Tag Archives: ai skills

This is the one AI skill everyone needs to have and understand

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The AI field is being revolutionized through Generative AI, and this will bring powerful new capabilities that can be turned into value for individuals, businesses, and society. It is a great time to work with AI. But it is also overwhelming, and challenging, and confusing for most people. If you are outside the AI world, you may be wondering, What really is AI? Is AI going to save the world? is AI going to destroy the world? And on top of that… what is Generative AI? There are so many AI fields, and subfields, and capabilities, and technologies, and algorithms, and frameworks that it is hard to know where to start to understand it. So start here… learn one thing: AI IS PROBABILISTIC!

What does probabilistic mean? it means, any answer you get from an AI system is not a precise answer but a prediction with a certain degree of confidence.

AI systems are not programmed like other deterministic technologies. In the world of technology outside of AI, you program a computer to make deterministic “precise” decisions: i.e.: If the user clicks on this button, then show them an image, if the account balance reaches $5, send the user an email, etc. AI systems, on the other hand, are “trained’ using data to either predict new data based on the input data (traditional AI) – i.e.: an inventory level prediction, a product you might like – , or to generate/create complex new data based on an input prompt (Generative AI) – i.e.: images, videos, code and text -. Training an AI model means you feed it existing data and then use mathematical models that align well with that data to predict future data. In other words, what is underneath these AI predictions is a collection of mathematical, probabilistic models. In essence, you have something similar to a weather forecast: There an x% of chance that tomorrow will rain. We all know how that goes right? maybe it will rain, maybe it won’t because a weather forecast is based on a mathematical probabilistic model. Mathematical probabilistic models are the foundation of any AI system. The problem is that unlike weather forecasts, AI systems are not telling us “there is an x% chance that what I am telling you is correct”. They just give us an answer, so the responsibility today is on us to understand this.

So now that you know that AI systems are probabilistic, how do you use this information? you must understand that any output from an AI system sometimes will be wrong, no matter how great it looks or how confident it sounds in the case of Generative Text models, and need to determine when it is safe to use them based on the impact of an incorrect output. Here are some examples of how to use this one skill to interact with AI:

  • Business Leaders. If you are a business leader driving an AI strategy, or aiming to leverage AI to impact your business area, know that with any AI implementation, not only you need to look at how to leverage AI to increase revenue, or reduce costs, or create new business models or new customer experiences. You also need to think about the increased need for risk management. What will happen in the % of cases that AI provides an incorrect answer? will the impact be minor? or will it be a disaster and guardrails need to be implemented?, or will it be a disaster of such magnitude that you can’t let this system be used unless there is a human in the loop? will a sector of the population be impacted different and negatively vs other segments of the population by automated AI-driven decisions? Always remember: AI outputs are not “precise” with 100% certainty. They are probabilistic with precision that is not 100%

  • Developers. Developers now have access to one of the greatest playgrounds in the world, with availability of multiple technologies to integrate AI capabilities into their applications. With just a call to an API, you can leverage pretrained AI models and add innovation to your applications without requiring the deep mathematical expertise required to train AI models. But if you haven’t worked with AI before, you are used to working with deterministic technologies. You call an API, you get a response, and that response should be 100% precise. With AI APIs, you are getting a probabilistic output which means, depending on the inputs you give the model you will get different results. These results may be very close to the answer you need if this input is similar to the data used to train the model, or completely nuts if this new input is significantly – mathematically speaking – different from any data used to train the model. Understanding this is key, as depending on the application where these capabilities are being integrated, you need to be mindful of causing no harm if the response this API produces is biased, or completely wrong. And if you are comparing models from different vendors, know that just testing models with a set of inputs may give you completely different results once you change the input sets. So do multiple tests. Don’t make the decision to leverage a model through an API, testing with a single set of inputs.

  • Data Scientists. You have got this. Even when you are not training Machine Learning models, but leveraging pretrained AI models, you are used to working in a probabilistic space. You need to be ambassadors of this understanding and drive other business and technical people in your organization, and friends in your circles, to internalize how to make decisions and evaluate outputs of AI technologies

  • Everybody else. The latest developments in the AI space have put AI in the hands of everyone. And more and more you will be using systems, tools, apps that have increased AI power. Just remember that any technology you are interacting with will give you results that are not correct. It doesn’t mean the system does not work – it is actually working as it should: in a probabilistic manner. It means you, the human, need to assess and think about the impact of the technology you are using and determine how safe it is for you to use it. For example, a tool that serves you an ice cream it “thinks” you should try, if you don’t have any allergies maybe is safe for you to use. But if you have a peanut allergy, maybe it is not safe to use unless there are guardrails that allow you to provide that restriction.

In summary, today the field of AI could not be more exciting or more full of potential for individuals, businesses and societies, but because AI systems don’t tell us their outputs are probabilistic, we need to always remember it. And not only remember it but assess the impact of any decisions being made by AI systems for us, or by us based on an AI system output. Keep in mind that these technologies are so sophisticated, that if we don’t remember they are just giving us a probabilistic prediction, it is very easy to think that they are in fact reasoning, and even acting like a “real” human.

Data Analysts, Data Scientists, ML and AI Specialists are the jobs with highest demand according to WEF’s 2020 Future of Jobs report

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Summary

In October 2020, The World Economic Forum published the report “The Future of Jobs”. This report has deep insights on technological adoption in the next five years, and it maps the jobs and skills of the future including a deep dive into Data and AI Skills. The report shows that technological adoption continues expanding, while skills availability remains the #1 barrier to that adoption. Businesses and governments around the world are investing significantly in upskilling and reskilling programs, with a significant percentage of that investment going towards transitions into Data and AI Jobs. Demand for Data Analysts, Data Scientists, and AI Specialists is high, but the skills gap that needs to be addressed to successfully transition into those roles is large.

