Few concepts and terminologies are as poorly understood as Artificial Intelligence and innovation. In the case of Artificial intelligence (AI) and according to Opinion surveys, even top business leaders lack a detailed understanding of what is Artificial Intelligence.
Most of them confuse it with super-robots or hyper-intelligent devices. Part of this problem is the lack of a clear, uniform definition of AI. The lack of clarity around the term has made technology pessimists fail to see AI’s potential as a tool for innovation.
Therefore, understanding what AI is and how organizations can leverage it for innovation is crucial for any business leader or entrepreneur.
What is Artificial Intelligence?
According to BuiltIn, Artificial Intelligence refers to a wide-ranging branch of computer science concerned with developing smart machines capable of learning and solving problems that need human intelligence. Its ideal characteristic is the ability to rationalize and take actions focused on achieving a certain goal.
At its core, AI is based on the principle that human intelligence and knowledge are representable to machines and computer systems so that machines can mimic intelligence easily and execute certain tasks. AI’s goal is to enhance, and increase efficiency by learning, reasoning, and executing tasks much faster and more accurately than humans.
How does Artificial Intelligence work?
Popular misconceptions place how AI works mostly in the fields of robotics and self-driving cars. However, this approach fails to point out the practical applications of AI, which is processing an extensive amount of data automatically and providing accurate results. Having a clear understanding of AI is the key to facilitating conversations on the practical applications of AI.
AI is a broad field that includes many technological theories and is categorized into the following major sub-fields;
1 – Machine Learning (ML)
Machine Learning is an application of AI that automates analytical model building. It provides computers or machines with the ability to learn and improve their accuracy without being programmed manually. The primary focus of Machine learning is developing algorithms that can analyze, learn, and make conclusions from small to medium-sized data sets.
2 – Computer Vision
Computer vision implements deep learning and pattern recognition to recognize what’s in a picture or video. With computer vision, computers can interpret and capture images and videos in real-time and provide real-time information.
3 – Neural Networks
Neural networks are associations developed by computer systems from modelling how the human brain works. Just like the way the brain uses neurons, computer systems use perceptron to create artificial neural networks. Through different learning models, the perceptron analyzes enormous sets of previously undefined data. Eventually, learning how to distinguish the data and make accurate conclusions or predictions.
4 – Deep Learning
Deep learning employs Neural Networks with multiple layers of processing units to learn complex patterns in large amounts of data. It then determines a single output from several inputs. Some Common applications of Deep Learning include image and speech recognition.
5 – Cognitive Computing
Cognitive computing is another crucial component of AI. Its primary purpose is to develop natural, human-like interactions with machines. One in which machines can simulate human-like processes through the ability to interpret images and speech and then respond coherently.
6 – Natural Language Processing | NLP
AI through Natural Language Processing allows computers or machines to analyze, understand, and generate human language and speech. The goal of NLP is to allow seamless interactions with machines using everyday human language.
Why is Artificial Intelligence Important?
According to Netapp, the amount of data being generated today far outpaces humans’ ability to analyze it and draw conclusions. As a simple example, most humans can figure out how not to lose at tic-tac-toe even though there are 255,168 unique moves, of which 46,080 lead to a draw. However, with more complex games like checkers, which have over 500 quintillions (50×108) of different potential moves, most humans cannot figure out effective moves.
AI forms the basis for the future of making complex decisions efficiently. Computer systems, when combined with AI, perform tasks much faster and more accurately than humans can.
Artificial Intelligence and Innovation
According to Fekry Ford, of all the technologies driving the fourth industrial revolution, most business leaders have termed Artificial Intelligence as perhaps the most disruptive of all. As automation becomes more sophisticated, there is no doubt the AI will cause radical changes or even cause disruption of entire industries. Therefore, having a clear understanding of how AI will affect different innovations is crucial.
Artificial Intelligence as a Disruptive Innovation
According to the Christensen Institute, disruptive innovation refers to the process through which a product or service initially takes root in simple applications at the bottom of a market, typically less expensive and more accessible. It relentlessly moves up the market to eventually displace established companies. Today, AI is redefining how industries operate by offering personalized and automated processes.
