How This Primer Is Organized:

  • Essential Reading: These are the some of the best resources on the topic. They range from the covering the history of AI looking at the applications that could change the world.
  • Glossaries: The main purpose of this resource is to get more people involved in conversations about AI. Jargon is a barrier that prevents the cross-discipline conversations are the origin of great ideas.
  • News Sources:  These are some of the sources that cover the day to day shifts within the technology. Everyone consumes news in a different way so resources represent diverse approaches.  Scan through the list and subscribe to a few to keep up to date on cutting edge advancements.
  • Podcasts and Immersive Media: To really understand complex topics, it’s important to understand the culture surrounding it and to hear conversations surrounding the topic. These resources provide an avenue to listen to conversations with experts and learn about the topic without diving through mountains of text.
  • Market PerspectivesThese transformative technologies are poised to radically change business models. These changes go beyond the mainstream disruptive effects on media and training.
  • Startups: These companies provide a window into what’s possible within AI.
  • Communities: Communities related to AI or ML to help you get started, network or push your knowledge further.

Essential Reading

These are some of the best resources involving AI, ML and computer science principles in general. They include both academic and theoretical approaches in addition to more practical for those looking to get into AI.

Approachable Foundations

Some may say this it may be difficult or you can’t go deeper into AI without academic rigor (but we are seeing more approachable text, methodologies and general information) these books are two main recommended text in order:

  1. AI: A modern Approach by Stuart Russel and Peter Norvig
  2. Deep Learning by Ian Goodfellow and Yoshua Bengio

In addition the following can be also extremely beneficial:

Deep Learning

Reinforcement Learning

For reinforcement learning there are quite a few and this is a good one to start with:

  • Reinforcement Learning An Introduction:
  • Please note that this book is expensive: Reinforcement Learning State-of-the-Art by Wiering and van Otterlo.  –
  • *Also as a note any workbook or tutorial on AI programming that involves Python is highly recommended. Python is a crucial tool to understand in the industry and it is consistently being used more to build AI, ML and Data Science projects (look at recent tech such as Pytorch).


Knowing the key terms for artificial intelligence can be a challenge since AI can now be applied more broadly.

Definitions pulled from the link above from Mighty AI include:

