What is Artificial Intelligence and How is it Used?
For some, Artificial Intelligence (AI) conjures up images of 2003's "Terminator 3: Rise of the Machines," wherein the malevolent AI Skynet sends a Terminator (Loken) back in time to ensure the rise of the machines by offing leaders of the future human resistance. This premise is fictional, of course. The reality is that we meet AI every day, from Alex and Siri to customer service chatbots. Artificial Intelligence, when applied ethically, has the potential to help businesses and society.
The Basics: What is AI?
Artificial intelligence
AI systems mimic human intelligence to complete tasks and improve themselves based on the collected information in its simplest form. AI comes in several forms – examples include:
- Chatbots that use AI to help customers, solve problems and offer valuable answers
- Intelligent Assistants use AI to analyze important information from large text datasets to enhance scheduling
- Recommendation Engines supply automated suggestions for TV shows based on users' watching habits
- The Software Testing Services that incorporates AI (specifically, machine learning) into automated assessment tools overcomes customary challenges of typical automated software testing
- Self-Driving Cars: visual recognition and artificial intelligence form the core intelligence of self-driving cars; they've moved from movies to reality in the past decade.
AI Subfields: Machine Learning and Deep Learning
Machine learning and deep learning are subsets of AI. AI is a universal term for applications that carry out complex tasks that in the past needed human input, like playing chess or chatting with customers online.
Machine Learning (ML) focuses on building systems that learn or improve performance based on their data. Often used interchangeably with AI, machine learning is not the same as AI. It's important to note that all machine learning is AI, yet all AI is not ML.
Machine learning works all around us. It's working when we shop online, use social media and interact with banks. Machine learning algorithms make user experience smooth, efficient and secure. There are three methods of learning – supervised, unsupervised, and reinforcement learning. The distinction between the two lies in how each learns about data to make predictions.
- Supervised Machine Learning algorithms are used most often. A data scientist acts as an instructor and teaches the algorithm what conclusions it must make. In supervised learning, the algorithm is trained by a labeled dataset with a predefined product.
- Unsupervised Machine Learning uses an independent approach. With this model, a computer learns to find complex processes and patterns without constant, close human help. Unsupervised machine learning entails training based on unlabeled data or data without a defined output.
- Reinforcement Learning is when an AI system receives feedback after unlabeled data sets perform one or more actions
Deep Learning is a subcategory of machine learning where algorithms known as neural networks consisting of three or more layers try to simulate human brain function. Layers of neural networks power deep learning; training these networks happens with large amounts of data to configure the neurons in the networks. When instructed, the deep learning model processes new data; the models take information from multiple data sources and analyze that data without human guidance. Deep learning drives many AI technologies that improve automation and analytical tasks – people meet deep learning while browsing the internet and using their mobile phones. In addition to countless applications, deep learning is what generates YouTube video captions, performs speech recognition on smartphones and smart speakers, facial recognition and enables autonomous vehicles. With data scientists and researchers taking on complex deep learning projects, this type of AI will become more prominent in our lives.
What are the Benefits of Deep Learning?
One of the most significant benefits of deep learning is its neural networks. These networks are used to reveal hidden insights and relationships from data. Since robust machine learning models can analyze extensive, complex data, enterprise improves fraud detection, supply chain management and cybersecurity by using:
- Analyzing Unstructured Data: deep learning algorithms are taught to look at text data by evaluating social media posts, news and surveys to deliver helpful business and customer insights.
- Data Labeling: labeled data is needed to train deep learning. Once trained, it can label new data and find different kinds of data by itself.
- Training: deep learning neural networks can be applied to many data and applications. Furthermore, a deep learning model is adaptable when re-trained with brand new data.
Uses for Deep Learning
Social Media: deep learning is used to analyze vast numbers of images; this helps social networks learn more about their users, which improves ad targeting and following suggestions
Finance: deep learning's neural networks are used to predict stock values and formulate trading strategies, find security threats and for fraud prevention
Healthcare: deep learning can be critical in life sciences by trend and behavior analysis to predict patient illnesses. Healthcare professionals can also use deep learning to choose the best treatments and tests for patients.
Cybersecurity: deep learning offers advanced threat detection by identifying suspicious activities
Digital Assistants are a few of the typical examples of deep learning. Digital assistants like Cortana, Siri, Google and Alexa use natural language processing (NLP)ⴕ to answer questions and familiarize themselves with user habits.
