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AI: what is Artificial Intelligence, main applications and tools to streamline and automate processe

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AI: what is Artificial Intelligence, main applications and tools to streamline and automate processe

robort - 2023-05-30 06:31:56

Artificial Intelligence is already part of our daily lives and is now used in more than half of large USA companies. Everyone knows self-driving cars or voice assistants like Apple's Siri, Microsoft's Cortana, or Google's Alexa, but there are many lesser-known examples. 
The intelligent algorithms, i.e. able to self-learn, suggest the products to buy, films or songs in line with our tastes, and know how to answer customers' questions via chat. 
They can recognize a person's face to enable access, sort documents based on content, support doctors in reading radiographic images and in diagnoses, filtering resumes to select the ideal candidate. And so on.

There are already numerous examples of how the introduction of Artificial Intelligence in business processes has brought positive impacts, automating repetitive and low value-added parts of the processes themselves, previously performed by humans, reducing errors, and allowing the development of new products and services. We are at the beginning, but in the coming years, we will see a quantum leap.

The major global corporations are taking the first steps in this direction: the first to arrive at concrete results will be able to benefit from an enormous competitive advantage.

The History of Artificial Intelligence

The first real research project attributable to AI dates back to 1943 when Warren McCulloch and Walter Pitt designed a neural network, i.e. mathematical algorithms that try to reproduce the functioning of our brain's neurons to solve problems. 
However, it is from the end of the 50s onwards that the greatest ferment is created, when the scientist Alan Turing (dated 1950) begins to theorize that a computer can behave like a human being.

The term Artificial Intelligence was coined by the American mathematician John McCarthy (1956) author of the first programming languages ​​(Lisp in 1958 and Prolog in 1973) specific for AI through which he began to develop general programs for solving problems.

From the 80s onwards we proceeded in alternating phases, with great advances on the front of mathematical models, increasingly sophisticated and able to imitate some cerebral functions such as the recognition of configurations, but a lesser interest in neural networks and, in hardware, which then reappeared in the 90s with the massive arrival of GPUs from the world of gaming (graphics processing units, processing chips much faster than CPUs, therefore able to support complex workloads much more quickly).

What is artificial intelligence: a definition

The Politecnico di Milano provides this definition of AI: "Artificial Intelligence, in English Artificial Intelligence (AI), is the branch of computer science that studies the development of hardware and software systems endowed with capabilities typical of human beings and able to autonomously pursue a defined purpose by making decisions that, until then, were usually entrusted to human beings”

The typical capabilities of the human being concern, specifically, the understanding and processing of natural language ( Nlp - Natural Language Processing ) and of images ( Image Processing ), learning, reasoning, and the ability to plan and interact with people, machines, and the environment.

Unlike traditional software, an AI system is not based on programming (that is, on the work of developers who write the operating code of the system) but on learning techniques: that is, algorithms have defined that process an enormous amount of data from which it is the system itself that must derive its own understanding and reasoning abilities.

Weak AI and Strong AI

In reality, there is no univocal definition of AI and the interpretations can vary according to the focus: on the one hand, one can concentrate on the internal reasoning processes, on the other on the external behavior of the systems, in principle always taking as a sort of “effectiveness measure” the similarity or closeness to human behavior.

Starting from these considerations, the scientific community has found an agreement in defining two different types of artificial intelligence, the weak one and the strong one:

– Weak Artificial Intelligence (weak AI): it contains systems capable of simulating some cognitive functions of man without however reaching the intellectual abilities typical of man; broadly speaking, these are problem-solving programs capable of replicating some human logical reasoning to solve problems, making decisions, etc. (as in the game of chess);

– Strong Artificial Intelligence (strong AI): systems capable of becoming wise (or even self-aware) fall into this category; there are theories that lead some scientists and experts to believe that one-day machines will have their own intelligence (therefore they will not emulate that of man), autonomous and probably superior to that of human beings (that moment is called Singularity).

The systems currently in use fall under the scope of weak intelligence, but progress is constant.

Machine Learning and Deep Learning

What characterizes Artificial Intelligence from a technological and methodological point of view is the learning method/model with which the intelligence becomes skilled in a task or action. These learning models are what distinguish Machine Learning and Deep Learning.

Machine Learning: these are systems that serve to "train" the software so that by correcting errors it can learn to carry out a task/activity autonomously.

For example, the mechanical arm supported by the AI, and therefore intelligent, is able to mount a piece even if this is not located where it should be because the control algorithm instead of providing the coordinates activates a visual recognition that searches for the piece in all the area that the arm can reach. And if the machine or the man handing over the pieces repeats the mistake several times, the robot learns that this is a new position and immediately goes looking for the piece there. Machine Learning is evolving along a line of research based on the use of neural networks organized in multiple levels of depth and for this reason, called Deep Learning.