  • Artificial General Intelligence (AGI) or Strong AI: A term for a hypothetical computer system able to learn, reason, and solve novel problems as a human can, or possibly even better. Not limited to one specific task, an AGI would be a true machine intelligence, capable of original thought. Nothing like this exists in the world today, and although there is a broad sense that such a thing should be possible, nobody has any idea how to even begin creating such a thing despite decades of research.
  • Classification: A kind of supervised learning task where the goal is to assign one or more labels to each input from a fixed, pre-defined set. All the examples in the training set must be labeled by humans before the system can be trained. In image classification, for example, the inputs are digital images, and the labels are the names of various objects that appear in these images (“cat”, “car”, “person”, etc.). To train a classifier, we need to not only label our data, but first define the set of labels we will use. The examples for different labels need to be distinguishable, and each label must have a reasonable number of example occurrences in our training set. Classifier training generally works best if the different labels are roughly “balanced,” that is, all have roughly the same number of examples. Popular machine learning systems for classification include neural networks, support vector machines, and random forests.
  • Neural Network: A particular kind of algorithm or architecture used in machine learning. Loosely inspired by the structure of the brain, a neural network consists of some number of discrete elements called “artificial neurons” connected to one another in various ways, where the strengths of these connections can be varied to optimize the network’s performance on the task in question. Although inspired by the brain, it is very, very important to keep in mind that artificial neural networks absolutely do not work the same way the human brain does! The similarities are often wildly overstated in the popular press.
    • Neurons in a neural network are organized into layers, where the output of one layer becomes the input to the next layer, until the final output is produced at the final layer. Neural networks can be “shallow” or “deep,” depending on how many layers they have. The basic “feed-forward” neural network architecture has no memory; It treats every input as an independent event, without consideration for sequence or timing.
  • Supervised Learning: A form of machine learning in which, for every input, there is one correct output that the system is being trained to predict. All the training examples it learns from have to be annotated before training the system with this correct output, by human beings. The system “learns” how to correctly generate outputs from inputs by looking at the human-annotated training data it is fed. Based on the human-labeled training data, the algorithm finds a mathematical way to generalize the patterns in this data and predict what the output ought to be on novel examples that no human has labeled. Classifiers are classic examples of supervised learning.
  • Unsupervised Learning: A form of machine learning in which there are no pre-existing labels or outputs defined on the input training data, and the system instead “learns” whatever patterns, clusters, or regularities it can extract from the training data. Clustering algorithms are classic examples of unsupervised learning. Another is the Google Brain project of 2012 that was fed millions of frames from YouTube videos without any labeling or annotation, and based on looking for common patterns, learned to recognize cat faces.
  • Reinforcement Learning: Reinforcement learning is a form of machine learning where the system interacts with a changing, dynamic environment and is presented with (positive and negative) feedback as it takes actions in response to this environment. There is no predefined notion of a “correct” response to a given stimulus, but there are notions of “better” or “worse” ones that can be specified mathematically in some way. Reinforcement learning is often used to train machine learning systems to play video games, or drive cars. The DeepMind system that learned to play Atari video games used reinforcement learning.
  • Convolutional Neural Network (CNN): A special neural network architecture especially useful for processing image and speech data. The difference between a normal feed-forward network and a convolutional network is primarily in the mathematical processing that takes place. Convolutional networks use an operation known as convolution to help correlate features of their input across space or time, making them good at picking out complex, extended features. However, they still treat each input separately, without a memory.
  • Recurrent Neural Network (RNN): Neural network architecture that maintains some kind of state or memory from one input example to the next, making it especially well-suited for sequential data like text. That is, the output for a given input depends not just on that singular input, but also on the last several input examples as well. There are many different recurrent architectures, but the most important now is known as the Long Short-Term Memory (LSTM) network. These can be combined with convolutional networks, too.
  • Computer Vision (CV): The application of machine learning to tasks involving digital images or video, such as identifying or tracking objects through a video sequence, or segmenting images into distinct objects. Convolutional neural networks are a powerful new tool widely used in computer vision.
  • Natural Language Processing (NLP): The application of AI to tasks involving human language, both written and spoken. NLP tasks can include both computer parsing of input natural language, and computer generation of naturalistic outputs in human language. Recurrent neural networks have become an important tool in this area recently. Chatbots and voice control are applications of NLP.
  • Overfitting: A problem that can occur in supervised learning tasks where the system learns patterns in the training data that are too specific or are there only by coincidence, so that it performs extremely well on examples it has been trained on but loses its ability to generalize and performs very poorly on anything new. Overfitting can be caused by an overly-complicated model, a limited training set without enough diversity, or by weaknesses in the training process itself.

News Sources:

Since we have seen AI grow exponentially throughout the last few years with significant companies integrating it into projects the field has also taken on a broad terminology and can also include machine learning and deep learning. That being said, the following news sources not only help gain knowledge covering the range of AI but to also examine concrete and up to date information within the discipline from a diverse source set:

  • Science Daily – AI news that covers a range of subjects and also is research based. It’s a great source if you want to find a diverse set of recent articles.
  • Machine Learning Mastery – – This is a great source to utilize for all ranges of learners especially beginners. It also provides relevant and new information for technologies currently used in the industry (for example Sci-kit learn, keras, etc.).  This is also a blog, so it can be considered on the list of resources for blog.
  • MIT News Artificial Intelligence – -This is another highly recommended resource to use since it covers a range of subjects and has a daily email that will help keep you in the loop for modern AI breakthroughs.
  • Harvard Business Review – – Although you need to purchase access to be able to have full content from the journal you can get executive summaries and access to recent articles online for free. Moreover, as a peer reviewed journal and similar to MIT publications the HBR publications always tend to be quality material
  • AI In the News – – This is an official publication source of the AAAI. It provides alerts, an AI magazine, classics and a brief history of AI. Publications on the site are useful and quality material.
  • Deep Learning Weekly –  This is a weekly newsletter and source for relevant deep learning information including topics that are currently of importance to the industry. For example you can find deep learning, NLP, quantum computing in addition to advice and tutorials for beginners.
  • Oreilly – – As a publisher it’s expected that this source contains relevant information but the key takeaway here is that they feature a range of information related to AI in addition to publishing new articles consistently along with highlights from conferences in the industry.
  • Open AI – Blog/Research – OpenAI is a non-profit conducting fantastic research. What are even better are the publications products along with using OpenAIgym, which is a source to build fun projects with relative ease. It’s a great way to introduce new students to concepts such as reinforcement learning.
  • Github for relevant repositories for AI software (Pytorch, NLTK, Tensorflow, OpenCV, etc.) or their corresponding websites. This recommendation is straightforward. Although it may not be suitable for a beginner if they are not ready to read code, it’s highly recommended to check the repositories and even source code for a higher level of understanding.
  • Import AI – It’s not just another newsletter but a weekly production from an employee of OpenAI and it provides a great source of reading material.
  • Deepmind – – Purchased by Google, it’s always a great idea to pay attention to what is going on with Deepmind along with their research into deep learning.
  • Machine Intelligence Research Institute – – More math based research but necessary if you are looking to take on some fundamental concepts for research aspects.
  • Nvidia – – Nvidia is producing some excellent articles again on a diverse subject base within AI. Use this source for news, practical approaches and industry trends.
  • Kaggle – – If you come from a development or software background you may have heard of Kaggle but it’s another incredible way to explore and build (more data science focused) from great datasets in addition to seeing how others approached the task. Competitions are also held for specific subjects and you can see an overlap with specific AI techniques.
  • StackOverflow – – Stackoverflow has to be mentioned since it is one of the go-to sources for questions, debugging and discussion on topics.
  • Wired AI – The list wouldn’t be complete without Wired. This recommendation is fairly straight forward, as Wired AI will bring you interesting and relevant information to the industry.
  • Academic recommendations to provide research based resources but they are also making it very approachable for beginners including the following:
  • Stanford AI Lab –
  • Carnegie Melon University AI – (You can also list MIT, Berkeley, etc.)
  • Coursera, EdX, Udacity, (and more) can be recommended for students to get a more hands on approach to building some fun projects, learning technologies for AI or taking classes to further learning.
  • Andrew Ng blog – – It’s not 100% strictly blog but has up to date information regarding Andrew Ng which can be useful for newcomers to AI.
  • Siraj Raval’s Youtube Channel – This is a great resource for those looking to get into AI and general AI information in a practical sense. Siraj brings you a range of AI/ML applications that beginners can take on.
  • Nathan Benaich’s  Blog – A blog to keep an eye on or more so a collection of writing by Nathan Benaich via Medium Nathan Benaich is the organizer of the London AI meetup and has some interesting readings for all levels.

West can help you navigate industry trends and build a strategy to succeed with machine learning and artificial intelligence.

Implications of AI:

Resources regarding how AI will affect jobs – as a note these can tend to be biased and categorized as either apocalyptic or useful. In my opinion (the condensed version) we have such tremendous benefit to be gained from incorporating AI and technology in general. The key factor is to institute sound policies (economic, social, educational) that will benefit the populous because changes are not just coming; they are here and will impact every industry.

A range of sources regarding the possible changes with the impact of AI:

As for incorporating AI into a project one of the key rules in the world of computational intelligence is that do not re-invent the wheel. These sources can help get you started forming an idea on when to use AI:


Podcasts are great tools to use to stay informed within AI. This list includes a variety of topics and approaches within AI, ML and even Data Science. The great thing about podcast is the ease of use to listen to them throughout your day. Some podcasts may be more appealing to your interest than others so examining the topics of each one and taking a listen can help you decide what you want to dedicate your time to.

