Among emerging technologies, there’s a culture of mistrust around artificial intelligence (AI) in Internet of Things (IoT) devices. IoT with AI integration is in nearly every home device on the market. From Google to Apple to Amazon, the technologies surrounding our lives provide sophisticated experiences.
From smartphones, smart TVs, and smart homes to smart cars and smart security systems, we are more dependent on technology now than any other time in human history. But in this world of innovation, there’s a shadow of uncertainty for how AI is affecting our privacy, control, and business responsibilities.
Almost every major brand is now using AI in some capacity to serve and collect big data. As privacy concerns grow, organizations need to cultivate trust in their brand’s AI capabilities. Ironically, it’s AI itself that can help seed that trust.
<h2 id="Mistrust-in-Technologies" data-renderer-start-pos="1020">Mistrust in Technologies</h2>
There’s no shortage of reasons for why people are concerned about what information AI is gathering and for what purpose. <a href="https://www.internetsociety.org/resources/doc/2019/trust-opportunity-exploring-consumer-attitudes-to-iot/" title="https://www.internetsociety.org/resources/doc/2019/trust-opportunity-exploring-consumer-attitudes-to-iot/" data-renderer-mark="true">Internet Society</a> reported that 63% of people surveyed find connected devices “‘creepy’ in the way they collect data about people and their behaviors.” Users attributed the creepiness factor to IoT devices gathering and using information without permission. One example is when advertisers misuse data that consumers thought was being collected for a different purpose.
Globally, consumers have shown concern about the ethics of this practice. Almost a third of survey participants revealed they are deterred from purchasing a smart device because of mistrust in how their data is used.
In similar findings, researchers from WMG and Computer Science at the University of Warwick conducted a <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0231615" title="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0231615" data-renderer-mark="true">survey of UK consumers</a> designed to measure the adoption and acceptability of smart home devices. They focused on attitudes toward awareness, ownership, experience, trust, satisfaction, and intention to use these technologies. They found that people aren’t convinced that their privacy and security will not be at risk when they use smart home devices.
Privacy and security are the primary reasons consumers won’t purchase smart technologies. But <a href="https://www.pewresearch.org/internet/fact-sheet/mobile/" title="https://www.pewresearch.org/internet/fact-sheet/mobile/" data-renderer-mark="true">96% of Americans own cellphones</a>, so the necessity and dependency on some type of AI devices with IoT integration still supersede privacy concerns.
Businesses need consumers to trust them. Using brand experience can help build relationships with customers while relying on big data to help make sound financial decisions.
<h2 id="Creating-Trust-in-AI-Systems" data-renderer-start-pos="2633">Creating Trust in AI Systems</h2>
Sowing the seeds of trust may appear a Sisyphean effort for businesses reliant on data to improve customer service and compete in a tough market. But there are ways to instill trust in AI-based technologies.
The best way to salve the mistrust is by providing services and solutions that feature <a href="https://www.forbes.com/sites/forbestechcouncil/2020/05/14/even-with-the-help-of-ai-human-conversation-is-essential-to-brand-trust/#c9fbb237f0f0" title="https://www.forbes.com/sites/forbestechcouncil/2020/05/14/even-with-the-help-of-ai-human-conversation-is-essential-to-brand-trust/#c9fbb237f0f0" data-renderer-mark="true">human interaction</a>. Consumers are willing to invest in companies where they feel valued and understood. Conversations about family, weather, or work— even trivial ones— can create bonds of trust and brand loyalty in a way that an automated service can’t. That’s because customers crave<a href="https://medium.com/@BULLDOGDRUMMOND/using-machine-learning-to-build-brand-trust-1d0117f29096" title="https://medium.com/@BULLDOGDRUMMOND/using-machine-learning-to-build-brand-trust-1d0117f29096" data-renderer-mark="true"> intimacy between themselves and the brand</a>.
The need for connecting human-to-human is as necessary as ever, especially during the coronavirus pandemic. There are stories of <a href="https://www.forbes.com/sites/blakemorgan/2020/08/20/10-examples-of-amazing-customer-service-during-covid-19/#b046c6448d18" title="https://www.forbes.com/sites/blakemorgan/2020/08/20/10-examples-of-amazing-customer-service-during-covid-19/#b046c6448d18" data-renderer-mark="true">customer service agents going out of their way</a> to deliver an above-and-beyond customer experience from companies like UPS and Zappos.
However, despite the human connection, organizations that sell devices with AI in IoT integration rely heavily on collecting data. It’s ironic that what’s gathered in devices like Siri, Alexa, and Nest delivers uniquely tailored and highly customized experiences.
The intimacy humans are developing with their smart devices is a double-edged sword of trust and mistrust.
That’s because intimacy begets brand transparency. More and more global businesses are transparent about what they do with the data they collect. Even if the details are in the fine print, consumers want to know how much an organization can protect their privacy and security. This transparency is not only ethical, but in many countries, <a href="https://gdpr.eu/what-is-gdpr/" title="https://gdpr.eu/what-is-gdpr/" data-renderer-mark="true">it’s also the law</a> for companies to disclose data collected from smart devices.
As AI innovations continue, humans will need to learn how to build intimacy and trust with these advanced technologies. Scientists are already observing how to do just that.
One such study is published in the journal Scientific Reports. Dr. Celso de Melo is a computer scientist with the <a href="https://www.nature.com/articles/s41598-020-71919-6" title="https://www.nature.com/articles/s41598-020-71919-6" data-renderer-mark="true">U.S. Army Combat Capabilities Development Command's Army Research Laboratory at CCDC ARL West</a> in Playa Vista, California. He collaborated with researchers from the University of Japan for a study on how people react to AI-controlled robots. They found that emotional expressions can influence human-AI cooperation.

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De Melo says, “Autonomous machines that act on people's behalf are poised to become pervasive in society; however, for these machines to succeed and be adopted, it is essential that people are able to trust and cooperate with them.”
That trust extends to smart systems. As the study shows, communicative expressions like voice and tone cues can lead to human cooperation with AI systems. Many IoT devices with AI are loaded with sentiment analysis that interpret and establish human communication.
Another study focused on how people’s <a href="https://www.sciencedirect.com/science/article/abs/pii/S074756322030354X?via%3Dihub" title="https://www.sciencedirect.com/science/article/abs/pii/S074756322030354X?via%3Dihub" data-renderer-mark="true">attachment styles affect their trust in AI</a>. Key findings show:
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People's attachment anxiety predicts less trust in artificial intelligence.
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Enhancing attachment anxiety reduces trust in artificial intelligence.
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Conversely, enhancing attachment security increases trust in artificial intelligence.
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Professor of Psychology, Omri Gillath, who led the study with an interdisciplinary team from the University of Kansas, says, “Most research on trust in artificial intelligence focuses on cognitive ways to boost trust. Here we took a different approach by focusing on a 'relational affective' route to boost trust, seeing AI as a partner or a team member rather than a device.”
The <a href="https://www.sciencedirect.com/science/article/abs/pii/S074756322030354X?via%3Dihub" title="https://www.sciencedirect.com/science/article/abs/pii/S074756322030354X?via%3Dihub" data-renderer-mark="true">study</a> is published in the journal Computers in Human Behavior. 
A partnership with AI technologies can help enhance customer experience and trust. Consumers have grown to enjoy the benefits of smart devices, smart programs, and smart solutions designed around AI. But as AI has penetrated so much of our lives, how can companies develop this trust with their brands?
<h2 id="Codazen-Solutions" data-renderer-start-pos="6586">Codazen Solutions</h2>
Organizations can develop trust with their customers when they take a strategy-driven approach. Codazen knows about building trustworthy AI solutions. This is why some of the world’s most influential brands trust us with their smart products.
We handle big data responsibly and ethically. Our team of data scientists embed with clients to help them develop AI-based solutions that can humanize customer service, create brand trust, and develop customized user experiences. Using big data, we’re able to craft services and products that are helpful and relevant to consumers.
Our team uses <a href="https://blog.codazen.com/data-in-the-driver-seat-for-auto-industry-future" title="https://blog.codazen.com/data-in-the-driver-seat-for-auto-industry-future" data-renderer-mark="true">machine learning</a> to dramatically upgrade the customer service experience. Your customers’ trust in your brand is priceless. We value it too and we can help you leverage technology ethically to preserve and protect that delicate relationship.
<a href="https://www.codazen.com/contact/" title="https://www.codazen.com/contact/" data-renderer-mark="true">Contact us</a> to learn more about how our capabilities can work for your smart products and services.

Handling data responsibly and ethically can help build user trust in smart technologies.

