Top AI Use Cases / Applications in 2019

AI is becoming more integrated into our lives with more AI use cases emerging. This leads to increased interest in AI.

According to a recent Gartner survey, 37% of organizations are still looking to define their AI strategies. To integrate AI into your own business, you need to identify how AI can serve your business, possible use cases of AI in your business. This article gathers the most common use cases covering marketing, sales, customer services, security, data, technology, and other processes. Read more

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Advantages of AI in 2019 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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Machine Learning Consulting in 2019: In-Depth Guide

Machine learning (ML) consulting, like AI consulting, is an emerging field where both traditional consultants and new startups compete. In this article, we will focus on ML specific challenges, for an overview of AI consulting and the players of the industry, please see our article on the topic.

What is the difference between ML and AI consulting?

Though machine learning (ML) is the subfield of AI with most commercial applications, it is best to distinguish between them

  • AI: includes all applications where the computer mimics human intelligence
  • ML: applications which use known data to create models that can be used to classify/process new data

Is ML consulting = deep learning consulting?

Not exactly. Deep learning is a subset of ML. However, deep learning is the most successful machine learning technique in terms of accuracy as of 2019 in most areas. Read more

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State of AI technology in 2019: 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 2019: 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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