Machine Learning & AI Involvement in the Way People Interact & Date Today

In recent years there has been an established relation between Artificial Intelligence (AI) and Machine Learning (ML). This represents a major step forward in how computers can learn.

ML can be used in many different ways. Usually smart businesses start with analytics departments and such visualization programs as Weka or What-If Tool(WIT) and then when businesses start to understand results and their product managers start to use this data in the beneficial for the business numbers way, it goes to the developers (or analytics with programming skills) who are capable to build proper models which can help businesses to grow.

Artificial intelligence is becoming good at many “human” jobs—diagnosing UX difficulties for heavy payers, translating languages, providing customer service. This has seen a relatively fast improvement. This is raising reasonable fears that AI will ultimately replace human workers throughout the economy. But that’s not the inevitable, or even most likely, outcome. Never before have digital tools been so responsive to us, nor we to our tools. While AI will radically alter how work gets done and who does it, the technology’s larger impact will be in complementing and augmenting human capabilities, not replacing them.

A Harvard Business research involving 1,500 companies found that firms achieve the most significant performance improvements when humans and machines work together. This collaboration plays into the strengths of both human and AI. What comes naturally to people (making a joke, for example) can be tricky for machines, and what’s straightforward for machines (analyzing gigabytes of data) remains virtually impossible for humans. Business requires both kinds of capabilities.

Principles of AI & Human Collaboration

To take full advantage of this collaboration, companies must understand how humans can most effectively augment machines, how machines can enhance what humans do best, and how to redesign business processes to support this partnership.

The machine learning in dating & social networks derives much effort from psychologists to build computational model for solving tasks like recognition, prediction, planning and analysis even in uncertain situations.

  1. Reimagine Business Processes – by taking into consideration the possibility of AI and Human collaboration, it is easier to view how processes may be changed. This leads to a discovery of proper areas of collaboration (and compartmentalization/exclusivity)

  2. Embrace Experimentation/Employee Involvement – being open to trying the AI and Human collaboration is an educational experience. It is enlightening and can serve as a framework for more solid planning.

  3. Actively Direct AI Strategy – an AI strategy works better when given a definitive purpose. By addressing a need or setting a target as well as adjusting as needed during the who process, we end up with more effective results that can be the basis for a final (defined) standard.

  4. Responsibly Collect Data – data is the building block for any collaborative work. It should be guided accordingly. A movement for ethical data collection has been around for as long as data has been collected. This allows for collection based on relevance, importance and proper use.

  5. Redesign Work to Incorporate AI and Cultivate Related Employee Skills – adapting accommodate AI and Human collaboration allows for the business to effectively incorporate new processes that greatly enhance employee skills while maximizing the capability and productivity of AI.

Top General Use Cases from the Industry

  1. Data Security
    Malware is a huge — and growing — problem. In 2014, Kaspersky Lab said it had detected 325,000 new malware files every day. But, institutional intelligence company Deep Instinct says that each piece of new malware tends to have almost the same code as previous versions — only between 2 and 10% of the files change from iteration to iteration. Their learning model has no problem with the 2–10% variations, and can predict which files are malware with great accuracy. In other situations, machine learning algorithms can look for patterns in how data in the cloud is accessed, and report anomalies that could predict security breaches.

  2. Fraud Detection
    Machine learning is getting better and better at spotting potential cases of fraud across many different fields. PayPal, for example, is using machine learning to fight money laundering. The company has tools that compare millions of transactions and can precisely distinguish between legitimate and fraudulent transactions between buyers and sellers.

  3. Recommendations
    You’re probably familiar with this use if you use services like Amazon or Netflix. Intelligent machine learning algorithms analyze your activity and compare it to the millions of other users to determine what you might like to buy or binge watch next. These recommendations are getting smarter all the time, recognizing, for example, that you might purchase certain things as gifts (and not want the item yourself) or that there might be different family members who have different TV preferences.

