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5 Hot AI & Machine Learning Trends for 2018

The mantra for technology evolution has been to replace or minimize human assistance with machines. Traditionally human support systems are rapidly being replaced by machines & by automation. The dependence on human decision-making is shrinking fast. If we take a domestic cooking gas stove as an example, we now have the automated safety gas stove where the gas supply can be cut off completely by itself in case of gas leakage or mishandling, thus avoiding fatal accidents and domestic fires. All this has been possible with the advent of automation in technology that has completely transformed our way of daily life.

Now coming to the technical field of working IT professionals and L&D leaders, new technologies are evolving at a rapid speed and it is imperative for them to keep up with it. In this article, I am going to focus on Machine Learning and Artificial Intelligence in particular which are two of the most important technologies in the technology market.

What are Machine Learning & AI?

First, let’s look at what these fancy technology terms actually mean. According to Arthur Samuel, a machine learning pioneer, Machine Learning is a field of study that gives computers the ability to learn without being explicitly programmed. AI, on the other hand, is not just one technology, it is a broad field constituting many disciplines from robotics to machine learning. The ultimate goal of AI, most of us affirm, is to build machines capable of performing tasks and cognitive functions that are otherwise only within the scope of human intelligence. In order to get there, machines must be able to learn these capabilities automatically instead of having each of them explicitly programmed end-to-end.

5 Trends in AI and Machine Learning to Watch Closely

  1. Supervised Learning-  is the task of inferring a function from labeled training data. It is based on data from the past. The supervised learning algorithm contains a set of inputs along with the labeled correct outputs. Based on the labeled outputs the inputs are compared. Depending on the variation between the two signals, an error value is computed and an algorithm is used to learn the mapping function from the input to the output. The goal is to approximate the mapping function so well that when we have new input data we can predict the output variables for that data. It is similar to the process of a teacher supervising the learning process. The learning stops when the algorithm achieves an acceptable level of performance.
  2. Reinforcement Learning- As we know, “to err is human” and humans learn new tasks mostly by trial-and-error. Reinforcement Learning is an area of machine learning that is inspired by this human facet and is concerned with calculating the results of certain actions to maximize the return. It involves having an agent tasked with observing its current state in a digital environment and taking actions that maximise the cumulative of a long-term reward it has been set. The agent has to weigh the actions and opt for the optimal strategies that best help or promote the progress to achieve the desired goal.
  3. Networks with Memory- One thing that separates human beings from machines is the ability to think and work discreetly. Machines can be programmed to do a certain task with unmatched accuracy but the problem occurs at the time of diverse environments. In order to be suitable for real-world environments, neural networks must be capable of sequential task learning without forgetting. Neural networks need to overcome catastrophic forgetting with the help of several powerful architectures such as long-short-term memory networks that can process and predict time series, the elastic weight consolidation algorithm that can slow down learning on account of importance noted from previously completed tasks and progressive neural networks that is immune to catastrophic forgetting and has the ability to extract useful features from previously learned networks for a new task.
  4. Building Smaller Models by Utilizing Less Data- Machine Learning traditionally involves studying and analyzing enormous amounts of data to perform a suitable task. Without large amounts of data, these models wouldn’t perform particularly well for complex models and tasks. But with the help of multiple collaborative neural networks it is possible to transfer information and thus the learning models needn’t contain extensive amounts of information in the individual networks. This would help in more efficient distributed training because data needs to be communicated between servers, less bandwidth to export a new model from the cloud to an edge device, and improved feasibility in deploying to hardware with limited memory.

  5. Simulation Environments- It’s a challenge to generate training data for AI systems because for them to be useful in the real world AI must generalize to many situations. For real-world operations, we need to develop digital environments to simulate the physics and behavior of the real world to measure and train the intelligent quotient of the AI models. Using the results from these simulation environments we can understand AI systems better and can improve them to perform effectively in real-world environments.  

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Tags: Data, Deep, Learning, Machine, Net, Neural, Science

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