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Creating Brand Trust With AI in IoT Integration

Artificial intelligence is part of a group of emerging technologies that over the past decade have changed the brand experience financial institutions offer clients. But the tools that create AI are rapidly progressing, and only the financial institutions that use them properly will be able to keep up in the race to the future of finance.
A 2017 <a href="http://www.evergage.com/wp-content/uploads/2016/06/2016-Trends-in-Personalization-Survey-Report-Evergage-final.pdf" title="http://www.evergage.com/wp-content/uploads/2016/06/2016-Trends-in-Personalization-Survey-Report-Evergage-final.pdf" data-renderer-mark="true">survey by Evergage</a>--a cloud-based software--found that 33% of marketers surveyed use AI to deliver personalized web experiences. But, since the onset of the novel coronavirus pandemic, there has been such a large shift in digital consumption that <a href="https://www.technologyreview.com/2020/05/11/1001563/covid-pandemic-broken-ai-machine-learning-amazon-retail-fraud-humans-in-the-loop/" title="https://www.technologyreview.com/2020/05/11/1001563/covid-pandemic-broken-ai-machine-learning-amazon-retail-fraud-humans-in-the-loop/" data-renderer-mark="true">many AI models have been thrown off</a>, forcing companies to retrain their AI algorithms to accurately reflect the changed spending patterns.
If there has ever been a time to catch up in the game, it’s now. Now is the moment when other companies are re-evaluating their AI interactions with society. Now is the time to consider how consumers will rely more on digital AI banking in a post-Covid-19 world.
AI is helping make online banking, digital marketing, social media, and other types of content much more efficient for financial institutions, employees, and clients. Below are five ways AI supports brand experience while helping organizations excel in their business goals:
<h3 id="1.-Recommendation-Systems" data-renderer-start-pos="1447">1. Recommendation Systems</h3>
Recommendation systems shape your world. When you type “What to eat in Paris,” the results that make it on your first page are not pulled from a hat: they are tailored for you. A recommendation system is a component of AI that uses data you provide to personalize experiences with the tools you use. The more data the recommendation system has, the better your personalized recommendations will be.
It’s this AI that keeps so many users scrolling on Facebook to see the next niche video, adding that particular one extra item to their Amazon cart before checkout, and discovering their next favorite series to binge watch on Netflix.
In banking, this same AI analyzes, manages, and optimizes paid ad campaigns targeting specific users. This helps banks respond to the needs of clients in a more specific way. It also helps match specific services to specific customers.
<h3 id="2.-Fraud-and-Theft-Prevention" data-renderer-start-pos="2347">2. Fraud and Theft Prevention</h3>
Fraud detection and theft prevention capabilities have come a long way as more and more people are using mobile devices. The AI toolbox also offers anomaly detectors: tools for finding the irregular patterns that betray fishy activity.
This form of AI helps prevent fraud and theft. <a href="https://www.cognilytica.com/2018/08/09/infographic-8-ways-in-which-ai-is-improving-your-banking-experience/" title="https://www.cognilytica.com/2018/08/09/infographic-8-ways-in-which-ai-is-improving-your-banking-experience/" data-renderer-mark="true">USAA uses AI</a> to monitor client behaviors using apps to analyze patterns of use and detect any anomalies. According to the <a href="https://emerj.com/ai-opportunity-landscape-finserv/" title="https://emerj.com/ai-opportunity-landscape-finserv/" data-renderer-mark="true">AI Opportunity Landscape</a> research, approximately 26% of the venture funding raised for AI in the banking industry is for fraud and cybersecurity applications, more than any other use-case category.
But this technology isn't only to protect clients. <a href="https://emerj.com/ai-sector-overviews/artificial-intelligence-fraud-banking/" title="https://emerj.com/ai-sector-overviews/artificial-intelligence-fraud-banking/" data-renderer-mark="true">Emerj, an artificial intelligence market research firm</a>, reports that detecting fraud within banking entities is a necessary capability. Fraud can happen between merchants and issuers with problems like pricing and the omission of unpaid merchandise.
Clearly, there is a need for these tools for finding fraud. However, this area of research is currently less developed. It can be reassuring that your bank has your back. But because the tools are still unrefined, certain non-threatening purchases will be flagged and require inconvenient approval, while larger costly purchases may fly under the radar. For instance, it seemed like overkill for the bank to need to validate my wife’s $40 purchase for blinds from Home Depot, just because it was her first venture into home improvement. A month later, her purchase of a $1000 electric standing desk required no such approval.
The future of detecting suspicious activity will rely not only on customer data, but on better understanding customers themselves to polish off what this tool in AI can offer. This is a ground on which those financial institutions who really succeed will likely get big returns in savings and in customer retention.
<h3 id="3.-Improve-Customer-Journey-Personalization" data-renderer-start-pos="4229">3. Improve Customer Journey Personalization</h3>
A good customer journey personalization makes for many-a-happy ROI. <a href="https://www.bcg.com/publications/2019/what-does-personalization-banking-really-mean.aspx" title="https://www.bcg.com/publications/2019/what-does-personalization-banking-really-mean.aspx" data-renderer-mark="true">Boston Consulting Group</a>, a global management consulting firm, estimated in a 2019 report that “for every $100 billion in assets that a bank has, it can achieve as much as $300 million in revenue growth by personalizing its customer interactions.”
You want to be validated and understood, right? Well, you’re not the only one. For that exact reason, people are drawn to organizations that reflect an understanding of their own interests. Within the past couple of years, game-changing techniques in deep learning have been added to the AI toolbox. This development has further expanded the space for everyday natural language to drive personalized systems that have been popular in a<a href="https://www.forbes.com/sites/blakemorgan/2019/04/25/20-examples-of-machine-learning-used-in-customer-experience/#18f70c454052" title="https://www.forbes.com/sites/blakemorgan/2019/04/25/20-examples-of-machine-learning-used-in-customer-experience/#18f70c454052" data-renderer-mark="true"> wide variety of customer experience domains</a>.
Because the customer journey is more about creating connections rather than creating sales (ironically thus creating sales), improving the journey means improving ways in which a financial institution guides the customer in their personalized needs. AI can help anticipate a customer’s needs through service and information on various channels, such as emailing product recommendations or inviting a customer to apply for a debt loan. This is a good formula for both quality customer experience and a pleasant brand experience.
<h3 id="4.-Investment-Analysis" data-renderer-start-pos="5602">4. Investment Analysis</h3>

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AI has helped leave the piles of data, human number crunching, and hours of analysis meetings behind. A decade ago, investors depended on the prowess of traders, but as computing technologies expanded, the human need of finding useful patterns in financial time-series data has grown in parallel. This has made it easier than ever for AI to be integrated with longstanding methods to help financial organizations manage wealth, trade, and finance. There’s also a myriad of laws regulating investments in the US, and AI can help financial institutions meet compliance.
Organizations also need to protect their investments. Banks must follow debt collection compliance, and AI can help banks collect in a more humane way. AI contributes to highly personalized user experiences by reaching out to customers through the channels they prefer. By allowing customers to choose how they want to be reminded of their <a href="https://emerj.com/ai-sector-overviews/artificial-intelligence-at-investment-banks-5-current-applications/" title="https://emerj.com/ai-sector-overviews/artificial-intelligence-at-investment-banks-5-current-applications/" data-renderer-mark="true">debt and payments</a>, banks are having higher rates of debt re-payments.
<h3 id="5.-Hyperpersonalization-of-Chatbots-and-Customer-Service" data-renderer-start-pos="6635">5. Hyperpersonal Chatbots and Customer Service</h3>
Chatbots can help financial institutions and customers save time. According to a study from the analyst group, <a href="https://www.juniperresearch.com/analystxpress/july-2017/chatbot-conversations-to-deliver-8bn-cost-saving" title="https://www.juniperresearch.com/analystxpress/july-2017/chatbot-conversations-to-deliver-8bn-cost-saving" data-renderer-mark="true">Juniper Research</a>, chatbots can save at least 4 minutes of a customer service agent’s time. Chatbots are connecting clients to the right service while handling simple requests. Many legacy banks serve clients all around the world and chatbots allow for 24/7 service.
Chatbots are becoming more sophisticated through natural language processing (NLP). They’re now more hyperpersonalized and customers are becoming more accustomed to interacting with them.
Hyperpersonalization also eradicates having too many choices for the customer. In banking, when a client needs to make an appointment for a specific type of loan, hyperpersonalization can steer them to the right loan officer and offer their available times. Recent advances in NLP have made big strides toward understanding human emotion through the rapidly growing subfield of sentiment analysis.
Both sentiment analysis and hyperpersonalization are sets of AI capabilities allowing for the automatic detection of the mood of a speaker, making writing tools such as <a href="https://www.grammarly.com/blog/tone-detector/#:~:text=By%20analyzing%20your%20word%20choice,sound%20to%20someone%20reading%20it.&amp;text=You%20may%20find%20that%20the,as%20sounding%20excited%20and%20joyful" title="https://www.grammarly.com/blog/tone-detector/#:~:text=By%20analyzing%20your%20word%20choice,sound%20to%20someone%20reading%20it.&amp;text=You%20may%20find%20that%20the,as%20sounding%20excited%20and%20joyful" data-renderer-mark="true">Grammarly possible</a>. These same tools can help a financial institution’s ROI when they are applied to areas such as customer service to automatically assess and analyze the feelings and responses of clients.
This kind of automatic analysis provides a guaranteed way to gather usable information from every customer call and interaction, rather than relying on the seemingly arduous process of customer satisfaction surveys to get the feedback you want.
The tech marketing blog, <a href="https://martechseries.com/mts-insights/guest-authors/leverage-ai-hyper-personalization-transform-mass-marketing-precision-marketing/" title="https://martechseries.com/mts-insights/guest-authors/leverage-ai-hyper-personalization-transform-mass-marketing-precision-marketing/" data-renderer-mark="true">MarcTech</a>, finds these AI capabilities have an incredible capacity for improving CX:
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It’s not just about who the customer is, however, it’s about very rich context, such as the use cases or problems they’re trying to solve, what other products they might be interested in, which country or regulations they operate under, their persona, their stage in the buying cycle, their pain points and other data.
</blockquote>
This kind of data analysis can yield a high rate of return on brand experience.
<h3 id="Codazen-Solutions" data-renderer-start-pos="8799">Codazen Solutions</h3>
From legacy banks to credit unions, financial institutions are evolving through a digital transformation. Their brand experience is rapidly changing to keep up with the digital demands of consumers in a world surviving through a pandemic.
Codazen works with clients to leverage their AI capabilities as the <a href="https://blog.codazen.com/three-emerging-technologies-taking-on-covid-19-challenges" title="https://blog.codazen.com/three-emerging-technologies-taking-on-covid-19-challenges" data-renderer-mark="true">demands of the digital landscape change</a>. Our team of data scientists find what’s hiding in your organization’s big data. We use Amazon Web Services to secure data and we also format and structure data features to input into an AI model. We embed with your team to work closely and bring creative solutions.
<a href="https://www.codazen.com/contact/" title="https://www.codazen.com/contact/" data-renderer-mark="true">Contact us to learn more</a> about how to maximize your AI capabilities. Codazen can help your institution keep up with the rapid changes of emerging technologies and demands of the market.

