Data science leader and regular author in “Towards Datascience’ medium publication
Have you ever wondered how the machine learning models are constructed? Today we will explore this and learn some quick techniques on how to find out which variables are influencing the model results and by how much.
We will use the FIFA 2018 dataset on Kaggle and explore the following models:
- Decision Tree model
- Random Forest model
This will be the agenda for today:
- Use the FIFA dataset to train the decision tree model
- Use the FIFA dataset to train the random forest model
- Explore the influential variables in the models
- Find the threshold of the influential variables
So without further ado let’s get started.
1. Use the FIFA dataset to train the decision tree model
Let us first talk a bit about decision trees.
Decision tree algorithms start with a root node from a data sample and then select features based on metrics like Gini impurity or information gain and splits the root nodes into leaf nodes/end nodes till no more split is possible. This is illustrated in the diagram below with a sample tree.
After the data and the libraries have been imported, the following lines of code will help to train the decision tree model.
#Create the dependent variable y = (data['Man of the Match'] == "Yes") #Create the independent variable feature_names = [i for i in data.columns if data[i].dtype in [np.int64]] x = data[feature_names] #Train the decision tree model train_x, test_x, train_y, test_y = train_test_split(x,y, random_state=1) dt_model = DecisionTreeClassifier(random_state=0, max_depth=5, min_samples_split=5).fit(train_x,train_y) pred_y = dt_model.predict(test_x) cm = confusion_matrix(test_y,pred_y) print(cm) accuracy_score(test_y,pred_y)
We will get the following output from the confusion matrix:
[[ 9 7] [ 6 10]] 0.59375
The accuracy of the decision tree model is moderate at 59.38% with (10+9) targets being corrected predicted and (7+6) being false positives and false negatives respectively.
2. Use the FIFA dataset to train the random forest model
Let us now learn a bit about the random forest model and then train the data with it.
Random forest is an ensemble learning algorithm that works by constructing multiple decision trees and outputs the class that is either the mode or the mean prediction of the individual decision trees.
An illustration is given below:
We will now use the code below to train the random forest model.
# Train the RF model rf_model = RandomForestClassifier(n_estimators=100, random_state=1).fit(train_x,train_y) pred_y = rf_model.predict(test_x) cm = confusion_matrix(test_y,pred_y) print(cm) accuracy_score(test_y,pred_y)
The output of the Random forest model is given below:
[[10 6] [ 3 13]] 0.71875
The random forest model has a better accuracy at 71.88% with (10+13) targets identified correctly and (6+3) targets mis-classified-6 being false positives and 3 being false negatives.
3. Explore the influential variables in the models
We will now look at the most influential variables in both the models and how they are affecting the accuracy. We will use ‘PermutationImportance‘ from the ‘eli5‘ library for this purpose. We can do this with only one line of code as given below.
# Import PermutationImportance from the eli5 library from eli5.sklearn import PermutationImportance # Influential variables for Decision Tree model eli5.show_weights(perm, feature_names = test_x.columns.tolist())
The influential variables in the decision tree model is :
The most influential variables in the decision tree model is ‘Goal scored’, ‘On-target’, ‘Distance Covered (Kms)’ and ‘Off-Target’. There are also variables that influence the accuracy negatively like ‘Attempts’ and ‘Corners’ – hence we can drop these variables from the model to increase the accuracy. Some variables like ‘Red’, ‘Ball Possession %’ etc has no influence on the accuracy of the model.
The weights indicate by how much percentage the model accuracy is impacted by the variable when the variables are re-shuffled. For eg: By using the feature ‘Goal Scored’ the model accuracy can be improved by 14.37% in a range of (+-) 11.59%.
The influential variables in the random forest model is :
As you can observe there are significant differences in the variables that influence the 2 models and for the same variable like say ‘Goal Scored’ the percentage of change in accuracy also differs.
4. Find out the threshold of the influential variable at which the changes to model accuracy is happening
Let us now take one variable say ‘Distance Covered (Kms)’ and try to find out the threshold at which the accuracy increases. We can do this easily with Partial dependence plots (PDP).
A partial dependence (PD) plot depicts the functional relationship between input variables and predictions. It shows how the predictions partially depend on values of the input variables.
