In the olden days, businesses were skill and art. Families kept their secrets to themselves and perfected their own methods of judgment to sustain their competitive advantage. However, in more modern days and age, these secrets are no longer hidden and anyone has the ability to learn those skills! The saturation of human management skills and its prone to erroneous decision making led to the need and demand for advanced technology. The world today is booming with cloud, big data, machine learning, deep learning, IoT and Artificial intelligence. The reason is that these technologies are bringing the world closer by integrating various systems and performing human-like tasks improving productivity and saving loads of money and time!
The terms AI and machine learning are often confused and used interchangeably. However, as we delve deeper into both concepts, we realize that both are quite different. Before jumping into the differences, let us understand some crucial information about both with common examples.
What is Artificial Intelligence?
Artificial intelligence is imparting a cognitive ability to a machine. The benchmark for AI is human intelligence regarding reasoning, speech, and vision. This benchmark is far off in the future
In practice today, we see AI in image classification for platforms like Pinterest, IBM’s Watson picking Jeopardy! answers, Deep Blue beating a chess champion, and A tennis game on Xbox, where you play against the computer, or human-AI interaction virtual assistants like Alexa, Cortana or Siri, who understand your questions and respond accordingly.To reference artificial intelligence is to allude to machines performing tasks that only seemed plausible with human thinking and logic.
In the real world, one of the most ubiquitous forms of AI might manifest themselves in the form of conversational AI. Conversation AI may include multimodal inputs (e.g. voice, facial recognition) with multimodal outputs (e.g image, synthesized voice). All of these modalities can be considered part of AI, as well as the integration of these modalities.
You may already be familiar with some of these modalities. A few years ago, Starbucks enhanced its mobile app by enabling ordering ahead via voice commands. Staples’ Easy System allows customers to order via voice commands. The National Hockey League rolled out a chatbot for easier communication with fans earlier this year. These applications of AI are examples of machines understanding human intents and returning relevant results.
Machine Learning Algorithms Create AI
Machine learning is the best tool so far to analyze, understand and identify a pattern in the data. One of the main ideas behind machine learning is that the computer can be trained to automate tasks that would be exhaustive or impossible for a human being. The clear breach from the traditional analysis is that machine learning can take decisions with minimal human intervention.
Machine learning uses data to feed an algorithm that can understand the relationship between the input and the output. When the machine finished learning, it can predict the value or the class of new data point.
Machine learning ,thus, is an approach used to achieve AI. Eventually, the goal of ML is that “Programming computers to learn from experience should eventually eliminate the need for much of this detailed programming effort.”
If AI is when a computer can carry out a set of tasks based on instruction, ML is a machine’s ability to ingest, parse, and learn from that data itself in order to become more accurate or precise about accomplishing that task. While other statistical methods for learning exist, through recent ML advancements, practitioners have revived the concept of neural networks, which are a series of algorithms that act—as one might assume—like the human brain.
Machine learning is prevalent anywhere AI exists, but it has some specific use cases with which we may already be familiar. Companies like Microsoft leverage predictive machine learning models to make better financial forecasts. These models make predictions on financial entities by learning from historical trends and generating forecasts of a stock’s movement.
Professional sports teams use machine learning to better project prospects during entry drafts and player transactions (trades and free agent signings). By feeding years of historical prospect data into machine learning algorithms, for example, draft teams can more accurately assess what types of statistical profiles are likely to lead to (good) professional players. In this application, algorithms learn how to better identify potential star players and, ideally, avoid draft busts.
Deep Learning & Neural Networks
Deep learning is a computer software that mimics the network of neurons in a brain. It is a subset of machine learning and is called deep learning because it makes use of deep neural networks. The machine uses different layers to learn from the data. The depth of the model is represented by the number of layers in the model. Deep learning is the new state of the art in term of AI. In deep learning, the learning phase is done through a neural network. A neural network is an architecture where the layers are stacked on top of each other
Within a neural network, each processor or “neuron,” is typically activated through sensing something about its environment, from a previously activated neuron, or by triggering an event to impact its environment. The goal of these activations is to make the network—which is a group of machine learning algorithms—achieve a certain outcome. Deep learning is about “accurately assigning credit across many such stages” of activation.
Google Brain may be the most prominent example of deep learning in action. Researchers presented to their neural network 10 million images of cats taken from YouTube videos without specifying any parameters for cat identification. The network successfully identified cat images without using labeled data.
In the real world, deep learning can also be used to synthesize new data instances, which is something traditional ML cannot do so well. One example of this in the real world is in medicine. Researchers at UCLA leveraged deep learning to enhance microscopy practices. With deep learning, researchers implemented a “framework [that] takes images from a simple, inexpensive microscope and produces images that mimic those from more advanced and expensive ones.”
Deep learning also often appears in the context of facial recognition software, a more comprehensible example for those of us without a research background. The face ID on iPhones uses a deep neural network to help phones recognize human facial features.
Active Learning chooses its own data
Most ML algorithms require substantial amount and types of data. However, with the right resources and the right amount of data, practitioners can leverage active learning. Active learning is the philosophy that “a machine learning algorithm can achieve greater accuracy with fewer training labels if it is allowed to choose the data from which it learns.” In order to choose the data from which it learns, an active learning-based AI can ask queries of humans in order to obtain more data.
In situations where data is not readily available or and providing labels for that data is difficult, active learning poses a helpful solution. Luckily, active learners can learn to label data themselves. If presented with a set of labeled data, active learning algorithms can ask human annotators to provide labels to unlabeled pieces of data. As humans label data, the algorithm learns what it should ask the human annotator next.
It is difficult to pinpoint specific examples of active learning in the real world. This is a difficult task in part because active learning is better thought of as a method of training machine learning algorithms, which means the technique may or may not be used in instances where machine learning drives artificial intelligence. In practice, the idea behind active learning is that data scientists can use poorly trained AI to help identify — through a Query Strategy, as outlined above — which pieces of data should be used to train a better version of that AI.
Human labelers are required for any sort of ML, but with Active Learning their work is significantly reduced by the machine selecting the most relevant data.
Deep learning is a more advanced form of machine learning, which is used to create artificial intelligence. Active learning leverages readily available, and often imperfect, AI to actively select new data that it believes would be most beneficial when developing the next, improved version of the AI. Active Learning, therefore, can significantly reduce the amount of data required to develop a performant AI system because it only learns from the most relevant data. All of these terms are interconnected, but each refers to a specific component of creating AI. With the right understanding of what each of these phrases entails, you can get off on the right foot creating our own AI. .
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