AI in Automation: Which tasks can we automate in 2019?

The advances in AI since the rise of deep learning can usher a new age of automation as machines go beyond human capabilities in a wide range of tasks.

The jobs that can be mostly automated include

  • predictable physical labor
  • white-collar back-office work: data collection and processing

Machines can now perform the activities involved in these jobs better/cheaper than humans. These activities include tasks that involve manipulating tools, extracting data from documents and other semi-structured data sources, making tacit judgments and even sensing emotions. In the next decade, driving is likely to become automated as well, enabling one of the most common professions to be automated. Read more

Share

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

Share

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

Share

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

Share

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

Share