Decoding Technical Terms

If you are non-technical but would like to implement technological solution in your organization, here is the utmost guide  decoding technical terms. Before we start let’s understand some of the AI terms are used primarily for marketing purposes, while others are more technical. Here are our translations for common terms you may hear, whether you’re thinking of implementing an AI product or partnering with a team of AI experts. It’s a great starting point for becoming an AI leader in your organization.


Artificial intelligence (AI): Marketing term that describes a continuum of non-living analytical power, fueled by fast processing and data storage’s declining costs. Applications today are termed weak AI (like IBM Watson), which are algorithms built to accomplish a specific task. Strong AI (like Skynet) is a term for hypothetical future applications that will replicate human intelligence.

Big data: Buzzword alluding to a machine’s ability to generate insights and learn from massive data sets, because sensors, software, and recordkeeping generate a lot of data. For example, The Weather Company and IBM researched weather’s impact on business by analyzing millions of data points from weather sensors, aircraft, smartphones, buildings, and vehicles.

Most important to remember

Machine learning: Method of automated analytical model building. Machine learning lets computers find hidden insights without being explicitly programmed where to look. For instance, Facebook’s machine learning software uses algorithms and data points to show a user suggested friends, display relevant ads, and detect spam.

Algorithm: Formula that represents a relationship between things. It’s a self-contained, step-by-step set of operations that automates a function, like a process, recommendation, or analysis. For example, Netflix’s recommendation algorithms can predict what movies a consumer might want to watch based on their viewing history.decoding technical terms


Deep learning: Branch of machine learning that uses multiple layers of distributed representations (neural networks) to recognize patterns in digital sounds, images, or other data. For example, Google’s DeepDream photo-editing software allows neural networks to “hallucinate” patterns and images in a photo.

Neural networks: Computational approach that loosely models how the brain solves problems with layers of inputs and outputs. Rather than being programmed, the networks are trained with several thousand cycles of interaction. Businesses can use these to do a lot with a little; for example, neural networks can generate image captions, classify objects, or predict stock market fluctuations.

Natural language processing: Field of study in which machines are trained to understand human language using machine-learning techniques. It’s useful for automatic translations, chatbots, or AI personal assistants. Think of the robot voice that picks up your helpline call and asks, “What can I help you with?” or an automated chatbot that responds to your texts.

Parsing: The process of evaluating text according to a set of grammar or syntax rules. You can build algorithms that parse text according to English grammar rules, for example, to aid natural language processing.

Hope the above information was helpful for you to decode the technical terms and apply necessary methods in your organization.


Artificial Intelligence: The Big Picture

  1. The Hype and Hope of Artificial Intelligence, The New Yorker
  2. What Counts as Artificially Intelligent? AI and Deep Learning, Explained, The Verge
  3. The Extraordinary Link Between Deep Neural Networks and the Nature of the Universe, MIT Technology Review
  4. The Competitive Landscape for Machine Intelligence, Harvard Business Review
  5. What Do People—Not Techies, Not Companies—Think About Artificial Intelligence? Harvard Business Review

How companies use AI today

  1. An Exclusive Look at Machine Learning at Apple, Wired
  2. Preparing for the Future of Artificial Intelligence, White House Blog
  3. Using Artificial Intelligence to Transform Healthcare with Pinaki Dsagupta, Hindsight, Startup Health
  4. Beyond Siri, The Next-Generation AI Assistants Are Smarter Specialists, Fast Company
  5. Infographic: What You Need to Know About Google RankBrain, Contently
  6. Facebook is Giving Away the Software it Uses to Understand Objects in Photos, The Verge
  7. How AI is Changing Human Resources, Fast Company
  8. Beyond Automation, Harvard Business Review

Ethical considerations

  1. The Head of Google’s Brain Team is More Worried about the Lack of Diversity in Artificial Intelligence than an AI Apocalypse, re/code
  2. The Tradeoffs of Imbuing Self-Driving Cars With Human Morality, Motherboard
  3. If We Don’t Want AI to Be Evil, We Should Teach It to Read, Motherboard
  4. The Ethics of Artificial Intelligence, Nick Bostrom
  5. Twitter Taught Microsoft’s AI Chatbot to be a Racist Asshole in Less Than a Day, The Verge
  6. Algorithms Are Biased Against Women and the Poor, According to a Former Math Professor, The Cut
  7. Elon Musk elaborates on his AI concerns, Sam Altman YouTube interview

Let us know what you think about AI in the comments section and how it can help shape the future of technology.

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