Leveraging AI can help a financial institution’s ROI while improving brand experience for customers.

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5 Ways AI Can Help the Financial Industry

Artificial intelligence (AI) can give investment firms a competitive edge. By creating better predictions, and therefore, better results, AI can improve the way firms make decisions, execute trades, and manage client portfolios.

Smart algorithms can change the way investment managers look at companies. Because most businesses release their financial statements weeks after a quarter ends, it’s difficult to obtain real-time public information.

But with more companies accelerating their shift toward digitization, there is now a multitude of websites for AI to crawl, tracking metrics such as a brand’s consumer reviews, inventory levels, and pricing strategies. Algorithmic programs utilizing this data can indicate the probability of whether a company will be a good investment, quickly adjusting strategies and optimizing trades based on that data.

According to statistics portal, Statista, stock markets in the[ U.S. accounted for 54.5% of the world’s stocks](https://www.statista.com/statistics/710680/global-stock-markets-by-country/) in 2020, with over half of adults in America investing money in the stock market. Apollo Global Management valued the world-wide [stock market at $95 trillion dollars](https://www.marketwatch.com/story/value-of-the-world-wide-stock-market-soars-to-record-95-trillion-despite-resurgence-of-coronavirus-11605196894).

With so much money on the line, AI-powered analysis has the ability to help investors maximize their returns while reducing their risk.

## Data-Driven Decisions
Artificial intelligence combined with human experts creates an investing dream team. By taking advantage of AI’s speed to analyze millions of data points in seconds, investors and money managers can make better strategic investment decisions.

Citi Treasury and Trade Solutions processes [over 9 million trades globally per year](https://www.datadriveninvestor.com/2020/01/17/artificial-intelligence-as-a-tool-for-investing/). Before implementing AI, employees would have to review each trade manually, conducting time-consuming research to ensure the trades met regulatory compliance. Now, Citi leverages AI to conduct the research, providing employees the data they need in an easy-to-understand format, decreasing both human error and fatigue.

AI is also helping asset managers optimize their portfolio construction process. By utilizing AI’s data analysis and statistical models, managers can better predict future economic outcomes to help investors minimize risk in their portfolios.

Gopal Appuswami, Practice Lead at LatentView Analytics, explains that these data-driven analytics can [track multiple economic indicators](https://money.usnews.com/investing/investing-101/slideshows/ways-artificial-intelligence-is-improving-investing) and regulatory trends “to produce insights and timely advice, which financial advisors can leverage to make proactive portfolio rebalancing recommendations or help customers build the right financial management solutions.”

The power of AI lies in its high-speed tracking and analyzing capability. This ability plays a crucial role in forecasting markets with greater accuracy and predicting future patterns.

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## Different Scenarios, Same Goal
While there are a multitude of strategies that can be implemented, all of them have one goal in common: maximizing returns. Smart machines can help investors increase their returns by analyzing data and calculating the probability of outcomes in real-time.

Trade Ideas, a stock market analysis company, utilizes an [AI-powered self-learning trading platform](https://builtin.com/artificial-intelligence/ai-trading-stock-market-tech) named “Holly” to go through over a million different scenarios using dozens of investment algorithms to come up with the most optimal trading strategies.

The strategies with at least a 60% probability of success and a 2:1 profit/loss ratio are shared with traders the following day. In 2018, Trade Ideas’ stock market return was 16%, compared to the S&P’s return of negative 1.0%.

Asset managers can further [leverage AI to forecast financial risk](https://money.usnews.com/investing/investing-101/slideshows/ways-artificial-intelligence-is-improving-investing). Mark Antonio Awada, Chief Risk and Investment Sciences Officer at Alpha Innovations Global Investment, says, “historical and live time series data provide subtle clues and autonomous patterns that can be used to predict future patterns with strikingly high accuracy.” This pattern analysis can significantly improve an investor’s risk/reward ratio.

The ability to track and analyze data to forecast trading outcomes is not limited to numbers. Artificial intelligence can also understand and analyze human speech to predict market sentiments.

## Understanding the Language of Investing
Online articles, reports, and press releases all provide vital information about how a company is performing. Artificial intelligence can use speech recognition and natural language processing (NLP) to save financial advisors and investment traders from the time-consuming task of sifting through conversations, notes, and financial reports.

Utilizing NLP, artificial intelligence can read, interpret, and understand human text to help investors gauge the sentiment in the text. Whether the tone in the news articles about a company is positive or negative can help predict how well that company’s stocks will do.

NLP can also detect [relationships between companies called intercompany momentum](https://www.gsam.com/content/gsam/global/en/market-insights/gsam-insights/gsam-perspectives/2016/big-data/gsam-roundtable.html). This focuses on how the price movement of a security might impact the price movement of other securities. Osman Ali, Managing Director at Goldman Sachs Asset Management, says, “these not-so-obvious relationships can be assembled from the clustering of companies in text-based data, appearing together in news articles, regulatory filings or research reports.”

Being able to analyze text is critical to making the best trading moves. With the [average stock market return at 10%](https://spendmenot.com/blog/average-stock-market-return/), investors that want a greater ROI need the right tools to make it happen.

## Codazen Solutions: Investing in AI
Investment firms who seek a competitive edge can find it in AI. By helping stock traders and asset managers [effectively analyze millions of data points](https://blog.codazen.com/information-wealth-in-big-data) in real-time, artificial intelligence can predict future trading outcomes to maximize returns while mitigating risks.

According to Michael Blum, one of our data scientists:


>AI is one way investors can ease the uncertainty of Wall Street. That’s because using data along with modern algorithms has become a sophisticated way of making accurate predictions. Markets give us tons of data to use. It’s a no-brainer that AI has a firm place in investing.

At Codazen, our team of data scientists and software engineers works closely with you to create effective and customized AI solutions for your investment needs. We take pride in integrating data-driven technology with personalized strategies to reach client goals.

[Contact us](https://www.codazen.com/contact/) to learn more about how we can help you leverage the power of artificial intelligence for your investments.


Artificial intelligence can help investment firms maximize returns while reducing risk.

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Smart Machines for Smart Investors

Artificial intelligence (AI) can increase a brand’s competitive advantage when it comes to digital advertising. From optimizing ad spend and improving performance to segmenting and targeting specific audiences, AI is transforming how companies market to consumers.
Customer relationship management systems and ad platforms provide a trove of data on impressions, click-through rates and demographics. While more data is better, humans can only process so much information. That’s where AI comes in.
With the right data, artificial intelligence can detect useful patterns, predicting campaign changes that would improve the effectiveness of each ad. And all this data analyzing can take seconds for the AI, instead of weeks for a human.
According to statistics portal Statista, <a href="https://www.statista.com/topics/1176/online-advertising/" title="https://www.statista.com/topics/1176/online-advertising/" data-renderer-mark="true">$330 billion is projected to be spent</a> on global digital advertising by 2021. With that much money on the line, it’s crucial for companies to leverage the power of AI to maximize ROI.
<h2 id="Optimizing-Ad-Spend-with-AI" data-renderer-start-pos="1170">Optimizing Ad Spend with AI</h2>
By watching campaign performance in real-time, artificial intelligence is able to help advertisers determine how much to spend on digital ads. The ad spend plans factor in not only the monetary aspect, but also the target audience to figure out the optimal cost of each ad.
AI systems monitor market shifts and the behavioral changes of consumers to adjust ad spend dollars. Naomi Simson, co-founder of online marketplace RedBalloon, was spending $45,000 each month on ad agencies and <a href="https://www.marketingaiinstitute.com/blog/how-one-ecommerce-company-used-ai-to-get-a-3000-return-on-ad-spend" title="https://www.marketingaiinstitute.com/blog/how-one-ecommerce-company-used-ai-to-get-a-3000-return-on-ad-spend" data-renderer-mark="true">paying at least $50 per customer acquisition</a>. Needing to cut costs, she switched to Albert, an AI marketing platform that uses machine learning to optimize ad campaigns by analyzing data across ad accounts and customer databases. With the AI solution, Simson now spends 25% less on marketing, achieves 30% better results and her return on ad spend hit 3,000%.
Perpetua, an advertising software for <a href="https://www.prnewswire.com/news-releases/perpetua-unlocks-scalable-omnichannel-growth-for-brands-with-ai-powered-optimization-and-intelligence-for-instacart-ads-301182087.html" title="https://www.prnewswire.com/news-releases/perpetua-unlocks-scalable-omnichannel-growth-for-brands-with-ai-powered-optimization-and-intelligence-for-instacart-ads-301182087.html" data-renderer-mark="true">Amazon and Instacart ads</a>, also leverages AI to make the best bid at the best time on keywords that are the most likely to bring conversions. These optimizations maximize ad spend efficiency so a company’s marketing dollars don’t go to waste.
With so much data available, being able to utilize responsive AI-based ad systems can give brands a competitive edge. But what if browsers stop collecting certain kinds of data?

