Artificial Intelligence: Using the Machine Learning Approach

Artificial intelligence (AI) has become one of the hottest buzzwords in the field of science and technology. AI refers to systems or machines that can think and behave like humans. It is the process of making a computerized system or software think as intelligently as the human mind. AI systems can carry out tasks and improve upon intelligence as they collect data and transform that data into intelligent information.

Results of a 2019 Gartner survey showed that 37% of organizations have implemented AI in "some form." AI has many forms. The field is vast and complex. For instance, many have heard the terms "machine learning" and "deep learning" and have made them synonymous with the term AI. However, AI is the umbrella term. Machine learning is currently the primary AI approach to making a machine intelligent. Furthermore, deep learning is one of the many tools used in machine learning for making a machine progressively smarter over time. The purpose of this article is to provide a better understanding of AI, look at the origins of AI, and point out some real-world applications of artificial intelligence.

Machine Learning as an Approach to Building intelligent Systems

Over the decades, there have been many different approaches to building intelligent systems. They include simulating the brain, modeling human problem solving, formalized logic, and utilizing large databases of knowledge. Machine learning (ML) is currently the dominant approach to building intelligent machines. It is a highly mathematical and statistical approach and is deemed by experts to be very successful in problem-solving.

Machine Learning can be defined as the process of training a computer system to learn patterns and make predictions or decisions based on data. It encompasses three major types: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model using labeled data, while unsupervised learning uncovers patterns and structures within unlabeled data. Reinforcement learning, on the other hand, teaches an agent to interact with an environment and learn through rewards and punishments.

Machine Learning Algorithms

Machine Learning algorithms serve as the building blocks that power AI systems. While we won't delve into specific algorithms in this article, it's worth mentioning that a multitude of algorithms exist within the realm of ML. These algorithms, such as linear regression, decision trees, random forests, support vector machines, clustering algorithms, and deep learning neural networks, form the backbone of ML models, enabling them to make predictions, classify data, and uncover hidden insights.

Characteristics of Machine Learning

Machine learning is characterized by computer scientists as sub-symbolic, neat, soft, and narrow.

• Sub-symbolic:
  sub-symbolic reasoning allows for inscrutable mistakes that arise out of human intuition (i.e. algorithmic bias).


• Neat:
  Neat achieves intelligent behavior through principles such like logic, optimization, and neural networks.


• Soft:
  Soft computing utilizes techniques such as genetic algorithms, fuzzy logic, and neural networks. These techniques tolerate imprecision, uncertainty, partial truth, and approximation.


• Narrow:
  AI researchers are divided as to whether to pursue the goals of artificial general intelligence and superintelligence (general AI) directly or to solve as many specific problems as possible (narrow AI) in hopes these solutions will lead indirectly to the field's long-term goals. Machine learning employs the narrow approach.

Applications of Machine Learning

The applications of Machine Learning are vast and diverse, transforming industries across the board. Let's explore some notable applications:

• Image and Object Recognition:
  Machine Learning has revolutionized computer vision, enabling systems to accurately identify and classify objects in images and videos. This technology finds applications in autonomous vehicles, surveillance systems, medical imaging, and more.


• Natural Language Processing:
  ML techniques are employed to comprehend and process human language. This has led to significant advancements in voice assistants, chatbots, sentiment analysis, language translation, and information extraction.


• Predictive Analytics and Forecasting:
  ML algorithms can analyze historical data to make predictions and forecasts, aiding decision-making in various domains such as finance, sales, marketing, and supply chain management.


• Recommender Systems:
  ML-powered recommender systems leverage user behavior and preferences to provide personalized recommendations for products, movies, music, and more. This drives customer engagement and enhances user experiences.


• Fraud Detection and Cybersecurity:
  ML algorithms help identify patterns and anomalies in data, enabling the detection of fraudulent activities and enhancing cybersecurity measures.


• Healthcare and Medical Diagnosis:
  Machine Learning is transforming the healthcare industry, enabling accurate diagnosis, personalized treatment plans, drug discovery, and improving patient outcomes.


• Autonomous Vehicles:
  ML algorithms are at the core of self-driving cars, enabling them to perceive the environment, make decisions, and navigate safely.


• Robotics and Industrial Automation:
  ML is crucial in enabling robots to learn and adapt to their environments, making them more autonomous and efficient in tasks ranging from manufacturing to healthcare.

Challenges and Ethical Considerations

As AI and ML continue to advance, several challenges and ethical considerations have come to the forefront:

• Data quality and bias issues:
  ML models heavily rely on the quality and diversity of training data. Biased or incomplete data can result in biased or inaccurate predictions, highlighting the need for robust data collection and preprocessing practices.