  • Nvidia AI Podcast – – Similarly to the blog and news source from Nvidia this is a well-rounded AI podcast on a range of interesting topics.
  • Linear Digressions – – Data Science focused podcast but it features relevant work being done in the industry that individuals will find useful.
  • Concerning AI – – excellent podcast that will help get you thinking on AI topics. For example their most recent podcast featured the 3rd in a series about narrow AI and AI being used for personal assistants.
  • This week in ML and AI –  Relatively new to the podcast scene but it has a great selection of guest speakers.
  • SDS podcast – – Although based in data science there is overlapping work and some interesting info for AI / ML


The following companies are some great examples on how AI is starting to be utilized and applied to projects. Overall, startups are assisting in leading the way for tech related projects incorporating AI. It’s now easier than ever to get your start up off the ground or to build a prototype with AI using the resources available online. While taking a look at this list it’s always a nice method to think in the back of your mind how you can apply AI to an idea, hobby or interest that you may have.

* You could easily pick any from this list and make a relevant connection to the industry

  • Veritone, Inc., AI operating system – Veritone is taking off in the AI space using cognitive AI approaches to process large quantities of data. For example, Veritone has developed the Veritone Platform, an open-developer ecosystem that combines the power of third-party cognitive engines and unlocks data from linear files like radio and TV broadcasts, police bodycam footage and call-center conversations.
  • Although not specifically a startup, Anything Andrew Ng related (Baidu, and
  • Not specifically “start up” – Purchased by Google in 2014, this London based startup is currently carrying out important research in the field of deep learning. They carry out ground breaking research in a variety of areas with deep learning and for example have used AI to reduce electric costs for sever rooms that can be applied to numerous industries in addition to natural language and neuroscience publications.
  • The tech giants (again not specifically startups) have to be mentioned since they are all integrating AI for specific purposes, new products and more. Amazon, Apple, Oracle, etc.
  • – Using AI approaches to process geospatial analytics
  • – AI for patient monitoring – “AiCure builds and deploys clinically-validated artificial intelligence technologies to optimize patient behavior and medication adherence. The company was founded in 2010 to revolutionize patient monitoring with the ultimate goal of reducing hospitalizations and extending life expectancy.” Imagine the changes in people’s lives and positive benefit if we can use AI to build programs that monitor individual’s health and can predict or optimize your wellness to maintain a healthy lifestyle.
  • – Naralogics is using AI to process enterprise data for real-time, context relevant recommendations and give the reasons behind them. Although to some AI in the realm of business analytics may seem not as interesting as other areas it has a huge impact since it can be used to run businesses more efficiently, analyze areas of the business for improvement and give you the tools to make better decisions for your business.
  • (robotics focus) – Another “has to be included” Boston Dynamics is one of the more well-known robotics companies but they are building impressive products.
  • – A company to pay attention to for deep learning based in Japan, they use A (deep learning) related to handling data.
  •– Frankfurt – Using AI for business process automation, straight forward (in it’s definition) but another one the list to keep an eye on.
  • Anki –  Bringing robotics to the consumer level.
  • – Health data and analysis. We do see quite a few startups launching in the health space but it’s no where near what is needed an this startup is using AI for processing health data and analysis.
  • Zero Zero Robotics – On our list we have another consumer robotics company with Zero Zero Robotics but that doesn’t mean any less irrelevant as they build great robotic products and push the consumer level further.
  • – Using an artificial intelligence engine for financial services.
  • – Using Machine learning and artificial intelligence to optimize performance metrics)
  • Fuzzyai allows the use of AI based API’s to carry out real time decision making. As we continue to progress, API’s allow individuals or companies to access AI models with ease to incorporate into their tech stack.
  • Auro is using AI and robotics to build self-driving shuttles for campuses. This area is only getting started in growth and hopefully more individuals take interest. The benefits of having automation regarding transportation will only provide us more benefits as individuals when needing to commute, reduce traffic fatalities and energy waste.
  • Enhance your operations by applying AI to increase key performance metrics.


Drone Based

Similar to autonomous vehicles drone technology is just starting to “take flight”. The applications are endless and it can end up saving human lives when inspecting dangerous equipment or delivering supplies to areas effected by natural disasters. In addition we are seeing companies bring affordable drones to market so that we as individuals interested in drones can get involved and build fun applications!