It isn’t easy to imagine a sector that has not yet or will not face disruption. Most AI innovations have not yet gone mainstream; hence many people are unaware of them. However, AI aims to replace current products, services, and business models to create new markets and replace existing ones. Below are some ways AI is taking root in operations and changing how companies operate.
Artificial Intelligence in Retail and E-commerce
According to Business Wire, global retail spending on AI will grow to over seven billion by 2022. They also predict that chatbots will replace 25% of the customer service task. AI chatbots have become common in both large and small-sized businesses. Retailers and E-commerce stores use these tools to collect enormous data from their customer’s requests and conversations.
Using machine learning and deep learning, chatbot algorithms can improve interactions with customers. In the future, chatbots will perform semantic analysis by capturing customer moods, understanding the natural human language, and providing personalized assistance. Ultimately, this analysis will help retailers and e-commerce stores better understand their customers and develop customer loyalty.
A good example is the H&M chatbot launched on the Kik messaging app ahead of its competitors in 2016. The chatbot allows customers on the Kik app to make purchases from the H&M store. With a few questions at the start, the chatbot understands customers’ preferences and provides personalized outfits.
Artificial Intelligence and the Future of Smart Homes
According to Statista, the smart appliances market brought in almost seventeen billion in 2019, and they expect this amount to grow twenty per cent by 2023. This number proves that smart appliances are slowly but increasingly being adapted and incorporated into people’s homes. Merging internet-connected homes and smart appliances brings forth incredible innovations.
For example, Chefling, a startup in Silicon Valley, is developing smart kitchen appliances. The startup leverages machine learning and connected appliances to create an integrated cooking experience. The application helps you take kitchen inventory in real-time, automate cooking appliances, and provide real-time cooking recipes and guides.
The population’s growing demand for smart homes is also driving the growth of voice assistance inside and outside homes. Companies like Amazon and Google are investing millions of dollars into voice technology companies like Drivetime.
How AI will Disrupt Health-care
Over the course of the past two decades, the medical industry has seen very disruptive changes. Now, rapid advancements are expected over the course of the next half-decade because of the integration of Artificial Intelligence into medical technology.
For example, on December 31, 2019, BlueDot’s AI health monitor systems notified clients of an outbreak, now known as COVID-19. Through automated epidemic detection, BlueDot detected the epidemic six days before the center for disease control and prevention, and nine days before the world health organization. Bluedot’s language processing systems and machine learning algorithms could detect where the incident occurred and how far it would spread using data from airline tickets.
From a video by the online platform, it is clear that AI is disrupting medical imaging. Radiologists have applied deep learning and neural networks to radiology and other imaging techniques to make medical imaging faster and more accurate. By leveraging AI, radiologists can accomplish imaging and analysis in real-time. The imaging models also provide real-time conclusions and diagnoses of patients.
AI is leaping in the healthcare industry, and it stands to disrupt the medical industry greatly. It may take a few years for the technologies to become mainstream, but when it does, most of the traditional medical approaches and techniques will become obsolete.
Artificial Intelligence as Radical Innovation
In today’s rapid technological change era, businesses need to innovate constantly to stay relevant and competitive. With radical innovations, businesses can leverage their existing infrastructure, assets, and core competencies to create products, services, and business models that change them for the better.
The accelerated pace of Artificial Intelligence developments has forced business leaders to look for ways to implement AI in their businesses. According to Global News Wire, AI is rapidly and radically changing how businesses operate, and the market demand for AI technologies is set to reach over two hundred billion by 2026.
Various startups and well-established companies are developing brilliant innovations with AI software to improve their products and take over their industries. If some of these innovations go mainstream, then most of the traditional operations will become outdated.
Artificial Intelligence and Natural Language Processing
According to Inside Big Data, data volume has been doubling every two years, but this period will reduce in the future.
