart Nation’ with a “Smart Financial Centre.”

To achieve the dream, the city-state has been showing constant efforts by using innovative technology. With this, it intends to pave the way for new opportunities, enhance efficiency and improve national management of financial risks.

Until 2019, Singapore was already home to over 600 fintech firms. These companies attracted more than half of the total funding for the same year. And amidst the COVID-19 pandemic, the Monetary Authority of Singapore (MAS) introduced two major support packages.

First on April 8, 2020, it announced a S$125 million COVID-19 care package for the financial and fintech sectors. This package aims at aiding the sectors in fighting the challenges from the COVID-19 health crisis. It will help in supporting workers, accelerate digitalisation, and improve operational readiness and resilience. 

Second, on May 13, 2020, MAS, the Singapore Fintech Association (SFA) and AMTD Foundation launched the MAS-SFA-AMTD Fintech Solidarity Grant. The S$6 million grant proposes to support Singapore-based fintech firms.

A specific focus is on managing cash flow, producing new sales and seeking growth strategies. At the individual level, many industry participants have launched their own initiatives to support the sector.

Hong Kong

HongKong’s fintech startup sector tells us a different story which involves the role of blockchain. Blockchain-based companies are dominating the city’s startup sector.

In 2019, enterprise DLT and crypto-assets exchanges earned rankings as the most popular sectors in Hong Kong’s fintech industry. The report comes from the Financial Services and Treasury Bureau. It confirms that blockchain startups make up 40% of the 57 Fintech firms established in the city in 2019.

As per reports, 45% of new companies are focused on developing applications for large businesses. This is the reason that enterprise blockchain firms were the most popular. Another 27% account for blockchain-related firms in Hong Kong involved in digital currency.  

The increase in the number of blockchain-based fintech startups is due to the Special Administrative Region of the People’s Republic of China. The authority introduced new policies towards blockchain tech development – making it a priority.

Blockchain is thriving in Hong Kong due to a number of reasons. The city has laid down clear regulatory guidelines for blockchain-related businesses. Many have leveraged the benefits of the QMAS program. It enables applicants to settle down in the region before having to look for employment. This has immensely encouraged several blockchain specialists to move to Hong Kong.

The city government is also entering partnerships to expand its fintech footprint in the right direction. For example, in November 2019, the government collaborated with Thailand’s officials to explore the development of Central Bank Digital Currencies (CBDCs). Blockchain is a promising technology for the fintech industry. It supports quick, secure and cost-effective transaction-related services.

More importantly, it provides transparency that other traditional technologies were not capable of. Thanks to the use of encrypted distributed ledgers. These enable real-time verification of transactions without the need for mediators such as correspondent banks.

Why Is Fintech Innovation Important For The Development Of Smart Cities?

Fintech Boosting Business And Growth Opportunities In Smart Cities

Advanced cities that are now smart cities have been using fintech for their development. With that, they are also leading the way for others to follow. Many experts confirm that innovation in fintech is a must for any city to become a ‘smart city.’

It enables easy national as well as international business. For the residents, it makes life more convenient by encouraging contactless, economical, sustainable and efficient payment-related operations. 

One important aspect that smart city development and fintech innovation has in common is their determination to cut bureaucracy. A city that manages to enable speedy and inexpensive international transfers will also enable its citizens with greater access to the global market. This is as said by Hans W. Winterhoff from KPMG in one of his articles.

Furthermore, fintech innovations of the past have demonstrated their success. Some fintech applications have simplified procedures that became unnecessarily complex over time. Traditional banking services are one of the biggest examples. 

The innovative fintech services opened doors for online shopping and easy international money transfers. Fintech is able to provide the same product or service to consumers. But that’s happening in less time, with fewer steps, and at more affordable rates.

Besides, transparency is another important factor that is allowing consumers to have faith in fintech services. With the current potential of fintech, we can now say that it is one of the essential pillars of successful smart city development. The results are already here in the age of this pandemic.

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What Waabi’s launch means for the self-driving car industry



Elevate your enterprise data technology and strategy at Transform 2021.

