Tag: implementation

The 12 Truths of Machine Learning – by Delip Rao

The 12 Truths of Machine Learning – by Delip Rao

In this original blog, with equally original title, Delip Rao poses twelve (+1) harsh truths about the real world practice of machine learning. I found it quite enlightning to read a non-hyped article about ML for once. Particularly because Delip’s experiences seem to overlap quite nicely with the principles of software design and Agile working.

Delip’s 12 truths I’ve copied in headers below. If they spark your interest, read more here:

  1. It has to work
  2. No matter how hard you push and no matter what the priority, you can’t increase the speed of light
  3. With sufficient thrust, pigs fly just fine. However, this is not necessarily a good idea
  4. Some things in life can never be fully appreciated nor understood unless experienced firsthand
  5. It is always possible to agglutinate multiple separate problems into a single complex interdependent solution. In most cases, this is a bad idea
  6. It is easier to ignore or move a problem around than it is to solve it
  7. You always have to tradeoff something
  8. Everything is more complicated than you think
  9. You will always under-provision resources
  10. One size never fits all. Your model will make embarrassing errors all the time despite your best intentions
  11. Every old idea will be proposed again with a different name and a different presentation, regardless of whether it works
  12. Perfection has been reached not when there is nothing left to add, but when there is nothing left to take away

Delip added in a +1, with his zero-indexed truth: You are Not a Scientist.

Yes, that’s all of you building stuff with machine learning with a “scientist” in the title, including all of you with PhDs, has-been-academics, and academics with one foot in the industry. Machine learning (and other AI application areas, like NLP, Vision, Speech, …) is an engineering research discipline (as opposed to science research).

Delip Rao via deliprao.com/archives/227

Delip [bio] is the VP of Research at AI Foundation where he leads speech, language, and vision research efforts for generating and detecting artificial content. You can find his personal webblog here.

Cover image via the-vital-edge.com/lie-detector

Checklist to Optimize Training Transfer in Organizations

Checklist to Optimize Training Transfer in Organizations

Ashley Hughes, Stephanie Zajac, Jacqueline Spencer, and Eduardo Salas wrote a recent research note for the International Journal of Training and Development. The research note is build around an evidence-based checklist of actionable insights for practitioners that will help to enhance the effectiveness of training interventions. These actionable insights would help to prevent ‘transfer problem’, meaning that trained skills are not being used on the job. 


Screenshot of the first page of the published research note, containing the abstract

Unfortunately, these published academic papers are often behind a paywall, but you may request a PDF from the authors here on ResearchGate.

Screenshot of the appendix of the research note containing the checklist for practitioners.

For the full details and scientific evidence behind each suggested action, I suggest you access the research note. Nevertheless, here’s my summary of their main advice on improving training transfer before, during, and after training implementation:

Before training

  • Conduct a training needs analysis to align the training’s content and participants with the organizational objectives
  • Involved stakeholders should be aware of training, understand its importance, and — obviously — be prepared for the training program. The scholars provide seven specific actions here, including the setting of personal training goals, and aligning resources and rewards with the training.
  • Training attendance should be framed as an opportunity, and the training’s anticipated benefits could be emphasized (e.g. improvement of work processes or on-the-job performance).
  • A climate which encourages learning should be created, with dedicated time (and opportunities) for post‐training learning 
    and a sense of accountability for using trained knowledge, skills, and abilities.

During training

  • Piloting the training with a single department or subset of trainees is highly encouraged. This is one way that greatly helps to assess whether the training design is appropriate in terms of content and delivery.
  • Error‐encouragement framing can influence a trainee’s learning orientation and thus errors made during training should be framed as growth opportunities.

After training

  • Use of the trained skills should be supported and planned. For instance, participants could be given a small workload reduction to provide opportunities to apply the learned knowledge and skills once they return to their position. 
  • Management and training participants should be held accountable for their use of skills on the job.
  • Think about using just‐in‐time or refresher training and coaching, if needed.
  • Assess training effectiveness criteria including training transfer using metrics and analytics. Specifically, the scholars propose that the criteria measured in the training evaluation should correspond to the training needs identified through the training needs analysis that was conducted before the training. 
  • Training evaluation criteria should consider the scope and timeframe of the training. Take into account that distal outcomes such as ROI may take longer to realize.