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AI has become quite the buzzword in the tech and business worlds. While artificial intelligence (AI) and its subset, machine learning (ML), are nothing new, they have recently started to make headlines. The topical fascination is largely thanks to new viral technologies like OpenAI’s ChatGPT, which saw a noteworthy adoption rate within the first five days of it becoming available, with 1 million users partaking in the AI assistive tool’s services during that introductory period.
However, despite its growing popularity, some people are seemingly unsure about what that means for their role in society and the workplace. According to Forbes Advisor, research shows that 77% of people are worried that AI will result in widespread job loss or lack of future employment opportunities. Reports have shown that it is likely that the evolution of AI could result in displaced workers worldwide, with about 15% of the global workforce affected between 2016 and 2030.
Regardless, the market for the compelling and sometimes controversial technology is expected to reach $407 billion by 2027. That’s quite a leap from its estimated revenue of $86.9 billion in 2022. Also, AI has been on the scene and infiltrating various industries for decades, so it’s unlikely that we should be thinking about being replaced by robots.
Instead, AI should be seen as complementary to human proficiencies and beneficial to our long-term success. According to research done by World Economic Forum and reported by Forbes, it’s estimated that AI will create around 97 million new jobs, countering the jobs lost. It also paves the way for increased creativity and productivity, with 64% of businesses expected to get more done by implementing AI technologies. The manufacturing industry should see the greatest financial impact on its bottom line, with gains of $3.8 trillion in expected revenue by 2035 due to AI.
The adoption of AI and ML will continue to grow beyond finance, healthcare, manufacturing, and other sectors where it’s already been making waves. As more and more businesses and industries realize its advantages, society might be more willing to accept its ability to improve human and business experiences without the fear of becoming obsolete.
What Is Artificial Intelligence (AI)?
Artificial intelligence, or AI, refers to the ability of machines or computers to perform tasks that normally require human intelligence. This can include reasoning, learning, perception, problem-solving, decision-making, and language understanding. AI can be divided into two main categories: narrow or “weak” AI and general or “strong” AI.
Narrow (Weak) Artificial Intelligence
Narrow AI is designed to perform specific tasks, whereas general AI can perform any intellectual task a human can do. It’s the type of AI we see most commonly today. Narrow AI includes image recognition, natural language processing (NLP), and recommendation systems. For example, recommendation systems like Netflix are trained on large amounts of data and use algorithms to find patterns and make predictions based on that data.
General (Strong) Artificial Intelligence
Unlike narrow AI, general AI is still largely a theoretical concept. For that idea to become a reality, machines need human-like cognitive abilities like reasoning, understanding context, and making decisions based on incomplete or ambiguous information. Humans do these things naturally, but replicating them is challenging for machines. Therefore, while significant progress has been made in both types of AI, general AI still exists mainly in science fiction.
Also, it’s important to note that even though narrow AI has been widely accepted as a valuable tool to assist humans, the development of general AI raises several ethical and societal questions (even leading to an AI Bill of Rights) that will need to be addressed as the technology advances.
What Is Machine Learning (ML)?
Machine learning (ML) is a type of artificial intelligence that involves training computer systems to learn and improve over time without being explicitly programmed. In other words, it’s a subset of AI that focuses on teaching machines to recognize patterns in data and make informed decisions accordingly.
In machine learning, a computer system is given access to large amounts of data and uses algorithms to identify patterns and relationships in the data. The system then uses these patterns to make predictions or decisions about new data it encounters.
The three main types of machine learning include:
Supervised Learning
In supervised learning, the system is given labeled examples of data and learns to make predictions based on those examples. In other words, the input data is already classified or categorized. Essentially, the labeled data is used to train the algorithm to classify data (i.e., find patterns or relationships between the input variables or features and the output variables or labels) and predict outcomes accurately about new or unseen data.
Supervised learning is used in various applications, such as image classification, speech recognition, natural language processing, and predicting stock prices. Common algorithms used in supervised learning include linear regression, logistic regression, decision trees, random forests, and neural networks.