For eg: We can create a partial dependence plot of the variable ‘Distance Covered (Kms)’ to understand how changes in the values of the variable ‘Distance Covered (Kms)’ affects overall accuracy of the model.
We will start with the decision tree model first.
# Import the libraries from matplotlib import pyplot as plt from pdpbox import pdp, get_dataset, info_plots # Select the variable/feature to plot feature_to_plot = 'Distance Covered (Kms)' # PDP plot for Decision tree model pdp_dist = pdp.pdp_isolate(model=dt_model,dataset=test_x, model_features=feature_names, feature= feature_to_plot) pdp.pdp_plot(pdp_dist, feature_to_plot) plt.show()
The plot will look like this:
If distance covered is 102 KM, then that influences the model positively, but if >102 Km is covered or <102 Km then that does not influence the model.
The PDP (Partial dependence plot) helps to provide an insight into the threshold values of the features that influence the model.
Now we can use the same code for the random forest model and look at the plot :
For the random forest model, the plot looks a bit different and here the performance of the model increases when the distance covered is 99 till about 102; post which the variables has little or no influence on the model as given by the declining trend and the flat line henceforth.
This is how we can use simple PDP plots to understand the behaviour of influential variables in the model. This information can not only draw insights about the variables that impact the model but is especially helpful in training the models and for selection of the right features. The thresholds can also help to create bins that can be used to sub-set the features that can further enhance the accuracy of the model.
Please refer to this link on Github for the full code.
Do reach out to me in case of any questions/comments.
 Abraham Itzhak Weinberg, Selecting a representative decision tree from an ensemble of decision-tree models for fast big data classification (Feb 2019), Springer
 Leo Breiman, Random Forests (Oct 2001), Springer
 Alex Goldstein, Adam Kapelner, Justin Bleich, and Emil Pitkin, Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual
Conditional Expectation (Mar 2004), The Wharton School of the University of Pennsylvania, arxiv.org
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The future growth of AI and ML
By Rachel Roumeliotis
We’ve all come to terms with the fact that artificial intelligence (AI) is transforming how businesses operate and how much it can help a business in the long term. Over the past few years, this understanding has driven a spike in companies experimenting and evaluating AI technologies and who are now using it specifically in production deployments.
Of course, when organisations adopt new technologies such as AI and machine learning (ML), they gradually start to consider how new areas could be affected by technology. This can range across multiple sectors, including production and logistics, manufacturing, IT and customer service. Once the use of AI and ML techniques becomes ingrained in how businesses function and in the different ways in which they can be used, organisations will be able to gain new knowledge which will help them to adapt to evolving needs.
By delving into O’Reilly’s learning platform, a variety of information about the different trends and topics tech and business leaders need to know can be discovered. This will allow them to better understand their jobs and will ensure that their businesses continue to thrive.
Over the last few months, we have analysed the platform’s user usage and have discovered the most popular and most-searched topics in AI and ML. We’ll be exploring some of the most important finding below which gives us a wider picture of where the state of AI and ML is, and ultimately, where it is headed.
AI outpacing growth in ML
First and foremost, our analysis shone a light on how interest in AI is continuing to grow. When comparing 2018 to 2019, engagement in AI increased by 58% – far outpacing growth in the much larger machine learning topic, which increased only 5% in 2019. When aggregating all AI and ML topics, this accounts for nearly 5% of all usage activity on the platform.
While this is just slightly less than high-level, well-established topics like data engineering (8% of usage activity) and data science (5% of usage activity), interest in these topics grew 50% faster than data science. Data engineering actually decreased about 8% over the same time due to declines in engagement with data management topics.
We also discovered early signs that organisations are experimenting with advanced tools and methods. Of our findings, engagement in unsupervised learning content is probably one of the most interesting. In unsupervised learning, an AI algorithm is trained to look for previously undetected patterns in a data set with no pre-existing labels or classification with minimum human supervision or guidance. In 2018, the usage for unsupervised learning topics grew by 53% and by 172% in 2019.
