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Author: Meghana Chinta

Adaptive AI Systems: One of the Top Trends of 2023

Artificial intelligence has bloomed into existence during this decade. It has boosted productivity, efficiency, and growth. Without artificial intelligence we couldn’t have Face IDs, fraud detection, or even travel navigation. With that being said, as companies take more time to research this newly developing technology they have made extensive progress in adaptive AI systems. Research shows that data seems to be moving in favor of Adaptive AI Systems. Keep reading to find out why.

What are Adaptive AI Systems?

The problem with traditional AI is the inability to learn from the data it receives. As the name suggests, Adaptive AI systems are adapting to new information. It takes in data continuously and uses that data continuously. It adapts to changing environments and shifts with market behavior. This cycle helps the framework be constantly updated, allowing for high performing activities. 

How are Adaptive AI Systems created?

It uses Machine Learning, a type of pattern recognition process that allows this system to understand what it takes in. Sort of how humans learn math. They see the pattern of how to do the problem and do it over and over until they understand the topic. By looking at the patterns of data, it allows the system to look at the environment and adapt to it instinctively. As it gets new data over time, it starts moving through this adaptive AI System. It cycles through a process: automated data preparation, model and strategy redevelopment, model and strategy and review, and push button deployment leading it to the decision platform. This creates a self updating, fully automated cycle. You don’t need to build a static model rather this changing automated system allows us to have a clean feedback loop. 

What are the advantages of Adaptive AI Systems? 

  • Traditional AI needs human intervention to make these changes while an Adaptive system doesn’t. What takes a human months to do a machine can do in a fraction of a second. This makes a huge difference in productivity levels and the output a company can produce. 
  • Adaptive Systems can fit into any situation. Let’s give learning as an example. Say you want to learn math and you go on a website that tracks how you do and makes the questions harder/easier depending on your performance. Well this is done by adaptive AI systems. Through learning your data and responses, this system can take into account your progress and make a learning experience that is unique for you. 
  • This system continues to perform at higher levels compared to Traditional AI systems. 

What are the disadvantages of Adaptive AI systems?

  • With any AI system, it can lack creativity and originality. It is best to understand that with an automated and computer organized system there will be a lack of creativity alongside it. 
  • Adaptive AI can be quite expensive to start up. The initial investment can be substantial making this a tough financial decision for businesses.

With Adaptive AI systems, businesses can help create their productivity levels and output. With its ability to change according to its environment, adaptive AI systems are becoming highly useful for data collection. Next time you witness data adapting to your circumstance or seeing an awfully good customer service robot make sure you give credit to Adaptive AI. Adaptive AI Systems can help your company. Contact Data Ninjas to provide additional information or help you set up a demo. This addition can take your company to your next level, making the investment well worth it.

Consumer Data Privacy Law Trends: Why businesses are becoming more responsible for consumer data

Privacy is becoming increasingly important for members of society. As data is becoming more and more widespread, people are more worried about where their data is going, how it’s being used, and how it’s facilitated. As technology has been around for around a century, trends towards more privacy have been increasingly important. 

Data Privacy Trends

1. AI Governance 

There are many forms of threats that can hurt a company: one being a data breach or a hacker. With the risk of having data taken away, AI governance is becoming an increasingly important priority. It will help take in information about the patterns of consumers, employee behaviors and other key metrics. This way consumer data is far more protected by risks out of businesses control.

2. Centralized Privacy User Experience

People have started demanding more and more privacy rights. After case and case of data mismanagement, it is becoming an important ideal for many consumers. Therefore, many businesses need to create a portal with consent management for data. This way consumers have the option to allow management or disallow it. Either way, privacy UX such as cookies and notices can be more manageable when put under one page allowing for easier usage and better comprehension of data rules.

3. Privacy at home

As school was switched to virtual, jobs taken at home, and life completely shut down, COVID has changed the way data is used and regulated. Now that everything is centralized at home, privacy risks are becoming more paramount. Businesses should make sure they are not monitoring data 24/7 and keeping it to a minimum. By communicating and being transparent with the data used, employees can feel safe and secure with their data. 

4. Consumer mistrust 

Many customers mistrust businesses’ usage of data. They are often becoming more and more aware of the amount of data that is being mishandled. Often read in many fine prints in popular social media is the usage of personal data for things other than commercial use. This is becoming increasingly hazardous for typical data users. I mean take Facebook for example, they allowed around 87 million people’s data to get into other businesses’ hands. With their abuse of their stated terms and conditions, Facebook got sued with the mismanagement of consumer’s data. 

5. Government action 

With the increasing power businesses now have with data, governments have now taken it upon themselves to protect digital rights. By requiring companies to keep up with up to 27 online privacy bills, the government is taking a step towards our security. For example, according to The Washington Post, governments are “proposing a bill that would allow users to opt out of targeted advertisements and to sue internet companies that improperly sell their data.” This being one of the many bills being made, it is increasingly important that businesses start using consumer’s data in a more secure way. 

Texas State Level Laws 

  • Texas Privacy Act: Created in 2019, this act makes companies tell consumers whether their data was leaked, within 60 days and if it affected more than 250 people. 
  • Student Privacy Act: This act took place in 2017 and protected students’ data. By disallowing companies to use and sell student’s data, the student privacy act keeps our children safe from personalized ads and data breaches
  • Identity Theft Law: This law makes it illegal to steal someone’s identity. This forces businesses to make sure their company isn’t illegally used.

