Whether you like it or not, algorithms and AI are all around us and we use them every day in our personal and professional lives. Beyond a trend, the history of algorithms dates back centuries to mathematicians, computer scientists and cryptographers worldwide who worked to develop encryptions, computer code, binary algebra, and computer programs.
One of the most significant advancements made was by Alan Turing, who in 1950 wrote a paper, “Computing Machinery and Intelligence,” in which he asked, “Can Machines Think?” To answer the question, Turing created a game known as the “Turing Test,” which pit the world’s most advanced AI programs against ordinary people to determine whether a computer can act “more human” than a person.
In 1991, Hugh Loebner and the Cambridge Center for Behavioral studies founded the Loebner Prize, an artificial intelligence competition based on the Turing test. Each year people compete. Roughly 18-years later, Brian Christian won the competition and was named the “The Most Human Human.”
A book with the same title was a Wall Street Journal bestseller, a New York Times Editors’ Choice, and a New Yorker favorite book of the year.
In a Quartz video about the competition, Christian said:
“I wanted to make a stand on behalf of humanity. I interviewed psychologists, linguists, philosophers, computer scientists, deposition attorneys, and dating coaches; people who specialize in human conversation and interaction and asked, ’What is the hallmark of human communication?’
We’ve substituted phone calls for face-to-face interaction, text messaging for emails, emoji for text and are steadily moving lower down the totem pole of bandwidth in terms of how we interact with each other. There’s a great quote from the Oxford philosopher John Lucas who says, ‘If machines pass the Turing test it is not because the machines are so intelligent, it’s because the humans are so wooden.’”
Since 2014, the contest has been held by The Society for the Study of Artificial Intelligence and Simulation of Behavior (AISB) and is the longest-running convention on artificial intelligence. The theme for 2018 was AI for the Digital Society.
From helping industry sectors, including marketing, technology, financial services, insurance, healthcare, entertainment, oil, gas, and refinery production, manufacturing, farming, sports, retail, consumer goods, transportation, human resources, social media and more, it’s important to understand the impact and growth of algorithms and AI.
What’s the Difference Between an Algorithm and AI?
An algorithm is a set of steps (the instructions), which is simple and well-defined. Each step is repeated to solve a specific problem. In non-tech terms, like a recipe, it incorporates ingredients, but needs to follow specific tasks to create a culinary dish.
The combination of many algorithms produces AI: a domain-specific illusion of intelligent behavior. For example, when I asked Alexa, Amazon’s digital AI assistant, for news flash #trending today, one of the stories was, “Cryptocurrency will be easier for the everyday customers to use.”
“These words are sent to a search engine — itself a huge pipeline of AI-algorithms that process the query and sends back an answer. Another algorithm formats the response into a coherent English sentence. A final algorithm verbalizes the sentence in a non-robotic-sounding way.
And that’s AI. Most AI systems, including, a self-driving car, a piece of software that monitors your credit card for fraud follow these same “pipeline of algorithms template”. The pipeline takes in data from some specific domain, performs a chain of calculations, and outputs a prediction or a decision.” — AIQ: How People and Machines Are Smarter Together by Nick Polson and James Scott
Data and machine learning are at the core of Alexa’s inner workings. Machine learning (ML) is an application of AI that enables systems to learn from data, such as patterns, training, and observations, which enable ML to get smarter over time.
In combination with natural language processing, a subset of AI, computer systems can decode a person’s written and verbal communications from data that is input into a computer system. Natural language is enhanced with machine learning and is the foundation of AI, which enables us to interact in human language.
“What you build on top of that, whether it’s predictive, prescriptive analytics, forecasting, optimization, wherever you want to go, that foundation always comes back to these technologies that have been around for decades,” said Mary Beth Moore, SAS Artificial Intelligence and Language Analytics Strategist. “It’s really [for] the semantic structure of the language. What are the nouns, what are the verbs, what is the vocabulary, how does [the] sentence structure fit together with your adverbs and pronouns? What are the different stems?”
In addition to using digital assistants in our homes, businesses are using chatbots to improve the customer experience (CX). Gartner predicts by the year 2021 “Conversational AI-first” user experience, or CUX, will be adopted by most enterprise organizations.
How People and Machines Can Work Together
To learn more, I participated in a workshop to learn how to create a chatbot. The goal was to create a chatbot for visitors attending a museum exhibition in Amsterdam. There were 12 groups of two: each had to write a scene about a painting of Cleopatra. Thankfully, I was partnered with a developer.
Since I don’t know how to code, I didn’t think I could help. My partner started writing the code, “Cleopatra, Antony is dead. Was he killed?” As a communications veteran, I said, “We need to use more emotion to make it interesting.” I added, “OMG, Antony died. How can I go on living?” I continued to provide more emotional responses, emoji’s and gifs. In the end, we created a funny and engaging chatbot.
I learned that coding without storytelling is just source code, a set of instructions for a computer to follow. Bots are relatable when they sound more human-like. Not just for developers, content marketers can help to improve customer experience with storytelling. In November, a global conference concluded machine translation is now “very close to the performance of human translators.”
Examples of Use Cases in AI
Customers want companies and brands to understand their needs and wants and to deliver personalized experiences. An experience is more personalized when you use algorithms and machine learning to focus on the individual. Machine learning systems access data to identify patterns and make predictive decisions.
