Ai therefore allows systems to perceive the surrounding environments, to relate to them and to act to achieve specific objectives by solving any problems that may arise. Systems operating on the basis of Ai analysis are in practice capable of adapting their own behavior and therefore that of the hardware they control, analyzing the effects of previous actions and working autonomously. They should therefore respond flexibly to the inputs they receive from outside by adapting to them, unlike traditional software that instead makes the systems they control operate in a rigid and immutable way. It is clear at this point that the ability of a machine to operate on the basis of artificial intelligence programs represents a significant technological plus and, as such, also “sellable” to the end user.
Precisely for this reason, at Eima many advertising brochures proposed the respective top-of-the-range operating solutions as vehicles capable of operating autonomously precisely on the basis of artificial intelligence software. In reality, this was not always the case, in the sense that the aforementioned machines often operated autonomously, but on the basis of traditional software that could not be considered as artificial intelligence programs in the strict sense.
There is also the “weak” Ai
This also applies to particularly advanced and complex software, programs that are close to those of artificial intelligence, but without being such and technically characterized as “Weak Ai” programs, in Italian “weak artificial intelligence”. The risk that the acronym “Ai” could be only an advertising reason for promotion is therefore concrete, also because the boundary between advanced software and an artificial intelligence program is increasingly labile and difficult to identify. A problem that in the future, however, will be overcome as Ai will end up taking over.
Early research in the 1950s
Studies on artificial intelligence date back to the 1950s and the work of the English mathematician Alan Mathison Turing, followed in the 1960s by those of the American computer scientist John McCarthy. It was the latter who developed the programming language “Lisp” which became a fundamental tool for subsequent research on AI. In the two decades that followed, studies then focused on issues related to machine learning processes and the development of artificial neural networks, a topic that was given a strong boost by the work of computer scientists Geoffrey Hinton, Yann LeCun and Yoshua Bengio, born in England, France and Canada respectively. In the 2000s, thanks to the exponential growth of computer processing power, machine learning and the development of so-called “decision trees” made great strides,
making it possible to tackle complex problems such as, to give just a few examples, image recognition, machine translation and personalized recommendations. These operational possibilities have in turn given rise to further developments that are revolutionizing many sectors including medicine, industry, finance and automation, but not without giving rise to ethical and social problems fundamentally linked to the protection of privacy and the impact that these technologies have on employment. Artificial intelligence is therefore opening up a future full of promise, but requires conscious and responsible management.
What’s the impact of artificial intelligence on energy demand?
The future of artificial intelligence
The future proposed by AI is full of possibilities and will significantly influence society and everyday life. Different trends and perspectives will shape its development, but certainly machine learning will be the driving force of future evolutions. Thanks also to the growing computing power of computers, machine learning algorithms will become increasingly sophisticated, allowing artificial intelligence to integrate into key sectors such as health, industry, agriculture and energy. It is expected that in industry and agriculture it will give rise to machines capable of completely replacing humans. However, humans will have increasingly effective systems of interaction with machines, arriving in a future before direct communications between the brain and digital devices. All this will obviously also impact society as there will be important changes in the structures of work and in the skills required that will require ad hoc training and adaptations. Intelligent automation could lead to a reduction in some jobs, but it could also create new job opportunities and allow greater efficiency and productivity. There is no doubt that complex ethical questions will be raised that will require in-depth reflection by all concerned. Rules and guidelines will need to be established to ensure that AI is used for the common good and to address social and economic challenges in a fair and sustainable way.
Many open challenges
Artificial Intelligence has all the potential to change the methods and timing of production cycles. An opportunity that was the topic of a meeting organized during Eima to explore the development prospects of AI in the field of agricultural mechanization. It remains true that "true" artificial intelligence based on advanced self-learning architectures, capable of closely simulating the functioning of the human mind, is still a long way off. In the immediate future, agriculture 4.0 systems will still dominate, with increasingly sophisticated digital solutions aimed at collecting large amounts of data in real time to support users in making informed decisions on the management of resources and operating cycles. The collection, integration and processing of data will therefore anticipate the development of advanced artificial intelligence, based on self-learning systems and algorithms capable of processing information and generating decisions without using predetermined mathematical or statistical models. But training algorithms so that they can learn from various environmental situations and respond appropriately, without human supervision, implies the use of enormous quantities of data
that today, in addition to being difficult to store and process, are often and willingly divided into subsystems that do not communicate with each other. The path to approaching a "true" artificial intelligence is therefore still very long and complex and the steps to be taken to aspire to open a door towards the birth of such a technology will depend on the ability to first give life to hardware architectures equipped with greater computational performance.