CCPA, California Consumer Privacy Act

  • It gives california consumers the right to know what personal information a business takes and how it is used or distributed 
  • Allows California residents delete their information collected from businesses 
  • Allows residents to opt out of selling their personal information 
  • Allows residents to non-discrimiantion if they use their CCPA rights 

Colorado Privacy Act

  • Requires businesses to include an opt-out function towards personal data usage 
  • Gives the right for consumers to opt-out from the sale of personal dta 
  • Gives the right for consumers to know if personal data is benign collected 
  • Allows consumers to fix/edit personal data
  • Allows consumers to delete their personal data 

Virginia Consumer Data Protection Act 

  • Gives consumers the right to see their personal data
  • Gives consumers the right to delete their personal data
  • Gives consumers the right to edit their personal data 
  • Gives consumers the right to opt out of giving personal data
  • Gives consumers the right to opt out of the sale of their personal data

Though just a couple examples of state level privacy regulation laws, it is becoming increasingly important to protect the rights of our children, our family and ourselves. 

Federal Level Regulation

As of right now there are no federal laws that control data privacy in the US. But there seems to be an increase in localized regulation such as in states and counties. The Federal trade commission act however has come to regulate some data, and enforce some privacy laws. However, it doesn’t explicitly state that in its purpose. 

With all this being said, consumer data privacy is increasingly becoming a big deal. Taking time to research and finding ways to secure consumer data can make your brand get more recognition and appreciation. To dive into data compliance in more depth check out our “Data Compliance – A Practical Guide”.

Data Ninja’s Guide: Machine Learning Use Cases in the Retail Industry

Machine learning in retail can be as simple as the self-checkout line, the computer kiosks, or the virtual shopping assistants. It helps facilitate existing customers while pulling in new customers. 

What is Machine Learning?

Machine learning takes large pieces of data and breaks them apart into key insights. It allows producers to take a look into demand, price, and customer behavior. It effectively predicts outcomes for the optimization of profits, creates a better 

understanding of customers’ behaviors and allows you to create new product discoveries. This flourishes your ability to topple other competitors. For more information on Machine Learning visit Machine Learning – A Practical Guide.

ML Use Cases 

  • Pricing strategy, Dynamic pricing and pricing optimization

The way you price an item can pull customers in or out. By using machine learning, it can help improve your pricing strategy. When a price is determined by one person, it is often different from the value held by another. Give a plant, for example, a nature aficionado may value it to be 10 dollars while you price it at 2. This is why this type of pricing can lead to inefficient results.

By using ML, it takes in data that consistently prices an item over and over again. It takes into account historical trends, present trends, and current behaviors to create the optimal price: for example like the brand, the fabric, the condition, etc. It even compares multiple vendors’ pricing against the market price to deliver the best deals. The amount you price your goods determines the profit you will make. ML takes in all economic variables to make a pricing strategy. By looking at how long a customer looks at a product, what products they buy, and what they browse through, ML prices the product efficiently. It even has a randomness function. Say the price has no variability, ML will change the price accordingly to see the profits and inputs of the decision. By analyzing the sales you get at every price point, ML can become smarter and make better price decisions for your company.

  • Product Matching

Product matching is when ML links similar items together. This is especially beneficial when you are comparing prices with competitors or have similar products. Since most customers compare prices before they buy, it is especially important that businesses utilize ML to track their competitor’s prices. When you have a larger catalog of prices, it may be harder to organize all of them and match them. ML can look through the attributes of each item such as its picture, description, and price to match them. If we take Target, for example, the website has many different products of just black jackets. But a typical algorithm will often not match them together. Why is this? Because typical algorithms do not have the strength or data managing capabilities ML has. 

  • Target Market Optimization 

Marketing is a core value in any business. You need to market to get your product out there and known. ML can crack into consumers’ social media and e-commerce platforms to get key metrics: age, gender, interests, persona, state, city, and time on site. Once the ML takes in these metrics it analyzes how they work together and it creates a relationship between the two. With this connection, it will personalize the ad toward the consumer. For example, say you were on social media looking at photos of sushi, ML can see that and connect an ad to that specific interest. This can make sure the target market is reached and the business seeks maximum profits. 

  • Prediction for Inventory Management 

Retailers need to know how much inventory they need to keep, buy or sell. ML will take a deep dive into the current atmosphere: special holidays, seasons, social media, etc. From that it will provide you with ideas for how much inventory you will need. For example, say you sell chocolates and valentine’s day is approaching, well ML will use the event to predict the inventory for that period. Additionally, by looking at past data ML can show when the inventory may be out of stock again. This way a business can let their consumers know without having the problem of insufficient supply.

  • Fraud detection 

In terms of businesses, it is always important to be safe from danger. Just like we humans get insurance, our technology needs fraud detection. ML allows you to take a deeper dive into missing items, suspicious activity, missing patterns, and weird behavior. Since ML looks at these trends when there is a spike in an account or a pattern it can warn you of suspicious activity.

  • Video surveillance and analytics 

Having video surveillance is important to notice any stolen items, broken goods, or suspicious activity. When ML is used in accordance with surveillance, it can analyze the material and make note of suspicious patterns. This highly intricate machine can take into account minute differences that can make or break a business. As humans, we cannot see everything in a video. We may misinterpret something or completely look over it. This isn’t the case with ML. It relies on visual data to make sense of distinct eccentric patterns.

  • Recommendation engines 

ML allows businesses to give a more personalized outlook on their products. It analyzes the type of customer it is dealing with and connects he/she with a product they like. By looking into personal data, patterns, purchases, and browsing history, ML connects the consumer with products they would like. This allows customers to get more recommendations for their interests which can help businesses raise profits.

With all that being said, Data Ninjas can help customers who are looking to implement Artificial Intelligence and Machine Learning solutions in their company. There are many benefits businesses can achieve implementing Machine learning solutions, from pricing strategy to product matching. Data Ninjas can help work through those processes and deliver amazing results. To contact Data Ninjas for your business needs visit https://yourdataninjas.com/contact/.