Financial Services
For example, I worked with ForwardLane, an AI FinTech company that aggregates, scales and organizes financial, investment, firm, industry, global news and sentiment data for financial services professionals.
Its technology uses AI, machine learning and natural language processing (NLP). NLP is a subset of AI that helps computers understand and interpret human language and the questions people ask.
If a wealth manager has 500, 300 or even 200 accounts, there’s just not enough time to read through all the data. In fact, 90 percent of data has been collected in the last two years. And, IDC predicts that our digital universe is growing by 40 percent each year, and that by 2020, it will contain nearly as many digital bits as there are stars in our physical universe.
Technologies like ForwardLane can deliver personalized and real-time insights and make recommendations for optimizing clients’ portfolios. If you have a portfolio manager, you should probably ask he or she if their company uses AI.
Insurance
Typically, people are frustrated with their insurance carriers in terms of lack of speed, inefficiencies in processing claims and other mistakes. NLP enables insurers to translate unstructured data into structured data to classify claims more quickly. Chatbots help customers get answers more quickly than being transferred to different agents and waiting on-hold.
In automotive claims, AI and machine learning can review a photograph of an accident, analyze millions of images and make financial predictions for settlements to determine loss. This reduces claim submission and payout times.
“While ML is exceptional at analyzing data to create models that make predictions, recognize patterns, and automate decisions, it lacks human reasoning capabilities,” Forrester analysts write. “ML’s strength is data. Knowledge engineering’s strength is human wisdom. Used together, enterprises can dramatically accelerate the development of AI applications.”
Healthcare
In healthcare, statistics show that 80 percent of serious medical errors are due to miscommunication between caregivers during patient transfer. AI can address communication issues between hospitals and patients with apps and chatbots.
In research by Accenture, effective application of AI to medical dosage error reduction can result in a savings of $16 billion for the healthcare industry by 2026. Medication and dosage errors are among the leading causes of unanticipated but easily preventable death or serious injury in the healthcare industry today.
AI will help make application and administration of medications with the precise dosage easy.
Entertainment
Since Netflix is not a broadcast network but an internet company, it has huge amounts of data about its customers. As Bill Murphy Jr. wrote for Inc.:
Because of the size of Netflix’s subscriber base its algorithm can deliver content to the members most likely to want to watch it and can turn in stunning total audience numbers for some new shows.
The individual episode numbers don’t matter much for Netflix (compared to what they’d mean for an advertiser-supported business model), but they’re a gigantic attraction for top Hollywood talent.
In video games, AI creates responsive, intelligent two-way interactions with non-player characters (NPCs). I’ve watched my boys play video games like Madden, NBA 2K19 or Red Dead Redemption 2. They look almost realistic. In Red Dead Redemption 2, characters wander around the Old West. There’s dust, tumbleweeds, horses, and cowboys, and killing. The NPCs operate based on what’s coded into them.
There are many other use cases of AI and algorithms. Some were highlighted here. In 2019, among companies using AI, 70 percent will obtain AI capabilities through the cloud. By 2020, the penetration rate of enterprise software with AI built in, and cloud-based AI development services, will reach an estimated 87 and 83 percent respectively, according to Deloitte.
How Your Day is Managed by Algorithms
Most of us wake up in the morning and ask digital assistants questions about the time and weather. Then we check our emails. If you use Gmail, you have automatic email filtering with primary, social and promotions.
You probably own a smartphone and check your calendar, texts, social media profiles, and send tweets, write posts or use automation tools. We search Google to make recommendations for us. As we search, it learns about our location, interests, purchasing habits and makes recommendations.
Chatbots answer our questions in real-time and are becoming more human-like in conversations.
Attorneys use LexisNexis for case searches. Financial professionals log into Bloomberg to get firm, financial, investment and global news for clients. Public relations professionals use Cision to search for and create media lists. Doctors use healthcare databases, and the list goes on.
On average, people spend 8-11 hours in meetings per week; many of which are video conferencing, webinars and screen sharing.
We use mobiles apps on our smartphones and tablets for mobile banking, dating, ride-hailing, reservations, directions, travel booking, social media, entertainment and more.
If you own a car, it probably has a GPS tracking device to help you get to where you’re going.
When we attempt to make an online purchase locally or while traveling abroad, banks identify possible fraud.
At the end of the day, we buy movies on Netflix and receive recommendations on television series, new movies and different categories by genre.
If you have children, they’re likely to be playing the latest video games.
Be honest with yourself. If you’re afraid that AI will replace your job, or you’re a student struggling to find a major, learn about an industry sector, a company or a startup’s needs.
There’s a huge talent shortage. The skills gap is widening, unemployment in the U.S. is at its lowest rate since 2000, and nearly 60 percent of employers struggle to fill job vacancies within 12 weeks.
Identify ways to build on your skills or move into a new area to remain relevant and competitive. There are many online courses, such as Coursera, Code Academy, and Udemy where you can learn programming, web development, data science, coding languages and more. Or, do research and read to learn more about 2019 business and industry trends to get ideas.
“If you are not willing to learn, no one can help you. If you are determined to learn, no one can stop you.” – Zig Zilgar.
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