But what’s driving this growth? While the names of its methods (clustering and association) and its applications (neural networks) are familiar, unsupervised learning isn’t as well understood as its supervised learning counterpart, which serves as the default strategy for ML for most people and most use cases.
This surge in unsupervised learning activity is likely driven by a lack of familiarity with the term itself, as well as with its uses, benefits, and requirements by more sophisticated users who are faced with use cases not easily addressed with supervised methods.
It is also likely that that the visible success of unsupervised learning in neural networks and deep learning has helped our interest, as has the diversity of open source tools, libraries and tutorials, that support unsupervised learning.
A Deep Learning Resurrection
While deep learning cooled slightly in 2019, it still accounted for 22% of all AI and ML usage. We also suspect that its success has helped spur the resurrection of a number of other disused or neglected ideas. The biggest example of this is reinforcement learning. This topic experienced exponential growth, growing over 1,500% since 2017.
Even with engagement rates dropping by 10% in 2019, deep learning itself is one of the most popular ML methods among companies that are evaluating AI, with many companies choosing the technique to support production use cases. It might be that engagement with deep learning topics has plateaued because most people are already actively engaging with the technology, meaning growth could slow down.
Natural language processing is another topic that has showed consistent growth. While its growth rate isn’t huge – it grew by 15% in 2018 and 9% in 2019 – natural language processing accounts for about 12% of all AI and ML usage on our platform. This is around 6x the share of unsupervised learning and 5x the share of reinforcement learning usage, despite the significant growth these two topics have experienced over the last two years.
Not all AI/ML methods are treated equally, however. For example, interest in chatbots seems to be waning, with engagement decreasing by 17% in 2018 and by 34% in 2019. This is likely because chatbots were one of the first application of AI and is probably a reflection of the relative maturity of its application.
The growing engagement in unsupervised learning and reinforcement learning demonstrates that organisations are experimenting with advanced analytics tools and methods. These tools and techniques open up new use cases for businesses to experiment and benefit from, including decision support, interactive games, and real-time retail recommendation engines. We can only imagine that organisations will continue to use AI and ML to solve problems, increase productivity, accelerate processes, and deliver new products and services.
As organisations adopt analytic technologies, they’re discovering more about themselves and their worlds. Adoption of ML, in particular, prompts people at all levels of an organisation to start asking questions that challenge what an organisation thinks it knows about itself.
With ML and AI, we’re training machines to surface new objects of knowledge that help us as we learn to ask new, different, and sometimes difficult questions about ourselves. By all indications, we seem to be having some success with this. Who knows what the future holds, but as technologies become smarter, there is no doubt that we will we become more dependent.
As Account Takeover Escalates, Enterprises Strengthen Identity Verification in 2021
Click to learn more about author Robert Prigge.
With over 36 billion records breached in 2020, the amount of personal information on the dark web has skyrocketed, allowing cybercriminals to easily obtain the data needed to impersonate users and take over accounts. In 2021, we’ll see these enterprises take the necessary and long-overdue steps to protect customer data from rising attacks and, in turn, see cybercriminals escalate their fraud game.
are a few trends we expect to see in 2021.
bias in AI algorithms will be a top priority, causing guidelines to be rolled
out for machine learning support of ethnicity for facial recognition.
Enterprises are becoming increasingly concerned about demographic bias in AI algorithms (race, age, gender) and its effect on their brand and potential to raise legal issues. Evaluating how vendors address demographic bias will become a top priority when selecting identity proofing solutions in 2021. According to Gartner, more than 95 percent of RFPs for document-centric identity proofing (comparing a government-issued ID to a selfie) will contain clear requirements regarding minimizing demographic bias by 2022, an increase from fewer than 15 percent today. Organizations will increasingly need to have clear answers to organizations who want to know how a vendor’s AI “black box” was built, where the data originated from, and how representative the training data is to the broader population being served.
As organizations continue to adopt biometric-based facial recognition technology for identity verification, the industry must address the inherent bias in systems. The topic of AI, data, and ethnicity is not new, but it must come to a head in 2021. According to researchers at MIT who analyzed imagery datasets used to develop facial recognition technologies, 77 percent of images were male and 83 percent were white, signaling one of the main reasons why systematic bias exists in facial recognition technology. In 2021, guidelines will be introduced to offset this systematic bias. Until that happens, organizations using facial recognition technology should be asking their technology providers how their algorithms are trained and ensure that their vendor is not training algorithms on purchased data sets.
fraud will become a national crisis.