Unsupervised Learning
In unsupervised learning, the system is given unlabeled data and must discover patterns and relationships on its own. Unlike supervised learning, there is no predefined output variable to predict. Instead, the algorithm clusters or groups similar data points together based on underlying commonalities or hidden structures without knowing what to look for.
Likewise, semi-supervised learning only labels a part of the input data. Both unsupervised and semi-supervised learning methods are desirable alternatives to supervised learning to save time and costs.
Unsupervised learning is used in anomaly detection, customer segmentation, recommendation systems, and data compression. Some common algorithms in unsupervised learning include k-means clustering, hierarchical clustering, and principal component analysis (PCA).
Reinforcement Learning
In reinforcement learning, the system learns through trial and error, exploring different strategies and receiving rewards (positive values) for making correct decisions and penalties (negative values) for incorrect decisions. An agent is a key component of reinforcement learning. This agent perceives and interacts with an environment to learn how to make decisions that maximize a cumulative reward. Over time, based on feedback received as either a reward or punishment, the agent adjusts its behavior to improve its future decision-making accordingly, learning to avoid negative feedback and actively seek the positive.
Reinforcement learning is often used in robotics, game-playing, and autonomous vehicles. Some common reinforcement learning algorithms include Q-learning, SARSA, and deep reinforcement learning algorithms like Deep Q-Networks (DQNs) and Actor-Critic methods.
What Is Deep Learning?
Deep learning is a subset of machine learning (ML), using artificial neural networks to enable machines to learn from data and make predictions or decisions without being explicitly programmed. This type of ML strongly imitates humans’ ability to gain certain information and is the closest form of human-level AI. As an element of data science involving statistics and predictive modeling, it helps collect, analyze, and interpret large amounts of data quickly, easily, and efficiently.
Traditional ML algorithms are linear. However, deep learning algorithms have multiple layers of neural networks that process nonlinear data as their input, identifying patterns within each given data set to derive a statistical output model. These layers are stacked hierarchically based on increasing complexity and abstraction, with each layer learning more complex features of the data until the output reaches maximum accuracy.
The network automatically determines which features to use for prediction and classification tasks. This approach is particularly effective for image and speech recognition, natural language processing, and autonomous decision-making in complex environments.
Deep learning algorithms are applied in a variety of fields, including:
- Self-Driving Cars
- Speech, Pattern, and Image Recognition
- Computer Programming
- Contextual Recommendations
- Fact-Checking
Deep learning has seen significant advancements in recent years thanks to the availability of vast amounts of data and powerful computing resources, such as GPUs or TPUs. It has revolutionized many fields, including computer vision, NLP, and robotics.
Looking at AI and ML Applications in the Real World
AI and machine learning are picking up steam. These technologies are used daily to improve our lives and make our interactions with the tech world more personalized and efficient. As AI and ML advance and evolve, we see more of their presence around us, and we might not even be aware of it.
One survey conducted in 2017 determined that while 84% of people were already using AI regularly, only 34% knew that’s what they were using.
Examples include:
- Digital assistants like Siri and Alexa
- Search engines like Google and Bing
- Social media platforms like Facebook and Twitter
- Email filtering to eliminate unwanted messages
- Fraud detection to detect or prevent fraudulent transactions
- Navigation apps like Google Maps and Waze
- Recommendation systems like online retailers and streaming services
How Is It Good for Businesses?
AI is a rapidly growing field that has the potential to revolutionize many industries, transforming the way we work and live. Overall, the technology allows businesses to be more creative and innovative, collaborate more effectively, and make more data-driven decisions. By leveraging AI technologies, businesses can stay ahead of the curve and continue to innovate and grow in an increasingly competitive marketplace.
As a subset of AI, machine learning can further impact the way we do business, improving the effectiveness of many business processes and leading to the following benefits:
- Improved Accuracy: Using algorithms to analyze large amounts of data quickly and accurately, identifying patterns that humans might miss.
- Increased Efficiency and Reduced Costs: Automating processes that would otherwise require human intervention.
- Personalization: Making recommendations based on a customer’s preferences or history to personalize products and services.
- Predictive Analytics: Predicting future events or outcomes, such as which customers are most likely to churn, or which products are most likely to sell.
