3 AI misconceptions IT leaders must dispel


 Artificial intelligence is rapidly changing many aspects of how we work and live. (How many stories did you read last week about self-driving cars and job-stealing robots? Perhaps your holiday shopping involved some AI algorithms, as well.) But despite the constant flow of news, many misconceptions about AI remain.

AI doesn’t think in our sense of the word at all, Scriffignano explains. “In many ways, it’s not really intelligence. It’s regressive.” 

IT leaders should make deliberate choices about what AI can and can’t do on its own. “You have to pay attention to giving AI autonomy intentionally and not by accident,”


  1. Tomi Engdahl says:

    Neural network reconstructs human thoughts from brain waves in real time

    Researchers from Russian corporation Neurobotics and the Moscow Institute of Physics and Technology have found a way to visualize a person’s brain activity as actual images mimicking what they observe in real time. This will enable new post-stroke rehabilitation devices controlled by brain signals. The team published its research as a preprint on bioRxiv and posted a video online showing their “mind-reading” system at work.

  2. Tomi Engdahl says:

    Finding Pre-Trained AI In A Modelzoo Using Python

    Training a machine learning model is not a task for mere mortals, as it takes a lot of time or computing power to do so. Fortunately there are pre-trained models out there that one can use, and [Max Bridgland] decided it would be a good idea to write a python module to find and view such models using the command line.


  3. Tomi Engdahl says:

    Spleeter is the Deezer source separation library with pretrained models written in Python and uses Tensorflow. It makes it easy to train source separation model (assuming you have a dataset of isolated sources), and provides already trained state of the art model for performing various flavour of separation :

    Vocals (singing voice) / accompaniment separation (2 stems)
    Vocals / drums / bass / other separation (4 stems)
    Vocals / drums / bass / piano / other separation (5 stems)


  4. Tomi Engdahl says:

    Aiming at everyone from hobbyists to educators, Teachable Machine requires no prior experience to build simple ML models.

    Google’s Teachable Machine Uses TensorFlow.js to Bring Code-Free Machine Learning to the Browser

    Aiming at everyone from hobbyists to educators, Teachable Machine requires no prior experience to build simple ML models.


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