Advantages of AI in 2020 according to top practitioners

Alternatives to modern AI approaches including machine learning include rule based systems and manual labor. AI systems can surpass both alternatives in terms of cost, speed and effectiveness in many areas.

Rule based systems are better than modern AI solutions where it is possible to formulate a fixed set of rules to solve a problem/make the optimal decision.

Manual labor is more preferable to modern AI solutions where AI solutions do not yield a sufficient ROI. This is likely to happen in cases where Read more

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State of AI technology in 2020: Comprehensive Guide

AI technology can be divided into 3 layers. While we will likely see incremental improvements in algorithm design and application of those algorithms to specific domains, step change is possible in computing if breakthroughs can be achieved in quantum computing.

  • Algorithms that enable machine decision making including artificial neural networks (ANN), Bayesian inference and evolutionary computing. ANNs can be categorized by their depth (i.e. number of layers) and structure (i.e. how nodes are connected). Most recent progress in AI was due to deep neural networks (also called deep learning).
  • Computing technology to run those algorithms: Computing is key in AI, advances in computing power enabled the wave of AI commercialization thanks to deep learning since the 2010s.
  • Application of those algorithms to specific domains including reinforcement learning, computer vision, machine vision, natural language processing (NLP), recommendation systems.

AI Algorithms

Algorithms are like recipes for AI systems to learn from data or to make decisions. Advances in algorithm design are key for advancement for AI.

Artificial Neural Networks (ANNs)

The neural network is a popular machine learning technique that is inspired by the human brain and the neural network in our brains. Read more

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Future of AI according to top AI experts of 2020: In-Depth Guide

Investment and interest in AI is expected to increase in the long run since major AI use cases (e.g. autonomous driving, AI-powered medical diagnosis) that will unlock significant economic value are within reach. These use cases are likely to materialize since improvements are expected in the 3 building blocks of AI: availability of more data, better algorithms and computing.

Short term changes are hard to predict and we could experience another AI winter however, it would likely be short-lived. Feel free to jump to different sections to see the latest answers to your questions about the future of AI: Read more

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AI-Powered Mobile Apps: A Brief Introduction

Are you looking to transform your mobile app with AI solutions? You can improve your user experience by integrating AI technologies to your app. We have compiled an introduction to AI-powered mobile apps to help you to understand how you can benefit from AI to improve your mobile apps.

How is AI impacting the mobile experience?

AI enables personalized mobile apps which helps improve user experience. This is the most common use case of AI in mobile apps. These apps can store many information about us such as our age, gender, location, hobbies, photos we like, the products we buy and much more. Read more

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Data-driven decision making in 2020: Step-by-step guide

Most successful organizations treat data-driven decision making as a primary objective and pursue it with religious zeal. However, data-driven decision making, the steps leading to it and how AI is changing it are not well-defined.

In an AI-first company, data-driven decision making means

  • Strategic decisions are made by a diverse group including executives that rely on a sufficiently comprehensive set of information. By definition, a bad strategic decision should be important enough to lead to failure of the company.
  • Most operational decisions are handled by continuously learning machine learning models which produce explainable decisions. Operational decisions are frequent (once a week or more frequent) and not critical (a single mistake is unlikely to lead to failure of the company).
  • Operational decisions which can not be automated with good accuracy are delegated to humans.
    • If data is lacking, opinion based decisions are made.
    • If data exists and has been analyzed, decision maker relies on analysis.
    • If data exists but is not analyzed yet, cost of analysis determines whether an opinion or data based decision will be made.

    Before settling on this framework for decision making for modern corporations, we need to identify how we can evaluate different decision making models. However, if you like you can directly skip to the sections that interest you: Read more

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