- Fraud Detection: Analyzing patterns in transaction data to detect fraudulent activity, such as credit card fraud.
- Improved Decision-Making: Providing insights and predictions based on data analysis to help organizations make more informed decisions.
- Automation of Repetitive Tasks: Taking over thoughtless tasks, freeing up humans to focus on more creative or strategic duties.
- Accessibility: Assisting people with disabilities by providing tools and technologies to accommodate a variety of impairments and improve their overall experience.
Specific Industry Use Cases
AI and ML have become ubiquitous across various industries. Natural language processing is used in chatbots, virtual assistants, and voice assistants, and AI-powered image and video analysis is used in the healthcare, retail, and security industries.
Predictive analytics is used in finance, healthcare, and other industries to predict future trends and behaviors. Fraud detection is used in finance, and robotics are used in manufacturing, healthcare, and other industries to automate repetitive or dangerous tasks.
AI is also used in e-commerce to personalize user experiences based on their preferences and behavior, and self-driving cars are powered by AI, able to perceive and react to their environment.
As the technology continues to advance, we can expect to see even more innovative and exciting applications emerge. It will surely become a significant cornerstone of our next industrial revolution.
Examples of how AI and ML are already impacting specific industries include:
Healthcare
AI and ML are used in healthcare for medical imaging analysis, drug discovery, and personalized medicine. For example, ML algorithms can analyze medical images and help doctors detect diseases like cancer and heart disease.
Finance
In finance, AI and ML are used for fraud detection, risk assessment, and algorithmic trading tasks. One instance is ML algorithms that can analyze large amounts of financial data to detect unusual patterns or fraudulent behavior.
Retail
Product recommendations, inventory management, and pricing optimization are some tasks in retail that AI and ML can handle. For example, ML algorithms can analyze customer data to suggest products they’re likely interested in.
Manufacturing
AI and ML can perform predictive maintenance, quality control, and supply chain optimization tasks in manufacturing. ML algorithms can analyze sensor data from machinery to predict when maintenance is needed to prevent breakdowns.
Transportation
Some ways AI and ML are used in transportation include route optimization, predictive maintenance, and autonomous vehicles. For instance, ML algorithms can analyze traffic patterns to suggest the most efficient route for a driver or a self-driving car.
Show Me Your Skills: The Biggest AI Challenge for Your Business
Research reported by Forbes shows that 85% of AI projects fail, even when businesses prioritize it over other IT initiatives.
Businesses face a handful of AI challenges when integrating AI into their existing systems and processes, such as data quality and availability, cost and infrastructure, regulatory compliance, and ethical considerations, including bias, privacy, and transparency. However, a primary challenge is the absence of available AI talent.
A lack of in-house skills keeps businesses from embracing 5th industrial wave technologies like AI and machine learning. As technology advances, companies are often scrambling to find individuals with the needed skill sets to keep them up to date.
There is currently a shortage of AI talent, and businesses may struggle to find skilled and knowledgeable data scientists, machine learning engineers, and other AI professionals to develop and implement AI solutions. It’s simply that the increased demand for AI talent has outpaced the lagging supply, leading to fierce competition for skilled professionals. Many businesses struggle to find and hire qualified candidates, particularly in regions with intense competition.
Even worse, carrying out AI initiatives without the appropriate cross-functional team with the expertise to understand which technologies perform which tasks and what is needed to enhance business operations can significantly waste time and money.
Oxford Can Help Fulfill Your AI Needs
Oxford has the expertise to deliver your AI requirements when you need them. We specialize in filling those skill gaps that can help move businesses into the future with ease and timeliness, keeping companies efficient, relevant, and one step ahead.
You can access our global AI talent on demand by partnering with Oxford. We strongly believe in proactivity versus reactivity, and it shows in our recruitment model. To ensure we can consistently offer The Right Talent. Right Now., we are always connecting with top talent before you approach us with specific needs. By the time you reach out to us, we already have pre-vetted candidates in mind to support your business, so there are no unnecessary delays.
AI isn’t just the future—it’s now. We’re ready.