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.

The Importance of E – Learning in Today’s Corporate World

The Importance of E- Learning in Today’s Corporate World

The concept of e-learning is not new to corporate organizations and individuals albeit the outbreak of the COVID-19 pandemic has fueled the need for organizations to leverage technology platforms to drive learning initiatives. In simple terms, e- Learning (Electronic Learning) is the act of learning (educating or training) through the use of digital resources such as computers, the internet, software programmes and mobile devices.

Now more than ever before, there is a heightened need for organizations to provide learning solutions to employees using digital platforms as the benefits far outweigh the demerits. With digital learning solutions, organizations are able to achieve much more than was possible in times past. This article brings to light the benefits of digital learning and reasons why organizations must rethink their learning strategies.

Why it is imperative for organizations to leverage digital platforms for learning

The use of digital platforms for learning is dramatically changing our education system and the corporate world should be willing to adopt this new reality. The days of classroom training are gradually coming to an end due to hi-speed internet and other advancements in technology. It is becoming more difficult for employees to stay engaged and attentive during lengthy classroom training sessions, the attention span of learners has declined significantly over the years. Research shows that e- Learning requires about 60% less time than learning the same information in a classroom setting. Training providers must look for ways to provide training programmes that ensure learners are properly engaged- leveraging technology makes this possible.

Advancement in technology has made access to information a lot easier and faster, individuals now have easy access to news articles, videos, podcasts and other forms of digital content. The benefits of e-learning are numerous, we have listed some of them below

1. E-Learning gives room for more flexibility in terms of training delivery method and timing.
2. Through digital learning, information can be accessed easily anywhere and at any time.
3. E-Learning courses are typically less expensive thus saving employers additional costs.
4. There is practically no limit to the number of learners that can take a course at the same time unlike a typical classroom training.
5. Digital learning allows learners learn at their own pace thereby increasing engagement and retention.

The future of corporate learning

For organizations to survive, they must constantly evolve to meet the needs of our ever-changing world and e-Learning is a key enabler for the success and growth of any organization. According to Forbes, “Companies like IBM, Sears, and Visa are starting to turn off their old systems and build a new generation of learning infrastructure that looks more like a ‘learning network’ and less like a single integrated platform.” Forward thinking organizations acknowledge the fact that employees are more receptive to e-Learning, they understand that the younger generation of the workforce grew up with technology embedded in their daily lives and education, hence they are tailoring their training to accommodate, interactive videos and other multimedia learning techniques. This is an important piece in the continued success of such organizations while the other organizations that fail to evolve will eventually fade away.

A study by the Journal of e-Learning and Higher Education states that “Satisfaction level with web-enhanced teaching increased to 95% in the 2011 – 2012 investigation, compared to 73% – 87% in the 2003 – 2004 one.” This study shows that in both investigations students were pleased with the concept of online learning, especially the students from 2011 – 2012. From such studies, organizations should realize that E- Learning is not simply an additional feature to education or training but is indeed a core and effective educational method that can and should be adopted, it is a powerful tool that provides businesses with highly skilled employees and also benefits the corporation economically. E- Learning is an important factor in training and education and it is here to stay.

The organizations that will survive now and in the future are those who constantly evolve their systems and processes to meet modern-day demands. Organizations that are serious about survival must not only update what their employees learn, they must also revamp how their employees learn.