As transactions have shifted online due to the COVID-19 pandemic, identity fraud will become a major concern across all sectors as institutions struggle to verify their online customers are who they claim to be. In fact, fraudsters have seized opportunities provided by this shift to online transactions, causing networks’ fraud rates to increase by 60 percent. Not only was there more fraud attempted, but the dollar value of each attempted fraudulent transaction was also 5.5 percent higher than it had been the six months preceding the pandemic. Organizations will shift from using data-based approaches of identity proofing (such as using credit bureau or census data) to document-centric identity proofing (using a government-issued ID and a selfie) to verify online users. With traditional authentication methods and data-based identity proofing, there is no way to know if a person logging in is the actual user or someone else using readily-available stolen information from the dark web. In 2021, enterprises will increasingly favor document-centric identity verification to deter fraudulent login attempts.
Government agencies and public institutions are likely to follow suit as COVID-19 related scams have targeted 31 percent of people around the world, and the FBI has specifically flagged a spike in fraudulent unemployment insurance claims related to the pandemic. The FBI’s advice to look out for suspicious communications and charges doesn’t cover all instances of unemployment fraud as fraudsters are able to bypass these communications channels, file fraudulent claims, and steal benefits. Government agencies will likely adapt to the modern fraud landscape by implementing stronger online identity verification to keep citizens safe in 2021 and beyond.
age verification will be essential in 2021 — and tech giants will be held
accountable for who accesses their sites.
As the social harm epidemic continues to accelerate with children being bullied, subjected to predators, and influenced by harmful content at a rapid rate online, technology companies need to take responsibility to protect minors on their platforms. The U.S. is likely to follow in the footsteps of Ofcom, the UK’s first internet watchdog, by implementing new legislation aimed to mitigate social harm, enforce age verification, and remove legal protections for tech companies that fail to police illegal content. And we’re likely to see enterprises start preparing for these laws in 2021. As learning, communications, and social interaction continue remotely into 2021, we’ll see online businesses implement stronger age verification methods (beyond self-reported age) to regulate age-restricted content and purchases while policing age on social platforms to protect minors and ultimately take a stand against social harm.
conversation about online voting for the 2024 U.S. election will start.
To ensure everyone has an equal opportunity to vote in the 2024 election, we can expect to see security professionals and the Cybersecurity and Infrastructure Security Agency (CISA) begin discussions around online voting. As the technology to ensure safe and secure online voting is available, we’ll see if online voting, coupled with online identity proofing, will become a reality as a safer, more secure, and cheaper alternative to mail-in and in-person voting.
will see the rise of stronger and more enforceable data privacy regulations.
With the passing of the California Privacy Rights and Enforcement Act (CCPA) of 2020 and pending legislation on the Improving Digital Identity Act, it’s clear protecting consumer data will be a top priority in 2021. States are likely to follow California in initiating legislation to expand consumers’ rights to prevent companies from being able to collect and share personal data without prior consent or knowledge. We’ll likely see the Improving Digital Identity Act passed, which will create a task force to protect individual privacy, direct the National Institute of Standards and Technology (NIST) to create new standards for government agencies’ digital identity verification services, and establish a grant program to help other states implement more secure digital identity verification.
stuffing will become the #1 global cybersecurity threat as account takeovers
The 36 billion records breached in 2020 will open the door for account takeover attacks via credential stuffing — a type of cyberattack where automated bots use exposed account credentials to gain unauthorized access to user accounts. As 71 percent of accounts are protected by passwords used on multiple websites, credential stuffing will become the top global cybersecurity threat as attacks will be successful in gaining access to multiple accounts, including social media profiles, education portals, banking applications, healthcare sites, and email domains. Once logged in, users can steal benefits, transfer funds, and lock the real user out. Traditional authentication methods (e.g., knowledge-based authentication and the common password) will no longer be relied on to keep accounts protected. In 2021, enterprises will look to stronger forms of biometric-based authentication to keep user data secured and out of the hands of fraudsters.
will weaponize AI in new ways for fraud.