It is not the best of times for self-driving car startups. The past year has seen large tech companies acquire startups that were running out of cash and ride-hailing companies shutter costly self-driving car projects with no prospect of becoming production-ready anytime soon.

Yet, in the midst of this downturn, Waabi, a Toronto-based self-driving car startup, has just come out of stealth with an insane amount of $83.5 million in a Series A funding round led by Khosla Ventures, with additional participation from Uber, 8VC, Radical Ventures, OMERS Ventures, BDC, and Aurora Innovation. The company’s financial backers also include Geoffrey Hinton, Fei-Fei Li, Peter Abbeel, and Sanja Fidler, artificial intelligence scientists with great influence in the academia and applied AI community.

What makes Waabi qualified for such support? According to the company’s press release, Waabi aims to solve the “scale” challenge of self-driving car research and “bring commercially viable self-driving technology to society.” Those are two key challenges of the self-driving car industry and are mentioned numerous times in the release.

What Waabi describes as its “next generation of self-driving technology” has yet to pass the test of time. But its execution plan provides hints at what directions the self-driving car industry could be headed.

Better machine learning algorithms and simulations

According to Waabi’s press release: “The traditional approach to engineering self-driving vehicles results in a software stack that does not take full advantage of the power of AI, and that requires complex and time-consuming manual tuning. This makes scaling costly and technically challenging, especially when it comes to solving for less frequent and more unpredictable driving scenarios.”

Leading self-driving car companies have driven their cars on real roads for millions of miles to train their deep learning models. Real-road training is costly both in terms of logistics and human resources. It is also fraught with legal challenges as the laws surrounding self-driving car tests vary in different jurisdictions. Yet despite all the training, self-driving car technology struggles to handle corner cases, rare situations that are not included in the training data. These mounting challenges speak to the limits of current self-driving car technology.

Here’s how Waabi claims to solve these challenges (emphasis mine): “The company’s breakthrough, AI-first approach, developed by a team of world leading technologists, leverages deep learning, probabilistic inference and complex optimization to create software that is end-to-end trainable, interpretable and capable of very complex reasoning. This, together with a revolutionary closed loop simulator that has an unprecedented level of fidelity, enables testing at scale of both common driving scenarios and safety-critical edge cases. This approach significantly reduces the need to drive testing miles in the real world and results in a safer, more affordable, solution.”

There’s a lot of jargon in there (a lot of which is probably marketing lingo) that needs to be clarified. I reached out to Waabi for more details and will update this post if I hear back from them.

By “AI-first approach,” I suppose they mean that they will put more emphasis on creating better machine learning models and less on complementary technology such as lidars, radars, and mapping data. The benefit of having a software-heavy stack is the very low costs of updating the technology. And there will be a lot of updating in the coming years as scientists continue to find ways to circumvent the limits of self-driving AI.

The combination of “deep learning, probabilistic reasoning, and complex optimization” is interesting, albeit not a breakthrough. Most deep learning systems use non-probabilistic inference. They provide an output, say a category or a predicted value, without giving the level of uncertainty on the result. Probabilistic deep learning, on the other hand, also provides the reliability of its inferences, which can be very useful in critical applications such as driving.

“End-to-end trainable” machine learning models require no manual-engineered features. This means once you have developed the architecture and determined the loss and optimization functions, all you need to do is provide the machine learning model with training examples. Most deep learning models are end-to-end trainable. Some of the more complicated architectures require a combination of hand-engineered features and knowledge along with trainable components.

Finally, “interpretability” and “reasoning” are two of the key challenges of deep learning. Deep neural networks are composed of millions and billions of parameters. This makes it hard to troubleshoot them when something goes wrong (or find problems before something bad happens), which can be a real challenge in critical scenarios such as driving cars. On the other hand, the lack of reasoning power and causal understanding makes it very difficult for deep learning models to handle situations they haven’t seen before.

According to TechCrunch’s coverage of Waabi’s launch, Raquel Urtasan, the company’s CEO, described the AI system the company uses as a “family of algorithms.”