Deep Learning:  these are recently developed learning models (since 2012) inspired by the structure and functioning of our brain, that is, which emulate the human mind. In this case, the mathematical model alone is not enough: Deep Learning requires ad hoc designed artificial neural networks (deep artificial neural networks) and a very powerful computational capacity capable of "holding" different layers of calculation and analysis (which is what happens with the neural connections of the human brain). It may seem like a futuristic level of technology but in reality, these are systems already in use in pattern recognition, voice or image recognition, and Nlp - Natural Language Processing systems.

A classification of Artificial Intelligence solutions: 8 classes

According to the Artificial Intelligence Observatory of the School of Management of the Politecnico di Milano, there are currently eight classes of Artificial Intelligence solutions:

  1. Autonomous Vehicle: refers to any self-driving vehicle used for any type of transport by road, water, or air, such as the self-driving car or the vehicle for home parcel deliveries.
  2. Autonomous Robot: robots, more or less anthropomorphic, capable of moving, manipulating objects, and performing actions without human intervention, drawing information from the surrounding environment and adapting to unforeseen or codified events. Boston Dynamics robots are among the best known: their ability to move today is such that they recently demonstrated parcour.
  3. Intelligent Object: all those objects, from glasses to suitcases, capable of performing actions and making decisions without requiring human intervention, interacting with the surrounding environment through sensors (thermometers, video cameras…) and actuators and learning from the actions of the people who they interact with them.
  4. Virtual Assistant and Chatbot: The most advanced systems are capable of understanding the tone and context of the dialogue, memorizing and reusing the information collected, and demonstrating resourcefulness during the conversation. These systems are increasingly used as the first level of contact with the customer for assistance through the company Customer Care.
  5. Recommendation: these are solutions aimed at addressing the user's preferences, interests, and decisions, based on information provided by him, indirectly or directly. Widely used in eCommerce or in video and music services (suggestions from Amazon, Netflix, and YouTube are an example), they can be placed at different points in the customer journey or, more generally, in the decision-making process.
  6. Image Processing: systems capable of analyzing images or videos for the recognition of people, animals, and things present in the image, biometric recognition, and, in general, the extraction of information from the image/video. For example, applications are being used for monitoring equipment rooms by utilities, or for evaluating auto damage in insurance accidents.
  7. Language Processing: provides language processing skills, for understanding the content, translating, up to the production of texts independently, starting from data or documents provided as input.
  8. Intelligent Data Processing: this broad category includes all those solutions that use artificial intelligence algorithms on structured and unstructured data to extract information: for example, systems for detecting financial fraud, pattern research, monitoring, and control systems, and predictive analysis. For risk prevention, highly sophisticated analyzes are carried out that correlate data, events, behaviors, and habits to understand any fraudulent activities in advance; these systems can also find application within other corporate contexts, for example for the protection of information and data in the fight against cybercrime.

Artificial Intelligence in business processes

AI was born in the 50s, but it is only today that the technological advances recorded in the field of computing power, data availability, and the ability to analyze them for solving complex problems have allowed applications to be born and spread.

The underlying technologies are mature, and APIs and cloud services are available at affordable costs. However, a design approach is needed to introduce AI into processes.

If up to 10 years ago the barriers to the introduction of businesses were linked to the lack of instrumentation, or inadequate analytical skills, the issue today is not technological, but mainly cultural and specific skills. According to experts, today 70% of the effort relating to an AI project is for the redesign of processes, 10% for writing the algorithms, and only 10% for the technological part.

Currently, the most advanced sectors in the adoption of artificial intelligence projects are banks, finance and insurance, automotive, energy, logistics, and telco. 

How Artificial Intelligence is Applied in everyday life

Even without being aware of it, each of us has already encountered artificial intelligence in our daily lives. The examples are many.

Let's think for example, as mentioned, of Netflix, Spotify, Amazon, or any e-commerce site. Everyone implements recommendation systems that recommend movies, books, music, purchases in line with our tastes and needs. How do they do it? The machine learning mechanism takes into consideration an enormous amount of cases, basically the behaviors of all its users over time, and extracts patterns, and common mechanisms, which it then applies to make predictions about us. Do we like horror? He probably won't suggest a romantic film, but a fantasy. Have we seen a film with Hugh Grant? The sympathy of the British actor will be proposed again. The correlations, of course, are not that simple.

Another application of daily use is in the email systems of Google and Microsoft. By now, as soon as we write the first letters of a word in the mail message, artificial intelligence suggests how to continue the entire sentence. And it is also able to suggest how to respond, and interpreting the text.