  4. Search
    Every time you execute a search, the program watches how you respond to the results. If you click the top result and stay on that web page, we can assume you got the information you were looking for and the search was a success.  If, on the other hand, you click to the second page of results, or type in a new search string without clicking any of the results, we can surmise that the search engine didn’t serve up the results you wanted — and the program can learn from that mistake to deliver a better result in the future.

  5. Neuro-Linguistic Programming (NLP)
    NLP is being used in all sorts of exciting applications across disciplines. Machine learning algorithms with natural language can stand in for customer service agents and more quickly route customers to the information they need. It’s being used to translate obscure legalese in contracts into plain language and help attorneys sort through large volumes of information to prepare for a case.

  6. Product Personalization
    Predict which offers will be most attractive to each individual customer, resulting in more targeted marketing campaigns and higher brand value.
    Problem / Pain:
    Consumers increasingly expect (even demand) individualized brand experiences. Businesses need to provide highly personalized product and service offerings – but it is not practical or scaleable for human teams to understand and adapt to the individual preferences of millions of customers.
    Modern machine learning algorithms accurately discover the preferences and purchasing behaviors of individual consumers. This knowledge enables businesses to target and personalize content and product recommendations, resulting in increased customer engagement, brand value, and sales.

  7. Finding Duplicate Customer Records in Your Database
    Clean up the records in your customer database, helping you stay up to date with best practices and avoid sending duplicate messages.
    Problem / Pain:
    Best practice marketing techniques require a single customer view, but your database may contain duplicate customer records. This may be due to spelling mistakes, changes in customer information, or even just because one record has the customer’s middle name and the other record doesn’t. These duplicates can be difficult and time-consuming to find and correct.
    By combining machine learning with fuzzy matching techniques, it’s easy to identify when multiple records are likely to be for the same customer, making sure your database is in prime condition.

  8. Loyalty Program Usage
    Predict which offers and program elements will encourage customers to use your loyalty program, boosting customer engagement and satisfaction.
    Problem / Pain:
    Loyalty programs are designed to improve customer engagement and reduce customer churn, but they are only effective when customers are actively participating. Choosing the best content and redemption offers for loyalty schemes gets program members more active and engaged, but it is difficult to know which activities will be effective.
    With machine learning, companies personalize redemption recommendations in loyalty schemes, resulting in increased point redemptions, more fulfilling experiences, and a more active membership base. For example, models predict the types of people that are more likely to travel, the types of travel people are likely to undertake, the prices that travellers are willing to pay, the importance of accommodation relative to travel, and the importance of experience compared to travel, all of which allows travel companies to tailor offerings and loyalty programs for maximum engagement and use.

  9. Next Best Offer
    Determine the next product to pitch to existing customers that will have the biggest impact on sales and deepen the customer relationship.
    Problem / Pain:
    Sending multiple or irrelevant product offers to a customer can cause them to think negatively about your company. Additionally, research shows that providing more choices can lower a customer’s satisfaction and make them less likely to purchase. Marketers need a way to determine when and how to contact customers so that they avoid spamming and improve product offerings.
    Machine learning algorithms determine the right product to recommend to each customer based on past purchase behavior, resulting in better ROI, increased customer satisfaction, higher brand value, and more sales.
    Next Best Action:
    Determine the message and medium that will resonate best with individual sales prospects, resulting in improved closing rates, increased engagement, and smoother buyer’s journeys (which marketing activity is most likely to move each individual customer closer to the payment).
  10. Customer Churn
    Predict which customers are likely to stop purchasing your products or services so you can undertake preventative action.
    Problem / Pain:
    Although acquiring new customers is important, it is just as important (and less expensive) to retain existing customers. However, customer retention teams have limited resources and are unable to devote the same level of attention to every customer. Businesses need to determine which customers are more likely to churn so they can prioritize their retention efforts.
    By discovering patterns in historical customer activity, modern machine learning algorithms accurately predict which of your current customers are most likely to defect to your competitors. This enables your retention team to focus their resources on the customers most at risk and offer them incentives to remain loyal. By retaining more customers, your business grows faster, increasing profits and building brand value.

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