Artificial intelligence

What is it

Artificial intelligence is a tool that allows you to solve problems by simulating human logic, generating new content.

There are two large subsets:

  • Machine Learning is a system trained to learn automatically from experiences;
  • The Deep Learning is a subset of machine learning that uses artificial neural networks to imitate the learning process of the human brain. The system is trained to learn on its own by recognizing patterns across multiple levels of processing.

In summary:

Machine LearningDeep learning
Subset of AISubset of Machine Learning
It can train on smaller data setsIt requires large amounts of data
It requires more human intervention to correct and learnIt learns on its own from the environment and past mistakes
Shorter training and lower accuracyLonger training and greater precision
Create simple, linear correlationsCreate nonlinear and complex correlations
Can run training on a CPU (central processing unit)It requires a specialized GPU (graphics processing unit) for training
It works with unstructured data such as images, videos, and audio
Ability to abstract and learn complex concepts, adapting to dynamic and complex environments

Examples:

Machine LearningDeep learning
Supervised: classify emails as spam or non-spam based on labeled data sets, including both inputs and outputsSocial Media – analysis of large quantities of images in order to detect users with specific content (for example for facial recognition) or in sentiment analysis to understand what public opinion thinks of a product or service, for example by scanning online reviews
Unsupervised: grouping data based on similarities, even without knowing in advance which categories they represent. Learning from unlabeled data, which includes only inputsFinance – Neural networks can predict company values, identify threats and develop trading strategies
Healthcare – helps understand patient behavior, facilitating diagnosis and treatment
Cyber Security – Algorithms can detect and mitigate threats such as viruses and malware
Digital Assistants – Natural language processing (NLP) allows Chatbots and digital assistants such as Siri, Google Assistant and Alexa to provide intelligent responses
Texts – machine translation, text generation

The advantages of Artificial Intelligence

What are the benefits?

Assistance

Digital assistance tools available 24×7 allow you to answer the most common questions, resolve issues or scale them to human agents, reducing repetitive tasks.

Reduction of errors

Correctly programmed algorithms allow you to increase accuracy and precision, for example through robotic surgery systems, improving safety and patient outcomes.

Zero risks

The use of robots eliminates risks for humans, for example for bomb defusing, interventions in the depths of the ocean, space travel or in fully automated dangerous production environments.

Repetitive tasks

Some repetitive and boring tasks can be automated, allowing people to focus on more complex or creative ones.

Impartial decisions

If the absence of biased opinions can be verified in the algorithm, the decision-making process can be more accurate, for example by selecting candidates based on knowledge and skills rather than demographic data.

What to pay attention to

Artificial intelligence can be useful for learning or solving problems, but there are some aspects that should be kept in mind because they could represent disabling elements or lead to a redefinition of its functioning.

Intellectual property

there are now few large concentrations of knowledge “drained” from the internet, with doubts about the protection of intellectual property

Fake news

Only human intelligence and sensitivity can distinguish between real and false facts and there is a risk of the proliferation of distorted information (for example through Deepfakes).
Organizations with resources can consider customizing generic models based on their specificities, but the problem of the accuracy and truthfulness of the available data remains

Prejudice and discrimination

Through predictive and generative statistical techniques, the results depend on the quality of the model and the source of the data. They may not be accurate or appropriate, if the model contains (in the algorithm) biased opinions (BIAS), generating prejudices and consequent discrimination

Data selection

Be careful when selecting the data used to “teach”, favoring small, specialized models.
It is important to also involve human resources in the process to validate the results of the AI models, before being published or used.
The risk may be to use inaccurate information, not contextualized to a specific case, obsolete and incorrect, generating responses that may be counterproductive

High costs

It is a resource-intensive technology that requires constantly updated hardware and software components, opening up reflections on environmental sustainability

Obsolete human work

It replaces some types of human work, at an exponential speed (probably) higher than that of the human being’s ability to acquire new skills and professionalism, resulting in the need to accompany change

Give meaning

It is not able to understand the meanings, as the results are generated by stochastic models, which follow causal, probabilistic laws

Ethics and emotions

Ethics and morals are human characteristics that cannot be incorporated into AI.
Feelings, the sense of team to achieve objectives cannot be replaced by computers

Rules and regulations

The above aspects need to be managed at a national and international level

Organizational Considerations and Aspects

The future of artificial intelligence will depend on the ability to enhance its advantages by canceling or mitigating possible threats to the well-being of the community.

Artificial intelligence should not be understood as an alternative to human intelligence, but as a complementary tool from which benefits can be drawn for the community, which can certainly be used when dealing with a large amount of data, “Big Data”, as it is capable of carrying out processing that is impossible for a human being in a short time.

Being an instrument it must be kept under the control, review and “approval” of the human brain. If only because that of Homo Sapiens has an evolution of no less than 50,000 years and even today neuroscience has difficulty understanding how it works (How far neuroscience is from understanding brains).

Organizational Design

The adoption of artificial intelligence tools in an organization involves a rethinking of the activities carried out by human resources.

The classic organizational design is based on an organizational structure with defined, differentiated and integrated roles with operational mechanisms. We need to find the right person, able to conform to the overall design, the culture, the rules (even if it is important to know how to generate diversity to innovate).

The roles are usually declined into profiles, activities, responsibilities and are subject to periodic maintenance as the context changes.

Today, in light of sudden changes (not only due to technological revolutions), we need to be able to equip ourselves to respond to challenges by asking ourselves who or what is able to process the right questions and provide the answers: human resources through development of knowledge and skills.

It is necessary to restore centrality to the human resource by opening a constructive, motivating dialogue, rediscovering the sense of knowing, knowing and participating, also well aware of the limits of each individual intelligence (the human brain is not perfect). This awareness can build a collaborative dialogue with new technologies, not an antithetical one.

A challenge that can be an opportunity

An opportunity for human beings to elevate their skills using the tools offered by artificial intelligence.

It would mean, for example, combining a role-based organization with a level expressed by the knowledge and skills expressed, demonstrated by the uniqueness of each individual person, to find solutions, innovate, support others. Remuneration should also be based on this.

Knowledge, skills, abilities and ideas of the future are the fruit of the unique history of each individual resource, who should have the possibility of making them explicit and developing them within team, area, organisation. The organization that aims to develop a competitive advantage should provide the concretely suitable context (organized knowledge, opportunities for micro learning, advanced training, etc.) the “humus”, the tools, the shared mechanisms. A level of true widespread, flat, non-hierarchical intelligence, but also on multiple “neuronal” levels/areas, capable of making decisions on the degree of adoption of artificial intelligence tools that can be integrated into the knowledge base.

AI must remain a Tool for organizational action

A level added to the normal organizational flow in which it is not the organization that tells you what you have to do, but it is you who – in the light of your knowledge – proposes what to do.

Discover the solution

how to introduce, implement and maintain the knowledge management platform in your organization