Artificial Intelligence - What is it

Artificial Intelligence - Limitations

It is claimed that AI is smarter than the average human. But what is meant by being smart? AI is 100% logical by nature. All calculations or lookups are based on logic. AI does not have traits like empathy, laziness, anger, joy, love. AI doesn't take irrational actions or do anything just for fun. Contrary to what some claim, AI cannot develop emotions, because AI does not possess intuition. All AI is logic, based on existing knowledge.

Research is being carried out in Artificial General Intelligence - AGI - which is a field within AI where attempts are made to create self-learning systems. The first results are expected during 2025 and then it will be seen what AGI can do at that time. The emotional part of the human is not part of the AGI, so there will be a deficiency in the AGI compared to the human intelligence.

Perhaps AI is smarter than humans in terms of logic, but humans possess properties that go beyond logic and enable us to observe with more properties than just logic. It can result in answers that logic cannot produce. AI can perform logical calculations and lookups faster than humans. But AI cannot invent new things by itself and AI does not question the processes that are carried out today. People have a curiosity, resentment, laziness or other drive that makes us ask questions about the state of things. And these are the questions that made us invent new things.

For AI, it does not matter that the toilet paper is folded in a special way or that there is a piece of chocolate in the bed when you arrive at a hotel. AI cannot distinguish between whether the chocolate should be a Mars bar or a piece of exclusive chocolate. As humans we will assume that one chocolate has been forgotten by a previous guest and the other is a special gift to us from the hotel. Things AI can't assess and that matter for humans.

The point is that AI is good, but only for what it is intended for. If we work with projects where we develop new things, then AI will be able to help us with calculations, input and lookups, but it will not be able to develop new products with no input from humans. It requires human input.

If we are working with projects that are repetitions of previous projects – i.e. no new development – and if the projects are repeated many times, then it pays to investigate some form of use of AI. Most often, a project template will be the cheapest and most effective solution.

In new development where analysis and comparisons with other products are necessary, AI can also be useful. But it is the human judgment that counts. AI only produces lists, statistics and calculations. We, as humans, have the irrational part of the brain, which AI does not possess, and which can see new possibilities and ideas where logic cannot see anything. And that's what makes the difference in new development – something that couldn't be done before is suddenly a possibility.

Monitoring the progress of projects is an obvious place to let AI solve. Just as automatic planning of both costs and schedules can prove to be cost-saving.

How to use AI in projects

Project processing is the development of known or the creation of new products or services. Companies often work with projects that are similar to each other, as the company has expertise in a specific field, and therefore it will be possible to incorporate AI in the project development.

AI does not ask questions about solutions - "isn't it a difficult method". AI can solve trivial problems, such as pattern recognition, verification of rules, etc. AI will therefore only be useful if there is sufficient data available and we can recognize data. Or the rules of data are well known (such as the laws of physics).

AI only does what you ask it to do. It does not prescribe new methods or ask questions about a solution. For well-known or defined problems, it is an advantage to use AI. In a project, AI can be used to relieve the team of carrying out all the routine tasks.

For specific tasks such as automatic planning, budget calculations and activity monitoring, it can be a great advantage to use AI. In programming, AI can suggest code and in mechanical construction, AI can assist in mechanical calculations.

The cost of developing AI solutions can be high and take time. Therefore, it must be considered whether it is financially profitable for the company to develop AI solutions for specific projects. If the company has a large turnover on projects where there are great similarities between the projects, it might pay off.

Often a simple AI solution such as a project template will be the best solution. The more logic you can build into the template, the easier the project will be to complete. And the more rigid the solution becomes. That is why it is suitable for projects with great uniformity.

For example a company that develops and produces conveyor belts will have a great deal of knowledge about conveyor belts. They will be able to develop a template in the form of a parameter-based solution for the design of conveyor belts, which is flexible and usable for several projects. Mechanical calculations, processes and approvals can be built into the project template, and provide the company with greater earnings.

Utilizing Proglar Project Templates

Project Templates

How to save time and costs by reusing knowledge from previous projects.

In projects where new invention is required, templates, parameter solutions and similar standards will not be applicable. ChatGBT or other standard AI solutions can contribute to this type of project in the form of searching for information and comparisons. AI will not propose new solutions and is limited in relation to analysis of the project. For example, AI will not be able to make a 360 degree evaluation in relation to technical, economic and market analysis. It will be an assessment that must be carried out by the project steering group.

It will be an advantage to use AI in project development. But it is not necessarily profitable to use an AI solution that is specific to the company.

Advantages of AI

The following are some advantages of AI:

• Usage in detail-oriented jobs. AI is a good fit for tasks that involve identifying subtle patterns and relationships in data that might be overlooked by humans.
• Efficiency in data-heavy tasks. AI systems and automation tools dramatically reduce the time required for data processing.
• Time savings and productivity gains. AI and robotics can not only automate operations but also improve safety and efficiency.
• Consistency in results and processes. Today's analytics tools use AI and machine learning to process extensive amounts of data in a uniform way, while retaining the ability to adapt to new information through continuous learning.
• Personalization & Customization. AI systems can enhance user experience by personalizing interactions and content delivery on digital platforms.
• 24 hours availability. AI programs do not need to sleep or take breaks.
• Scalability. AI systems can scale to handle growing amounts of work and data. This makes AI well suited for scenarios where data volumes and workloads can grow exponentially.
• Accelerated research and development. AI can speed up the pace of R&D in fields such as materials science. By rapidly simulating and analyzing many possible scenarios, AI models can help researchers discover new materials or compounds more quickly than traditional methods.
• Sustainability and conservation. AI and machine learning are increasingly used to monitor environmental changes, predict future weather events and manage conservation efforts.
• Process optimization. AI is used to streamline and automate complex processes across various industries.

Disadvantages of AI

Some disadvantages of AI:

• High costs. Developing AI can be very expensive. Building an AI model requires a substantial upfront investment in infrastructure, computational resources and software to train the model and store its training data. After initial training, there are further ongoing costs associated with model inference and retraining.
• Technical complexity. Developing, operating and troubleshooting AI systems - especially in real-world production environments - requires a great deal of technical know-how. In many cases, this knowledge differs from that needed to build non-AI software. For example, building and deploying a machine learning application involves a complex, multistage and highly technical process, from data preparation to algorithm selection to parameter tuning and model testing.
• Algorithmic bias. AI and machine learning algorithms reflect the biases present in their training data - and when AI systems are deployed at scale, the biases scale, too. In some cases, AI systems may even amplify subtle biases in their training data by encoding them into reinforceable and pseudo-objective patterns.
• Difficulty with generalization. AI models often excel at the specific tasks for which they were trained but struggle when asked to address novel scenarios. This lack of flexibility can limit AI's usefulness, as new tasks might require the development of an entirely new model. An NLP model trained on English-language text, for example, might perform poorly on text in other languages without extensive additional training.
• Security vulnerabilities. AI systems are susceptible to a wide range of cyberthreats, including data poisoning and adversarial machine learning. Hackers can extract sensitive training data from an AI model, for example, or trick AI systems into producing incorrect and harmful output.