2020: The Year Of Artificial Intelligence For Your Business

  • “Close to 40% of businesses use artificial intelligence (AI). This number was estimated at 5% to 10%, but a survey by IBM showed that we grossly underestimated the scale of AI’s existence.
  • This number is set to grow to 80% to 90% over the next 18-24 months.
  • Here are some processes and areas AI is making inroads in with organizations around the world:
  • 1. Hiring and retaining people

    2. Gathering and using data

    3. Decision-making

    4. Measuring results”


    AI (Artificial Intelligence): What We Can Expect In The New Year

    • “2020 will be the year of practical AI: using cool technology to solve “boring” problems.
    • Deep Learning goes industrial. Dedicated DL chipsets are accelerating trial and error opportunities across industries
    • adversaries are going to use the best technology to accomplish their goals…Adversaries will try to create wedges and divides in society.
    • 2020 will usher in the year of ‘AI in the Enterprise.’ AI will get an upgrade from being an ingredient to a first class citizen”


    Image Credit: AI, Artificial Intelligence concept,3d GETTY

    8 biggest AI trends of 2020, according to experts

    1. “AI will make healthcare more accurate and less costly
    1. Explainability and trust will receive greater attention
    2. AI will become less data-hungry
    3. Improved accuracy and efficiency of neural networks
    4. Automated AI development
    5. AI in manufacturing
    6. The geopolitical implications of AI
    7. AI in drug discovery”


    This is what the AI industry will look like in 2020

    “With that in mind, my predictions for 2020 attempt to balance both aspects, with an emphasis on real value for companies, and not just ‘cool things’ for data science teams.

    1. Data science and AI roles continue the trend towards specialization
    2. Executive understanding of data science and AI becomes more important
    3. End-to-end model management becomes the best practice where production is required
    4. Data science and AI ethics continue to gain momentum and are starting to form into a distinct discipline
    5. The convergence of tools causes confusion
    6. Efforts to ‘democratize’ and ‘automate’ data science and AI redouble, with parties that over-promise failing
    7. Architecture at the Edge and Fog starts to enter the mainstream
    8. The hype cycle and deluge of definitions are shifting
    9. Competition enters the AI chip market
    10. It is still easier to teach data science and AI and sell tools than to actually make it work in practice”