The past decade has given rise to an entire cybercrime ecosystem on the dark web. Increasingly, cybercriminals have gained access to new and emerging technologies to automate their attacks on a massive scale. The dark web has also become a virtual watercooler for cybercriminals to share tips and tricks for scanning for vulnerabilities and perpetrating fraud. The evolution and sophistication of cybercrime will continue in 2021 as criminals leverage artificial intelligence and bots more than ever before.
organizations have adopted artificial intelligence to shore up the attack
surface and thwart fraud, fraudsters are using artificial intelligence to carry
out attacks at scale. In 2021, we will essentially witness an AI arms race, as
companies attempt to stay ahead of the attack curve while criminals aim to
overtake it. We anticipate this at unprecedented levels across several key
- Machine Learning: Bad actors will leverage machine learning (ML) to accelerate attacks on networks and systems, using AI to pinpoint vulnerabilities. As companies continue to digitally transform, spurred by the COVID-19 pandemic, we will witness more fraudsters rapidly leveraging ML to identify and exploit security gaps.
- Attacks on AI: Yes, AI systems can be hacked. Attacks on AI systems are different from traditional attacks and exploit inherent limitations in the underlying AI algorithms that cannot be fixed. The end goal is to manipulate an AI system to alter its behavior — which could have widespread and damaging repercussions, as AI is now a core component in critical systems across all industries. Imagine if someone changed how data is classified and where it is stored at scale. We expect more attacks on AI systems in 2021.
- AI Spear-Phishing Attacks: AI will be used to increase the precision of phishing attacks in 2021. AI-powered spear-phishing email campaigns are hyper-targeted with a specific audience in mind. Scouting information from social media and tailoring attacks to a specific victim can increase the click-through rate by as much as 40 times, and all of this can be automated through sophisticated AI technology. In 2021, cybercriminals will continue to model phishing attacks after human behavior, replicating specific language or tone to drive higher levels of ROI on attack investments.
- Deepfake Videos: Deepfake technology uses AI to combine existing imagery to replace someone’s likeness, closely replicating both their face and voice. Increasingly in 2020, deepfake technology was leveraged for fraud. As more companies adopt biometric verification solutions in 2021, deepfakes will be a highly coveted technology for fraudsters to gain access to consumer accounts. Conversely, technology capable of identifying deepfakes will be of equal importance to organizations leveraging digital identity verification solutions. Organizations must be sure any solution they implement has the sophistication in place to stop these growing attacks, which will be highly utilized by fraudsters in 2021.
By implementing document-centric identity verification, stricter age verification, and new data privacy regulations, enterprises will be equipped to tackle emerging fraud threats in 2021 and in the future.
China Wants to Be the World’s AI Superpower. Does It Have What It Takes?
China’s star has been steadily rising for decades. Besides slashing extreme poverty rates from 88 percent to under 2 percent in just 30 years, the country has become a global powerhouse in manufacturing and technology. Its pace of growth may slow due to an aging population, but China is nonetheless one of the world’s biggest players in multiple cutting-edge tech fields.
One of these fields, and perhaps the most significant, is artificial intelligence. The Chinese government announced a plan in 2017 to become the world leader in AI by 2030, and has since poured billions of dollars into AI projects and research across academia, government, and private industry. The government’s venture capital fund is investing over $30 billion in AI; the northeastern city of Tianjin budgeted $16 billion for advancing AI; and a $2 billion AI research park is being built in Beijing.
On top of these huge investments, the government and private companies in China have access to an unprecedented quantity of data, on everything from citizens’ health to their smartphone use. WeChat, a multi-functional app where people can chat, date, send payments, hail rides, read news, and more, gives the CCP full access to user data upon request; as one BBC journalist put it, WeChat “was ahead of the game on the global stage and it has found its way into all corners of people’s existence. It could deliver to the Communist Party a life map of pretty much everybody in this country, citizens and foreigners alike.” And that’s just one (albeit big) source of data.
Many believe these factors are giving China a serious leg up in AI development, even providing enough of a boost that its progress will surpass that of the US.