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<h2 id="No-More-Cookies" data-renderer-start-pos="2537">No More Cookies</h2>
Cookies are bits of data loaded onto a computer’s browser that lets marketers track what users do online. Apple’s Safari currently <a href="https://www.cnet.com/news/apple-updates-safari-on-ios-and-mac-to-block-third-party-cookies/" title="https://www.cnet.com/news/apple-updates-safari-on-ios-and-mac-to-block-third-party-cookies/" data-renderer-mark="true">blocks all third-party cookies</a> and Google’s Chrome is planning to do the same. Without this tracking technology to follow consumers around the internet, how will advertisers reach potential customers? AI is the answer.
Artificial intelligence can recognize patterns that cookies can’t. It can see that a user lives in Los Angeles, note that the weather is forecast to be 98 degrees and pitch an ad for sunscreen without needing any personally identifiable data.
According to Randi Stipes, <a href="https://www.forbes.com/sites/paultalbot/2020/11/17/inside-the-world-of-ai-at-ibm-watson-advertising/?sh=504bb33d3f70" title="https://www.forbes.com/sites/paultalbot/2020/11/17/inside-the-world-of-ai-at-ibm-watson-advertising/?sh=504bb33d3f70" data-renderer-mark="true">CMO at IBM Watson Advertising</a>:
<blockquote>
Because of its inherent identifier-free data processing and machine learning capabilities, we believe that AI will be the backbone of the new digital economy and provide stakeholders across the ecosystem with greater value, confidence and trust.
</blockquote>
By utilizing one of Watson Advertising’s AI solutions to identify brand-safe social media influencers, Hyundai was able to increase its positive sentiment score to 100%. Another retail brand that leveraged Watson Advertising’s AI capabilities to deliver tone-appropriate messages to a target audience increased their campaign performance by 206%.
GumGum, a contextual intelligence platform, developed an artificial intelligence called Verity that relies on <a href="https://aithority.com/technology/native-and-programmatic-advertising/mediavine-expands-access-to-contextual-ad-targeting/" title="https://aithority.com/technology/native-and-programmatic-advertising/mediavine-expands-access-to-contextual-ad-targeting/" data-renderer-mark="true">contextual data instead of the behavioral data</a> that third-party cookies track. It helps advertisers target ads using keyword and contextual categorization. GumGum CEO Phil Schraeder says that Verity “considers images, text and metadata when scoring relevance and determining suitability, ensuring brand safety and campaign success.”
Artificial intelligence can help pave the way for advertisers to navigate through a cookie-less future and provide the most effective customized campaigns.
<h2 id="At-the-Right-Place-With-the-Right-Message-at-the-Right-Time" data-renderer-start-pos="4407">At the Right Place With the Right Message at the Right Time</h2>
With customized advertising, companies can personalize ads for target audiences by leveraging AI’s ability to process and detect patterns in real-time. This <a href="https://blog.codazen.com/data-in-the-driver-seat-for-auto-industry-future" title="https://blog.codazen.com/data-in-the-driver-seat-for-auto-industry-future" data-renderer-mark="true">deep customer analytics</a> helps marketers better understand just who it is they’re trying to appeal to.
Artificial intelligence not only offers precision targeting of advertisements, but can also self-adjust and optimize individual campaigns. By assessing websites for content and context, AI can analyze specific pages where ads show up, raising targeting accuracy rates and reducing the risk of ads appearing next to content that is negatively related to the ad. For example, there are plenty of websites focusing on diet plans, nutrition and healthy eating. Since the pages are full of food content, they’re also full of advertising deals for fast food chains and delivery services. AI context analysis can help prevent this irony.
Dun &amp; Bradstreet, a commercial data analytics company, created D&amp;B Buyer Intent which utilizes <a href="https://martechseries.com/analytics/b2b-data/dun-bradstreet-launches-buyer-intent-help-increase-roi-uk-b2b-marketing-teams/" title="https://martechseries.com/analytics/b2b-data/dun-bradstreet-launches-buyer-intent-help-increase-roi-uk-b2b-marketing-teams/" data-renderer-mark="true">Natural Language Processing and billions of unique web engagements</a> to identify in-market buyers. Marketers using Buyer Intent have found an increase in ROI, with their email open rates and click-through rates going up by 37% and 22% respectively, as well as a 13% increase in conversions.
Marketing and sales teams who want a competitive edge can benefit from AI’s audience targeting capabilities. By predicting the best ads to run in the best places and adjusting in real-time, advertising campaigns can become more engaging and attractive to customers who appreciate personalization.
<h2 id="Codazen-Solutions:-Advertising-and-AI" data-renderer-start-pos="6043">Codazen Solutions: Advertising and AI</h2>
Artificial intelligence is changing the advertising world. It’s helping companies optimize their campaigns to reduce ad spend while making those ads more relevant to individual audiences. With more personalized and effective ads, not only will marketers get better results for each ad dollar spent, but because the AI is analyzing trillions of data points to predict human needs, customers will also be able to get ads for products they would actually want.
The more artificial intelligence advances, the greater a role it will play in the advertising industry. Codazen’s data scientists have the skills necessary to help accelerate a brand’s digital transformation by maximizing the benefits of AI .
According to Michael Blum, one of our data scientists:
<blockquote>
Finding the right advertisement to show someone is not an easy task, as it’s difficult to capture someone’s interest when they’re likely focused on something else. There are a ton of factors to consider when launching an ad campaign, which can be overwhelming for people to handle on their own. Personalized ads are always better at getting my own interest, and AI is the most effective way forward to handle those types of campaigns.
</blockquote>
Our innovative team of data scientists and software engineers works closely with you to create effective and customized AI solutions for your advertising needs. We take pride in integrating data-driven tech with personalized brand strategies to reach client goals.
<a href="https://www.codazen.com/contact/" title="https://www.codazen.com/contact/" data-renderer-mark="true">Contact us</a> to learn more about how we can help you leverage the power of artificial intelligence for your campaigns.

Artificial intelligence can customize digital ads and predict changes in real-time to improve the effectiveness of advertising campaigns.

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Artificial Intelligence Is Changing the World of Advertising

One of the hardest parts about building a machine learning (ML) pipeline is making sure you have enough clean labeled data to train a model. Good data can occasionally be easy to come by, but good labeled data is much more rare and it can become quite expensive to label a dataset manually. That is why data scientists use Amazon Mechanical Turk (MTurk). MTurk uses contracted or temporary workers – commonly referred to as “gig workers” – that can help label massive amounts of information.

Gig workers are part of many startups and Big Tech organizations. In 2018, the Bureau of Labor Statistics <a href="https://www.forbes.com/sites/shephyken/2018/07/29/the-gig-economy-opens-the-door-for-employment-opportunities/#7b1372be7662" title="https://www.forbes.com/sites/shephyken/2018/07/29/the-gig-economy-opens-the-door-for-employment-opportunities/#7b1372be7662" data-renderer-mark="true">reported</a> that 55 million people in the U.S. are gig workers. These free-agent workers can complete a variety of tasks.
Even though many companies are putting their focus on<a href="https://blog.codazen.com/the-digital-transformation-has-arrived" title="https://blog.codazen.com/the-digital-transformation-has-arrived" data-renderer-mark="true"> emerging technologies</a>, gig workers are still relevant to helping a company meet financial and technical goals. As an online marketplace for temporary workers, MTurk is part of the growing digital transformation.
<a href="https://www.mckinsey.com/featured-insights/employment-and-growth/independent-work-choice-necessity-and-the-gig-economy" title="https://www.mckinsey.com/featured-insights/employment-and-growth/independent-work-choice-necessity-and-the-gig-economy" data-renderer-mark="true">The McKinsey Global Institute</a>, an economic research firm, found that up to 162 million people in Europe and the United States—or 20 to 30 percent of the working-age population—engage in some form of independent, gig work.
The digital age has created a litany of jobs requiring outsourcing — not only to intelligent machines, but to a variety of humans. Part of strengthening ML capabilities involves occasionally relying on gig workers. There are still many jobs that require a human touch, such as gathering information via conversations or writing product reviews.
Gig workers help data scientists gather data for ML. This data can be used by developers to build algorithms. Depending on the scope of the project, the process can be time-consuming and expensive. MTurk can help large-scale organizations advance their ML techniques.
<h2 id="Training-for-Machine-Learning-Models" data-renderer-start-pos="1937">Training for Machine Learning Models</h2>
MTurk is a way for companies to access the power of a large swath of temporary workers who can bring diversity in talents, ages, ethnicities, cultures and locations, while collecting data and labeling it for ML.
It can take years for an organization to develop this type of workforce. With so many workers readily available, there is also inherent flexibility that employers and gig workers can take advantage of. Both can use MTurk’s large workforce capabilities on an as-needed basis and can help mitigate stressed budgets while helping workers subsidize their income, especially during a recession.
Because a large workforce brings so much diversity, MTurk has been used in <a href="https://theintercept.com/2020/01/16/pete-buttigieg-amazon-mechanical-turk-gig-workers/" title="https://theintercept.com/2020/01/16/pete-buttigieg-amazon-mechanical-turk-gig-workers/" data-renderer-mark="true">political campaigns</a> for canvassing,<a href="https://www.washingtonpost.com/news/monkey-cage/wp/2015/05/04/researchers-are-rushing-to-amazons-mechanical-turk-should-they/" title="https://www.washingtonpost.com/news/monkey-cage/wp/2015/05/04/researchers-are-rushing-to-amazons-mechanical-turk-should-they/" data-renderer-mark="true"> in social science for research </a>and in <a href="https://psychcentral.com/blog/mechanical-turk-to-the-rescue-of-psychology-research/" title="https://psychcentral.com/blog/mechanical-turk-to-the-rescue-of-psychology-research/" data-renderer-mark="true">psychology to gauge opinions that are representative of the general population</a>. For organizations who have sensitive data that can only be shared with a private group, MTurk helps provide labeling options such as choosing one of the third-party vendors pre-selected by AWS.
One of MTurk’s biggest perks is its integration with another one of Amazon’s services, SageMaker. Together, they form a third service called Amazon SageMaker Ground Truth. This combination builds labeled data that can be used to train ML models with a public workforce. Developers can use it to label their data using human annotators through MTurk, third-party vendors or their own employees.
<a href="https://aws.amazon.com/sagemaker/groundtruth/customers/" title="https://aws.amazon.com/sagemaker/groundtruth/customers/" data-renderer-mark="true">Several global companies use MTurk’s Amazon SageMaker</a> for ML. Airbnb uses MTurk to generate and maintain high quality data in order to train and test ML models. T-Mobile uses labeled data to create high-performing ML models and to lighten the task loads of their data scientists and software engineers. ZipRecruiter uses it to help their ML model extract relevant data automatically from uploaded resumes. Codazen also utilizes MTurk as well to gather data used in creating several of our innovative projects in computer vision and deep learning.
The<a href="https://blog.mturk.com/aws-introduces-a-new-way-to-label-data-for-machine-learning-with-mturk-2f9c19866a98" title="https://blog.mturk.com/aws-introduces-a-new-way-to-label-data-for-machine-learning-with-mturk-2f9c19866a98" data-renderer-mark="true"> Amazon Mechanical Turk Blog</a> describes the process:
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Amazon SageMaker Ground Truth learns from these annotations in real time and can automatically apply labels to much of the remaining dataset, reducing the need for human review. Amazon SageMaker Ground Truth creates highly accurate training data sets, saves time and complexity, and reduces costs by up to 70 percent when compared to human annotation.
</blockquote>