Artificial Intelligence in Data Science & Analytics
Some say the era of the spreadsheet is finished. Whether it's a passport scan, an online search, shopping history or Facebook posts, they all contain collectible data that can be monetized and analyzed. Computers and algorithms allow people to understand growing amounts of data in real time; CPUs will likely have the human brain's processing power in less than one decade.
The Numbers
According to the International Data Corporation's (IDC) Worldwide Semiannual Artificial Intelligence Tracker (February 2022), the worldwide AI market is expected "to grow 19.6% year over in 2022 to $432.8 billion." It will break $500 billion in 2023. These worldwide revenues include software, hardware and services.
According to Ritu Jyoti, IDC's Group Vice President, Worldwide Artificial Intelligence and Automation Research:
"AI has emerged as the next major wave of innovation. AI solutions currently focus on business process problems and range from human augmentation to process improvement to planning and forecasting, empowering superior decisioning [sic]and outcomes. Advancements in language, voice and vision technologies, and multi-modal AI solutions are revolutionizing human efficiencies. Overall, AI plus human ingenuity is the differentiator for enterprises to scale and thrive in the era of compressed digital transformation."
To summarize, many business leaders look for ways to innovate. If it's a product or service, they look for answers in data analytics to get insights on the market, demand and target customers. Because of this, businesses adopt AI and machine learning at increasing rates, and the rates will continue to surge.
Ethical Challenges
The swift expansion of AI development and popularity introduces risk to consumers. For example, there is no significant legislation about the privacy of personal health information (PHI) collected by AI devices. Here are a few significant ethical challenges of artificial intelligence:
Biases: AI algorithms must be trained with data, and it's our job to eliminate bias within that data. For example, consider the ImageNet database. ImageNet is an "image dataset organized according to the WorldNet hierarchy" (image-net.org). ImageNet describes its relationship to the WorldNet as follows:
"Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a 'synonym set' or 'synset.' There are more than 100,000 synsets in WordNet; the majority of them are nouns (80,000+). In ImageNet, we aim to provide, on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, we hope ImageNet will offer tens of millions of cleanly labeled and sorted images for most of the concepts in the WordNet hierarchy."
What does this mean? So, the ImageNet database has more white faces than non-white faces. When AI algorithms are trained to recognize facial features using a database with an off-balance of human faces, the algorithm doesn't function well on non-white faces. This sets up an integral bias with enormous impact.
Morality and Control: the more AI we use, the more we ask machines to make progressively essential decisions. For example, there is an international treaty controlling autonomous drone use. Suppose a person, nation or group has a drone with a missile. In that case, there must be a human involved in decision-making before the rocket is used. We've navigated around the vital AI control issues with a hodgepodge of regulations and rules.
Here is the problem: AI must make increasingly immediate decisions. Consider high-frequency trading – more than 90% of financial trades are algorithm-driven. This means that there's zero chance of humans controlling the choices. The same goes for autonomous vehicles; they must react instantly if a child runs onto the road. Therefore, AI must manage the situation, creating interesting ethical challenges around AI and control.
Privacy and consent are long-standing predicaments of AI. There must be data to train AIs, but where do we get that data, and how is it used? Often, we assume most data comes from competent adults who are entirely able to decide for themselves how they want their data used – but this is not always the case.
For example, there is an AI-enabled Barbie doll that children can converse with – but what does this mean when it comes to ethics? Think about it: there is an algorithm collecting data from your child's conversations with this doll. Where does the data go, and what is done with it? We've seen in the news that many companies harvest data and sell it to other companies. Consider this: what are the regulations about this type of data collection, and do we need consumer protection laws?
Big Guys vs. Little Guys: large corporations like Facebook, Amazon, and Google use AI to conquer competitors and become unstoppable, and governments use it, too. For example, China has bold government-backed AI strategies. China isn't alone. In a 2017 Forbes article, Russia, the United States, Japan, Israel and Canada are pulling resources together to benefit from AI.
So, how can we ensure the monopolies we've created distribute their wealth equally and that only one or two countries are eons ahead of the rest of the world? Finding a power balance is a critical AI challenge.
Ownership and Responsibility: who is responsible for what AI creates? Artificial intelligence is used to generate text, bots and misleading deepfake videos. Who owns this material, and what should we do with this "fake news" when it proliferates across the internet?
The Environment: since most of us assume cloud data trains an algorithm and the data run recommendation engines on websites, environmental impact isn't our first thought. However, training AI can produce seventeen times the CO2 emissions of an average American in one year. Consider how we can use this energy for the ultimate good and use AI to solve some of the world's most immense and urgent problems? When we only use AI because it's there, we should rethink our choices.