But there’s more to AI than data, and there’s more to progress than investing billions of dollars. Analyzing China’s potential to become a world leader in AI—or in any technology that requires consistent innovation—from multiple angles provides a more nuanced picture of its strengths and limitations. In a June 2020 article in Foreign Affairs, Oxford fellows Carl Benedikt Frey and Michael Osborne argued that China’s big advantages may not actually be that advantageous in the long run—and its limitations may be very limiting.
Moving the AI Needle
To get an idea of who’s likely to take the lead in AI, it could help to first consider how the technology will advance beyond its current state.
To put it plainly, AI is somewhat stuck at the moment. Algorithms and neural networks continue to achieve new and impressive feats—like DeepMind’s AlphaFold accurately predicting protein structures or OpenAI’s GPT-3 writing convincing articles based on short prompts—but for the most part these systems’ capabilities are still defined as narrow intelligence: completing a specific task for which the system was painstakingly trained on loads of data.
(It’s worth noting here that some have speculated OpenAI’s GPT-3 may be an exception, the first example of machine intelligence that, while not “general,” has surpassed the definition of “narrow”; the algorithm was trained to write text, but ended up being able to translate between languages, write code, autocomplete images, do math, and perform other language-related tasks it wasn’t specifically trained for. However, all of GPT-3’s capabilities are limited to skills it learned in the language domain, whether spoken, written, or programming language).
Both AlphaFold’s and GPT-3’s success was due largely to the massive datasets they were trained on; no revolutionary new training methods or architectures were involved. If all it was going to take to advance AI was a continuation or scaling-up of this paradigm—more input data yields increased capability—China could well have an advantage.
But one of the biggest hurdles AI needs to clear to advance in leaps and bounds rather than baby steps is precisely this reliance on extensive, task-specific data. Other significant challenges include the technology’s fast approach to the limits of current computing power and its immense energy consumption.
Thus, while China’s trove of data may give it an advantage now, it may not be much of a long-term foothold on the climb to AI dominance. It’s useful for building products that incorporate or rely on today’s AI, but not for pushing the needle on how artificially intelligent systems learn. WeChat data on users’ spending habits, for example, would be valuable in building an AI that helps people save money or suggests items they might want to purchase. It will enable (and already has enabled) highly tailored products that will earn their creators and the companies that use them a lot of money.
But data quantity isn’t what’s going to advance AI. As Frey and Osborne put it, “Data efficiency is the holy grail of further progress in artificial intelligence.”
To that end, research teams in academia and private industry are working on ways to make AI less data-hungry. New training methods like one-shot learning and less-than-one-shot learning have begun to emerge, along with myriad efforts to make AI that learns more like the human brain.
While not insignificant, these advancements still fall into the “baby steps” category. No one knows how AI is going to progress beyond these small steps—and that uncertainty, in Frey and Osborne’s opinion, is a major speed bump on China’s fast-track to AI dominance.
How Innovation Happens
A lot of great inventions have happened by accident, and some of the world’s most successful companies started in garages, dorm rooms, or similarly low-budget, nondescript circumstances (including Google, Facebook, Amazon, and Apple, to name a few). Innovation, the authors point out, often happens “through serendipity and recombination, as inventors and entrepreneurs interact and exchange ideas.”
Frey and Osborne argue that although China has great reserves of talent and a history of building on technologies conceived elsewhere, it doesn’t yet have a glowing track record in terms of innovation. They note that of the 100 most-cited patents from 2003 to present, none came from China. Giants Tencent, Alibaba, and Baidu are all wildly successful in the Chinese market, but they’re rooted in technologies or business models that came out of the US and were tweaked for the Chinese population.
“The most innovative societies have always been those that allowed people to pursue controversial ideas,” Frey and Osborne write. China’s heavy censorship of the internet and surveillance of citizens don’t quite encourage the pursuit of controversial ideas. The country’s social credit system rewards people who follow the rules and punishes those who step out of line. Frey adds that top-down execution of problem-solving is effective when the problem at hand is clearly defined—and the next big leaps in AI are not.
