Understanding Artificial Intelligence, Machine Learning and Deep Learning

Artificial Intelligence (AI) and its subsets Machine Learning (ML) and Deep Learning (DL) are playing a major role in Data Science. Data Science is a comprehensive process that involves pre-processing, analysis, visualization and prediction. Lets deep dive into AI and its subsets.

Artificial Intelligence (AI) is a branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is mainly divided into three categories as below

  • Artificial Narrow Intelligence (ANI)
  • Artificial General Intelligence (AGI)
  • Artificial Super Intelligence (ASI).

Narrow AI sometimes referred as ‘Weak AI’, performs a single task in a particular way at its best. For example, an automated coffee machine robs which performs a well-defined sequence of actions to make coffee. Whereas AGI, which is also referred as ‘Strong AI’ performs a wide range of tasks that involve thinking and reasoning like a human. Some example is Google Assist, Alexa, Chatbots which uses Natural Language Processing (NPL). Artificial Super Intelligence (ASI) is the advanced version which out performs human capabilities. It can perform creative activities like art, decision making and emotional relationships.

Now let’s look at Machine Learning (ML). It is a subset of AI that involves modeling of algorithms which helps to make predictions based on the recognition of complex data patterns and sets. Machine learning focuses on enabling algorithms to learn from the data provided, gather insights and make predictions on previously unanalyzed data using the information gathered. Different methods of machine learning are

  • supervised learning (Weak AI – Task driven)
  • non-supervised learning (Strong AI – Data Driven)
  • semi-supervised learning (Strong AI -cost effective)
  • reinforced machine learning. (Strong AI – learn from mistakes)

Supervised machine learning uses historical data to understand behavior and formulate future forecasts. Here the system consists of a designated dataset. It is labeled with parameters for the input and the output. And as the new data comes the ML algorithm analysis the new data and gives the exact output on the basis of the fixed parameters. Supervised learning can perform classification or regression tasks. Examples of classification tasks are image classification, face recognition, email spam classification, identify fraud detection, etc. and for regression tasks are weather forecasting, population growth prediction, etc.

Unsupervised machine learning does not use any classified or labelled parameters. It focuses on discovering hidden structures from unlabeled data to help systems infer a function properly. They use techniques such as clustering or dimensionality reduction. Clustering involves grouping data points with similar metric. It is data driven and some examples for clustering are movie recommendation for user in Netflix, customer segmentation, buying habits, etc. Some of dimensionality reduction examples are feature elicitation, big data visualization.

Semi-supervised machine learning works by using both labelled and unlabeled data to improve learning accuracy. Semi-supervised learning can be a cost-effective solution when labelling data turns out to be expensive.

Reinforcement learning is fairly different when compared to supervised and unsupervised learning. It can be defined as a process of trial and error finally delivering results. t is achieved by the principle of iterative improvement cycle (to learn by past mistakes). Reinforcement learning has also been used to teach agents autonomous driving within simulated environments. Q-learning is an example of reinforcement learning algorithms.

Moving ahead to Deep Learning (DL), it is a subset of machine learning where you build algorithms that follow a layered architecture. DL uses multiple layers to progressively extract higher level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces. DL is generally referred to a deep artificial neural network and these are the algorithm sets which are extremely accurate for the problems like sound recognition, image recognition, natural language processing, etc.

To summarize Data Science covers AI, which includes machine learning. However, machine learning itself covers another sub-technology, which is deep learning. Thanks to AI as it is capable of solving harder and harder problems (like detecting cancer better than oncologists) better than humans can.

How Technology is Shaping the Future of Learning

Technology has transformed many aspects of our lives, including the way we learn. With the rapid development of new technologies such as artificial intelligence, machine learning, and augmented reality, the future of learning looks very different from what it did just a few years ago. In this article, we will explore how technology is shaping the future of learning.

The first major impact of technology on learning is the availability of online resources. Today, students can access a wealth of information through online platforms like YouTube, Khan Academy, and Coursera. These platforms offer thousands of courses on various subjects, ranging from basic math to advanced scientific topics. This means that anyone with an internet connection can learn almost anything they want, regardless of their location or background.

Another significant impact of technology on learning is the rise of personalized learning. Personalized learning involves tailoring education to the needs and interests of individual students. Technology makes it possible to gather data on each student’s progress and adapt the curriculum accordingly. For example, an AI-powered system can determine which areas a student struggles with and provide additional support in those areas. This approach to learning can result in faster progress and better outcomes for students.

Gamification is another trend that is shaping the future of learning. Gamification involves using game elements, such as points, badges, and leaderboards, to motivate students and make learning more engaging. Games have been used in education for decades, but technology has taken gamification to the next level. Today, educators can create interactive games that simulate real-life scenarios, allowing students to develop practical skills in a safe environment.

Virtual and augmented reality are also transforming the way we learn. Virtual reality (VR) allows students to experience things that would be impossible in the real world. For example, medical students can use VR simulations to practice surgeries without risking patient safety. Augmented reality (AR) overlays digital information onto the real world, creating an immersive learning experience. AR can be used to provide additional information about historical sites or to teach students about scientific concepts.

Artificial intelligence (AI) is yet another technology that is shaping the future of learning. AI-powered systems can analyze data on student performance, identify areas for improvement, and provide personalized feedback. AI can also automate administrative tasks, such as grading and record-keeping, freeing up teachers’ time to focus on teaching.

Finally, technology is making education more accessible to people with disabilities. For example, text-to-speech software can help students with visual impairments to access written material, while closed captioning makes videos accessible to students with hearing impairments. Assistive technology like screen readers and speech recognition software can help students with a range of disabilities to participate fully in the learning process.

In conclusion, technology is transforming the way we learn, making education more accessible, engaging, and effective. From online resources to personalized learning, gamification, VR and AR, AI, and assistive technology, the possibilities for learning are endless. As technology continues to evolve, we can expect even more exciting developments in the future of learning.