With the coming decade, placing learners and trainers at the heart of training based on data provided by digital systems becomes more and more plausible. These data provide an essential basis for artificial intelligence to take over the training. Between the promises of a better world (more personalization, a better impact of training on each learner, less repetitive tasks …) and the fears aroused by AI (confronting the role of the trainer, confidentiality …), it today becomes essential to understand the reality that it covers and to anticipate its impacts.
Artificial intelligence is now … or almost
Another study by 360digitmg on the use of artificial intelligence in many training like artificial intelligence course in Bangalore. After survey 360digitmg indicates that: 19% of organizations are already using artificial intelligence in one way or another. 31% of organizations plan to use AI in the short term. 26% plan to use AI in the medium term and 24% plan to use AI in a few years It, therefore, becomes essential for all Learning & Development (L&D) stakeholders to negotiate the AI shift. Just as the 2000s saw the advent and the applied democratization of the Internet, the 2020s will be those of an AI accessible and at the service of L&D actors. We, therefore, offer you a series of posts on the subject, structured as follows:
- What is AI and what are its foreseeable impacts on L&D?
- What are the different use cases of AI in an L&D framework?
- What ethics for AI in training?
- Why we talking about exactly?
AI refers to a computer program that can simulate human capacities (recognition of shape, image, sound, machine translation), manages a human/machine dialogue (question/answer) or quite simply process an immense amount of data to guide decisions.
We could distinguish two types of AI:
Weak AI: Performs simple and repetitive tasks but can evolve while remaining in a limited area.
Strong IA: Able to produce reflections, understand a context and develop their own reasoning. We also speak of the notion of machine consciousness.
At this point, in the L&D universe, we only encounter weak AIs. We will, therefore, zoom in on these later in our posts.
Focus on weak artificial intelligence
The principle of weak AI is to reproduce “simple” tasks and to Improve people thinking and mind with the help of Artificial intelligence training in Bangalore. The goal is not to conceive of an intelligence strictly speaking but to faithfully copy human actions with the help of an automaton or a program, by bringing more reliability, and less painfulness. The program only performs actions for which it was created and is not able to evolve on its own.
Beware of the word “weak”, which does not mean “idiot”: it should rather be seen as a conception which is not in a position to take a problem as a whole, nor to understand its environment (what is it? refers to exist).
Let’s take a concrete example to illustrate this principle of algorithms: we are in a company that seeks to understand the impact of some of its training on its employees. They have become accustomed to reacting to training on the corporate social network. This impact measurement could, of course, be taken care of by humans, by listing all the messages published, classifying them, analyzing them… but that would be very long and tedious! So, this company builds, on the one hand, a dictionary of positive/neutral/negative words, and on the other hand, a solution allowing to suck the content of the publications on the internal social network.
The algorithm will then analyze the words used in the publications, and assign them notes in connection with the initial dictionary:
- -1 for words reflecting a negative perception
- 0 for neutral words
- +1 for positive words
Each publication will thus be evaluated. The overall analysis will make it possible to produce a dashboard highlighting the learners’ perception of the targeted training. CQFD: It’s a weak AI process.
The AI pyramid
In this example, we follow a logic of RPA (Robotic Process Automation), at the bottom of the AI pyramid. It mainly aims to automate a process that could be performed by a human.
Now let’s add cognitive services, including voice recognition. It aims to analyze all video publications associated with the training, and automatically post responses consistent with the content of the video. In short, these cognitive services create rich interactions with users. We now have a CRPA (Cognitive Robotic Process Automation).
Finally, imagine that our solution spots other initially unlisted words that are associated with publications of a positive nature, that it begins to feed its own dictionary, and that it gradually sharpens it as the messages appear.