Today, much of the data produced from the internet, businesses, and social media platforms is textual data. With this exponential growth in text data, most of which is generated by the internet, businesses need ways to quickly and accurately analyze this data.
Up until recent times, businesses had been analyzing customer actions to understand market trends and customer needs. Unfortunately, this technique is becoming outdated. Social media platforms now perform the analysis of their user attitudes, moods, and preferences under semantic analysis which businesses can now tap into.
Natural Language Process provides businesses a chance for semantic analysis. NLP makes customer data, including text data, more user-friendly using machine learning and deep learning. NLP also allows businesses to develop smart digital assistance. In a way, this breaks the language barrier in customer service and allows human-like interactions with chatbots and smart assistants.
Tech giants like Google, Amazon, and Twitter are employing NLP in their processes to assess their customers and create personalized products and services.
Artificial Intelligence and Autonomous Cars
The autonomous car innovation will profoundly change the world. The current car utilization process is inefficient. Although the process of manufacturing cars is very efficient, our utilization of cars is sparkly different. According to Morgan Stanley, most car owners only drive their cars 4% of the time. This stands for 8.4 trillion hours of idle time annually for the global fleet of cars. If you consider the number of vehicles globally and what they are worth, the amount of money wasted is staggering.
According to PWC, introducing autonomous cars could impact almost 90% of the global car fleet, resulting in a reduction of 250 million cars to only 2.5 million in the US alone. Therefore, the potential impact is enormous. By introducing machine learning and deep learning, the era of autonomous driving does not seem far-fetched.
Currently, companies like Waymo and Tesla have proposed techniques that leverage AI to improve autonomous taxis. However, among the self-driving car companies, Tesla currently holds the top position with the ability to train neural networks at the scale of billions of miles. Therefore, if Tesla’s self-driven cars go mainstream, then the existing car industry could be made obsolete.
A look at the Downside of Artificial Intelligence in Innovation
Artificial intelligence is still a very young technology, and it is still not clear what it will bring to organizations and people’s lives. Additionally, it is also unclear whether the outcomes of AI will be positive or negative or whether the negative impacts will outweigh the positive ones. Currently, several challenges have created concerns around AI and slowed down its adoption.
AI should always apply to problems that humans can track the outcomes of and how decisions were made. The parameters should be clear and precise if not well-coded into the algorithms. When we have too many unknown variables, it becomes difficult to deploy AI. Hence the reason why autonomous cars are still not available to the public. Many concerns arise when we propose to delegate responsibility for the safety of car passengers, other drivers, and pedestrians into the hands of a machine. The variables involved in autonomous driving are many, and a glitch or wrong decision by AI could cause catastrophic outcomes.
The Blind reliance on AI
Artificial intelligence is an excellent servant but a poor master. Most of the AI algorithms function with an accuracy of about 90%. The remaining 10% require human help. Although they may perform almost ten times better than humans, they still need human oversight, this fact will be For example, when making ethical or legal decisions.
Vast unemployment looms in the future as AI continues to develop. With capital-intensive technologies, most of the tasks done by humans will be made obsolete. For example, AI innovations have triggered the layoff of many cashiers because of smart cashier services in banks and stores. It’s possible that the continued replacement of human workers by AI could lead to a decline in a country’s GDP. With high unemployment rates, a country’s GDP remains stagnant or plunges. It’s all good that AI will replace human workers, but will this lead to new jobs being created for humans to perform.
Many organizations are working on exciting AI innovation initiatives across different industries. While many startups and big tech companies have reported acceleration of their AI projects, those sitting on the sidelines observing will be pleased to know that there are still vast amounts of opportunities in the AI land grab. With these opportunities come challenges that those considering AI adoption should be prepared to address as they launch their AI projects.
There has never been a time in history when the convergence of human minds, algorithms, hardware, and financial resources have been dedicated to a technology pursuit comparable to Artificial Intelligence. However, this is not to say that we are at the peak of AI adoption or that the road ahead will be smooth. There are a plethora of disruptive and radical AI innovations yet to come. AI’s potential is enormous, and we have only scratched the surface of its application.