Develop your Digital Fluency by Learning AI

16/06/2021 84

In a recent study from Linkedin entitled “Workplace Learning Report: Skill Building in the New World of Work” some key data for the business world is offered, for example that almost 60% of leaders consider that acquiring new skills (upskilling ) and retraining ourselves professionally (reskilling) are the main priority over leadership and management. The list of most important skills in order of importance for the year 2021 are:

1) Resilience and adaptability

2) Technological skills / digital fluency

3) Communication through remote or distributed teams

4) Emotional intelligence

5) Collaboration between functions

6) Leading through change

7) Change management

8) Manage stress / be more mindful

9) Time management an

10) Creativity

Regarding the second skill on the list, the study marks a difference between digital fluency and digital transformation. The latter is the change associated with the application of digital technology in all aspects of human society. Digital transformation restructures all aspects of a business.

In other words, it’s the way organizations use technology, talent, and processes to innovate and impact performance. However, from an employee learning and development perspective, digital transformation is a learning program, while being digitally fluent means that a learner has the technology skills to operate effectively in an increasingly digital world.

It includes everything from understanding how to use the Microsoft Office suite to more advanced artificial intelligence. These data are aligned with those published in the latest Stanford University Human-Centered Artificial Intelligence “Artificial Intelligence Index Report 2021”. This study addresses the most relevant technological performances of AI in five areas:

The first analyzes how everything is generative. Artificial intelligence systems can now compose text, audio and images of such a high level that humans are beginning to have trouble differentiating the artificial from the natural. This development opens up a huge range of AI applications but is also prompting researchers to invest in technologies to detect generative models and DeepFakes.

humans are beginning to have trouble differentiating the artificial from the natural.

The second is related to the industrialization of computer vision. This technology has seen immense progress in the last decade, mainly due to the use of machine learning techniques. Companies are investing increasing amounts of computational resources to train computer vision systems at a faster rate than ever before. Meanwhile, technologies for use in deployed systems, such as object detection frameworks for video still-frame analysis, are rapidly maturing, indicating increased deployment of AI.

The third studies how natural language processing (NLP) is outperforming its evaluation metrics. AI systems with enhanced language capabilities are being developed and have started to have a significant economic impact on the world. Progress in NLP is being exponential and has started to exceed benchmarks to test them. This can be seen in the emergence of systems that achieve human-level performance in SuperGLUE, an NLP assessment suite developed in response to previous NLP progress that surpasses the capabilities assessed by GLUE.

The fourth reviews the new analysis on reasoning. New analytics developed for the AI ​​Index provide metrics that allow an evolving benchmark and attribution to individual credit systems for a portion of the overall performance of a group of systems over time. These apply to two symbolic reasoning problems, the automated proving of theorems and the satisfaction of Boolean formulas.

AI systems with enhanced language capabilities are being developed and have started to have a significant economic impact on the world.

The fifth argues that machine learning is revolutionizing healthcare and biology. The landscape of the healthcare and biology industries has evolved substantially with the adoption of machine learning. For example, PostEra, an artificial intelligence startup, used ML-based techniques to accelerate COVID-related drug discovery during the pandemic. Another case that we already mentioned in another post is how the company Sherpa.ai designed an application for the Basque Health Service (Osakidetza) that allows anticipating the evolution of Covid-19 in the Basque Country.

The Stanford study highlights how the world’s best universities have increased their investment in AI education in the past four years. The number of courses that teach students the skills necessary to build or implement a practical AI model at the undergraduate and graduate levels has increased by 102.9% and 41.7%, respectively, in the last four years academics. Over the past 10 years, AI-related PhDs have risen from 14.2% in the United States, to about 23% as of 2019.