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<h2 id="Codazen-Solutions:-Making-Data-Collection-Easy" data-renderer-start-pos="4388">Codazen Solutions: Making Data Collection Easy</h2>
Human intelligence collaborating with artificial intelligence is the trend of the future. However, companies must know not only how to use Amazon Mechanical Turk or other gig work providers, but also how to use it so they meet bottom line revenue while leveraging the right technologies.
MTurk can come in handy because it allows the tedious tasks of data collection to be completed by gig workers without burdening the workflow processes of developers and data scientists.
Codazen can help organizations leverage MTurk as a way to speed up data collection and labeling, while staying on track with project timelines and leveraging AI capabilities. Labeled data is integral to building sophisticated algorithms—especially on a gig economy platform. After our team collects massive amounts of data, we work on training machine learning algorithms.
Datasets for deep learning are an integral part of this process and our experts collaborate with client teams to provide creative, technical solutions. We work with companies who have existing AI capabilities and those without. Codazen has the expertise and experience many companies need, but may lack, to make full use of the MTurk platform.
Codazen developers and data scientists create effective projects, experiments, and surveys using MTurk. We can help organizations minimize the costs and maximize the returns of tedious workflows and complex machine learning models.
To learn more about how to leverage the multiple benefits of MTurk and ML, <a href="https://www.codazen.com/contact/" title="https://www.codazen.com/contact/" data-renderer-mark="true">contact Codazen</a>.

Machine learning techniques using MTurk can accelerate training data.

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Easing the Burden of Building Good Training Datasets

The demands of paperwork on doctors and nurses can be a tedious burden on medical care workflow. On top of one-on-one patient care, a doctor’s day can easily entail hours of charting, following up on paperwork, and tending to business needs that eat up a lot of time. Natural Language Processing (NLP) can help.
NLP is when machines read, listen, and process spoken words and written text. NLP can detect differences and similarities between words and phrases.
In medical practices, NLP is being used to prevent documentation burnout for physicians. This natural language processing software takes speech and transcribes it to where it’s relevant on medical records, doctors are able to spend more time with patients, ease workflow, and save lives.
A <a href="https://www.acpjournals.org/doi/10.7326/M18-3684" title="https://www.acpjournals.org/doi/10.7326/M18-3684" data-renderer-mark="true">study published in the Annals of Internal Medicine</a> found that physicians spend an average of 16 minutes and 14 seconds per encounter. The study has data from some 100 million patient encounters with about 155,000 physicians from 417 health systems. The most time-consuming tasks were: chart review (33%), documentation (24%), and ordering (17%). While tasks vary based on industry and specialities, these appeared as the most common.
A day in the life of physicians is a balancing act of conducting business and human healthcare. But NLP could help doctors ease the workflow by cutting the time spent on these tasks.
<h2 id="NLP-to-the-Rescue:-Saving-Time,-Saving-Lives" data-renderer-start-pos="1510">NLP to the Rescue: Saving Time, Saving Lives</h2>

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Most people are familiar with speech-to-text capabilities via their smartphones that come with Siri, Alexa, or Google. The feature is meant to serve as a hands-free way of easing communication and as a timesaver.
When it’s used in a medical workflow, it can help increase the quality of healthcare to patients. A 2018 study published in the<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5961784/" title="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5961784/" data-renderer-mark="true"> AMIA Joint Summits on Translational Science </a>explored the time crunch of physicians burdened with documentation.
The study followed workflows of doctor-client sessions and the time doctors spent on post-session documentation. It found the majority of respondents noticed an improvement in documentation quality and completeness as well as a decrease in time spent on documenting a patient encounter after implementing a speech recognition solution.
NLP software eavesdrops on conversations between a physician and patient. The language processing system is programmed to look for keywords and patterns. The words are data that the machine categorizes and matches to other data within a doctor’s patient documentation. It looks for red flags.
For example, if there is a patient that has a pattern of symptoms, the machine is able to match them to potential diseases. It can also find if there are other patients who have expressed similar symptoms. This helps doctors to better care for patients. It gives them a more informed practice by helping them see patterns they may have missed on their own.
Microsoft recently partnered with clinical documentation company, Nuance Communications. The tech giant has developed a “software that understands patient-clinician conversations and automatically integrates that data into the patient's medical record,” according to health industry journal <a href="https://www.healthcaredive.com/news/microsoft-lands-new-partner-in-race-to-reliable-speech-to-text-ehr-documen/565297/" title="https://www.healthcaredive.com/news/microsoft-lands-new-partner-in-race-to-reliable-speech-to-text-ehr-documen/565297/" data-renderer-mark="true">HealthDrive</a>.
As speech-to-text becomes more common in the medical industry, this type of NLP technology will need a place to store information.
Amazon has tapped into this niche with <a href="https://hitinfrastructure.com/news/amazon-announces-new-healthcare-data-lake-service" title="https://hitinfrastructure.com/news/amazon-announces-new-healthcare-data-lake-service" data-renderer-mark="true">Amazon HealthLake</a>. This cloud-based service allows for information to be stored and shared between health systems and with third-party applications. In the patient care workflow, healthcare providers will be able to collaborate more effectively and ensure patients access to their personal medical information.
HealthLake, like any other technology, has to be HIPAA-approved before using. That helps protect client confidentiality with data.
<h2 id="Codazen-Solutions:-Speech-to-Text-Capabilities" data-renderer-start-pos="3955">Codazen Solutions: Speech-to-Text Capabilities</h2>
At Codazen, we know time is of the essence in organizational workflow and management. In an industry like healthcare, where the demands for human interaction are great, AI can really assuage the documentation-heavy workflow.
Our team of data scientists can help leverage NLP technologies for health-based organizations. We take audio transcriptions and turn them into text using open-source and in-house speech recognition packages in Python.
We also help figure out what speech is about by summarizing or classifying speech data. Our data science team can provide any sort of analysis that finds patterns in phrases or audio.
Whether your company needs assistance automatically transcribing thousands of pages of text, or analyzing the sentiment and finding patterns throughout the corpora, our team of dedicated and experienced data scientists can help you do just that.
We can help ease the demands on physicians and provide better healthcare to patients. <a href="https://www.codazen.com/contact/" title="https://www.codazen.com/contact/" data-renderer-mark="true">Learn more</a> about partnering with us.

Natural language processing can ease a physician’s paperwork load and improve client care.

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NLP Helps Doctors Save More Than Just Time

Artificial intelligence (AI) is leveling up the video gaming and online gaming industry. By freeing developers and designers from time-consuming tasks while creating content faster and at a lower cost, AI has become increasingly popular for game manufacturers. Players also reap the benefits with higher quality graphics and better gameplay on their consoles, PCs and phones.
According to commerce platform PYMNTS, online gaming makes up almost <a href="https://www.pymnts.com/news/security-and-risk/2020/biometrics-battles-evil-on-gaming-platforms/" title="https://www.pymnts.com/news/security-and-risk/2020/biometrics-battles-evil-on-gaming-platforms/" data-renderer-mark="true">75% of all in-app spending</a>, with the gaming industry expected to generate $159.3 billion in global revenues in 2020. In the U.S. alone, 3 out of 4 consumers interact with gaming platforms, spending up to 14 hours a week on them, which is up from 12 hours in 2018. A <a href="https://www2.deloitte.com/us/en/insights/industry/technology/video-game-industry-trends.html" title="https://www2.deloitte.com/us/en/insights/industry/technology/video-game-industry-trends.html" data-renderer-mark="true">2020 digital media trends</a> survey by Deloitte found that during the Covid-19 crisis, a third of consumers have subscribed to a video gaming service, used a cloud gaming service or watched an esports event for the first time in their lives. With numbers like these, it’s essential for companies to leverage AI to increase player interest, engagement and satisfaction.
The power of this cutting-edge technology lies in its speed. AI bots can play years’ worth of game time at an accelerated rate. <a href="https://www.theverge.com/2018/6/25/17492918/openai-dota-2-bot-ai-five-5v5-matches" title="https://www.theverge.com/2018/6/25/17492918/openai-dota-2-bot-ai-five-5v5-matches" data-renderer-mark="true">OpenAI’s co-founder</a> and CTO Greg Brockman says that if a human player takes between 12,000 and 20,000 hours of gameplay to become a professional, then OpenAI’s agents can “play 100 human lifetimes of experience every single day.”
AI is helping make gaming more efficient for manufacturers and more realistic for players. Below are five ways the video gaming and online gaming industry can leverage artificial intelligence.
<h2 id="1.-QA-Testing" data-renderer-start-pos="1845">1. QA Testing</h2>
Software glitches and bugs are an inevitable part of the game development process, and extensive testing is required to achieve the best possible product. The problem is that testing is extremely time-consuming and repetitive. The solution? AI can run thousands of tests in a fraction of the time a human tester can.
Video game company Larian Studios built a proprietary <a href="https://www.polygon.com/2020/10/16/21519815/baldurs-gate-3-testing-ai-super-gamer-larian-studios-development-pc-steam" title="https://www.polygon.com/2020/10/16/21519815/baldurs-gate-3-testing-ai-super-gamer-larian-studios-development-pc-steam" data-renderer-mark="true">AI that it calls the World Tester</a>. When a new build for a game is put together, the World Tester pushes the game’s boundaries, going through combat, dialogue options and menus in search of bugs faster than any human QA team could ever hope to do. Human testers no longer have to do the grunt work of pressing different combinations of buttons and input commands to see what will break. The World Tester can even juggle multiple builds at the same time, simulating hours of gameplay at once.
<h2 id="2.-Automation-of-Graphics" data-renderer-start-pos="2724">2. Automation of Graphics</h2>
Artificial intelligence’s ability to automate the animation process can save game makers millions in expenses while making games more lifelike and efficient. In order to <a href="https://www.wired.com/story/game-makers-inject-ai-develop-more-lifelike-characters/" title="https://www.wired.com/story/game-makers-inject-ai-develop-more-lifelike-characters/" data-renderer-mark="true">speed up the development process</a>, a team from gaming company Electronic Arts and the University of British Columbia are using a machine learning technique called reinforcement learning to automatically animate humanoid characters. When fed enough data, AI can automate the creation of photo-realistic characters and scenes, helping designers cut production time and costs.
Microsoft is also utilizing machine learning in its latest Xbox console to generate <a href="https://venturebeat.com/2020/10/06/xbox-series-xs-machine-learning-auto-hdr-renders-stunning-results/" title="https://venturebeat.com/2020/10/06/xbox-series-xs-machine-learning-auto-hdr-renders-stunning-results/" data-renderer-mark="true">High Dynamic Range (HDR)</a> content from much less detailed Standard Dynamic Range (SDR) content. Artificial intelligence fills in the gaps from the limited input data to process the HDR image and get the desired effect of expanded contrast and color in SDR games.

