The impact of artificial intelligence on the IT industry: Innovations, professions and the future of technology

Artificial intelligence, originally created to simplify and automate labour-intensive tasks, now encompasses methods of creating machines that mimic human intelligence. According to an AI professor at the Sorbonne, AI is transforming workplaces in the same way that electricity and the steam engine once did. This presents an opportunity to engage in large-scale societal change as AI revolutionises many areas of business and society.

The basics of working with AI

Artificial Intelligence is rapidly evolving and it requires flexible thinking and a good understanding of the basics to be successful in this field. Studying AI involves mastering computer science and the techniques that make it possible to build machines that perform tasks traditionally done by humans. It requires specialised training and an understanding of several levels of skill.

Studying Artificial Intelligence

To familiarise yourself with the basics of artificial intelligence, Pôle emploi, together with the Institut Montaigne and OpenClassrooms, offers the MOOC ‘Objectif IA: initiez-vous à l'intelligence artificielle’ (Objective AI: introduction to artificial intelligence), which requires no prerequisites. Various online learning organisations also offer a more or less in-depth introduction to the subject, including courses for project managers and company directors on the benefits of implementing AI. By the end of the course, learners will be able to:

• Explain what artificial intelligence is;
• Identify the challenges and opportunities associated with artificial intelligence;
• Understand the nature of the AI project and its sub-disciplines - machine learning and deep learning.

University programmes and higher education institutions

Many universities have already developed comprehensive courses to specialise in AI. These programmes start with general computer science courses and progress to undergraduate and postgraduate courses. In addition, engineering and computer science schools also offer specialised programmes.

Courses in machine learning and deep learning can last up to 5 years of higher education or even up to doctoral level for those pursuing a research career. Short courses are developed to provide practical skills that can be applied in companies. However, long courses remain a priority given the complexity of artificial intelligence.

Lifelong learning

As artificial intelligence is constantly evolving, it will require a policy of lifelong learning supported by R&D programmes. To enable professionals to keep their knowledge current and master new AI-related technologies, various organisations have made this their speciality. CNAM, a French higher education institution, offers AI training courses at all levels, available throughout France.

Modelling artificial intelligence applications

Here's what the process of creating and implementing artificial intelligence in a business looks like, from defining goals to exploiting it:

1. Goals and Model: When an entrepreneur sets out to create artificial intelligence, they must realise that it is not an end in itself. What matters is what the artificial intelligence is supposed to produce - the desired outcome.
2. Choosing an AI model: Depending on these goals, an artificial intelligence model will need to be chosen. If the entrepreneur is not a scientist and does not understand coding, he or she can build an application based on existing models, approach professionals in the field or simply hire a team of experts in the field.
3. Data set: This is one of the most important parts of building AI: choosing a data set, collecting and labelling it. The more complex the expected answer, the larger the data set needs to be collected.
4. Data labelling: You'll have to consider many possible combinations to determine the data needed for the algorithm to work properly. For example, if the goal of the application is to recognise the species of an animal, you will need to collect a sample of all its varieties: colour, size range, different breeds or subspecies.
5. Training: The algorithm behaves like a brain: to perform a task efficiently, it needs to be trained until it has mastered it. To do this, the AI will perform its own analysis based on the labelling of the data.
6. Creating a programme: It will examine the data and its characteristics, and then identify recurring elements. This stage helps in creating a programme at the heart of the application that will be able to provide answers based on the incoming data.
7. Prediction relevancy: The aim is to make the predictions made by the AI as relevant as possible. This operation requires a lot of computing power, time and specialised equipment.
8. Outsourcing training: This is why many companies prefer to outsource this step. This allows them to optimise resources and reduce equipment costs.
9. Testing and operation: Testing is done to compare the model with reality through performance analysis. If the algorithm level is insufficient, the procedure will have to be repeated.
10. Repeating the procedure: Data modification, labelling and training will be repeated until the expected results are obtained. The level of reliability expected from artificial intelligence depends on the application area of the algorithm.
11. Reliability of AI: The more consequences an answer generates, the more reliable the AI should be. This is the case with forest fire detection, for example.
12. Transition to use: Once the testing is complete, the company can move on to using the AI. This stage is important to start the actual operation of the system.

Launching an AI startup

To create and market an AI startup, an entrepreneur needs to consider the following aspects:

1. Understanding the environment: To launch an AI startup, it is necessary to study the market well. What are the opportunities in this sector that the entrepreneur is interested in and what are the existing products and their shortcomings?
2. Gathering a team: To create and launch an AI application, it is necessary to gather representatives from various technical and commercial professions. Even if the idea can be developed independently, refining it and bringing it to market requires specialised skills.
3. Finding funding: To launch a startup, you need to get initial funding. The first round will be followed by several more to help the startup grow and bring the product to market.
4. Defining the business plan: The chosen business model must be clear and relevant to the product being promoted. A global vision as well as a plan for creating and marketing the AI needs to be developed.
5. Maintaining effective customer relationships: In order to grow an AI startup, it is necessary to maintain close relationships with customers. This starts with market research that will show whether the app can be a success with the target audience.

Conclusion

Artificial intelligence is having a profound impact on the IT industry, transforming jobs and opening up new business opportunities. It is important to adapt to these changes and invest in AI training and development. The future of technology depends on how successfully we integrate AI into our lives and work.