• Interpretability and transparency:
  Some ML models, particularly deep learning neural networks, operate as "black boxes," making it difficult to understand the reasoning behind their decisions. Efforts are being made to develop explainable AI techniques for increased transparency and accountability.


• Privacy and security concerns:
  The abundance of data and the power of ML raise concerns about privacy and data security. Safeguarding personal information and ensuring secure data handling practices are critical.
• Job displacement and workforce impact:
  The automation potential of ML raises concerns about job displacement. However, it also presents opportunities for upskilling and reimagining work roles.


• Bias and fairness in decision-making:
  ML models can unintentionally perpetuate biases present in the training data, leading to discriminatory outcomes. Addressing bias and ensuring fairness in AI systems is crucial for ethical deployment.


• Legal and regulatory challenges:
  The rapid development of AI and ML has created challenges in establishing legal frameworks and regulations to govern their use, ensuring accountability and preventing misuse.

Future Perspectives and Emerging Trends

The future of Machine Learning is incredibly promising, with several emerging trends and advancements on the horizon:

• Deep Learning and Neural Networks:
  Deep learning techniques, powered by neural networks, are revolutionizing AI capabilities, enabling complex pattern recognition, natural language understanding, and image processing.


• Transfer Learning and Federated Learning:
  Transfer learning allows models to leverage knowledge from one task and apply it to another, while federated learning enables ML models to be trained collaboratively across decentralized devices, addressing privacy concerns.


• Explainable AI and Model Interpretability:
  The quest for explainability aims to develop AI systems that can provide transparent explanations for their decisions, allowing users to understand and trust their outputs.


• Edge Computing and IoT Integration:
  ML models are being deployed on edge devices, bringing intelligence closer to the data source, reducing latency, and enabling real-time decision-making. The integration of ML with the Internet of Things (IoT) holds great potential for smart and autonomous systems.


• Ethics and Governance in AI:
  As AI becomes increasingly integrated into our lives, ethical considerations and governance frameworks are crucial for responsible AI development and deployment.


• Interdisciplinary approaches and collaboration:
  The future of ML lies in multidisciplinary collaborations, bringing together experts from diverse fields such as computer science, neuroscience, psychology, and ethics to address complex challenges and unleash the full potential of AI.

AI Timeline

• 1950- Alan Turing published "Computing Machinery and Intelligence." Turing's essay began, "I propose to consider the question, 'Can machines think?'" It then laid out a scenario that came to be known as a Turing Test. Turing proposed that a computer could be considered intelligent if a person could not distinguish the machine from a human being.


• 1956-the first artificial intelligence conference was hosted. The conference, Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI), was organized by John McCarthy and Marvin Minsky. The conversations served as a springboard for future AI research.


• 1960s- With this period came the development of robots and several problem-solving programs. During this time there was the development of a program which entailed the simulation of psychotherapy. The program was called ELIZA and demonstrated smart communication between a human and a machine. In 1969, Stanford Research Institute (SRI International) developed Shakey. It was the first general-purpose mobile robot known to have ever been built.


• 1970s-80s- Although limited in its capabilities, Mercedes-Benz introduced the first autonomous vehicle. Government funding for AI research saw a deep decline leading to a period referred to as the "AI Winter."


• 1990s- The Artificial Linguistic Internet Computer Entity (ALICE) chatbot reached beyond ELIZA showing that human-computer communication could be more natural. In 1997, IBM released Deep Blue, a supercomputer that played chess. The program won against the reigning world chess champion.


• Early 2000s & Present - In the early 2000s, Robotics gained momentum. The iRobot company developed an autonomous home vacuuming cleaner called "Roomba." NASA developed robots to explore Mars. Innovation did not stop there. AI technological advancement continued. Speech recognition, robotic process automation (RPA), a dancing robot, smart homes, and other innovations have made their debut. IBM's Watson, Siri, Alexa, Cortana, and AlphaGo have all arrived on the scene. The newest rage is ChatGPT (Chat Generative Pre-trained Transformer), a highly advanced chatbot developed by OpenAI.

Companies Actively Utilizing AI

• Walmart
• Exxon
• Apple
• Berkshire Hathaway
• Amazon
• United Health
• McKesson
• CVS Health
• AT&T

Conclusion

Machine Learning is propelling the rapid advancement of Artificial Intelligence, revolutionizing industries, and transforming the way we live and work. Its applications span from image recognition to healthcare, predictive analytics to autonomous vehicles. However, the journey is not without challenges and ethical considerations. As we navigate this exciting realm, it is vital to address bias, ensure transparency, and develop robust governance frameworks. By embracing the potential of Machine Learning responsibly, we can shape a future where AI serves as a powerful tool for positive change and human advancement.