It’s debatable how strongly a culture of social conformism can impact technological innovation, and of course there can be exceptions. But a relevant historical example is the Soviet Union, which, despite heavy investment in science and technology that briefly rivaled the US in fields like nuclear energy and space exploration, ended up lagging far behind primarily due to political and cultural factors.
Similarly, China’s focus on computer science in its education system could give it an edge—but, as Frey told me in an email, “The best students are not necessarily the best researchers. Being a good researcher also requires coming up with new ideas.”
Winner Take All?
Beyond the question of whether China will achieve AI dominance is the issue of how it will use the powerful technology. Several of the ways China has already implemented AI could be considered morally questionable, from facial recognition systems used aggressively against ethnic minorities to smart glasses for policemen that can pull up information about whoever the wearer looks at.
This isn’t to say the US would use AI for purely ethical purposes. The military’s Project Maven, for example, used artificially intelligent algorithms to identify insurgent targets in Iraq and Syria, and American law enforcement agencies are also using (mostly unregulated) facial recognition systems.
It’s conceivable that “dominance” in AI won’t go to one country; each nation could meet milestones in different ways, or meet different milestones. Researchers from both countries, at least in the academic sphere, could (and likely will) continue to collaborate and share their work, as they’ve done on many projects to date.
If one country does take the lead, it will certainly see some major advantages as a result. Brookings Institute fellow Indermit Gill goes so far as to say that whoever leads in AI in 2030 will “rule the world” until 2100. But Gill points out that in addition to considering each country’s strengths, we should consider how willing they are to improve upon their weaknesses.
While China leads in investment and the US in innovation, both nations are grappling with huge economic inequalities that could negatively impact technological uptake. “Attitudes toward the social change that accompanies new technologies matter as much as the technologies, pointing to the need for complementary policies that shape the economy and society,” Gill writes.
Will China’s leadership be willing to relax its grip to foster innovation? Will the US business environment be enough to compete with China’s data, investment, and education advantages? And can both countries find a way to distribute technology’s economic benefits more equitably?
Time will tell, but it seems we’ve got our work cut out for us—and China does too.
Digital commerce predictions for 2021
Prior to 2020, many Canadian businesses were lagging in terms of digital transformation. The global pandemic has permanently altered the way businesses operate. This year, nearly six in 10 organizations have accelerated their digital transformations to meet health and safety guidelines and keep up with changing consumer shopping behaviours. While online shopping isn’t new, this year proved that it’s here to stay, and there is no turning back.
What does the future of digital commerce look like following a year of accelerated transformation? These are some of my predictions on some of the top trends of 2021 as the pandemic, and other factors continue to influence consumer buying behaviour.
Augmented reality redefines retail
Augmented Reality (AR) applications have been on the rise in recent years with virtual “try-before-you-buy” experiences. Due to pandemic related lockdowns, the trend is only increasing as retail brands no longer have the live interaction with their customers they previously depended on.
AR helps bridge the gap between online and offline retail experiences with use cases ranging from previewing furniture and products in your home – from brands like IKEA and Home Depot – to virtually trying on a pair of glasses in Warby Parker’s mobile app without ever having to leave the couch. In terms of more recent innovation, just last week, Google announced its move into the AR space by launching an AR-powered cosmetics try-on experience on Google Search. Once a nice-to-have feature, AR is quickly becoming an essential technology for retailers.
AI takes customer service to new heights
While the past few years have allowed many companies to dip their toe into artificial intelligence, 2020 proved to be the year to dive in headfirst. Newer developments have seen AI-driven technology moving from back-of-house to front-of-house, with customer-facing initiatives such as AI-powered chatbots that enable users to perform their everyday tasks more efficiently, automate customer conversations, predict customer behaviour and increase customer retention.
In 2021, AI will be leveraged to enhance the customer experience by delivering uber personalized guidance and recommendations. For businesses that leverage AI to collect data, the more data they continue to collect and optimize a customer and predict products and services, the more they can build a compelling shopping experience. Add more data, and AI can learn and infer user preference, delivering best-in-class personalization.