Chabot’s may seem straightforward but with the integration of AI they have been able to unleash exponential potential. Before AI we had developers coding extensive for loops, for example: If individual x says Hello, write Hi and this would be extensive. We are now able to build and deploy chatbots that analyze each part of speech when interacting and will respond intelligently reducing the need to code basic answers. We are seeing huge shifts in the industry for companies adding chatbots to their applications. You may have seen them when logging on a website and a small pop up box opens asking if you need help.

  • Using AI to power Chatbots (and we have recently seen a huge shift in chatbot technologies to integrate AI)
  • Purchased an AI and Chatbot startup last month for $50 million, it will be a good idea to see how they use it to integrate into their products.


For the following companies AI is being used for cyber security purposes. As we have seen recently and normally each week we see a new hack leading to the breach of sensitive financial data, incorporating AI and ML concepts is not only a good idea but is necessary so we can protect our data.

  • Fraud prevention with Machine Learning for financial services.
  •  Automatically and grades risky behavior in mobile applications to identify risky behavior.
  • Using AI to predict and prevent cybersecurity threats.
  • using advanced mathematics and machine learning to detect anomalous behavior in company’s networks.
  • WSJ’s one of the top tech companies to watch, Illusive Networks proactively deceive and disrupt in progress attacks.  This is something a bit newer since it serves as a more proactive approach (usually cybersecurity is either strictly defensive or figuring out what happened after the attack).


Each of these companies has a bit of a unique twist integrating AI into their projects.

  • Tetra uses AI to take notes on your phone calls so that you can remember everything.  Sometimes it’s easy to get caught up in the conversation while trying to remember important points.
  • Uses AI to help consumers increase wellness and productivity. For BioBeats what is a nice aspect is that since we have focused so much on technology this in a sense uses technology to perform better as a human.
  • – Atomwise is the creator of AtomNet – the first deep learning technology for small molecule discovery, used by research groups for drug discovery programs.  Health startups are crucial and this is one to watch as well. Using AI and ML to increase drug discovery will be highly beneficial.
  • – Building delivery robots to carry packages to consumers that are now being tested and can be seen in real environments.  This is another company to watch since we can actual see them currently in the world.
  • Purchased Skry in February to bring AI and Machine Learning to blockchain. (Blockchain technologies and crypto currency are seeing huge growth right now).  It will be very important to pay attention to see how AI/ML will be used within the crypto industry.
  • – Building an interactive, versatile personality for your code.
  • – Building avatars with AI that are “genuinely caring avatars who can build trusting, enduring relationships.
  • Data mining creating a customer “fingerprint” that helps reach your prospects better.
  • Appling things learned in nature to inspire factory and process automation.
  • Cancer detection with AI. We are starting to see machines make more accurate predictions when the data is available than the acting physicians.
  • Computer vision based platform-using AI to solve real world problems such as following a person (with a drone) without GPS signal using vision detection and real time localization via webcam. Computer vision is being adopted into autonomous vehicles, phones, cameras and much more. This area is growing consistently and it’s a fun area to get involved in!
  • Using AI to track where users are looking on your website. Not the typical tracking where users click or mouse movement, this is using AI to track where users look through eye tracking.  This is a newer tech or approach with AI as well instead of using the traditional tracking click method.
  • Arial imagery to enhance agriculture and farming projects.
  • Using AI and computer vision for blind or visually impaired individuals. It aims to solve three main problems including obstacle avoidance, geo-navigation and image-recognition.
  • Using AI to drive creative thinking and social intelligence for children.
  • Acquired by John Deere, it uses AI and computer vision to detect and identify each individual plant in a field aimed to reduce chemicals used to rid weeds and other harmful plans for agriculture.



To reiterate the following list includes highly recommended events involving the AI community.  As mentioned below to get started it’s always a great idea to check your local meetups for those that are AI or ML related to help you get started, network or push your knowledge further.  In addition if you are interested in one it’s always a great idea to keep an eye on it for scheduling information in addition to general news and publications.

Online Communities and Forums

Conferences and Meetups

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