“When combined, the developer can trace back the decision process of the AI system and incorporate prior knowledge so they don’t have to teach the AI system everything from scratch,” TechCrunch wrote.

self-driving car simulation carla

Above: Simulation is an important component of training deep learning models for self-driving cars. (credit: CARLA)

Image Credit: Frontier Developments

The closed-loop simulation environment is a replacement for sending real cars on real roads. In an interview with The Verge, Urtasan said that Waabi can “test the entire system” in simulation. “We can train an entire system to learn in simulation, and we can produce the simulations with an incredible level of fidelity, such that we can really correlate what happens in simulation with what is happening in the real world.”

I’m a bit on the fence on the simulation component. Most self-driving car companies are using simulations as part of the training regime of their deep learning models. But creating simulation environments that are exact replications of the real world is virtually impossible, which is why self-driving car companies continue to use heavy road testing.

Waymo has at least 20 billion miles of simulated driving to go with its 20 million miles of real-road testing, which is a record in the industry. And I’m not sure how a startup with $83.5 million in funding can outmatch the talent, data, compute, and financial resources of a self-driving company with more than a decade of history and the backing of Alphabet, one of the wealthiest companies in the world.

More hints of the system can be found in the work that Urtasan, who is also a professor in the Department of Computer Science at the University of Toronto, does in academic research. Urtasan’s name appears on many papers about autonomous driving. But one in particular, uploaded on the arXiv preprint server in January, is interesting.

Titled “MP3: A Unified Model to Map, Perceive, Predict and Plan,” the paper discusses an approach to self-driving that is very close to the description in Waabi’s launch press release.

MP3 self-driving neural networks probablistic deep learning

Above: MP3 is a deep learning model that uses probabilistic inference to create scenic representations and perform motion planning for self-driving cars.

The researchers describe MP3 as “an end-to-end approach to mapless driving that is interpretable, does not incur any information loss, and reasons about uncertainty in the intermediate representations.” In the paper researchers also discuss the use of “probabilistic spatial layers to model the static and dynamic parts of the environment.”

MP3 is end-to-end trainable and uses lidar input to create scene representations, predict future states, and plan trajectories. The machine learning model obviates the need for finely detailed mapping data that companies like Waymo use in their self-driving vehicles.

Raquel posted a video on her YouTube that provides a brief explanation of how MP3 works. It’s fascinating work, though many researchers will point out that it not so much of a breakthrough as a clever combination of existing techniques.

There’s also a sizeable gap between academic AI research and applied AI. It remains to be seen if MP3 or a variation of it is the model that Waabi is using and how it will perform in practical settings.

A more conservative approach to commercialization

Waabi’s first application will not be passenger cars that you can order with your Lyft or Uber app.

“The team will initially focus on deploying Waabi’s software in logistics, specifically long-haul trucking, an industry where self-driving technology stands to make the biggest and swiftest impact due to a chronic driver shortage and pervasive safety issues,” Waabi’s press release states.

What the release doesn’t mention, however, is that highway settings are an easier problem to solve because they are much more predictable than urban areas. This makes them less prone to edge cases (such as a pedestrian running in front of the car) and easier to simulate. Self-driving trucks can transport cargo between cities, while human drivers take care of delivery inside cities.

With Lyft and Uber failing to launch their own robo-taxi services, and with Waymo still away from turning One, its fully driverless ride-hailing service, into a scalable and profitable business, Waabi’s approach seems to be well thought.

With more complex applications still being beyond reach, we can expect self-driving technology to make inroads into more specialized settings such as trucking and industrial complexes and factories.

Waabi also doesn’t make any mention of a timeline in the press release. This also seems to reflect the failures of the self-driving car industry in the past few years. Top executives of automotive and self-driving car companies have constantly made bold statements and given deadlines about the delivery of fully driverless technology. None of those deadlines have been met.

Whether Waabi becomes independently successful or ends up joining the acquisition portfolio of one of the tech giants, its plan seems to be a reality check on the self-driving car industry. The industry needs companies that can develop and test new technologies without much fanfare, embrace change as they learn from their mistakes, make incremental improvements, and save their cash for a long race.

Ben Dickson is a software engineer and the founder of TechTalks. He writes about technology, business, and politics.

This story originally appeared on Copyright 2021


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