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<h2 id="3.-Toxicity-Monitoring" data-renderer-start-pos="3671">3. Toxicity Monitoring</h2>
Because of the intense nature of online gaming, some players take their in-game speech from competitive to abusive. Finding a way to ensure game chats are free from toxic language has been challenging for developers. That is, until AI came along.
Artificial intelligence has helped <a href="https://www.explica.co/toxicity-in-overwatch-dropped-thanks-to-the-use-of-artificial-intelligence/" title="https://www.explica.co/toxicity-in-overwatch-dropped-thanks-to-the-use-of-artificial-intelligence/" data-renderer-mark="true">Blizzard police game chats</a> in Overwatch and Heroes of the Storm by verifying player reports of abusive behavior and issuing punishments. By continually looking for patterns in player reports, the machine learning algorithm gets better at identifying offensive communications.
Blizzard president J. Allen Brack says the use of AI “allows us to do appropriate penalties faster, and we’ve seen an incredible decrease in not just toxic text in chat, but an overall decrease in recidivism rates.”
<h2 id="4.-Player-Verification" data-renderer-start-pos="4472">4. Player Verification</h2>
With real money being gambled, it’s essential for online casinos to screen their players to make sure that the people signing up on their site are not only real people, but are also of age. UK-based tech company Innovative Technology’s (ITL) Intelligent Identification system, which it dubs ICU, uses AI to verify the age of players, blocking children from gambling.
Marcus Tiedt, Sales Director at <a href="https://www.biometricupdate.com/202001/biometrics-ai-machine-learning-innovations-to-boost-gaming-industry-growth" title="https://www.biometricupdate.com/202001/biometrics-ai-machine-learning-innovations-to-boost-gaming-industry-growth" data-renderer-mark="true">Innovative Technology</a>, says, “ICU uses artificial intelligence and ITL’s own algorithms to instantly verify age, and the next iteration of ICU can now identify faces. ICU Age Verification can therefore ensure compliance to prevent underage gambling and safeguard children.” So far, ICU has a 96% success rate in identifying underage gambling, far surpassing the human accuracy rate of 69%.
<h2 id="5.-Game-Creation" data-renderer-start-pos="5287">5. Game Creation</h2>
Imagine being able to create a video game without knowing how to write a single line of code. AI makes it possible. Nvidia, a company that designs graphics processing units, along with researchers from the University of Toronto and MIT developed GameGAN, an <a href="https://www.wired.com/story/game-makers-inject-ai-develop-more-lifelike-characters/" title="https://www.wired.com/story/game-makers-inject-ai-develop-more-lifelike-characters/" data-renderer-mark="true">AI engine that can recreate Pac-Man</a> without any of the game’s original code. By watching the video game screen and monitoring the controls used during thousands of games of Pac-Man, GameGAN was able to create its own version of the classic game with new, never-before-seen levels. If given enough data, GameGAN can generate entirely new games that don’t require any coding.
Matthew Guzdial, an AI Researcher and Assistant Professor at the University of Alberta, and his team trained their own AI to build games. By having it watch hours of videos showing people playing different video games, the AI was able to figure out the rules of each game. <a href="https://time.com/5779217/artificial-intelligence-video-games/" title="https://time.com/5779217/artificial-intelligence-video-games/" data-renderer-mark="true">It then combined the styles and rules</a> of the various games to create something new. Guzdial says that rather than replacing game creators, his goal is to use AI to reduce the barrier of entry to game creation. With AI, gamers can build their own novel game levels without first having to learn to code.
<h2 id="Codazen-Solutions:-Level-Up-With-AI" data-renderer-start-pos="6515">Codazen Solutions: Level Up With AI</h2>
Artificial intelligence is revolutionizing how we live our lives. Not only do we ask Amazon’s Alexa about the weather and watch <a href="https://blog.codazen.com/data-in-the-driver-seat-for-auto-industry-future" title="https://blog.codazen.com/data-in-the-driver-seat-for-auto-industry-future" data-renderer-mark="true">Tesla’s self-driving cars</a> zoom down the freeway, but we also enjoy video games brought to us by the power of machine learning.
The more artificial intelligence advances, the more embedded it will be in the gaming industry. However, there are still many companies that have yet to take advantage of the opportunities AI has to offer in improving developer efficiency and player experience. Codazen has the skills necessary to help these companies explore those opportunities in a cost-effective way.
One of our data scientists, Neal Munson, believes that the fast-paced advances in AI should be embraced. He says:
<blockquote>
Our teams evolve with ever-changing technologies, including how to leverage AI. People talk about how challenging it is to keep up with new technology in gaming. The weather may change every hour, but the weatherman who checks on it every minute keeps up with it no sweat. Here at Codazen, we make it our job to be familiar with the newest algorithms and techniques to tackle the AI problems our game manufacturing clients want to take on.
</blockquote>
Our innovative team of data scientists and software engineers works closely with you to create powerful AI engines capable of generating customized gaming experiences.
<a href="https://www.codazen.com/contact/" title="https://www.codazen.com/contact/" data-renderer-mark="true">Contact us</a> to learn how we can help you leverage the power of artificial intelligence to level up your game.

Artificial intelligence in the video game industry can free workers from time-consuming tasks while creating content faster and at a lower cost.

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Power-Up: Five Ways the Video Game Industry Can Leverage Artificial Intelligence

Amazon Rekognition is using digital image processing to help people find love while preventing fraud and decreasing manual labor.

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Amazon Rekognition: Recognizing Love

Big data is necessary for achieving key insights that lead to maximizing competitive advantage and ROIs. Companies can have all the information in the world, but without a team of data scientists, they can easily miss blindspots that can cost millions in revenue. In other words, you can have all the bricks in the world, but without an architect, tools and labor know-how, you won’t have a well-built house.