Have you received a report of an anomalous use of your credit card because you used it in London, where you had never been before? Also, in this case, a Machine Learning algorithm trained for fraud detection suggests that the bank contact you. Many insurance and financial companies use AI to look for suspicious behavior and intervene promptly to protect their customers.

Another growing use is in video surveillance. Modern cameras are associated with image analysis systems, another technology that uses artificial intelligence. The algorithm is able to "watch" the videos and interpret them, and this is used for example in cities, to identify any anomalous or dangerous situations, in pedestrian areas but also in car traffic.

The use of systems that, through voice or text, answer questions, the chatbots, is also growing. In addition to helping users find repetitive information in a simpler and more immediate way (usually the same ones already found in the FAQs), they can provide customers with information about the products or services offered by the company, such as timetables, but they find space also internally to companies, for example, to provide information on holidays, leave, contributions in the HR area.

The project map

intelligent data processing

The most significant share of the Italian market of AI applications (34%) concerns Intelligent Data Processing solutions, which allow to analyze and extract information from data. In particular, the focus is on forecasting applications in areas such as investment management, budgeting, and business planning.

Natural Language Interpretation (NLP)

Also relevant is the whole area of ​​interpretation of spoken or written language (28%), which includes chatbots and NLP. Generative AI applications such as DALL-E or ChatGPT belong to this area. These allow to automatically extract and process information from written documents such as contracts, policies, or judicial documents, as well as eMails, posts, and social comments.

Recommendation engines

In 19% of cases, companies work in the area of ​​algorithms that suggest products or content to customers in line with their specific preferences (Recommendation System).

Computer vision

10% of the initiatives implemented can be traced back to the area of ​​artificial vision, i.e. systems that analyze the content of an image in contexts such as monitoring production lines or video surveillance in public or private places.

Intelligent RPA (Robotic Process Automation)

9% of projects refer to solutions where algorithms are used to automate repetitive tasks and workflows in the back office.

What Italian users think

But what is the perception of Italians regarding the widespread application of AI algorithms in their daily life? 93% of Italians have already heard of Artificial Intelligence, 55% recognize that AI is very present in their daily lives and 37% use it for their work.

However, there is no shortage of doubts and perplexities: 73% of those interviewed have fears, in particular as regards the effects on the world of work. However, only 1/5 (19%) of the population says they are against the use of AI in the professional sphere.

The posture of companies in the Belpaese

The Observatory also analyzed the level of maturity of large organizations in the process of adopting Artificial Intelligence, identifying five different profiles.

34% of large companies are in the Implementation Age, so they have the skills and technologies necessary to autonomously develop and bring AI initiatives into production.

Of these, 9% belong to the avant-garde category, i.e. companies that independently manage the entire value chain of AI projects. Followed by Apprentices (25%) with numerous fully operational projects spread throughout the organization, who begin to wonder about the potential ethical risks of AI solutions. These organizations will have to create coordination mechanisms between internal skills, also increasing the pervasiveness of Artificial Intelligence by involving all stakeholders.

The remaining 66% includes organizations on the way (33%), who do not perceive the topic of AI as relevant and do not have an adequate IT infrastructure but are equipped with enabling elements.

If we look at SMEs, however, the adoption rate drops dramatically: only 15% have at least one AI project started (it was 6% in 2021), but one in three plans to start new initiatives in the next two years.

Process automation: Robotic Process Automation and AI

RPA (Robotic Process Automation) solutions have been used for several years to streamline onerous processes, automating simple repetitive operations, especially on legacy information systems (for example, extracting data from an ERP system and inserting it into another software). Their use is aimed at efficiency: they save a lot of time and allow resources to be dedicated to activities with greater added value and to the resolution of complex scenarios that automation is unable to manage, enhancing creativity and initiative.

By integrating AI with RPA, which are in fact complementary, it is possible to take a step forward: if before the most complex parts of the process were entrusted to humans, now it is possible to automate them, through training.

For example, it can be said that the RPA acts like a robot (in fact solutions are also called bots) sitting in front of the PC and capable of carrying out a limited number of activities, which is replaced by a robot to manage more complex scenarios with better preparation.

Process automation with RPA falls within the scope of BPM (Business Process Management) and is now implemented with Agile methodologies that allow you to obtain results quickly, proceeding by project "sprints" with weekly or short-term frequencies period.

The range of RPA solutions is wide: top vendors include Automation Anywhere, UI Path, Blue Prism, and Nice, which are present in the Italian market through a partnership with Avanade.

