AI Revolution: ML and DL

AI is not a thing of the future that is only in the science fiction books; it is a changing factor that is transforming industries and the lives of people. The primary drivers of this revolution are machine learning (ML) and deep learning (DL), two branches of AI that facilitate machines to learn from data and perform activities that humans typically perform, as seen in Figure 1. This article examines machine learning and deep learning, as well as their uses and future implications.

Figure 1

Artificial Intelligence (AI)

Artificial intelligence is a broad area that covers creating systems that can perform tasks that would usually be done by human intelligence. These tasks include problem-solving, speech recognition, decision-making, and language translation. Narrow AI is created for a particular task, whereas general AI is made to do any intellectual task that a human can do (Russell et al., 2016).

Machine learning

Machine learning, which is a branch of AI, is the process of teaching algorithms to find the patterns in data and make decisions based on those patterns, as shown in Figure 2. As in the case of conventional programming, where the instructions are given, in the case of ML, the algorithms learn from the data and get better and better with time. The problem-solving capability of ML makes it very powerful in tasks like recommendation systems, fraud detection, and predictive analytics (Jordan et al., 2015).

Figure 2

Machine learning can be further classified into three types:

Supervised learning: The algorithm is trained on a labeled dataset, meaning each training instance is associated with a label. The principal aim is to establish a link between the input and output. Spam detection in emails and image classification are among the other fields that the supervised learning applies to.

Unsupervised learning: The algorithm uses unlabelled data and looks for hidden patterns or internal structures in the input data. The main applications of these methods are clustering and association.

Reinforcement learning: Reinforcement learning (RL) is a machine learning type about an agent that learns decisions in an environment to make the maximum cumulative reward. In this method, the agent is the one that acquires knowledge and makes decisions to accomplish the given objectives. In contrast, the environment is the external system the agent interacts with. The agent examines summarized and probabilistic values to enhance strategy or action. It is used in numerous domains, such as playing games, robotics, and self-driving vehicles, in problems where it is difficult to state all rules explicitly, but it’s possible to learn from experience.

Diving into Deep Learning

Deep learning, a unique subset of machine learning, uses neural networks with many layers (hence, “deep”) to analyze the different aspects of data. These neural networks, which are the structure of the human brain, are made of interconnected nodes (neurons) that process the input data in several layers, and each layer extracts higher-level features (LeCun et al ., 2015). Deep learning is optimal for processing very large unstructured data like images, audio, or text. It can be credited with having brought breakthroughs in areas such as the following: This, in my opinion, is the main factor for the following progress, as shown in Figure 3.

Figure 3

Computer Vision: Deep learning models can locate any object in an image or video with the highest accuracy. It happens when the face recognition, autonomous vehicles, or medical image analysis domain is included.

Natural Language Processing: Models that comprehend and generate human language are employed in chatbots, language translation, and sentiment analysis, respectively.

Speech Recognition: The DL models are employed to transcription the spoken language, which is the main task that voice assistants like Siri and Alexa do to comprehend and respond to the user’s orders.

Influence of ML and DL

The industries show how these technologies change the world and how we live and work. Mixing machine learning and deep learning with various industries is the core reason for creating new ideas and achieving a high productivity level.

Healthcare: ML and DL models are the main tools for diagnosing diseases, personalizing treatment plans, and predicting patient outcomes. DL models, which can analyze medical images and detect anomalies with high accuracy (Goodfellow et al., 2016), prove this.

Finance: AI algorithms detect fraudulent transactions, assess credit risk, and automate trading strategies. Machine learning boosts customer experiences by providing banking services tailored to each customer’s needs.

Retail: ML models enable recommendation engines that recommend products to customers according to their browsing history and preferences. Inventory management and demand forecasting are also the territories of their optimization by these technologies.

Manufacturing: Predictive maintenance, backed by machine learning, keeps the equipment in service before failures; hence, it decreases downtime and cost. Image recognition technologies enhance quality control processes.

Prospects of AI

The constant improvement of machine learning and deep learning will change more areas of life and work in the future. As computational power grows and more data is collected, these models will become even more complicated and skilled. Along with moral issues like data privacy and algorithmic fairness, they will be the key factors in the development of AI (Marr, 2018).

To sum up, the AI revolution, powered by machine learning and deep learning, is changing the way we live, work, and interact with technology. By comprehending the basic concepts and using these technologies, we can realize their possibilities and navigate the changing world of AI.

The Next 10 Years of AI: A View of the Future

The future of AI in the next decade will be the age of unique achievements, and the integration in different areas will be very massive.

1. Ubiquitous AI Integration: AI will be a part of our lives as smart homes, personal assistants, autonomous vehicles, and smart cities. Natural language processing will be advanced, and human-computer interaction will be better; thus, AI will become a part of daily life smoothly (Marr, 2018).

2. Advancements in Healthcare: AI is going to revolutionize the healthcare industry with more accurate diagnoses, personalized treatment plans, and advanced predictive analytics. AI-based tools will assist doctors in making the right decisions, and thus, patients will be given superior care (Topol, 2019).

3. Ethical AI and Regulation: AI is becoming more and more influential, and thus, the need for solid ethical guidelines and regulations is also on the rise. The problems of data privacy, algorithmic bias, and transparency will be the main topics of AI governance. Therefore, AI technologies will be used responsibly (Vinuesa et al ., 2020).

4. AI in Education: AI will revolutionize education through individual learning, intelligent tutoring systems, and administrative automation. AI-driven analytics will help teachers adjust teaching methods to individual student needs, improving learning outcomes (Holmes et al ., 2019).

5. Economic and Workforce Impact: AI will be a great determinant of the workforce, as it will automate routine tasks and create new job openings. Even though some jobs will disappear, new ones that require knowledge and skills of AI will be created, therefore requiring retraining and upgrading programs.

6. Enhanced Cybersecurity: AI will lead cybersecurity by monitoring and dealing with the threats in real time. The advanced machine learning algorithms will identify anomalies and predict possible breaches, safeguarding the digital infrastructure. In a nutshell, the AI revolution is driven by machine learning and deep learning, reshaping how we live, work, and communicate with technology. The technologies can be explained through the basic principles and then put into practical use to know them better, and then we can see their potential and move in the right direction in the changing world of AI. The following years will be the years of great discoveries that will transform the world, where AI is a part of every life domain while dealing with ethical and social issues.

References

  • Russell, S. J., & Norvig, P. (2016). Artificial intelligence : a modern approach.
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016, November 18). Deep Learning. MIT Press.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015, May 27). Deep learning. Nature. https://doi.org/10.1038/nature14539
  • Jordan, M. I., & Mitchell, T. M. (2015). Machine Learning: Trends, Perspectives, and Prospects. Science, 349(6245), 255-260.
  • Marr, B. (2018). How AI And Machine Learning Are Transforming Business. Forbes.
  • Topol, E. (2019, March 12). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Hachette UK.
  • Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Nerini, F. F. (2020, January 13). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1), 233.
  • Holmes, W., Bialik, M., & Fadel, C. (2019, February 28. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.