Most modern corporations have more data than they know what to do with. The Forbes Technology Council compiled a list of the most <a href="https://www.forbes.com/sites/forbestechcouncil/2020/01/15/dont-make-these-14-common-big-data-mistakes-at-your-business/#560dcdfd3ca8" title="https://www.forbes.com/sites/forbestechcouncil/2020/01/15/dont-make-these-14-common-big-data-mistakes-at-your-business/#560dcdfd3ca8" data-renderer-mark="true">common mistakes businesses make</a> when dealing with data, such as analysis paralysis and letting data sit in a silo. It’s crucial for companies to avoid these pitfalls on their path to digital transformation.
That’s where data science comes in. It’s the data scientist’s job to gain actionable insights from all those gigabytes of information. When strategies are data-driven, they bear powerful results.
<a href="https://www.wired.co.uk/article/how-do-netflixs-algorithms-work-machine-learning-helps-to-predict-what-viewers-will-like" title="https://www.wired.co.uk/article/how-do-netflixs-algorithms-work-machine-learning-helps-to-predict-what-viewers-will-like" data-renderer-mark="true">Netflix uses big data analytics</a> for targeted advertising, collecting search and watch history from its millions of subscribers to gain insights on what keeps viewers hooked. The streaming platform’s recommendation system is powered by machine learning (ML) and sophisticated algorithms, sending personalized suggestions of what viewers should watch next. This data-driven personal touch amps up customer experience, keeping viewers subscribing month after month.
<a href="https://www.mentionlytics.com/blog/5-real-world-examples-of-how-brands-are-using-big-data-analytics/" title="https://www.mentionlytics.com/blog/5-real-world-examples-of-how-brands-are-using-big-data-analytics/" data-renderer-mark="true">PepsiCo also takes a data-driven analysis approach</a> to business. The global drink brand analyzes metrics collected from its warehouse and point of sale inventories to forecast production and shipment needs, ensuring retailers consistently have the correct products in the correct amount.
Big data can help businesses increase ROI, drive customer retention and mitigate risk factors. Unlocking the power of that data starts with the Discovery Phase, Codazen’s unique approach to analyzing our clients’ data and putting it to work. Here are the five steps our data scientists use, from nailing down the initial problem to building out machine learning models.
<h2 id="Step-1:-Understand-the-Problem" data-renderer-start-pos="2237">Step 1: Understand the Problem</h2>
The first step of big data analytics is understanding the context of the problem that needs solving, with a focus on the motivation. Even the biggest, most well-funded corporations struggle with understanding what data they already have, what they still need and how to leverage it to achieve their goals. To find answers, they need data scientists to ask the right questions.
Codazen Data Scientist, Neal Munson, explains the importance of understanding the business directive in order to find real insights specific to the data project. According to Neal:
<blockquote>
We may find a lot of correlations from the data we get, but the key is to sort out which ones are useful. For example, data from a restaurant could say that food is cooked before it’s served, but that information probably isn’t new and wouldn’t provide additional value. Discovering that particular tables at the restaurant tend to produce higher paying customers could be game-changing.
</blockquote>
Identifying in detail the needs and uses of each project is integral to maximizing the benefits of big data.
<h2 id="Step-2:-Access-the-Data" data-renderer-start-pos="3331">Step 2: Access the Data</h2>
Once the goal of the project is figured out, the next step is to look at the information. Connecting to your company’s database or using APIs are two common ways of obtaining usable metrics.
Maryam Farboodi, assistant professor of finance at the <a href="https://sloanreview.mit.edu/article/the-problem-with-big-data/" title="https://sloanreview.mit.edu/article/the-problem-with-big-data/" data-renderer-mark="true">MIT Sloan School of Management</a>, and her colleagues calculated the different availability of data in small and large companies, looking at how data interacted with the growth of processing power. Farboodi says that since large companies produce more data than smaller companies, this abundance in data “accelerates advances in processing speeds and computing and helps investors view these large companies as a less risky bet.”
But what happens if a client has access to only one type of data when other types are needed? Data scientists can access the missing information by looking for open sources on the internet. If a zoo wants metrics on the weather to figure out how to keep its animals comfortable but it hasn’t collected any weather data, scientists can acquire what they need from open platforms online. These open datasets are an incredible resource of additional information.
Once a company has collected the required data, our scientists can confidently assess how projects can benefit from artificial intelligence.
<h2 id="Step-3:-Identify-the-Value-of-AI" data-renderer-start-pos="4635">Step 3: Identify the Value of AI</h2>
Although artificial intelligence is a powerful tool, data scientists need to first understand how to go about solving the problem without AI before they try to automate the solution or create an ML model.
Let’s say you’re trying to classify dog breeds. You have a video of different dogs and you want to know which breeds are in the video. As a human, you can watch the video and figure out the breeds visually from your limited knowledge of dogs. An ML model, however, can be trained to recognize hundreds of different dog breeds from what they look like and potentially other aspects of the dogs such as their bark. It can do so much quicker and with much more accuracy than a human who’s visually identifying the breeds.
In the vast majority of cases, the question isn’t whether an ML model should be used to solve the problem, but the best way to have an ML model fit into a solution. Determining how AI can use information from the data is the key to delivering the desired results.

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<h2 id="Step-4:-Clean-the-Data" data-renderer-start-pos="5689">Step 4: Clean the Data</h2>
The next step is to transform the data into a useful format for ML models. Making sure data is homogeneous and clean can be time-consuming. According to a survey by <a href="https://www.anaconda.com/state-of-data-science-2020" title="https://www.anaconda.com/state-of-data-science-2020" data-renderer-mark="true">data science platform, Anaconda</a>, 45% of a data scientist’s time is spent loading and cleaning data before they can use it to develop models and visualizations.
If videos are being used, they must be transformed into sets of images, which must again be transformed into matrices of numbers. To classify dog breeds by bark, each bark needs to be broken down by volume and pitch with a scale for every millisecond. This preprocessed data is then fed into an ML model.
Raw data can also be cleaned by combining it with other raw data using feature extraction. For example, the number of customers at a store can be combined with the sum of money that store made to figure out per capita profits. Combining different variables into features reduces the amount of data that needs to be processed, turning it into a format that can easily be used by ML models.
<h2 id="Step-5:-Create-the-Model" data-renderer-start-pos="6736">Step 5: Create the Model</h2>
This step in the Discovery Phase includes clustering data points together, creating algorithms used in predictions and building specific processes for turning data into desired insights or results. It’s crucial to determine the kind of ML model that’s helpful for a project.
For example, unsupervised algorithms can be used to find patterns or draw inferences from unlabeled data. Supervised learning, on the other hand, can be used in training models to predict or classify variables such as height, price or number of customers. Reinforcement learning is used to attain a complex objective or maximize a specific dimension over many steps.
<a href="https://venturebeat.com/2020/09/14/how-amazon-is-using-machine-learning-to-eliminate-915000-tons-of-packaging/" title="https://venturebeat.com/2020/09/14/how-amazon-is-using-machine-learning-to-eliminate-915000-tons-of-packaging/" data-renderer-mark="true">Amazon uses ML tools</a> built on data from product descriptions and customer feedback to identify the best packaging with the least waste, reducing the use of boxes from 69% to 42%. This reduction not only saves the company money, but helps the environment.
Another example is with the medical industry: Aarthi Janakiraman, <a href="https://www.prnewswire.com/news-releases/data-science-to-accelerate-drug-discovery-with-artificial-intelligence-and-machine-learning-says-frost--sullivan-301140011.html" title="https://www.prnewswire.com/news-releases/data-science-to-accelerate-drug-discovery-with-artificial-intelligence-and-machine-learning-says-frost--sullivan-301140011.html" data-renderer-mark="true">Technical Insights Research Manager</a> at Frost &amp; Sullivan, a research and consulting firm, says, “Integrating AI and ML methods into drug discovery pipelines would cut down cost and time, and increase the efficiency of the entire research and development (R&amp;D) process.”
Choosing the right ML model maximizes ROI. Our data scientists use pre-trained models available online or build their own from scratch.
<h2 id="Step-6:-Refine-the-Solution" data-renderer-start-pos="8136">Step 6: Refine the Solution</h2>
The Discovery Phase doesn’t end after the ML model is built. In order for the model to remain accurate, it needs to be continuously improved, given new data and retrained. Iteration is key to unlocking the power of information.
Adjusting the model appropriately produces more accurate results. Since data can change over time, predictions offered by static machine learning tools begin to <a href="https://machinelearningmastery.com/gentle-introduction-concept-drift-machine-learning/" title="https://machinelearningmastery.com/gentle-introduction-concept-drift-machine-learning/" data-renderer-mark="true">degrade as time goes on</a>, becoming less accurate and, therefore, less useful.
Companies can overcome this by continuously updating and retraining their ML models with new data and identifying and implementing new features. Our data scientists can provide these sustainable solutions to keep your data working for you.
<h2 id="Codazen-Solutions:-Data-Discovery" data-renderer-start-pos="8875">Codazen Solutions: Data Discovery</h2>
Information is only useful with the right understanding. Our in-house data science team analyzes your data and builds machine learning models to provide effective solutions for your business.
From retail and banking to the auto industry and the medical field, companies can benefit from discoveries in big data to accelerate their timelines. But, to truly make use of it, organizations need to have a data science-driven strategy that aligns with business goals.
Codazen’s data scientists know how to leverage data to increase revenues and customer satisfaction while reducing costs and risk. We specialize in data collection methods and partner with clients to propel their digital transformation.
<a href="https://www.codazen.com/contact/" title="https://www.codazen.com/contact/" data-renderer-mark="true">Contact us</a> to learn more about how our unique Discovery Phase can help you harness the power of data.

Our data science team uses a unique approach to discover the richness of big data and how it can help organizations grow.

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Information Wealth in Big Data

Big data collection is shifting into high gear in the auto industry. Advancements in sensor technology and emerging markets such as autonomous vehicles are disrupting the traditional ways of building cars and providing services. In order to succeed, dealers and manufacturers must adapt quickly to embrace the power of consumer data.