AI for HR: hunting for the best talent

The search for the ideal candidate is a demanding and strategic activity, which takes up a lot of the HR team's time. Analyzing CVs is increasingly difficult and a quick reading of the data does not always guarantee an effective match to the skills and abilities that the company needs. An in-depth analysis is required, for example by comparing the experiences and background of an employee already present in the company, verifying the actual level of knowledge of a software based on the years in which it has been used.

 Artificial Intelligence comes to the rescue. As-a-service solutions capable of supporting the recruiting activity are arriving on the market, such as the one developed by Avanade and based on Cortana, currently used internally by the same company.

The tool supports HR managers in selecting a shortlist of candidates for open positions in the company, leaving the final decision to HR managers and reference managers. 
It is made up of a dialogue interface integrated with RPA and internal company databases which searches for candidates online (particularly on Linkedin) and matches their skills with those present in the company employee DB, returning a list of profiles with percentage values ​​of compatibility.

The solution, therefore, maintains a human-centered approach but is enhanced by the ability of automation and AI, thus redesigning the talent search experience, drastically reducing the time required for research, and increasing the quality of the results.

From AI, a support for the professional development of employees

In the field of Human Resources, artificial intelligence is not only seen by e-recruiters as a cutting-edge technology to support them in their activities but also workers, for their part, perceive the opportunities of AI in directing them along a path of professional growth.

According to the global study conducted by Oracle and the research and consultancy company Workplace Intelligence, not only 83% of respondents believe that these AI technologies are better support than a human being when it comes to career choices, because they give recommendations not influenced by bias/prejudice (37%), respond quickly (33%) and help to find new job positions in line with the skills possessed (32%). But 85% of respondents in the study also want AI technology to help them define their future: to identify the skills they need to develop and propose ways to acquire them (36%) or to suggest steps to take to pursue those career goals (32%).

Artificial Intelligence Marketing (AIM): chatbots and sentiment analysis

In Marketing we have seen for some time now AI systems used in different activities and with various objectives; the most important undoubtedly concerns the management of the relationship with users, which has always been the "cross and delight" of any brand, even in the BtoB world.

The AI ​​technologies employed range from vocal/virtual assistants (chatbots and systems such as Apple's Siri or Microsoft's Cortana) which exploit artificial intelligence algorithms both for natural language recognition and for learning and analyzing habits and user behaviors, up to the most sophisticated engagement mechanisms that contemplate the real-time analysis of large amounts of data (particularly on social media) to understand the "sentiment" and needs of people with activities that go as far as a prediction of purchasing behavior from which to derive communication strategies and/or service proposals.

Chatbots and other NLP-based systems are now widely used even within the departments that deal with customer assistance, service, and support (contact center, customer service, maintenance and support, etc.). According to Forrester, Marketing, and Sales drive investments in AI, followed by Customer Care. Analysts suggest not to underestimate the human factor and to always support these systems with a flesh-and-blood expert ready to intervene in case of problems with the customer, to avoid unpleasant situations that could be counterproductive.

AI and Supply Chain Management

The theme of risk management is also of fundamental importance for the optimization and management of the supply and distribution chain where, in addition to sophisticated analyzes, "intelligent systems" are also needed that are able to connect and monitor the entire supply chain and all the players involved.

One of the most interesting cases of the use of AI, in this area, concerns the Order Management activities within which the technologies that exploit artificial intelligence not only aim at simplifying processes but also at their total integration, from purchases up to the inventory, from the warehouse to sales up to the integration with Marketing for the preventive management of supplies according to promotional activities or communication campaigns.

Artificial Intelligence App

Tools that work with text, audio, video, and images proliferate on the web. They range from the generation of photos (one has even won a prestigious award ) to that presentations, passing through podcasts, and content marketing.

A (non-exhaustive) list of the main tools that companies have at their disposal is this:

This system allows you to create chatbots specialized in conversation with human users, which self-learn and improve the effectiveness of their intervention over time. The same tool also allows you to create podcasts.

The AI-based system allows you to create realistic images and artistic compositions from a description imparted through natural language.

It is a generative AI platform that allows you to easily create customizable content in seconds starting from a keyword.

This app allows you to create professional videos in minutes without having to invest in specific equipment, simply by typing a text (120 different languages ​​are available).

A useful tool for marketers, it allows you to create blog posts, images, social media content, searches, and ADV content in just a few clicks.

This platform favors corporate Project Management and the dissemination of best practices within geographically distributed teams.

The AI-based system offers a 360° automated production and management of corporate storytelling, with blog posts and interactive web content.

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Robort Gabriel

Lagos, Nigeria

Freelance Web Developer, Native Android Developer, and Coding Tutor.

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