Some of the key findings:

  1. Skills gaps continue to be high. This includes skills like critical thinking, analysis, problem-solving, and skills in self-management such as active learning, resilience, stress tolerance and flexibility. On average, companies estimate that around 40% of workers will require reskilling of six months or less and 94% of business leaders report that they expect employees to pick up new skills on the job, a sharp uptake from 65% in 2018. 
  2. Online learning is on the rise. There has been a four-fold increase in the numbers of individuals seeking out opportunities for learning online through their own initiative, a five-fold increase in employer provision of online learning opportunities to their workers and a nine-fold enrollment increase for learners accessing online learning through government programs
  3. The window of opportunity to reskill and upskill workers has become shorter. The share of core skills that will change in the next five years is 40%, and 50% of all employees will need reskilling 
  4. The large majority of employers recognize the value of human capital investment. 66% of employers surveyed expect to get a return on investment in upskilling and reskilling within one year. Employers expect to offer reskilling and upskilling to over 70% of their employees, but employee engagement into those courses is lagging, with only 42% of employees taking up employer-supported reskilling and upskilling opportunities. 

Over the past decade, a set of ground-breaking, emerging technologies have signaled the start of the Fourth Industrial Revolution. By 2025, the capabilities of machines and algorithms will be more broadly employed than in previous years, and the work hours performed by machines will match the time spent working by human beings. This augmentation of work will disrupt the employment prospects of workers across a broad range of industries and geographies, and we will see job growth in the ‘jobs of tomorrow’— such as roles at the forefront of the data and AI economy, as well as new roles in engineering, cloud computing and product development. 

Technological Adoption

The past two years have seen a clear acceleration in the adoption of new technologies. Cloud computing, big data and e-commerce remain high priorities, following a trend established in previous years. However, there has also been a significant rise in interest in encryption, and a significant increase in the number of firms expecting to adopt robots and artificial intelligence.  These new technologies are set to drive future growth across industries, as well as to increase the demand for new job roles and skill sets. Figure 1 shows technologies likely to be adopted by 2025 (by share of companies surveyed).

By 2025 the average estimated time spent by humans and machines at work will be at parity based on today’s tasks. Algorithms and machines will be primarily focused on the tasks of information and data processing and retrieval, administrative tasks and some aspects of traditional manual labor. The tasks where humans are expected to retain their comparative advantage include managing, advising, decision-making, reasoning, communicating and interacting. 

Emerging Jobs

Similar to the last survey in 2018, the leading positions in growing demand are roles such as Data Analysts and Scientists, AI and Machine Learning Specialists, Robotics Engineers, Software and Application developers as well as Digital Transformation Specialists. However, job roles such as Process Automation Specialists, Information Security Analysts and Internet of Things Specialists are newly emerging among a cohort of roles which are seeing growing demand from employers. The emergence of these roles reflects the acceleration of automation as well as the resurgence of cybersecurity risks. Figure 2 shows the top 20 job roles in increasing demand across industries, with Data Analysts, Data Scientists, and AI Specialists ranked with the highest demand overall. 

Figure 2 – Top 20 job roles in increasing demand across industries

These emerging jobs have been organized in clusters, and this report presents a unique analysis which examines key learnings gleaned from job transitions into those emerging clusters using LinkedIn and Coursera data gathered over the past five years.  The main clusters are: Data and AI, Cloud Computing, Engineering, Content Production, Marketing, People and Culture, and Product Development and Sales. Figure 3 shows Data and AI roles organized according to the scale of each opportunity within the cluster.

Figure 3 – Data and AI Job Cluster

Emerging Skills

The ability of global companies to harness the growth potential of new technological adoption is limited by skills shortages. Figure 4 shows that skills gaps in the local markets and inability to attract the right talent remain among the leading barriers to the adoption of new technologies. 

Figure 4 -Perceived barriers to the adoption of new technologies

Skill shortages are more acute in emerging professions. Business leaders consistently cite difficulties when hiring for Data Analysts and Scientists, AI and Machine Learning Specialists as well as Software and Application Developers. 

To address skills shortages, companies are investing in upskilling and reskilling programs. However, employee engagement into those courses is lagging, with only 42% of employees taking up employer-supported reskilling and upskilling opportunities. There are however significant challenges in the amount of skills that need to be developed especially for emerging roles in Data Science and Artificial Intelligence.  Figure 5 illustrates the skills gap that needs to be closed for individuals to transition into these roles, with Artificial Intelligence, NLP, Data Science and Signal Processing representing the largest amount of skills needed to be developed for a successful transition.

Figure 5 – Typical skills gaps across successful job transitions

Furthermore, the report uses data from Coursera learners to estimate the distance from the optimal level of mastery for learners targeting to transition into Data and AI, and quantifies the days of learning needed for the average worker to gain that level of mastery. (Figure 6).

Figure 6 – Top 10 skills by required level of mastery and time to achieve that mastery

Mastery score is the score attained by those in the top 80% on an assessment for that skill. Mastery gap is measured as a percentage representing the score among those looking to transition to the occupation as a share of the score among those already in the occupation. 

In conclusion, technological adoption continues expanding, and skills availability remains the #1 barrier to that adoption. Businesses and governments around the world are investing significantly in upskilling and reskilling programs, with a significant percentage of that investment going towards transitions into Data and AI Jobs. Demand for Data Analysts, Data Scientists, and AI Specialists is high, but the skills gap that needs to be addressed to successfully transition into those roles is large.