Modern cars contain at least <a href="https://www.mygreatlearning.com/blog/analytics-is-driving-the-automotive-industry-from-good-to-great/" title="https://www.mygreatlearning.com/blog/analytics-is-driving-the-automotive-industry-from-good-to-great/" data-renderer-mark="true">50 sensors which collect information</a> on driver behavior, like speed, mileage, and braking. Data scientists use this data to find patterns and to develop features that enhance the vehicle-driver experience and create innovations that bring us one step closer to the world of The Jetsons.
<h2 id="Data-Analysis-for-Autonomous-Vehicles" data-renderer-start-pos="859">Data Analysis for Autonomous Vehicles</h2>
Nothing says “car of the future” like a self-driving one. According to market-foresight advisory firm ABI Research, <a href="https://www.abiresearch.com/press/abi-research-forecasts-8-million-vehicles-ship-sae-level-3-4-and-5-autonomous-technology-2025/" title="https://www.abiresearch.com/press/abi-research-forecasts-8-million-vehicles-ship-sae-level-3-4-and-5-autonomous-technology-2025/" data-renderer-mark="true">8 million driverless cars</a> will be added to the road in 2025, with a projected <a href="https://www.researchandmarkets.com/research/bbbzmt/u_s_autonomous?w=4" title="https://www.researchandmarkets.com/research/bbbzmt/u_s_autonomous?w=4" data-renderer-mark="true">20 million autonomous vehicles</a> in operation in the U.S. by 2030.
<a href="https://www.cio.com/article/3433931/tesla-the-data-company.html" title="https://www.cio.com/article/3433931/tesla-the-data-company.html" data-renderer-mark="true">Tesla is building its Autopilot system</a> using real-world data and machine learning algorithms. Every time a driver moves the steering wheel, presses on the pedals or shifts gears, that information is logged and uploaded into Tesla’s database. The automaker treats each car, each sensor and each human interaction as data points.
Unlike other automakers working towards autonomous driving, Tesla doesn’t rely on an internal test fleet to collect its data. The company instead utilizes its massive customer fleet, consisting of over half a million vehicles on the road. According to Andrej Karpathy, Tesla’s head of <a href="https://electrek.co/2020/04/22/tesla-autopilot-data-3-billion-miles/" title="https://electrek.co/2020/04/22/tesla-autopilot-data-3-billion-miles/" data-renderer-mark="true">AI and computer vision</a>, as of February 2020, Tesla vehicles have logged over 3 billion miles while using Tesla Autopilot. All those data points from all those miles are used to refine existing algorithms and develop new ones. These improvements are sent via wireless connection to every Tesla vehicle to enhance their capabilities.
Tesla isn’t the only player in the driverless car game. Waymo, which started as the <a href="https://venturebeat.com/2020/01/06/waymos-autonomous-cars-have-driven-20-million-miles-on-public-roads/" title="https://venturebeat.com/2020/01/06/waymos-autonomous-cars-have-driven-20-million-miles-on-public-roads/" data-renderer-mark="true">Google Self-Driving Car Project</a>, has collected 20 million miles on public roads and tens of billions of miles through computer simulations since 2009. According to Waymo, the data from its autonomous vehicles is building “the world’s most experienced driver.”
<a href="https://venturebeat.com/2020/06/21/waymo-and-uber-propose-ai-techniques-to-improve-self-driving-systems/" title="https://venturebeat.com/2020/06/21/waymo-and-uber-propose-ai-techniques-to-improve-self-driving-systems/" data-renderer-mark="true">Ridesharing company Uber</a> has created its own self-driving system called V2VNet to improve the reliability and safety of autonomous automobiles. Cars using the V2VNet system exchange data sets, timestamps and location information, using an AI model to select only relevant data from the data sets.
The autonomous vehicle market is expected to have <a href="https://www.marketwatch.com/press-release/self-driving-car-market-2018-2023-trends-industry-size-growth-and-region-forecasts-impacted-by-covid19-2020-04-27?tesla=y#:~:text=Apr" title="https://www.marketwatch.com/press-release/self-driving-car-market-2018-2023-trends-industry-size-growth-and-region-forecasts-impacted-by-covid19-2020-04-27?tesla=y#:~:text=Apr" data-renderer-mark="true">a global revenue</a> of $173.15 billion by 2023. With so much profit at stake, it’s imperative for car companies to leverage driver behavior data.

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<h2 id="Big-Data-for-Better-Drivers" data-renderer-start-pos="2973">Big Data for Better Drivers</h2>
Automakers can develop new services based on driver data to improve customer experience, making the everyday task of driving safer and less costly.
One such service is Toyota Connected. The average Toyota customer drives 48 minutes per day. <a href="https://towardsdatascience.com/data-science-at-toyota-connected-69bf50982b09" title="https://towardsdatascience.com/data-science-at-toyota-connected-69bf50982b09" data-renderer-mark="true">Toyota Connected cars</a> collect and transmit comprehensive driving data that ranges from speed and distance to steering angle--even whether the windows are up. Over 500 data points are generated every 200 milliseconds, so that 48 minutes amounts to 7.2 million data points per vehicle per day.
Data scientists analyze all this information to develop a driver score, focusing on g-forces, location, speed and brake pressure. Using this individualized score, Toyota is able to provide the customer with driving tips to help them get better miles per gallon. And better mileage isn’t the only perk. Those with good scores can obtain discounts from insurance companies.
Hyundai is also helping drivers equate safe driving with saving money. Hyung Soon Park, Data Analytics Team Manager at <a href="https://tech.hyundaimotorgroup.com/article/how-will-big-data-improve-the-auto-industry/" title="https://tech.hyundaimotorgroup.com/article/how-will-big-data-improve-the-auto-industry/" data-renderer-mark="true">Hyundai Motor</a>, echoes the positive effect that data-based services can have on consumers’ daily lives. He explains Hyundai’s own usage-based insurance program that will lower premiums for drivers, saying:
<blockquote>
Hyundai will utilize the driving data via its Blue Link connected car service from consenting car owners, and assess individual driving behavior. Thanks to the Big Data analysis by Blue Link, Hyundai can encourage safer and more efficient driving.
</blockquote>
Real-time data collection, monitoring and analysis can also give drivers peace of mind during tiring commutes. <a href="https://www.fjmercedes.com/mercedes-benz-attention-assist/" title="https://www.fjmercedes.com/mercedes-benz-attention-assist/" data-renderer-mark="true">Mercedes-Benz’s Attention Assist</a> uses a sensor and detailed algorithm to analyze driving behavior and detect fatigue. Taking parameters like steering wheel movement and driver interaction with vehicle controls into account, Attention Assist sends alerts to the driver to pull over and take a break if it determines that the driving behavior is due to a lack of alertness.
While there is inherent controversy regarding the privacy issues that data collection brings, Lauren Smith of the <a href="https://www.cbsnews.com/news/carmakers-are-collecting-your-data-and-selling-it/" title="https://www.cbsnews.com/news/carmakers-are-collecting-your-data-and-selling-it/" data-renderer-mark="true">Future of Privacy Forum</a> explains “that yes, our cars are learning more about us, but what they learn may save our lives.” Automotive businesses can utilize driver data to incentivize safer habits while improving customer satisfaction.

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<h2 id="Data-for-Safer-Cities" data-renderer-start-pos="5351">Smart Data for Safer Cities</h2>
Data can also be used to build safer cities by identifying streets which, if no precautions are taken, are most likely to be where future traffic accidents occur.
<a href="https://media.ford.com/content/fordmedia/feu/en/news/2018/11/29/ford-reveals-how-big-data-could-help-make-cities-safer--by-highl.html" title="https://media.ford.com/content/fordmedia/feu/en/news/2018/11/29/ford-reveals-how-big-data-could-help-make-cities-safer--by-highl.html" data-renderer-mark="true">Ford Smart Mobility</a> spent 2018 recording over 621,000 miles of vehicle and driver data in and around London, England. Detailed driving data like severity of braking and hazard light application helped identify near misses on the road which were then cross-referenced against data on existing accident reports. With this information, Ford was able to build an algorithm to determine the probability and location of future accidents. Sarah-Jayne Williams, Director of Smart Mobility for Ford of Europe says:
<blockquote>
The Ford City Data Report is a showcase of what we at Ford can do with connected vehicle data, smart infrastructure, and our analytical capabilities. We are calling on cities to work with us to collectively solve problems that they can become even better places to live and work in.
</blockquote>
Ford also completed an 18-month trial run in 2019 of its <a href="https://cities-today.com/ford-to-test-mobility-solutions-with-six-cities/" title="https://cities-today.com/ford-to-test-mobility-solutions-with-six-cities/" data-renderer-mark="true">City Insights Platform</a> in Ann Arbor, Michigan. The platform utilizes safety, parking and transit data to build interactive models that the city can use to solve mobility issues like traffic congestion and pedestrian safety. By analyzing data collected from Ford connected vehicles, the platform provides a real-time look at traffic flow.
Honda is also contributing to city safety with its <a href="https://csr.honda.com/2018/10/04/honda-demonstrates-new-smart-intersection-technology/" title="https://csr.honda.com/2018/10/04/honda-demonstrates-new-smart-intersection-technology/" data-renderer-mark="true">“Smart Intersection” technology</a> for vehicle-to-everything (V2X) communication that it’s developing in partnership with Marysville, Ohio. Utilizing object recognition software and V2X communication in conjunction with cameras mounted on Marysville’s intersections, Honda cars can virtually see through walls and around buildings to help alert drivers to hidden hazards.
With intersection collisions causing 20% of traffic-related deaths, Ted Klaus, vice president of strategic research at Honda R&amp;D Americas, Inc believes that Honda’s “V2X technology is an essential component of a smarter and safer transportation ecosystem and can play a role in our dream for a zero-collision society.”
An ounce of prevention really is worth a pound of cure. If automakers’ safety algorithms can alert cities of problematic roadways and officials enact changes, the data can help save lives from traffic accidents.
<h2 id="Codazen-Solutions:-Putting-Big-Data-Analysis-into-Action" data-renderer-start-pos="7683">Codazen Solutions: Putting Big Data Analysis into Action</h2>
As sensors in cars become as commonplace as the radio and glove compartment, automakers who want to thrive in our technological world need to adapt the way they build vehicles and provide services. By collecting real-world information and analyzing it to create algorithms, automobile manufacturers can develop services that enhance customer experience as well as improve driver and city safety. They can also catapult transportation itself into the future with autonomous vehicles.
At Codazen, we know the positive impact user data can have on our clients and projects. That’s why we work closely with organizations to leverage that data, utilizing machine learning and AI to <a href="/amazon-rekognition-recognizing-love" rel="noopener" target="_blank">detect and analyze images and objects</a>.
One of our data scientists, Neal Munson, believes that data analysis in the automotive industry has the potential to make our lives better and less stressful. He says:
<blockquote>
Data science applied appropriately to cars can help people form better driving patterns, or it can uphold the AI that drives for them. Either way, there is a lot that can happen that currently isn’t commonplace. Sure, my car can sound a bell if I’m not buckled, but we live in a time when it should ask if I want to refuel before driving out too far past town.
</blockquote>
Codazen believes in creating optimized experiences and improving customer satisfaction. Our enterprise teams and data scientists provide data management and analysis to meet those goals.
<a href="https://www.codazen.com/contact/" title="https://www.codazen.com/contact/" data-renderer-mark="true">Contact us</a> to learn more about how we can help you harness the power of <a href="https://blog.codazen.com/information-wealth-in-big-data" title="https://blog.codazen.com/information-wealth-in-big-data" data-renderer-mark="true">big data analysis </a>to drive your organization’s digital transformation forward.

Big data analysis is changing the automotive industry through autonomous vehicles, safe driver incentives and precautionary city planning.

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