The Human Aspect: think about how artificial intelligence makes human beings feel. AI is so powerful, fast and efficient that it may leave people feeling inferior. It will keep on automating jobs; so, what are our contributions as human beings? It's unlikely that AI will replace all jobs, but it will enhance them. Humans must learn to work with intelligent machines to manage change and respect people and technology gracefully.
Artificial Intelligence: Business Applications and Benefits
Software Testing: As artificial intelligence permeates our world, it's increasingly vital to validate systems are functional, safe, secure, performant, available and resilient. An IT services provider like V2Soft enhances quality assurance efficiencies using an analytics-driven software and application QA approach.
Customer Service: customer service must be a significant focus for every business. Returns are better when existing customers are happy because it's expensive to continuously find new ones. Technological advancements supply companies with added tools and resources to transform customer service interactions, improving response times and enhancing interaction quality. AI helps businesses improve customer loyalty, brand reputation and enables employees to focus on tasks that bring higher returns.
AI-bots as customer service agents able to oversee tasks are transforming customer-company relationships. Bots run assorted tasks like troubleshooting or engaging with potential customers. Since AI bots can help many people 24 hours a day, seven days a week, they are used as the front line for customer engagement.
Business Intelligence: yes, AI can do the work of calculating profit and loss figures to generate a result. What's more helpful is giving AI the context to understand those numbers. With the correct data inputs, particularly location intelligence, it's possible to harness AI to improve decision-making and assist business leaders in dialing in on trends.
Personalized and Targeted Marketing: customers can make or break any brand. Therefore, companies must analyze their customer base to strategize for increased engagement. It was difficult for brands to find performance insights since many interactions happened in person. AI allows companies to survey customers for richer feedback than historical data analysis. These surveys offer correct information and help perform strategies for improved engagement, which increases sales and customer experiences. This creates customer-centric companies that make more money and increase customer loyalty.
Product Recommendations and Predictive Analysis: AI makes astute predictions with recommender systems. Artificial intelligence no longer needs massive data inputs of historical data before getting to work; it uses reinforcement learning (mentioned above) when there's no known answer. Machines process through scenarios in a trial-and-error way to find the best solution.
Hiring and Recruiting: hundreds of candidates will apply for a single job at a company, which results in a tedious task for an HR team to rifle through every resume to select the ideal candidate. This is where Natural Language Processing (NLP) comes in – NLP filters resumes to fund candidates who meet their requirements. NLP analyzes different attributes like location, skills, education, etc. Furthermore, AI and NLP also recommend eligible candidates for other job openings -- allowing for candidate choice in an unbiased and practical way, saving time and energy for the HR department.
In a March 7, 2022, article in Forbes, Mari Kemp of the Forbes Business Council said:
"As more companies begin using AI and automation tools to make HR workflows more efficient, this could open the door for HR leaders to evolve beyond mundane tasks and paperwork. At my company, we use tools that aid us in tracking and assessing performance data, like Lattice. From benefits administration to employee reviews, I believe AI and automation could be drivers of digital transformation in HR."
Intelligent Supply Chains: organizations across industries are using AI to improve the management of their supply chains since machine learning algorithms can forecast organizational needs and timing. Machine learning algorithms predict what is needed and the best time to move supplies.
Robotics: Intelligent robots have varied uses, from tedious tasks to dangerous ones. The intelligence of these robots improves every day. The imagination of their service is the only limiting factor.
Artificial Intelligence: Business Challenges
Challenges to AI's growth in business are as follows:
Data Scarcity: Even with the abundance of data available to companies today, the adoption of artificial intelligence in some respects stays challenging. For machine learning, which powers most of the applications of artificial intelligence in business, to work, large amounts of data are needed to train the model. This limits the use of AI in new business areas where there is no data available. The massive amount of data we have is (generally speaking) unstructured and unlabeled. With most AI applications including supervised training on labeled data, this poses a challenge to artificial intelligence in business.
Algorithm Bias: we mentioned this above in "ethics" -- it is a societal and a business challenge. Recently, Microsoft and Amazon suspended the sale of their AI face recognition software to law enforcement agencies because of ethnic, racial and gender biases. This illustrates a significant challenge of AI, depicting how bad the algorithm can function when trained on biased data. In the future, AI systems will address these biases appropriately. For now, it poses a severe threat to the adoption of AI in some areas of application.
Threats to Data Security and Privacy – while AI has the potential to answer and solve business problems, such as finding suspicious charges among thousands of invoices and predicting what customers desire. However, artificial intelligence requires people's sensitive information, leading to privacy concerns. ¬The data that feeds AI-powered algorithms creates new pieces of sensitive information without consumer and employee knowledge. What does this mean? AI could create personal data without an individual's permission or knowledge.