The online shopping experience gets personal
The pandemic is driving e-commerce competition to record heights as Canadian consumers spend $52 billion on retail e-commerce this year, increasing 20.7 percent compared to 2019. While sales are booming, it is increasingly challenging for retailers to cut through the noise. Consumers now seek richer experiences when they shop. With the absence of brick-and-mortar, the digital shopping experience requires immersive, informative, and personalized tools.
The future of personalized experiences will be one where retailers can leverage customer data to create one-to-one personalization. More and more, customers will start to receive offers that are highly targeted at them, as individuals, with products, offers, and communications that are uniquely relevant to them. International cosmetics retailer, Sephora, is a prime example of online personalization done right from its personalized emails, Beauty Insider loyalty program and in-store technology. Shoppers’ Beauty Insider profiles are unified across Sephora.com and its mobile app and can be accessed in store to personalize consumers’ shopping experiences, no matter their entry point.
We’re likely to see brands increase their investment in personalization tools to help them stand out in a crowd and prove they know their customers best.
The rise of voice commerce
Voice assistants are seeing their use cases expand beyond checking the weather, operating smart home devices, or searching fun facts on Google. They’ve been quietly taking over the e-commerce industry. Given that we spent more time at home this year, we’ve seen increased interactions with voice assistants as families adopted new technologies and habits around their newly disrupted routines.
The more opportunities consumers have to engage with new technology, such as voice assistants, the more it allows long-lasting habits to set in. This is likely why this year saw a significant growth of independent voice assistants. For example, Houndify is a voice AI platform that allows brands to add smart, conversational interfaces to an Internet connection.
In terms of where voice can take retailers, it has the potential to help brands improve the way they interact with customers, providing more seamless, conversational customer journeys that shepherd them through purchase funnels, customer service encounters, and other types of payments and transactions.
Social commerce is trending
Social commerce made major strides this year as social platforms evolved to meet consumers where they spend most of their time, and I anticipate this trend will only continue to accelerate in 2021. In May, Facebook launched Facebook Shops, enabling businesses to set up a single online store for customers to access Facebook and Instagram. Businesses small and large now have a simplified way to build an e-commerce outlet on the world’s leading social network, and consumers are presented with a frictionless shopping experience without ever leaving the social app. More recently, Instagram has been testing its Shop tab as an evolving e-commerce tool, providing businesses with new revenue opportunities.
While social commerce enables brands to focus on direct selling as the key priority, it also presents an opportunity to increase audience engagement and create awareness as over half (60 per cent) of Instagram’s users learn about new products on the app. Given three in four Canadians (77 per cent) use Facebook daily, and Instagram coming in a close second with 69 per cent daily users, 2021 will be the year to capitalize on this market and increase conversions via social commerce.
These are just two examples of social platforms with built-in checkout functionalities designed to streamline the online shopping experience and facilitate more immediate purchase behaviour in response to user actions. With the increased usage of TikTok and YouTube, the possibilities of social commerce are endless.
Optimize the shopping experience for mobile
As lockdown rules change on a week-by-week basis, many consumers are left with lingering fears over renewed outbreaks, making them wary of returning to stores. Discomfort with physical shopping forced consumers to try digital and mobile commerce in new areas. Grocery shopping is a prime example.
Research from earlier this year found that nearly half of Canadians surveyed said they had bought groceries online in the past six months. Among those who ordered groceries online, 53 per cent said this would be something they would continue to do so long after the pandemic is over. To reach the rising number of mobile customers, Walmart Canada rolled out mobile check-in across the country this fall, so customers can check-in for their grocery orders while on route, making the pickup speed quicker.
As customers continue to make more purchases using mobile devices, brands will need to provide consistent experiences across all devices, from desktop to tablet to mobile, for their online store or risk shopping cart abandonment and above-average bounce rates. The mobile shopping gains we’ve seen this year will likely stick post-pandemic, and I expect the effects of the pandemic will only accelerate long-term trends in mobile usage.
With roughly half (48 per cent) of Canadians using e-commerce platforms more often now than pre-pandemic, technology needs to be part of your go-to-market and business strategy. Consider if your brand is leveraging any of the above-mentioned trends, and if not, how could adoption help your company keep pace with changing consumer habits?
If 2020 has taught us anything, it’s that digital transformation isn’t a concept but a reality.
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