Artificial Intelligence and Machine Learning Deep Dive

Last updated:
June 14, 2021

What Is Artificial Intelligence (AI)?

Artificial intelligence is a broad term that can be applied to many different aspects of our lives. It has been used in the past for computer simulations, robot design, and natural language processing. However, AI has become more popular recently due to its use in big data analysis and machine learning. So what exactly is artificial intelligence? Read on to find out!


  • Artificial intelligence can be used in many different ways, which means it is difficult to define.
  • According to AI founder John McCarthy, artificial intelligence “consists of the study of intelligent agents: how they perceive and act within an environment” Unlike machine learning, AI does not focus on interpreting data; instead, it focuses on interacting with people or environments.
  • One way that we see this interaction is through natural language processing (NLP). NLP makes use of algorithms called deep neural networks as well as a large database of texts so that computers are able to understand human speech and text for things like translation purposes.[ii]. As computer programs start doing more complicated tasks such as translating languages across borders,
  • AI in a nutshell comprises complex algorithms that could leverage machine learning technique/machine learning algorithm to build predictive models.
  • AI can leverage deep learning algorithms which will be explained in the blog below.
  • AI can have a learning process to build their predictive models, hence there are some initial trial and error.

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What Are the Different Types of Systems?

Artificial intelligence can be divided into two different categories:

  • Weak.
  • Strong.


What Is Weak AI?

Weak artificial intelligence is a system that does not have the abilities of strong AI but instead relies on either human input or pre-programmed algorithms. Weak AI’s are often used in websites and mobile apps as they don't need to be continuously updated with new data. The use of weak AIs also prevents security breaches which can happen when information is stored centrally by one company. Systems like these may still seem intelligent, yet their intelligence comes from elsewhere - usually, humans who make adjustments based on feedback loops or programmers who set them up initially.


What Is Strong AI?

Strong Artificial Intelligence refers to machines which one day will become as intelligent as (or even smarter) than humans when they reach their full potential with advancements in computing power, robotics research and other technologies related thereto; these neural networks are modelled after those found inside the brain.

When artificial intelligence is strong, it can do a variety of things. These include playing chess and Go better than any human could or driving cars without steering wheels. Strong AI would have the ability to think for itself in ways that people cannot conceive of today. It will be able to autonomously make decisions based on what it has learned from experience, just like humans do now but much faster and more accurately because its computational abilities are so great relative to ours.

Think of strong AI as evolving by using reinforcement learning with deep learning models. It's always self-correcting the machine learning models to be more accurate. It can mimic human intelligence by leveraging various machine learning algorithms.

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What Are the Benefits of Artificial Intelligence?

There are various forms of AI including predictive analytics which uses computer programs to analyze data sets and make predictions about future events, robotics where machines or robots complete tasks autonomously without human input, natural language processing (NLP) that analyzes how people communicate verbally or in written form in order to understand meaning and intent etc. 

All these fields use artificial intelligence as their base and so it's understandable just how broad its applications really are. With so many organizations adopting AI into their practices, it's only a matter of time before we'll see some astonishing results.

One such example is DeepMind which was acquired by Google in 2014 for $400 million dollars to help the company tackle various problems like climate change, healthcare or education. 

In November 2016 they were able to use AI to substantially reduce energy consumption at one of Google's data centers - cutting costs by 40% without any noticeable impact on performance levels!

These are just two examples out of many that show how artificial intelligence has been having an immense effect not only on business but also on society as a whole. There are many uses of AI, but with the coming wave of automation, it is important to ask ourselves how we can make sure that artificial intelligence works for society and not against us. With AI,  there is no need for a human to do the same task over and over again.

However, it is important that these technologies are transparent in order to maintain trust among society when using AI technology like self-driving cars or medical diagnosis software.

What Is Deep Learning?

Deep learning is the process of using artificial neural networks to extract or find patterns in large data sets. This technique has been used for everything from image recognition, natural language processing (NLP), and automatic speech recognition (ASR) technology that's now standard on most mobile devices. Deep learning entails training artificial neural systems called "neural nets" with a two-part system: an input layer and an output layer. The input layer feeds information into the net, which processes it through nodes called neurons that generate outputs given their inputs. In machine learning tasks like optical character recognition (OCR).

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What Is Machine Learning?

Machine learning is the science of getting computers to act without being explicitly programmed. It’s also a predictive analytics technique that has been around for decades, but it has exploded in popularity and use over recent years with massive improvements in computational power and algorithmic sophistication (e.g., deep neural networks).

These days machine learning helps us find patterns that were either too complex or couldn’t be found manually. For example, consider all the images on Facebook—with billions of photos uploaded every day—and then think about how many different types there might be: landscapes, selfies, food shots, pictures of friends from high school you haven't seen since graduation...​ Imagine trying to label each one by hand!

But by leveraging machine learning it can process all the data, it can find patterns and make predictions about what type of photo is in each folder.