Implementation – quality data drives and develops AI systems, which means the data must be valid. It is sometimes difficult to decide which data to use because different data flows across organizations. Replacing old infrastructure is a big challenge for many companies and organizations since AI systems have high computational speed. An AI system can perform exceptionally when a business has an extensive infrastructure and advanced processors.
Complex Algorithms and AI Training Models – business intelligence operations rely heavily on AI algorithms to perform and function. Companies planning to employ AI must know how these solutions function and their potential to transform their systems.
Artificial Intelligence: Societal Benefits
There are many unseen benefits of AI; this technology makes strides to create a better world. As with all new and emerging technologies, it is not only easy to focus on the possible downsides but also natural. Most living things will not make it far if they are not cautious. However, society is more accepting of innovation than in the past.
Let us look at AI's potential societal benefits:
Healthcare: artificial intelligence can improve healthcare, and the most valuable advancement is disease diagnoses. Deep learning has the potential to lower costs and enhance radiographic imaging. Cancer patients in particular benefit because early detection is key to survival. A 2018 article in the scientific journal Nature Reviews Cancer states:
"Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images to detect, characterize, and monitor diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics."
While we mentioned the healthcare industry above, it is essential to note that the industry uses artificial intelligence and machine learning products to analyze enormous amounts of data collected over recent decades. AI and ML products uncover patterns and insights that humans can't find on their own. Furthermore, intelligent tools also help clinicians develop more individualized treatment plans designed for maximum efficiency for each unique patient. AI-enabled virtual assistants are reducing unnecessary hospital visits and the patient care load. Workflow assistants help doctors free up their schedules; pharmaceutical companies research life-saving medicines in a fraction of the time and cost it traditionally takes. A recent example was the use of AI in creating and testing COVID19 vaccines. Typically, the process would have taken much longer.
Environmental Preservation: there is much potential for AI to help conservation and environmental initiatives, from fighting climate change to advancing recycling systems. AI and robotics could change the recycling industry with improved sorting of recycling materials. Furthermore, Columbia University studied ways for artificial intelligence to manage renewable energy, forecast energy demand in large metropolitan areas, streamline agricultural practices and make them environmentally friendly. Moreover, AI helps to protect animals and habits around the world. For example, Google founded a Global Fishing Watch platform that uses machine learning and Google Earth to find fishing vessels and protect marine habitats (Business Standard, May 21, 2022).
Natural Disaster Prediction: tornadoes, hurricanes, floods, and more can strike suddenly; this leaves people little time to prepare. These natural disasters wreak havoc and have a significant impact, often affecting millions of people with extended recovery periods. While AI cannot prevent natural disasters, it can aid in disaster prediction, allowing people to prepare.
Educational Improvements: artificial intelligence can teach every hour of the day, potentially offering individualized instruction to all students.
Reducing on-the-job Incidents: people often worry about machines taking over their jobs. While AI causes understandable concerns, it's situated to improve everyone's working situations by reducing workplace hazards. Workers are safer when robots perform dangerous tasks such as bomb disposal and hazardous material handling. Another benefit is health risk mitigation, including ending repetitive movement injuries, toxin exposures or driving in unsafe conditions.
Consider computer vision, an AI technology. It is a promising tool that conducts risk assessment using a computer rather than a human onlooker to identify human posture, movement and hand activity to assess injury risk in the workplace. It's a tool widely used by professional ergonomists and safety experts; however, it's important to note that these evaluations are subjective and lead to biased interpretations.
AI and V2Soft
Artificial intelligence helps companies automate their business processes and operations to drive growth and efficiency. Many companies in the artificial intelligence space continue to work in technical automation, capturing expert knowledge and growing expertise in machine learning, natural language processing, knowledge virtualization, decision management and robotic process automation.
At V2Soft, we deliver the highest performance to our clients by developing applications that specifically cater to their requirements and maximize their ROI by automating their business operations. Our ability extends to all AI technologies, including machine learning, natural language processing, speech recognition, etc.
AI-driven testing reimagines user experiences, speeds up release cycles and gets products to market faster. The latest AI and ML technologies need a new approach to software testing while managing complex systems and functionalities. Testing AI platforms empowers enterprises to support sufficient security measures for their complex applications.
Our AI and ML services include consulting and AI solutions, such as potential use cases, strategy and technology roadmaps, conversational bots, NLP, image recognition, machine vision and analytical models.