Machine learning powers almost every technology that you use on a daily basis—from search engines to language translators (e.g., Google Translate), from spam filters for your email account to speech recognition systems such as Siri’s or Alexa's. The even better news? Machine learning will only become more important going forward, so it might be time to start brushing up on these skills now if you're not already familiar with them!


What Is Supervised Machine Learning?

In supervised machine learning, the algorithm tries to identify patterns in data that can be used to predict future outcomes. This is accomplished by feeding past observations into a model and using it to make future predictions, or making inferences about unobserved events based on known correlations between variables.

Supervised models are usually defined through an objective function (or loss function) which assigns weights (costs) for each class of errors: underfitting, overfitting and generalization error. Supervised modelling techniques include classification trees such as logistic regression; linear discriminant analysis; kernel methods like support vector machines, Gaussian process machines and adaptive boosting algorithms such as AdaBoost, CGBoost and XGBoost; Bayesian networks; cluster etc. Furthermore, with the use of a technique called backpropagation, these models are then able to automatically learn and optimize the weights for different classes of errors.

An important part of supervised machine learning is feature engineering, which includes techniques like preprocessing (e.g., rescaling features), finding predictive features or transforming types of input data through normalization (e.g., by scaling). These methods can be useful for avoiding overfitting problems in high-dimensional spaces where dimensionality reduction is not feasible.

The common goal with all supervised ML algorithms is comparing an unknown function f(x) against its training set {f(xi)}xi∈S, either on new instances xi outside S or on predictions made from previously unseen points.

A supervised machine learning algorithm is given training data that contains both input information (x) and desired output values, y. The algorithm then learns to produce a function f(x), which can be used to generate an accurate prediction of the classification result for any new value of x in its application space.

The main types of supervised algorithms are regression analysis, where the goal is predicting real-valued outputs; classification, where the goal is predicting discrete values from continuous inputs; and clustering, grouping similar objects together based on their attributes or features.

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What Is Unsupervised Machine Learning?

Traditionally, machine learning has been focused on supervised methods of analyzing data. In this type of analysis, the computer is given a sample set with a labelled input and expected output value for each element (called training examples). The goal here is to allow the algorithm or program to learn from these samples in order to make predictions about new inputs that were not part of the original dataset.

An unsupervised learning method does not have any labels for its input data but rather analyzes it by finding patterns within it without being told what they are explicit. One example would be performing clustering algorithms where you give your machine learning system many different pieces of information and let them find natural groupings between them-seeing if certain people like certain foods based on patterns. Moreover, the input data can be an unlabeled grouping, such as a dataset of pixels, and the algorithm will find patterns in that to label.

This type of machine learning doesn’t require any explicit labels or programming; it just learns from looking at the different cases.

The upside is that this method does not need anything predefined but instead finds more complex insights on its own. The downside is that there may be nothing for your program to learn if you don't have enough examples-you might end up with too many very similar outputs making all predictions quite simple due to no variation between them. In addition, unsupervised methods are more computationally demanding and may require more memory.

The downside of this type of machine learning is that if you don't have enough examples, there can be nothing for your program to learn from which results in making predictions too simple due to no variation between them. Alternatively, unsupervised methods are less computationally intensive but still require a lot of memory space on their own end.


Uses of Machine Learning in Real Life

  • Molecular Biology Machine Learning Applications The advances made in DNA sequencing technologies have led to increased interest in using machine learning techniques for analyzing genomes (Komorowski et al., 2016). For example, researchers at UC Berkeley found they could predict genome mutations by looking at patterns.
  • Machine Learning and Artificial Intelligence in Data Science AI are making waves across many industries, from financial services to healthcare. Some key use cases include predictive analysis (Dinov et al., 2016), speech recognition for automated customer service (Allen et al., 2017) or sentiment analysis of social media traffic that helps firms monitor reputation on the web (O'Keefe, 2015).
  • Financial Services Machine Learning Applications Lending applications are an example of machine learning being used by banks; providers can offer competitive rates without relying on a fixed set of criteria because the system learns what works best over time. In addition to lending applications, investment decision support systems are also popular among large institutions looking to utilize artificial intelligence technologies such as machine learning.
  • Healthcare: Machine Learning has been used to identify genetic mutations that cause diseases, predict which treatments will be the most effective for patients and reduce false-positive diagnoses.
  • Law enforcement: Police can analyze footage from CCTV cameras or witness videos using machine vision algorithms to detect criminals. The AI can process technology is also used to identify faces in a crowd.
  • Automotive: Cars can use machine learning algorithms to analyze driving patterns, apply for insurance and detect rogue behaviour like speeding or violating traffic laws
  • Web applications: Google's web crawlers employ natural language processing techniques that let search engines learn about more complex queries without the need for extra programming information.
  • Retail: Machine learning can be used to analyze customer data, find similarities between products and demographics.

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